Initial commit

.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/../../../../../../../:\Users\Pica\worker\MaxCompute-Spark\spark-2.x\.idea/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/.name

+spark-examples_2.11
\ No newline at end of file

.idea/codeStyles/Project.xml

+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <ScalaCodeStyleSettings>
+      <option name="MULTILINE_STRING_CLOSING_QUOTES_ON_NEW_LINE" value="true" />
+    </ScalaCodeStyleSettings>
+  </code_scheme>
+</component>
\ No newline at end of file

.idea/codeStyles/codeStyleConfig.xml

+<component name="ProjectCodeStyleConfiguration">
+  <state>
+    <option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" />
+  </state>
+</component>
\ No newline at end of file

.idea/compiler.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CompilerConfiguration">
+    <annotationProcessing>
+      <profile name="Maven default annotation processors profile" enabled="true">
+        <sourceOutputDir name="target/generated-sources/annotations" />
+        <sourceTestOutputDir name="target/generated-test-sources/test-annotations" />
+        <outputRelativeToContentRoot value="true" />
+        <module name="spark-2.x" />
+      </profile>
+    </annotationProcessing>
+    <bytecodeTargetLevel>
+      <module name="spark-2.x" target="6" />
+    </bytecodeTargetLevel>
+  </component>
+</project>
\ No newline at end of file

.idea/jarRepositories.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="RemoteRepositoriesConfiguration">
+    <remote-repository>
+      <option name="id" value="central" />
+      <option name="name" value="Central Repository" />
+      <option name="url" value="http://maven.aliyun.com/nexus/content/repositories/central/" />
+    </remote-repository>
+    <remote-repository>
+      <option name="id" value="central" />
+      <option name="name" value="Maven Central repository" />
+      <option name="url" value="https://repo1.maven.org/maven2" />
+    </remote-repository>
+    <remote-repository>
+      <option name="id" value="jboss.community" />
+      <option name="name" value="JBoss Community repository" />
+      <option name="url" value="https://repository.jboss.org/nexus/content/repositories/public/" />
+    </remote-repository>
+  </component>
+</project>
\ No newline at end of file

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/101tec/zkclient/0.10/zkclient-0.10-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/101tec/zkclient/0.10/zkclient-0.10-sources.jar!/" />
+    </SOURCES>
+  </library>
+</component>
\ No newline at end of file

.idea/libraries/Maven__com_alibaba_fastjson_1_2_45.xml

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/alibaba/fastjson/1.2.45/fastjson-1.2.45-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/alibaba/fastjson/1.2.45/fastjson-1.2.45-sources.jar!/" />
+    </SOURCES>
+  </library>
+</component>
\ No newline at end of file

.idea/libraries/Maven__com_aliyun_datahub_aliyun_sdk_datahub_2_9_4_public.xml

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+    <CLASSES>
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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/datahub/aliyun-sdk-datahub/2.9.4-public/aliyun-sdk-datahub-2.9.4-public-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/datahub/aliyun-sdk-datahub/2.9.4-public/aliyun-sdk-datahub-2.9.4-public-sources.jar!/" />
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+  </library>
+</component>
\ No newline at end of file

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/emr/emr-datahub_2.11/1.6.0/emr-datahub_2.11-1.6.0-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/emr/emr-datahub_2.11/1.6.0/emr-datahub_2.11-1.6.0-sources.jar!/" />
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+  </library>
+</component>
\ No newline at end of file

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/cupid-sdk/3.3.8-public/cupid-sdk-3.3.8-public-javadoc.jar!/" />
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+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/cupid-sdk/3.3.8-public/cupid-sdk-3.3.8-public-sources.jar!/" />
+    </SOURCES>
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+</component>
\ No newline at end of file

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/hadoop-fs-oss/3.3.8-public/hadoop-fs-oss-3.3.8-public-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/hadoop-fs-oss/3.3.8-public/hadoop-fs-oss-3.3.8-public-sources.jar!/" />
+    </SOURCES>
+  </library>
+</component>
\ No newline at end of file

.idea/libraries/Maven__com_aliyun_odps_odps_sdk_commons_0_30_8_public.xml

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/odps-sdk-commons/0.30.8-public/odps-sdk-commons-0.30.8-public-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/odps-sdk-commons/0.30.8-public/odps-sdk-commons-0.30.8-public-sources.jar!/" />
+    </SOURCES>
+  </library>
+</component>
\ No newline at end of file

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/odps-sdk-core/0.30.8-public/odps-sdk-core-0.30.8-public-javadoc.jar!/" />
+    </JAVADOC>
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+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/odps-sdk-core/0.30.8-public/odps-sdk-core-0.30.8-public-sources.jar!/" />
+    </SOURCES>
+  </library>
+</component>
\ No newline at end of file

.idea/libraries/Maven__com_aliyun_odps_odps_spark_datasource_2_11_3_3_8_public.xml

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+    <CLASSES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/odps-spark-datasource_2.11/3.3.8-public/odps-spark-datasource_2.11-3.3.8-public.jar!/" />
+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/odps-spark-datasource_2.11/3.3.8-public/odps-spark-datasource_2.11-3.3.8-public-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/odps-spark-datasource_2.11/3.3.8-public/odps-spark-datasource_2.11-3.3.8-public-sources.jar!/" />
+    </SOURCES>
+  </library>
+</component>
\ No newline at end of file

.idea/libraries/Maven__com_aliyun_odps_streaming_lib_3_3_8_public.xml

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/streaming-lib/3.3.8-public/streaming-lib-3.3.8-public-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/odps/streaming-lib/3.3.8-public/streaming-lib-3.3.8-public-sources.jar!/" />
+    </SOURCES>
+  </library>
+</component>
\ No newline at end of file

.idea/libraries/Maven__com_aliyun_openservices_aliyun_log_0_6_15.xml

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+    <CLASSES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/openservices/aliyun-log/0.6.15/aliyun-log-0.6.15.jar!/" />
+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/openservices/aliyun-log/0.6.15/aliyun-log-0.6.15-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/openservices/aliyun-log/0.6.15/aliyun-log-0.6.15-sources.jar!/" />
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+  </library>
+</component>
\ No newline at end of file

.idea/libraries/Maven__com_aliyun_openservices_log_loghub_producer_0_1_11.xml

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/openservices/log-loghub-producer/0.1.11/log-loghub-producer-0.1.11-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/openservices/log-loghub-producer/0.1.11/log-loghub-producer-0.1.11-sources.jar!/" />
+    </SOURCES>
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+</component>
\ No newline at end of file

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+    </CLASSES>
+    <JAVADOC>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/openservices/loghub-client-lib/0.6.3/loghub-client-lib-0.6.3-javadoc.jar!/" />
+    </JAVADOC>
+    <SOURCES>
+      <root url="jar://$PROJECT_DIR$/../../../../../repository/com/aliyun/openservices/loghub-client-lib/0.6.3/loghub-client-lib-0.6.3-sources.jar!/" />
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\ No newline at end of file

.idea/libraries/Maven__com_aliyun_oss_aliyun_sdk_oss_2_2_1.xml

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+    <JAVADOC>
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\ No newline at end of file

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\ No newline at end of file

.idea/libraries/Maven__com_chuusai_shapeless_2_11_2_3_2.xml

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+    <JAVADOC>
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\ No newline at end of file

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+    <JAVADOC>
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\ No newline at end of file

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\ No newline at end of file

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\ No newline at end of file

.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

hs_err_pid136340.log

+#
+# There is insufficient memory for the Java Runtime Environment to continue.
+# Native memory allocation (malloc) failed to allocate 159056 bytes for Chunk::new
+# Possible reasons:
+#   The system is out of physical RAM or swap space
+#   In 32 bit mode, the process size limit was hit
+# Possible solutions:
+#   Reduce memory load on the system
+#   Increase physical memory or swap space
+#   Check if swap backing store is full
+#   Use 64 bit Java on a 64 bit OS
+#   Decrease Java heap size (-Xmx/-Xms)
+#   Decrease number of Java threads
+#   Decrease Java thread stack sizes (-Xss)
+#   Set larger code cache with -XX:ReservedCodeCacheSize=
+# This output file may be truncated or incomplete.
+#
+#  Out of Memory Error (allocation.cpp:390), pid=136340, tid=200496
+#
+# JRE version: Java(TM) SE Runtime Environment (8.0_51-b16) (build 1.8.0_51-b16)
+# Java VM: Java HotSpot(TM) 64-Bit Server VM (25.51-b03 mixed mode windows-amd64 compressed oops)
+# Failed to write core dump. Minidumps are not enabled by default on client versions of Windows
+#
+
+---------------  T H R E A D  ---------------
+
+Current thread (0x00000000202ba800):  JavaThread "C2 CompilerThread2" daemon [_thread_in_native, id=200496, stack(0x0000000020940000,0x0000000020a40000)]
+
+Stack: [0x0000000020940000,0x0000000020a40000]
+Native frames: (J=compiled Java code, j=interpreted, Vv=VM code, C=native code)
+V  [jvm.dll+0x32b4ca]
+
+
+Current CompileTask:
+C2:   3215  129  s    4       sun.misc.URLClassPath::getNextLoader (88 bytes)
+
+
+---------------  P R O C E S S  ---------------
+
+Java Threads: ( => current thread )
+  0x0000000020352800 JavaThread "Service Thread" daemon [_thread_blocked, id=200520, stack(0x0000000020b40000,0x0000000020c40000)]
+  0x00000000202bd800 JavaThread "C1 CompilerThread3" daemon [_thread_blocked, id=200508, stack(0x0000000020a40000,0x0000000020b40000)]
+=>0x00000000202ba800 JavaThread "C2 CompilerThread2" daemon [_thread_in_native, id=200496, stack(0x0000000020940000,0x0000000020a40000)]
+  0x00000000202ba000 JavaThread "C2 CompilerThread1" daemon [_thread_in_native, id=200504, stack(0x0000000020840000,0x0000000020940000)]
+  0x00000000202b9000 JavaThread "C2 CompilerThread0" daemon [_thread_in_native, id=200500, stack(0x0000000020740000,0x0000000020840000)]
+  0x000000001e70d000 JavaThread "JDWP Command Reader" daemon [_thread_in_native, id=198720, stack(0x000000001ee70000,0x000000001ef70000)]
+  0x000000001e70a800 JavaThread "JDWP Event Helper Thread" daemon [_thread_blocked, id=200360, stack(0x000000001ed70000,0x000000001ee70000)]
+  0x000000001e6ff800 JavaThread "JDWP Transport Listener: dt_socket" daemon [_thread_blocked, id=199312, stack(0x000000001ec70000,0x000000001ed70000)]
+  0x000000001e6f6000 JavaThread "Attach Listener" daemon [_thread_blocked, id=200336, stack(0x000000001eb70000,0x000000001ec70000)]
+  0x000000001e6f5800 JavaThread "Signal Dispatcher" daemon [_thread_blocked, id=200352, stack(0x000000001ea70000,0x000000001eb70000)]
+  0x0000000002c7d000 JavaThread "Finalizer" daemon [_thread_blocked, id=200344, stack(0x000000001e570000,0x000000001e670000)]
+  0x0000000002c75000 JavaThread "Reference Handler" daemon [_thread_blocked, id=200324, stack(0x000000001e470000,0x000000001e570000)]
+  0x000000000297e000 JavaThread "main" [_thread_in_native, id=200284, stack(0x0000000002850000,0x0000000002950000)]
+
+Other Threads:
+  0x000000001c66a000 VMThread [stack: 0x000000001e370000,0x000000001e470000] [id=200340]
+  0x00000000202be000 WatcherThread [stack: 0x0000000020c40000,0x0000000020d40000] [id=200512]
+
+VM state:not at safepoint (normal execution)
+
+VM Mutex/Monitor currently owned by a thread: None
+
+Heap:
+ PSYoungGen      total 75776K, used 29142K [0x000000076b580000, 0x0000000770a00000, 0x00000007c0000000)
+  eden space 65024K, 44% used [0x000000076b580000,0x000000076d1f5bf0,0x000000076f500000)
+  from space 10752K, 0% used [0x000000076ff80000,0x000000076ff80000,0x0000000770a00000)
+  to   space 10752K, 0% used [0x000000076f500000,0x000000076f500000,0x000000076ff80000)
+ ParOldGen       total 173568K, used 0K [0x00000006c2000000, 0x00000006cc980000, 0x000000076b580000)
+  object space 173568K, 0% used [0x00000006c2000000,0x00000006c2000000,0x00000006cc980000)
+ Metaspace       used 3400K, capacity 4716K, committed 4864K, reserved 1056768K
+  class space    used 348K, capacity 424K, committed 512K, reserved 1048576K
+
+Card table byte_map: [0x0000000012040000,0x0000000012840000] byte_map_base: 0x000000000ea30000
+
+Marking Bits: (ParMarkBitMap*) 0x0000000059b64040
+ Begin Bits: [0x0000000013590000, 0x0000000017510000)
+ End Bits:   [0x0000000017510000, 0x000000001b490000)
+
+Polling page: 0x0000000000d10000
+
+CodeCache: size=245760Kb used=1320Kb max_used=1334Kb free=244439Kb
+ bounds [0x0000000002c80000, 0x0000000002ef0000, 0x0000000011c80000]
+ total_blobs=390 nmethods=142 adapters=169
+ compilation: enabled
+
+Compilation events (10 events):
+Event: 3.197 Thread 0x00000000202bd800  133       1       java.security.ProtectionDomain::getCodeSource (5 bytes)
+Event: 3.198 Thread 0x00000000202bd800 nmethod 133 0x0000000002dc3310 code [0x0000000002dc3460, 0x0000000002dc3570]
+Event: 3.206 Thread 0x00000000202bd800  139   !   3       java.util.zip.InflaterInputStream::read (138 bytes)
+Event: 3.207 Thread 0x00000000202bd800 nmethod 139 0x0000000002dc5ad0 code [0x0000000002dc5d60, 0x0000000002dc6e28]
+Event: 3.207 Thread 0x00000000202bd800  143       1       java.net.URL::getFile (5 bytes)
+Event: 3.208 Thread 0x00000000202bd800 nmethod 143 0x0000000002dc2c50 code [0x0000000002dc2da0, 0x0000000002dc2eb0]
+Event: 3.208 Thread 0x00000000202bd800  142       1       java.net.URL::getPort (5 bytes)
+Event: 3.208 Thread 0x00000000202bd800 nmethod 142 0x0000000002dc77d0 code [0x0000000002dc7920, 0x0000000002dc7a30]
+Event: 3.208 Thread 0x00000000202bd800  140       3       java.util.zip.InflaterInputStream::ensureOpen (18 bytes)
+Event: 3.208 Thread 0x00000000202bd800 nmethod 140 0x0000000002dc7a90 code [0x0000000002dc7c00, 0x0000000002dc7e68]
+
+GC Heap History (0 events):
+No events
+
+Deoptimization events (0 events):
+No events
+
+Internal exceptions (10 events):
+Event: 2.973 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc18638) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.974 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc187f0) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.974 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc199a8) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.974 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc19b60) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.974 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc1ac68) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.974 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc1ae20) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.975 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc1bfa0) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.975 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc1c158) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.975 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc1d1e8) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+Event: 2.975 Thread 0x000000000297e000 Exception <a 'java/security/PrivilegedActionException'> (0x000000076cc1d3a0) thrown at [C:\re\workspace\8-2-build-windows-amd64-cygwin\jdk8u51\3951\hotspot\src\share\vm\prims\jvm.cpp, line 1382]
+
+Events (10 events):
+Event: 3.190 loading class scala/collection/GenSeqLike
+Event: 3.190 loading class scala/collection/GenSeqLike done
+Event: 3.191 loading class scala/collection/SeqLike
+Event: 3.191 loading class scala/collection/SeqLike done
+Event: 3.193 loading class scala/reflect/api/TypeCreator
+Event: 3.193 loading class scala/reflect/api/TypeCreator done
+Event: 3.205 loading class scala/Serializable
+Event: 3.205 loading class scala/Serializable done
+Event: 3.207 loading class com/aliyun/odps/spark/examples/sparksql/SessionProcess$$typecreator13$1
+Event: 3.207 loading class com/aliyun/odps/spark/examples/sparksql/SessionProcess$$typecreator13$1 done
+
+
+Dynamic libraries:
+0x00007ff6d9cc0000 - 0x00007ff6d9cf7000 	D:\Program Files\Java\jdk1.8.0_51\bin\java.exe
+0x00007ffb863c0000 - 0x00007ffb8658f000 	C:\Windows\SYSTEM32\ntdll.dll
+0x00007ffb85480000 - 0x00007ffb8552d000 	C:\Windows\System32\KERNEL32.DLL
+0x00007ffb83390000 - 0x00007ffb835ae000 	C:\Windows\System32\KERNELBASE.dll
+0x00007ffb85890000 - 0x00007ffb85936000 	C:\Windows\System32\ADVAPI32.dll
+0x00007ffb84ee0000 - 0x00007ffb84f7e000 	C:\Windows\System32\msvcrt.dll
+0x00007ffb85190000 - 0x00007ffb851eb000 	C:\Windows\System32\sechost.dll
+0x00007ffb85360000 - 0x00007ffb8547d000 	C:\Windows\System32\RPCRT4.dll
+0x00007ffb85e20000 - 0x00007ffb85f86000 	C:\Windows\System32\USER32.dll
+0x00007ffb837b0000 - 0x00007ffb837ce000 	C:\Windows\System32\win32u.dll
+0x00007ffb84fb0000 - 0x00007ffb84fe4000 	C:\Windows\System32\GDI32.dll
+0x00007ffb837d0000 - 0x00007ffb83954000 	C:\Windows\System32\gdi32full.dll
+0x00007ffb76150000 - 0x00007ffb763ca000 	C:\Windows\WinSxS\amd64_microsoft.windows.common-controls_6595b64144ccf1df_6.0.14393.4169_none_7de0bbf28341b1f2\COMCTL32.dll
+0x00007ffb85b20000 - 0x00007ffb85de6000 	C:\Windows\System32\combase.dll
+0x00007ffb82bb0000 - 0x00007ffb82ca4000 	C:\Windows\System32\ucrtbase.dll
+0x00007ffb82890000 - 0x00007ffb828fc000 	C:\Windows\System32\bcryptPrimitives.dll
+0x00007ffb85df0000 - 0x00007ffb85e1e000 	C:\Windows\System32\IMM32.DLL
+0x0000000057ec0000 - 0x0000000057f92000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\msvcr100.dll
+0x0000000059360000 - 0x0000000059be3000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\server\jvm.dll
+0x00007ffb85aa0000 - 0x00007ffb85aa8000 	C:\Windows\System32\PSAPI.DLL
+0x00007ffb75ed0000 - 0x00007ffb75ed9000 	C:\Windows\SYSTEM32\WSOCK32.dll
+0x00007ffb80c70000 - 0x00007ffb80c93000 	C:\Windows\SYSTEM32\WINMM.dll
+0x00007ffb7bce0000 - 0x00007ffb7bcea000 	C:\Windows\SYSTEM32\VERSION.dll
+0x00007ffb85ab0000 - 0x00007ffb85b1a000 	C:\Windows\System32\WS2_32.dll
+0x00007ffb80c10000 - 0x00007ffb80c3b000 	C:\Windows\SYSTEM32\WINMMBASE.dll
+0x00007ffb82900000 - 0x00007ffb82942000 	C:\Windows\System32\cfgmgr32.dll
+0x0000000059270000 - 0x000000005927f000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\verify.dll
+0x0000000059240000 - 0x0000000059269000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\java.dll
+0x0000000057ce0000 - 0x0000000057d15000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\jdwp.dll
+0x0000000057cd0000 - 0x0000000057cd8000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\npt.dll
+0x0000000057ca0000 - 0x0000000057cc3000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\instrument.dll
+0x0000000059220000 - 0x0000000059236000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\zip.dll
+0x00007ffb83960000 - 0x00007ffb84e65000 	C:\Windows\System32\SHELL32.dll
+0x00007ffb82cb0000 - 0x00007ffb83383000 	C:\Windows\System32\windows.storage.dll
+0x00007ffb82820000 - 0x00007ffb8286c000 	C:\Windows\System32\powrprof.dll
+0x00007ffb85a40000 - 0x00007ffb85a92000 	C:\Windows\System32\shlwapi.dll
+0x00007ffb82870000 - 0x00007ffb8287f000 	C:\Windows\System32\kernel.appcore.dll
+0x00007ffb829f0000 - 0x00007ffb82a98000 	C:\Windows\System32\shcore.dll
+0x00007ffb82800000 - 0x00007ffb82814000 	C:\Windows\System32\profapi.dll
+0x0000000057c90000 - 0x0000000057c99000 	D:\Program Files\Java\jdk1.8.0_51\jre\bin\dt_socket.dll
+0x00007ffb82130000 - 0x00007ffb8218d000 	C:\Windows\system32\mswsock.dll
+
+VM Arguments:
+jvm_args: -agentlib:jdwp=transport=dt_socket,address=127.0.0.1:5920,suspend=y,server=n -javaagent:C:\Users\Pica\AppData\Local\JetBrains\IntelliJIdea2020.3\captureAgent\debugger-agent.jar -Dfile.encoding=UTF-8 
+java_command: com.aliyun.odps.spark.examples.sparksql.SessionProcess
+java_class_path (initial): C:\Users\Pica\AppData\Local\Temp\classpath1804223408.jar;C:\Users\Pica\AppData\Local\JetBrains\IntelliJIdea2020.3\captureAgent\debugger-agent.jar
+Launcher Type: SUN_STANDARD
+
+Environment Variables:
+JAVA_HOME=D:\Program Files\Java\jdk1.8.0_51
+PATH=D:\Program Files\Java\jdk1.8.0_51\bin;D:\Program Files (x86)\NetSarang\Xftp 7\;D:\Program Files (x86)\NetSarang\Xshell 7\;D:\hadoop-3.0.0-cdh6.2\bin;D:\work\scala-2.11.12\bin;D:\work\gradle-6.2.1\bin;C:\Users\Pica\AppData\Local\Microsoft\WindowsApps;
+USERNAME=Pica
+OS=Windows_NT
+PROCESSOR_IDENTIFIER=Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
+
+
+
+---------------  S Y S T E M  ---------------
+
+OS: Windows 10.0 , 64 bit Build 14393 (10.0.14393.4651)
+
+CPU:total 8 (4 cores per cpu, 2 threads per core) family 6 model 142 stepping 10, cmov, cx8, fxsr, mmx, sse, sse2, sse3, ssse3, sse4.1, sse4.2, popcnt, avx, avx2, aes, clmul, erms, 3dnowpref, lzcnt, ht, tsc, tscinvbit, bmi1, bmi2, adx
+
+Memory: 4k page, physical 16639044k(3340644k free), swap 29877824k(5200k free)
+
+vm_info: Java HotSpot(TM) 64-Bit Server VM (25.51-b03) for windows-amd64 JRE (1.8.0_51-b16), built on Jun  8 2015 18:03:07 by "java_re" with MS VC++ 10.0 (VS2010)
+
+time: Wed Feb 23 15:03:06 2022
+elapsed time: 3 seconds (0d 0h 0m 3s)
+

pom.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <properties>
+        <spark.version>2.3.0</spark.version>
+        <cupid.sdk.version>3.3.8-public</cupid.sdk.version>
+        <scala.version>2.11.8</scala.version>
+        <scala.binary.version>2.11</scala.binary.version>
+    </properties>
+
+    <groupId>com.aliyun.odps</groupId>
+    <artifactId>spark-session_${scala.binary.version}</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+    <packaging>jar</packaging>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-core_${scala.binary.version}</artifactId>
+            <version>${spark.version}</version>
+            <scope>provided</scope>
+            <exclusions>
+                <exclusion>
+                    <groupId>org.scala-lang</groupId>
+                    <artifactId>scala-library</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.scala-lang</groupId>
+                    <artifactId>scalap</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_${scala.binary.version}</artifactId>
+            <version>${spark.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-mllib_${scala.binary.version}</artifactId>
+            <version>${spark.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+            <version>${spark.version}</version>
+            <scope>provided</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>com.aliyun.odps</groupId>
+            <artifactId>cupid-sdk</artifactId>
+            <version>${cupid.sdk.version}</version>
+            <scope>provided</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>com.aliyun.odps</groupId>
+            <artifactId>hadoop-fs-oss</artifactId>
+            <version>${cupid.sdk.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>com.aliyun.odps</groupId>
+            <artifactId>odps-spark-datasource_${scala.binary.version}</artifactId>
+            <version>${cupid.sdk.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>org.scala-lang</groupId>
+            <artifactId>scala-library</artifactId>
+            <version>${scala.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>org.scala-lang</groupId>
+            <artifactId>scala-actors</artifactId>
+            <version>${scala.version}</version>
+        </dependency>
+
+        <!-- datahub streaming依赖 -->
+        <dependency>
+            <groupId>com.aliyun.emr</groupId>
+            <artifactId>emr-datahub_${scala.binary.version}</artifactId>
+            <version>1.6.0</version>
+        </dependency>
+
+        <dependency>
+            <groupId>org.scala-lang</groupId>
+            <artifactId>scala-xml</artifactId>
+            <version>2.11.0-M4</version>
+        </dependency>
+
+        <dependency>
+            <groupId>com.aliyun.datahub</groupId>
+            <artifactId>aliyun-sdk-datahub</artifactId>
+            <version>2.9.4-public</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>net.jpountz.lz4</groupId>
+                    <artifactId>lz4</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-streaming-kafka-0-10_${scala.binary.version}</artifactId>
+            <version>${spark.version}</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>net.jpountz.lz4</groupId>
+                    <artifactId>lz4</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql-kafka-0-10_${scala.binary.version}</artifactId>
+            <version>${spark.version}</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>net.jpountz.lz4</groupId>
+                    <artifactId>lz4</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+
+        <dependency>
+            <groupId>com.aliyun.odps</groupId>
+            <artifactId>streaming-lib</artifactId>
+            <version>3.3.8-public</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>net.jpountz.lz4</groupId>
+                    <artifactId>lz4</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.scala-lang</groupId>
+                    <artifactId>scala-library</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.scala-lang</groupId>
+                    <artifactId>scalap</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+
+        <dependency>
+            <groupId>org.apache.zeppelin</groupId>
+            <artifactId>spark-interpreter</artifactId>
+            <version>0.8.1</version>
+        </dependency>
+
+        <!--For usage of log4j2 interface-->
+        <!-- https://mvnrepository.com/artifact/org.apache.logging.log4j/log4j-core -->
+        <dependency>
+            <groupId>org.apache.logging.log4j</groupId>
+            <artifactId>log4j-core</artifactId>
+            <version>2.12.1</version>
+            <scope>provided</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>jindofs</groupId>
+            <artifactId>sdk</artifactId>
+            <version>3.7.2</version>
+            <scope>system</scope>
+            <systemPath>${project.basedir}/libs/jindofs-sdk-3.7.2.jar</systemPath>
+        </dependency>
+    </dependencies>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-shade-plugin</artifactId>
+                <version>2.4.3</version>
+                <executions>
+                    <execution>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>shade</goal>
+                        </goals>
+                        <configuration>
+                            <minimizeJar>false</minimizeJar>
+                            <shadedArtifactAttached>true</shadedArtifactAttached>
+                            <artifactSet>
+                                <includes>
+                                    <!-- Include here the dependencies you
+                                        want to be packed in your fat jar -->
+                                    <include>*:*</include>
+                                </includes>
+                            </artifactSet>
+                            <filters>
+                                <filter>
+                                    <artifact>*:*</artifact>
+                                    <excludes>
+                                        <exclude>META-INF/*.SF</exclude>
+                                        <exclude>META-INF/*.DSA</exclude>
+                                        <exclude>META-INF/*.RSA</exclude>
+                                        <exclude>**/log4j.properties</exclude>
+                                    </excludes>
+                                </filter>
+                            </filters>
+                            <transformers>
+                                <transformer
+                                        implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                                    <resource>reference.conf</resource>
+                                </transformer>
+                                <transformer
+                                        implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                                    <resource>
+                                        META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+                                    </resource>
+                                </transformer>
+                            </transformers>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>net.alchim31.maven</groupId>
+                <artifactId>scala-maven-plugin</artifactId>
+                <version>3.3.2</version>
+                <executions>
+                    <execution>
+                        <id>scala-compile-first</id>
+                        <phase>process-resources</phase>
+                        <goals>
+                            <goal>compile</goal>
+                        </goals>
+                    </execution>
+                    <execution>
+                        <id>scala-test-compile-first</id>
+                        <phase>process-test-resources</phase>
+                        <goals>
+                            <goal>testCompile</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <source>6</source>
+                    <target>6</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+
+</project>

replay_pid136340.log

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+ciMethod sun/misc/URLClassPath$3 <init> (Lsun/misc/URLClassPath;Ljava/net/URL;)V 4097 1 569 0 -1
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+ciInstanceKlass java/util/AbstractList 1 1 167 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 100 100 100 7 7 100 100 100 100 100 100 100 100 100 100 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 1 1
+instanceKlass java/util/Stack
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+staticfield java/util/ArrayList EMPTY_ELEMENTDATA [Ljava/lang/Object; 0 [Ljava/lang/Object;
+staticfield java/util/ArrayList DEFAULTCAPACITY_EMPTY_ELEMENTDATA [Ljava/lang/Object; 0 [Ljava/lang/Object;
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+instanceKlass java/util/LinkedHashMap$Entry
+ciInstanceKlass java/util/HashMap$Node 1 1 85 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 100 100 7 100 100 100 1 1 1 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 9 9 9 9 10 10 10 10 10 10 10 11 11 1
+ciInstanceKlass java/security/PrivilegedExceptionAction 1 0 15 1 1 1 1 1 1 1 1 1 100 100 100 1 1
+ciInstanceKlass java/io/PrintStream 1 1 282 8 8 8 8 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 7 100 100 100 7 100 100 7 7 7 100 100 100 100 100 100 100 100 100 100 100 100 100 100 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 1
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+staticfield sun/misc/URLClassPath JAVA_VERSION Ljava/lang/String; "1.8.0_51"
+staticfield sun/misc/URLClassPath DEBUG Z 0
+staticfield sun/misc/URLClassPath DEBUG_LOOKUP_CACHE Z 0
+staticfield sun/misc/URLClassPath DISABLE_JAR_CHECKING Z 0
+ciInstanceKlass sun/net/util/URLUtil 1 1 113 8 8 8 8 8 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 100 7 7 7 100 100 100 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 1
+ciInstanceKlass sun/misc/URLClassPath$3 1 1 91 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 100 7 7 7 100 7 7 7 7 100 1 1 1 1 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 12 9 9 9 10 10 10 10 10 10 10 10 10 10 1 1 1 1
+instanceKlass sun/misc/URLClassPath$FileLoader
+instanceKlass sun/misc/URLClassPath$JarLoader
+ciInstanceKlass sun/misc/URLClassPath$Loader 1 1 123 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 100 100 100 100 7 100 100 100 100 100 100 100 100 100 7 100 100 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 1 1 1
+ciInstanceKlass sun/misc/URLClassPath$JarLoader 1 1 444 8 8 8 8 8 8 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 100 100 7 7 7 100 7 7 7 100 100 7 100 100 100 7 7 100 100 7 100 100 7 7 7 100 7 7 100 7 7 100 7 7 100 7 100 7 100 7 7 7 100 7 100 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 1 1 1
+staticfield sun/misc/URLClassPath$JarLoader zipAccess Lsun/misc/JavaUtilZipFileAccess; java/util/zip/ZipFile$1
+ciInstanceKlass sun/misc/URLClassPath$FileLoader 1 1 125 8 8 8 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 100 100 100 7 7 100 100 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 1 1 1
+instanceKlass java/io/FileNotFoundException
+ciInstanceKlass java/io/IOException 1 1 27 1 1 1 1 1 1 1 1 1 1 1 1 5 0 100 7 1 12 12 12 12 10 10 10 10 1
+ciInstanceKlass java/security/PrivilegedActionException 1 1 59 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 0 100 7 100 100 100 100 7 1 1 1 1 1 1 12 12 12 12 12 12 12 9 10 10 10 10 10 10 10 1
+compile sun/misc/URLClassPath getNextLoader ([II)Lsun/misc/URLClassPath$Loader; -1 4 inline 9 0 -1 sun/misc/URLClassPath getNextLoader ([II)Lsun/misc/URLClassPath$Loader; 1 84 sun/misc/URLClassPath getLoader (I)Lsun/misc/URLClassPath$Loader; 2 13 java/util/ArrayList size ()I 2 33 java/util/Stack empty ()Z 3 1 java/util/Vector size ()I 2 123 java/util/ArrayList size ()I 2 190 java/util/ArrayList get (I)Ljava/lang/Object; 3 2 java/util/ArrayList rangeCheck (I)V 3 7 java/util/ArrayList elementData (I)Ljava/lang/Object;

src/main/java/com/aliyun/odps/spark/sparksql/JavaSparkSQL.java

+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ * <p>
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * <p>
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.aliyun.odps.spark.sparksql;
+
+import com.aliyun.odps.Odps;
+import com.aliyun.odps.cupid.CupidSession;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.apache.spark.sql.types.StructField;
+
+public class JavaSparkSQL {
+
+    public static void main(String[] args) {
+        SparkSession spark = SparkSession
+                .builder()
+                .appName("SparkSQL-on-MaxCompute")
+                .config("spark.sql.broadcastTimeout", 20 * 60)
+                .config("spark.sql.crossJoin.enabled", true)
+                .config("odps.exec.dynamic.partition.mode", "nonstrict")
+                .getOrCreate();
+        JavaSparkContext sparkContext = new JavaSparkContext(spark.sparkContext());
+
+
+        String tableName = "mc_test_table";
+        String tableNameCopy = "mc_test_table_copy";
+        String ptTableName = "mc_test_pt_table";
+
+
+        spark.sql("DROP TABLE IF EXISTS " + tableName);
+        spark.sql("DROP TABLE IF EXISTS " + tableNameCopy);
+        spark.sql("DROP TABLE IF EXISTS " + ptTableName);
+
+        spark.sql("CREATE TABLE " + tableName + " (name STRING, num BIGINT)");
+        spark.sql("CREATE TABLE " + ptTableName + " (name STRING, num BIGINT) PARTITIONED BY (pt1 STRING, pt2 STRING)");
+
+        List<Integer> data = new ArrayList<Integer>();
+        for (int i = 0; i < 100; i++) {
+            data.add(i);
+        }
+
+        JavaRDD<Row> dfRDD = sparkContext.parallelize(data, 2).map(new Function<Integer, Row>() {
+            public Row call(Integer i) {
+                return RowFactory.create(
+                        "name-" + i.toString(),
+                        Long.valueOf(i));
+            }
+        });
+
+        JavaRDD<Row> ptDfRDD = sparkContext.parallelize(data, 2).map(new Function<Integer, Row>() {
+            public Row call(Integer i) {
+                return RowFactory.create(
+                        "name-" + i.toString(),
+                        Long.valueOf(i),
+                        "2018",
+                        "0601");
+            }
+        });
+
+        List<StructField> structFilelds = new ArrayList<StructField>();
+        structFilelds.add(DataTypes.createStructField("name", DataTypes.StringType, true));
+        structFilelds.add(DataTypes.createStructField("num", DataTypes.LongType, true));
+        Dataset<Row> df = spark.createDataFrame(dfRDD, DataTypes.createStructType(structFilelds));
+
+        structFilelds.add(DataTypes.createStructField("pt1", DataTypes.StringType, true));
+        structFilelds.add(DataTypes.createStructField("pt2", DataTypes.StringType, true));
+        Dataset<Row> ptDf = spark.createDataFrame(ptDfRDD, DataTypes.createStructType(structFilelds));
+
+        // 写 普通表
+        df.write().insertInto(tableName); // insertInto语义
+        df.write().mode("overwrite").insertInto(tableName);// insertOverwrite语义
+
+        // 读 普通表
+        Dataset<Row> rdf = spark.sql("select name, num from " + tableName);
+        System.out.println("rdf count: " + rdf.count());
+        rdf.printSchema();
+
+        //create table as select
+        spark.sql("CREATE TABLE " + tableNameCopy + " AS SELECT name, num FROM " + tableName);
+        spark.sql("SELECT * FROM " + tableNameCopy).show();
+
+        // 写 分区表
+        // DataFrameWriter 无法指定分区写入 需要通过临时表再用SQL写入特定分区
+        df.registerTempTable(ptTableName + "_tmp_view");
+        spark.sql("insert into table " + ptTableName + " partition (pt1='2018', pt2='0601') select * from " + ptTableName + "_tmp_view");
+        spark.sql("insert overwrite table " + ptTableName + " partition (pt1='2018', pt2='0601') select * from " + ptTableName + "_tmp_view");
+
+        ptDf.write().insertInto(ptTableName);// 动态分区 insertInto语义
+        ptDf.write().mode("overwrite").insertInto(ptTableName); // 动态分区 insertOverwrite语义
+
+        // 读 分区表
+        Dataset<Row> rptdf = spark.sql("select name, num, pt1, pt2 from " + ptTableName + " where pt1 = '2018' and pt2 = '0601'");
+        System.out.println("rptdf count: " + rptdf.count());
+        rptdf.printSchema();
+
+        Odps odps = CupidSession.get().odps();
+        System.out.println(odps.tables().get(ptTableName).getPartitions().size());
+        System.out.println(odps.tables().get(ptTableName).getPartitions().get(0).getPartitionSpec());
+    }
+}
\ No newline at end of file

src/main/java/com/aliyun/odps/spark/utils/ConfigLog4j2.java

+package com.aliyun.odps.spark.utils;
+
+import org.apache.logging.log4j.Level;
+import org.apache.logging.log4j.LogManager;
+import org.apache.logging.log4j.core.Appender;
+import org.apache.logging.log4j.core.LoggerContext;
+import org.apache.logging.log4j.core.appender.ConsoleAppender;
+import org.apache.logging.log4j.core.config.AppenderRef;
+import org.apache.logging.log4j.core.config.Configuration;
+import org.apache.logging.log4j.core.config.LoggerConfig;
+import org.apache.logging.log4j.core.layout.PatternLayout;
+
+public class ConfigLog4j2 {
+
+  private static final LoggerContext CONTEXT;
+  public static final String DEFAULT_APPENDER = "MY_STDOUT";
+  public static final String
+      DEFAULT_PATTERN =
+      "%d{yyyy-MM-dd HH:mm:ss.SSS} [%t] %-5level %logger - %msg %ex %n";
+
+  static {
+    CONTEXT = (LoggerContext) LogManager.getContext(false);
+  }
+
+  /**
+   * @Description: add specific logger for specific package
+   * @Param: packageName, such as com.xx.yy
+   * @return: void
+   * @Author: lcj265802@alibaba-inc.com
+   * @Date: 2020/12/29
+   */
+  public static void initPackageLogger(String packageName) {
+    LoggerContext loggerContext = CONTEXT;
+    Configuration config = loggerContext.getConfiguration();
+
+    ConsoleAppender.Builder builder = ConsoleAppender.newBuilder();
+    builder.setName(DEFAULT_APPENDER);
+    builder.setLayout(PatternLayout.newBuilder().withPattern(DEFAULT_PATTERN).build());
+    Appender stdoutAppender = builder.setTarget(ConsoleAppender.Target.SYSTEM_OUT).build();
+    stdoutAppender.start();
+
+    config.addAppender(stdoutAppender);
+
+    AppenderRef ref = AppenderRef.createAppenderRef(DEFAULT_APPENDER, null, null);
+    AppenderRef[] refs = new AppenderRef[]{ref};
+
+    LoggerConfig
+        loggerConfig =
+        LoggerConfig.createLogger(false, Level.INFO, packageName,
+                                  "true", refs, null, config, null);
+    loggerConfig.addAppender(stdoutAppender, null, null);
+    config.addLogger(packageName, loggerConfig);
+
+    loggerContext.updateLoggers();
+  }
+}

src/main/python/spark_oss.py

+# -*- coding: utf-8 -*-
+import sys
+from pyspark.sql import SparkSession
+
+try:
+    # for python 2
+    reload(sys)
+    sys.setdefaultencoding('utf8')
+except:
+    # python 3 not needed
+    pass
+
+if __name__ == '__main__':
+    spark = SparkSession.builder\
+        .appName("spark write df to oss")\
+        .getOrCreate()
+
+    data = [i for i in range(0, 100)]
+
+    df = spark.sparkContext.parallelize(data, 2).map(lambda s: ("name-%s" % s, s)).toDF("name: string, num: int")
+
+    df.show(n=10)
+
+    # write to oss
+    pathout = 'oss://yeshan01/test.csv'
+    df.write.csv(pathout)

src/main/python/spark_sql.py

+# -*- coding: utf-8 -*-
+import sys
+from pyspark.sql import SparkSession
+
+try:
+    # for python 2
+    reload(sys)
+    sys.setdefaultencoding('utf8')
+except:
+    # python 3 not needed
+    pass
+
+if __name__ == '__main__':
+    spark = SparkSession.builder\
+        .appName("spark sql")\
+        .config("spark.sql.broadcastTimeout", 20 * 60)\
+        .config("spark.sql.crossJoin.enabled", True)\
+        .config("odps.exec.dynamic.partition.mode", "nonstrict")\
+        .getOrCreate()
+
+    tableName = "mc_test_table"
+    ptTableName = "mc_test_pt_table"
+    data = [i for i in range(0, 100)]
+
+    # Drop Create
+    spark.sql("DROP TABLE IF EXISTS %s" % tableName)
+    spark.sql("DROP TABLE IF EXISTS %s" % ptTableName)
+
+    spark.sql("CREATE TABLE %s (name STRING, num BIGINT)" % tableName)
+    spark.sql("CREATE TABLE %s (name STRING, num BIGINT) PARTITIONED BY (pt1 STRING, pt2 STRING)" % ptTableName)
+
+    df = spark.sparkContext.parallelize(data, 2).map(lambda s: ("name-%s" % s, s)).toDF("name: string, num: int")
+    pt_df = spark.sparkContext.parallelize(data, 2).map(lambda s: ("name-%s" % s, s, "2018", "0601")).toDF("name: string, num: int, pt1: string, pt2: string")
+
+    # 写 普通表
+    df.write.insertInto(tableName) # insertInto语义
+    df.write.insertInto(tableName, True) # insertOverwrite语义
+
+    # 写 分区表
+    # DataFrameWriter 无法指定分区写入 需要通过临时表再用SQL写入特定分区
+    df.createOrReplaceTempView("%s_tmp_view" % ptTableName)
+    spark.sql("insert into table %s partition (pt1='2018', pt2='0601') select * from %s_tmp_view" % (ptTableName, ptTableName))
+    spark.sql("insert overwrite table %s partition (pt1='2018', pt2='0601') select * from %s_tmp_view" % (ptTableName, ptTableName))
+
+    pt_df.write.insertInto(ptTableName) # 动态分区 insertInto语义
+    pt_df.write.insertInto(ptTableName, True) # 动态分区 insertOverwrite语义
+
+    # 读 普通表
+    rdf = spark.sql("select name, num from %s" % tableName)
+    print("rdf count, %s\n" % rdf.count())
+    rdf.printSchema()
+
+    # 读 分区表
+    rptdf = spark.sql("select name, num, pt1, pt2 from %s where pt1 = '2018' and pt2 = '0601'" % ptTableName)
+    print("rptdf count, %s" % (rptdf.count()))
+    rptdf.printSchema()
+
+

src/main/scala/com/aliyun/odps/spark/SparkPi.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark
+
+import org.apache.spark.sql.SparkSession
+
+import scala.math.random
+
+object SparkPi {
+  def main(args: Array[String]) {
+    val spark = SparkSession
+      .builder()
+      .appName("SparkPi")
+      .config("spark.master", "local[4]") // 需设置spark.master为local[N]才能直接运行，N为并发数。
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    try {
+      val slices = if (args.length > 0) args(0).toInt else 2
+      val n = math.min(100000L * slices, Int.MaxValue).toInt // avoid overflow
+      val count = sc.parallelize(1 until n, slices).map { i =>
+        val x = random * 2 - 1
+        val y = random * 2 - 1
+        if (x * x + y * y < 1) 1 else 0
+      }.reduce(_ + _)
+      println("Pi is roughly " + 4.0 * count / n)
+    } finally {
+      sc.stop()
+    }
+  }
+}
\ No newline at end of file

src/main/scala/com/aliyun/odps/spark/WordCount.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark
+
+import org.apache.spark.sql.SparkSession
+
+object WordCount {
+  def main(args: Array[String]) {
+    val spark = SparkSession
+      .builder()
+      .appName("WordCount")
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    try {
+      sc.parallelize(1 to 100, 10).map(word => (word, 1)).reduceByKey(_ + _, 10).take(100).foreach(println)
+    } finally {
+      sc.stop()
+    }
+  }
+}
\ No newline at end of file

src/main/scala/com/aliyun/odps/spark/graphx/PageRank.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.graphx
+
+import org.apache.spark.graphx.{Edge, Graph, VertexId}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+object PageRank {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName("PageRank")
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // build vertices
+    val users: RDD[(VertexId, Array[String])] = sc.parallelize(List(
+      "1,BarackObama,Barack Obama",
+      "2,ladygaga,Goddess of Love",
+      "3,jeresig,John Resig",
+      "4,justinbieber,Justin Bieber",
+      "6,matei_zaharia,Matei Zaharia",
+      "7,odersky,Martin Odersky",
+      "8,anonsys"
+    ).map(line => line.split(",")).map(parts => (parts.head.toLong, parts.tail)))
+
+    // build edges
+    val followers: RDD[Edge[Double]] = sc.parallelize(Array(
+      Edge(2L, 1L, 1.0),
+      Edge(4L, 1L, 1.0),
+      Edge(1L, 2L, 1.0),
+      Edge(6L, 3L, 1.0),
+      Edge(7L, 3L, 1.0),
+      Edge(7L, 6L, 1.0),
+      Edge(6L, 7L, 1.0),
+      Edge(3L, 7L, 1.0)
+    ))
+
+    // build graph
+    val followerGraph: Graph[Array[String], Double] = Graph(users, followers)
+
+    // restrict the graph to users with usernames and names
+    val subgraph = followerGraph.subgraph(vpred = (vid, attr) => attr.size == 2)
+
+    // compute PageRank
+    val pageRankGraph = subgraph.pageRank(0.001)
+
+    // get attributes of the top pagerank users
+    val userInfoWithPageRank = subgraph.outerJoinVertices(pageRankGraph.vertices) {
+      case (uid, attrList, Some(pr)) => (pr, attrList.toList)
+      case (uid, attrList, None) => (0.0, attrList.toList)
+    }
+
+    println(userInfoWithPageRank.vertices.top(5)(Ordering.by(_._2._1)).mkString("\n"))
+  }
+}

src/main/scala/com/aliyun/odps/spark/log4j2/Logger.scala

+package com.aliyun.odps.spark.log4j2
+
+import org.apache.logging.log4j
+import org.apache.logging.log4j.LogManager
+
+trait Logger {
+  val log: log4j.Logger = LogManager.getLogger(this.getClass)
+  log
+}

src/main/scala/com/aliyun/odps/spark/log4j2/SimpleWordCount.scala

+package com.aliyun.odps.spark.log4j2
+
+import com.aliyun.odps.spark.utils.ConfigLog4j2
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
+object SimpleWordCount extends Logger {
+  def main(args: Array[String]): Unit = {
+
+    ConfigLog4j2.initPackageLogger("com.aliyun.odps.spark.examples.log4j2")
+    val spark: SparkSession = SparkSession
+      .builder()
+      .appName("WordCount")
+      .getOrCreate()
+
+    log.info("My Test!")
+    val wordList = List("Hello", "World", "Hello")
+    val rdd: RDD[String] = spark.sparkContext.parallelize(Seq(wordList: _*)).cache()
+    val resultRDD: RDD[(String, Int)] = rdd.map(w => (w, 1)).reduceByKey(_ + _)
+    resultRDD.collect().foreach(v => {
+      log.info(s"${v._1} has num ${v._2}")
+    })
+
+    spark.stop()
+  }
+}

src/main/scala/com/aliyun/odps/spark/mllib/KmeansModelSaveToOss.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.mllib
+
+import org.apache.spark.mllib.clustering.KMeans._
+import org.apache.spark.mllib.clustering.{KMeans, KMeansModel}
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.sql.SparkSession
+
+object KmeansModelSaveToOss {
+  val modelOssDir = "oss://bucket/kmeans-model"
+
+  def main(args: Array[String]) {
+
+    //1. train and save the model
+    val spark = SparkSession
+      .builder()
+      .config("spark.hadoop.fs.oss.credentials.provider", "org.apache.hadoop.fs.aliyun.oss.AliyunStsTokenCredentialsProvider")
+      .config("spark.hadoop.fs.oss.ststoken.roleArn", "acs:ram::****:role/aliyunodpsdefaultrole")
+      .config("spark.hadoop.fs.oss.endpoint", "oss-cn-hangzhou-zmf.aliyuncs.com")
+      .appName("KmeansModelSaveToOss")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
+    val points = Seq(
+      Vectors.dense(0.0, 0.0),
+      Vectors.dense(0.0, 0.1),
+      Vectors.dense(0.1, 0.0),
+      Vectors.dense(9.0, 0.0),
+      Vectors.dense(9.0, 0.2),
+      Vectors.dense(9.2, 0.0)
+    )
+    val rdd = sc.parallelize(points, 3)
+    val initMode = K_MEANS_PARALLEL
+    val model = KMeans.train(rdd, k = 2, maxIterations = 2, runs = 1, initMode)
+    val predictResult1 = rdd.map(feature => "cluster id: " + model.predict(feature) + " feature:" + feature.toArray.mkString(",")).collect
+    println("modelOssDir=" + modelOssDir)
+    model.save(sc, modelOssDir)
+
+    //2. predict from the oss model
+    val modelLoadOss = KMeansModel.load(sc, modelOssDir)
+    val predictResult2 = rdd.map(feature => "cluster id: " + modelLoadOss.predict(feature) + " feature:" + feature.toArray.mkString(",")).collect
+    assert(predictResult1.size == predictResult2.size)
+    predictResult2.foreach(result2 => assert(predictResult1.contains(result2)))
+  }
+}

src/main/scala/com/aliyun/odps/spark/oss/JindoFsDemo.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.oss
+
+import org.apache.spark.{SparkConf, SparkContext}
+
+object JindoFsDemo {
+  def main(args: Array[String]): Unit = {
+    val aliyunUid : String = args(0)
+    val role : String = args(1)
+    val bucket : String = args(2)
+    val ossPath : String = args(3)
+
+    // using ram-role assume
+    val conf = new SparkConf()
+      .setAppName("jindo-fs-demo")
+      .set("spark.hadoop.fs.AbstractFileSystem.oss.impl", "com.aliyun.emr.fs.oss.OSS")
+      .set("spark.hadoop.fs.oss.impl", "com.aliyun.emr.fs.oss.JindoOssFileSystem")
+      .set("spark.hadoop.fs.oss.endpoint", "oss-cn-hangzhou-internal.aliyuncs.com")
+      .set("spark.hadoop.fs.jfs.cache.oss.credentials.provider", "com.aliyun.emr.fs.auth.CustomCredentialsProvider")
+      .set("spark.hadoop.aliyun.oss.provider.url", s"http://localhost:10011/sts-token-info?user_id=${aliyunUid}&role=${role}")
+
+
+    //using access-key-id/access-key-secret
+//    val conf = new SparkConf()
+//      .setAppName("jindo-fs-demo")
+//      .set("spark.hadoop.fs.AbstractFileSystem.oss.impl", "com.aliyun.emr.fs.oss.OSS")
+//      .set("spark.hadoop.fs.oss.impl", "com.aliyun.emr.fs.oss.JindoOssFileSystem")
+//      .set("spark.hadoop.fs.oss.endpoint", "oss-cn-hangzhou-internal.aliyuncs.com")
+//      .set("spark.hadoop.fs.oss.accessKeyId", "xxx")
+//      .set("spark.hadoop.fs.oss.accessKeySecret", "xxx")
+
+    val sc = new SparkContext(conf)
+
+    try {
+      read_oss_dir(sc, "demo", s"oss://${bucket}/${ossPath}")
+    } finally {
+      sc.stop()
+    }
+  }
+
+  /**
+    * compute cost time using jindo sdk
+    */
+  def read_oss_dir(sc: SparkContext, job_des:String, ossPath: String): Unit = {
+    val startTime: Long = System.currentTimeMillis()
+    val inputData = sc.textFile(ossPath, 20)
+    val cnt = inputData.count
+    val endTime:Long = System.currentTimeMillis()
+    val cost:Long = endTime - startTime
+    println(s"job:$job_des, count:$cnt, consume:$cost")
+  }
+}

src/main/scala/com/aliyun/odps/spark/oss/SparkUnstructuredDataCompute.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.oss
+
+import org.apache.spark.sql.SparkSession
+
+object SparkUnstructuredDataCompute {
+  def main(args: Array[String]) {
+    val spark = SparkSession
+      .builder()
+      .config("spark.hadoop.fs.oss.credentials.provider", "org.apache.hadoop.fs.aliyun.oss.AliyunStsTokenCredentialsProvider")
+      .config("spark.hadoop.fs.oss.ststoken.roleArn", "acs:ram::****:role/aliyunodpsdefaultrole")
+      .config("spark.hadoop.fs.oss.endpoint", "oss-cn-hangzhou-zmf.aliyuncs.com")
+      .appName("SparkUnstructuredDataCompute")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
+    try {
+      val pathIn = "oss://bucket/inputdata/"
+      val inputData = sc.textFile(pathIn, 5)
+      val cnt = inputData.count
+      println(s"count: $cnt")
+    } finally {
+      sc.stop()
+    }
+  }
+}
\ No newline at end of file

src/main/scala/com/aliyun/odps/spark/sparksql/SessionProcess.scala

+package com.aliyun.odps.spark.sparksql
+
+import config.MyConfigSession
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
+import org.apache.spark.sql.expressions.{Window, WindowSpec}
+import org.apache.spark.sql.functions.lag
+import org.apache.spark.storage.StorageLevel
+import utils.{DateUtils, StringUtils, UseUtil}
+
+import scala.collection.mutable.ListBuffer
+
+/**
+ *
+ * 处理埋点流量表,导入到DW层pica_dw.dw_fact_log_session
+ *
+ * @Author weicheng.mao
+ * @Date 2022/02/23 14:23
+ * @Version 1.0
+ */
+
+object SessionProcess {
+  def apply(): SessionProcess = new SessionProcess()
+
+  def main(args: Array[String]): Unit = {
+
+    var scnData: String = DateUtils.getYesterdayDate
+    if(args.length>=1){
+      scnData = args(0)
+    }
+    println(s"scnData=${scnData}")
+    //设置任务开始时间,格式是2019-09-12 14:03:30
+    val startTime: String = DateUtils.getTodayTime
+    //存储SQL中的参数
+    val insertArr: Array[String] = Array[String](scnData, startTime)
+
+    try {
+      val sessionProcess: SessionProcess = SessionProcess()
+      //step1:获取源数据,重新分区,产生shuffle,Spark读Hive默认的分区数太少,并对数据去重
+      var sourceDF: DataFrame = sessionProcess.sparkSession.sql(MyConfigSession.SOURCE_SQL+s" and created_day='${scnData}'").repartition(200).distinct()
+      sourceDF.show()
+      var conditionGroup = List( "<='2'","between '3' and '5'","between '6' and '8'","between '9' and 'b'",">='c'" )
+      var dataCount = 0
+      var index = 0
+      sourceDF.persist(StorageLevel.MEMORY_AND_DISK_SER)
+      for(condition <- conditionGroup){
+        index += 1
+        val slideDF = sourceDF.where(s" SUBSTRING(pseudo_session,1,1)  ${condition}").repartition(100)
+        //step2:抽取出当天pseudo_session对应的非空的device_token,doctor_id,mobile,补充到对应的pseudo_session下这几项为空的记录中
+        val groupRdd = slideDF.rdd.groupBy(r => r.getAs[String]("pseudo_session"))
+        val resRdd = groupRdd.flatMap(g => {
+          val pseudo_session = g._1
+          val resList: ListBuffer[Row] = new ListBuffer[Row]()
+          var rowList = g._2
+          rowList = rowList.toList.sortWith((x,y)=>x.getAs[String]("created") > y.getAs[String]("created"))//按created由大到小排序
+          var thisDeviceToken = ""
+          var thisDoctorId = "0"
+          var thisMobile = ""
+
+          rowList.foreach(row => {
+            var deviceToken = row.getAs[String]("device_token")
+            var doctorId = row.getAs[String]("doctor_id")
+            var mobile = row.getAs[String]("mobile")
+            val created = row.getAs[String]("created")
+            if(deviceToken!=null && !deviceToken.equals("") ){
+              thisDeviceToken = deviceToken
+            }else {
+              deviceToken = thisDeviceToken
+            }
+            if(doctorId!=null && !doctorId.equals("") && !doctorId.equals("0") ){
+              thisDoctorId = doctorId
+            }else {
+              doctorId = thisDoctorId
+            }
+            if(mobile!=null && !mobile.equals("") ){
+              thisMobile = mobile
+            }else {
+              mobile = thisMobile
+            }
+            resList += (Row(row.getAs[String]("pseudo_session"),
+              doctorId,
+              mobile,
+              deviceToken,
+              row.getAs[String]("user_token_tourist"),
+              row.getAs[String]("class_name"),
+              row.getAs[String]("view_path"),
+              row.getAs[String]("action"),
+              row.getAs[String]("component_tag"),
+              row.getAs[String]("app_version"),
+              row.getAs[String]("device_type"),
+              row.getAs[String]("device_brand"),
+              row.getAs[String]("device_model"),
+              row.getAs[String]("network_type"),
+              row.getAs[String]("created")))
+          })
+          resList.iterator
+        })
+
+        import sessionProcess.sparkSession.implicits._
+        //step3:根据映射表来进行action_type和class_name数据过滤
+        val resDF = sessionProcess.sparkSession.createDataFrame(resRdd,sourceDF.schema)
+        resDF.persist(StorageLevel.MEMORY_AND_DISK_SER)
+        println("resDF.show=======>")
+        //            resDF.show()
+        val data: RDD[Row] = resDF.rdd.mapPartitions(sessionProcess.filterRows)
+        println("---------------------------------------process columns-------------------------------------------")
+
+        val baseDF: DataFrame = data.mapPartitions(sessionProcess.processColumns)
+          .toDF("pseudo_session", "user_id", "mobile", "device_token", "user_token", "view_class", "view_path",
+            "action_type", "component_tag", "menu_code", "action_code", "position", "label_value","label_class","module_class1","module_class2", "app_version",
+            "device_type", "device_brand", "device_model", "device_system", "net_type", "created_time", "date_time")
+        println("----------------------------------compute session id---------------------------------------------")
+        val sessionIdDF: DataFrame =  sessionProcess.getSessionId(baseDF,sessionProcess)
+        //默认缓存级别是:MEMORY_AND_DISK
+        sessionIdDF.persist(StorageLevel.MEMORY_AND_DISK_SER)
+        println("sessionIdDF.show=======>")
+        println("-------------------------------match user_id 逻辑-------------------------------------------------")
+        val dwFactLogSession: DataFrame = sessionProcess.matchUserId(sessionIdDF,sessionProcess.sparkSession,scnData)
+        println("dwFactLogSession.show=======>")
+        println("-----------------create view fact_log_session and load to dw_fact_log_session--------------------")
+        dwFactLogSession.createOrReplaceTempView("fact_log_session")
+        var insertMode = "insert overwrite"
+        if(index!=1){
+          insertMode = "insert into"
+        }
+        val loadDataSql =
+          s"${insertMode} table ${MyConfigSession.HIVE_TABLE1} partition(created_day='${scnData}') select * from fact_log_session distribute by rand()"
+        sessionProcess.sparkSession.sql(loadDataSql)
+        dataCount = dataCount + dwFactLogSession.count().toInt
+      }
+
+      println("----------------------------------update task record table---------------------------------------")
+
+      sessionProcess.sparkSession.stop()
+    } catch {
+      case e: Exception => {
+        println(s"-----------------------------------任务异常---------------------------------------------------")
+        e.printStackTrace()
+
+      }
+    }
+  }
+
+
+
+}
+
+class SessionProcess extends java.io.Serializable{
+  def getSparkSession(appName: String): SparkSession = {
+//    val sparkSession = SparkSession
+//      .builder()
+//      .appName("SparkPi")
+//      .config("spark.master", "local[1]") // 需设置spark.master为local[N]才能直接运行，N为并发数。
+//      .config("spark.hadoop.odps.project.name", "pica")
+//      .config("spark.hadoop.odps.access.id", "LTAI5tQEnb9VREcPdDAR32bg")
+//      .config("spark.hadoop.odps.access.key", "RYazt1JIYZnRcEdlntarZ1Ov21zoNi")
+//      .config("spark.hadoop.odps.end.point", "http://service.cn.maxcompute.aliyun.com/api")
+//      .config("spark.sql.catalogImplementation", "odps")
+//      .getOrCreate()
+
+        val sparkSession = SparkSession
+          .builder()
+          .config("spark.hadoop.odps.project.name", "pica")
+          .appName("SessionProcess")
+          .getOrCreate()
+
+    sparkSession
+  }
+
+  val sparkSession: SparkSession = getSparkSession("SessionProcess")
+
+  //获取符合要求的actionType广播变量
+  val actionTypeBroad =
+    UseUtil.getBroadcast(sparkSession, MyConfigSession.ACTION_TYPE_SQL, "action_type", "is_valid")
+  //获取clasName广播变量
+  val classNameBroad =
+    UseUtil.getBroadcast(sparkSession, MyConfigSession.CLASS_NAME_SQL, "class_name", "is_valid")
+  //获取menu_code广播变量
+  val menuCodeBroad =
+    UseUtil.getBroadcast(sparkSession, MyConfigSession.MENU_CODE_SQL, "view_path", "menu_code")
+  //获取actionCategory变量
+  val actionCategory =
+    UseUtil.getBroadcast(sparkSession,MyConfigSession.ACTION_CATEGORY_SQL,"action_type","action_category")
+
+
+  //定义函数式变量,过滤映射表数据
+  val filterRows: Iterator[Row] => Iterator[Row] = (rows: Iterator[Row]) => {
+    val rowList: ListBuffer[Row] = new ListBuffer[Row]()
+    val actionTypeMap: Map[String, String] = actionTypeBroad.value
+    val classNameMap: Map[String, String] = classNameBroad.value
+    //关联到action_category的映射表广播变量
+    val actionCategoryMap: Map[String, String] = actionCategory.value
+    rows.toList.foreach(row => {
+      //筛选action的条件
+      val action: String = StringUtils.getNotNullString(row.getAs[String]("action"))
+      //说明该action类型即为所要的
+      if (actionTypeMap.getOrElse(action, "-1").equals("1")) {
+        //将action转化为映射表中对应的标准actionCategory
+        val action_type: String = actionCategoryMap.getOrElse(action,"ACTION")
+        //                val action_type = UseUtil.getActionType(action)
+        //action为其中的任何一个
+        if (action_type.equals("ACTION_CLICK") || action_type.equals("ACTION_EXPOSE")) {
+          //判断 component_tag 必须要包含 "#"
+          if (row.getAs[String]("component_tag") != null
+            && row.getAs[String]("component_tag").contains("#")) {
+            rowList += row
+          }
+        } else if (action_type.equals("ACTION_VIEW")) {
+          rowList += row
+          //非上述三种action_type,那么需要过滤掉映射表中class_name为"0"对应的那些数据
+        } else if (row.getAs[String]("class_name") != null
+          && !classNameMap.getOrElse(row.getAs[String]("class_name"), "-1").equals("0")) {
+          rowList += row
+        }
+      }
+    })
+    rowList.iterator
+  }
+
+  //处理字段,得到需要的字段值
+  val processColumns = (rows: Iterator[Row]) => {
+    //        val baseList = new ListBuffer[(String, String, String, String, String, String, String, String, String,
+    //                String, String, String, String, String, String, String, String, String, String, String, String,String,String,String)]()
+    val baseList = new ListBuffer[SessionLog]()
+    //关联到menu_code的映射表广播变量
+    val path_menu: Map[String, String] = menuCodeBroad.value
+    //关联到action_category的映射表广播变量
+    val actionCategoryMap: Map[String, String] = actionCategory.value
+    rows.toList.foreach(row => {
+      //1.获取网络类型
+      //2G,3G,4G,2G/3G/4G,WIFI,WLAN,或者为空字符串
+      val net_type = UseUtil.netTypeMatch(StringUtils.getNotNullString(row.getAs[String]("network_type")))
+
+      //2.修改action类型
+      var action_type: String = ""
+      if (row.getAs[String]("action") !=null) {
+        action_type = actionCategoryMap.getOrElse(row.getAs[String]("action"),"ACTION")
+      }
+      //3.拆分 component_tag字段
+      val component_tag: String = StringUtils.getNotNullString(row.getAs[String]("component_tag"))
+      var menu_code: String = ""
+      var action_code: String = ""
+      var position: String = ""
+      var label_value: String = ""
+      var label_class:String = ""
+      var module_class1:String=""
+      var module_class2:String=""
+      //将符合要求的component_tag进行切割,获取 aciton_code,label_value
+      if (component_tag.contains("#")) {
+        //按照#号切割
+        val strs: Array[String] = component_tag.split("#")
+        strs.length match {
+          case 1 => {
+            menu_code = strs(0)
+          }
+          case 2 => {
+            menu_code = strs(0)
+            action_code = strs(1)
+          }
+          case 3 => {
+            menu_code = strs(0)
+            action_code = strs(1)
+            position = strs(2)
+          }
+          case 4 => {
+            menu_code = strs(0)
+            action_code = strs(1)
+            position = strs(2)
+            label_value = strs(3).substring(0,math.min(250,strs(3).length))
+          }
+          case 5 => {
+            menu_code = strs(0)
+            action_code = strs(1)
+            position = strs(2)
+            label_value = strs(3).substring(0,math.min(250,strs(3).length))
+            label_class = strs(4)
+          }
+          case 6 => {
+            menu_code = strs(0)
+            action_code = strs(1)
+            position = strs(2)
+            label_value = strs(3).substring(0,math.min(250,strs(3).length))
+            label_class = strs(4)
+            module_class1=strs(5)
+          }
+          case _ => {
+            menu_code = strs(0)
+            action_code = strs(1)
+            position = strs(2)
+            label_value = strs(3).substring(0,math.min(250,strs(3).length))
+            label_class = strs(4)
+            module_class1=strs(5)
+            module_class2=strs(6)
+          }
+        }
+      }
+      //匹配menu_code:如果上述截取出来的menu_code为(''||null||0||length(menu_code)>3 ) and action is ACTION_VIEW
+      if ((menu_code.equals("")|| menu_code.equals("null") || menu_code.equals("0") || (menu_code.length> 3 && !component_tag.contains("#") ))
+        && action_type.equals("ACTION_VIEW")) {
+        menu_code = "0" //关联不上的显示为0
+        import scala.util.control.Breaks._
+        breakable {
+          //利用menu_code映射表匹配
+          for (tuple <- path_menu) {
+            //源数据view_path的字符串包含映射表view_path的字符串
+            if (StringUtils.getNotNullString(row.getAs[String]("view_path")).contains(tuple._1)) {
+              //满足条件后,修改源数据的menu_code
+              menu_code = tuple._2
+              println("--------------------menu_code match successfully-----------------------")
+              //结束遍历
+              break()
+            }
+          }
+          //经过上述匹配,如果menu_code仍然为空串,那么置为component_tag字段一样
+          if (menu_code.equals("")) {
+            menu_code = component_tag
+          }
+        }
+      }
+      //一行数据添加到List中
+      baseList += SessionLog(StringUtils.getNotNullString(row.getAs[String]("pseudo_session")),
+        StringUtils.getNotNullString(row.getAs[String]("doctor_id")),
+        StringUtils.getNotNullString(row.getAs[String]("mobile")),
+        StringUtils.getNotNullString(row.getAs[String]("device_token")),
+        StringUtils.getNotNullString(row.getAs[String]("user_token_tourist")),
+        StringUtils.getNotNullString(row.getAs[String]("class_name")),
+        StringUtils.getNotNullString(row.getAs[String]("view_path")),
+        action_type, component_tag, menu_code, action_code, position, label_value,label_class,module_class1,module_class2,
+        StringUtils.getNotNullString(row.getAs[String]("app_version")),
+        StringUtils.getNotNullString(row.getAs[String]("device_type")),
+        StringUtils.getNotNullString(row.getAs[String]("device_brand")),
+        StringUtils.getNotNullString(row.getAs[String]("device_model")),
+        "", net_type,
+        StringUtils.getNotNullString(row.getAs[String]("created")),
+        DateUtils.milliSecondsFormatTime(StringUtils.getNotNullString(row.getAs[String]("created"))))
+    })
+    baseList.iterator
+  }
+  //按照time_gap 切割session,计算session_id
+  val computeSessionId = (tuple: (String, Iterable[Row])) => {
+    //先按照 pseudo_session 的值命名 sessionID
+    val sessionID: String = tuple._1
+    val rowList: Iterable[Row] = tuple._2
+    //定义一个累加量
+    var count: Int = 0
+    //存储一行的数据
+    //        val list = new ListBuffer[(String, String, String, String, String, String, String, String, String,
+    //                String, String, String, String, String, String, String, String, String, String, String, String, String,String, String)]()
+    val list = new ListBuffer[SessionLog]()
+    rowList.toList.foreach(row => {
+      val created: String = StringUtils.getNotNullString(row.getAs[String]("created_time"))
+      val refer_created: String = StringUtils.getNotNullString(row.getAs[String]("refer_created"))
+      val time_diff: Long = created.toLong - refer_created.toLong
+      //相邻的时间差小于等于30分钟,就是同一个 sessionID
+      if (time_diff > MyConfigSession.SESSION_GAP) {
+        count = count + 1
+      }
+      //添加到List
+      list +=  SessionLog(sessionID + count,
+        StringUtils.getNotNullString(row.getAs[String]("user_id")),
+        StringUtils.getNotNullString(row.getAs[String]("mobile")),
+        StringUtils.getNotNullString(row.getAs[String]("device_token")),
+        StringUtils.getNotNullString(row.getAs[String]("user_token")),
+        StringUtils.getNotNullString(row.getAs[String]("view_class")),
+        StringUtils.getNotNullString(row.getAs[String]("view_path")),
+        StringUtils.getNotNullString(row.getAs[String]("action_type")),
+        StringUtils.getNotNullString(row.getAs[String]("component_tag")),
+        StringUtils.getNotNullString(row.getAs[String]("menu_code")),
+        StringUtils.getNotNullString(row.getAs[String]("action_code")),
+        StringUtils.getNotNullString(row.getAs[String]("position")),
+        StringUtils.getNotNullString(row.getAs[String]("label_value")),
+        StringUtils.getNotNullString(row.getAs[String]("label_class")),
+        StringUtils.getNotNullString(row.getAs[String]("module_class1")),
+        StringUtils.getNotNullString(row.getAs[String]("module_class2")),
+        StringUtils.getNotNullString(row.getAs[String]("app_version")),
+        StringUtils.getNotNullString(row.getAs[String]("device_type")),
+        StringUtils.getNotNullString(row.getAs[String]("device_brand")),
+        StringUtils.getNotNullString(row.getAs[String]("device_model")),
+        StringUtils.getNotNullString(row.getAs[String]("device_system")),
+        StringUtils.getNotNullString(row.getAs[String]("net_type")),
+        created, StringUtils.getNotNullString(row.getAs[String]("date_time")))
+    })
+    list
+  }
+  /**
+   * @Description     按照TimeGap切割session,重命名session_id
+   * @param dataFrame 要处理的DataFrame
+   * @param sessionProcess   SessionProcess对象,包含SparkSession 环境
+   * @return org.apache.spark.sql.Dataset<org.apache.spark.sql.Row>
+   **/
+  def getSessionId(dataFrame: DataFrame,sessionProcess: SessionProcess):DataFrame = {
+    import sessionProcess.sparkSession.implicits._
+
+    //先按照 pseudo_session 分组,然后按照 created 排序,组件一个窗口
+    val pSessionWinSpec: WindowSpec = Window.partitionBy("pseudo_session").orderBy("created_time")
+    //增加一个字段 refer_created ,这个字段的值是上一条记录 created 字段的值,方便后面通过两者差值计算出 session_id
+    val rcreDF: DataFrame =
+      dataFrame.withColumn("refer_created", lag(dataFrame("created_time"), 1).over(pSessionWinSpec))
+
+    //执行COALESCE,目的是为了去掉 refer_created 为Null的值
+    val coalesceDF: DataFrame = rcreDF.selectExpr(
+      "pseudo_session", "user_id", "mobile", "device_token",
+      "user_token", "view_class", "view_path", "action_type",
+      "component_tag", "menu_code", "action_code", "position", "label_value","label_class","module_class1","module_class2",
+      "app_version", "device_type", "device_brand", "device_model",
+      "device_system", "net_type", "created_time", "date_time",
+      "COALESCE(refer_created,created_time) as refer_created")
+
+    //按照 pseudo_session聚合,计算 session_id
+    val groupRDD: RDD[(String, Iterable[Row])] =
+      coalesceDF.rdd.groupBy(row => row.getAs[String]("pseudo_session"))
+
+    //计算两者之差,这时候就得到了 session_id
+    val sessionIdDF: DataFrame = groupRDD.map(sessionProcess.computeSessionId).flatMap(it => it)
+      .toDF("session_id", "user_id", "mobile", "device_token", "user_token",
+        "view_class", "view_path", "action_type", "component_tag",
+        "menu_code", "action_code", "position", "label_value","label_class","module_class1","module_class2",
+        "app_version", "device_type", "device_brand", "device_model",
+        "device_system", "net_type", "created_time", "date_time")
+    sessionIdDF
+  }
+
+
+  /**
+   * @Description         匹配user_id,补全数据中的user_id字段
+   * @param dataFrame     筛选后的数据
+   * @param sparkSQLSession  SparkSession 环境
+   * @param created_day   当前数据的日期,格式 "2020-03-01"
+   * @return org.apache.spark.sql.Dataset<org.apache.spark.sql.Row>
+   **/
+  def matchUserId(dataFrame: DataFrame,sparkSQLSession: SparkSession,created_day:String):DataFrame={
+    //追加:将dataFrame与pica_ds.pica_doctor根据user_id进行匹配,匹配不上的user_id置为'0'
+    println("matchUserId开始执行-----------------------------------")
+    dataFrame.createOrReplaceTempView(MyConfigSession.VIEW_SESSION_ODS)
+    val DF = sparkSQLSession.sql(MyConfigSession.INIT_USER_ID_SQL)
+    //以下的所有逻辑是为了补全user_id字段
+    //第一步：首先筛选出不符合的use_id数据,将这些user_id置为字符串'0'
+    val noMatchUserIdDF: Dataset[Row] = DF.where("user_id ='' OR user_id = '0' OR LENGTH(user_id) = 24")
+      .selectExpr("session_id","'0' as user_id", "mobile", "device_token", "user_token",
+        "view_class", "view_path", "action_type", "component_tag",
+        "menu_code", "action_code", "position", "label_value","label_class","module_class1","module_class2",
+        "app_version", "device_type", "device_brand", "device_model",
+        "device_system", "net_type", "created_time", "date_time")
+    noMatchUserIdDF.createOrReplaceTempView(MyConfigSession.VIEW_SESSION_NO_MATCH)
+
+    //1.筛选出上一步没有匹配到的user_id,先按照mobile_phone进行匹配
+    val mobilePhoneDF: DataFrame = sparkSQLSession.sql(MyConfigSession.MOBILE_PHONE_SQL)
+    mobilePhoneDF.createOrReplaceTempView(MyConfigSession.VIEW_MOBILE_PHONE)
+    //2.使用临时表equiment,筛选出为1的那条最新数据
+    var equipmentInfoSql =  MyConfigSession.EQUIPMENT_INFO_SQL
+    if(!created_day.equals(DateUtils.getYesterdayDate)){//如果不是跑昨天的数据,使用equipment拉链表
+      equipmentInfoSql =  MyConfigSession.EQUIPMENT_INFO_SQL_ARGS+ s"'${created_day}'"
+    }
+    println(s"equipmentInfoSql=${equipmentInfoSql}")
+    val equipmentDF: DataFrame = sparkSQLSession.sql(equipmentInfoSql).where("row_d =1")
+    equipmentDF.createOrReplaceTempView(MyConfigSession.VIEW_EQUIPMENT_INFO)
+
+    //3.将第2步筛选出来的数据按照device_token进行匹配,获得user_id
+    val deviceTokenDF: DataFrame = sparkSQLSession.sql(MyConfigSession.DEVICE_TOKEN_SQL)
+
+    //4.将上述三者union,最终导入表中的数据
+    val rightUserId: Dataset[Row] = DF.where("user_id !='' and user_id != '0' and LENGTH(user_id) !=24")
+    val mobilePhoneResDF: Dataset[Row] = mobilePhoneDF.where("user_id !='0'")
+    val dwFactLogSession: Dataset[Row] = rightUserId.union(mobilePhoneResDF).union(deviceTokenDF)
+    dwFactLogSession.createOrReplaceTempView(MyConfigSession.VIEW_DEVICE_TOKEN)
+    //根据pica_doctor补充user_id_int字段（字段类型转换成int型）, 限制 delete_flag = 1 and creat_time截止昨日创建,未关联上显示为0
+    val USER_ID_INT_SQL:String=
+      s"""
+         |SELECT   concat(regexp_replace( '${created_day}',"-","") ,cast(row_number() over(partition by 1 order by created_time) as string)) as id,
+         |ss.session_id, ss.user_id,COALESCE(b.id,0) user_id_int, ss.mobile, ss.device_token, ss.user_token,
+         |ss.view_class,ss.view_path,ss.action_type,ss.component_tag, ss.menu_code,
+         |ss.action_code, ss.position,ss.label_value,ss.label_class,ss.module_class1,ss.module_class2,ss.app_version, ss.device_type,
+         |ss.device_brand, ss.device_model,ss.device_system,ss.net_type,ss.created_time,
+         |ss.date_time from ${MyConfigSession.VIEW_DEVICE_TOKEN}  AS ss
+         |left join (select  id,cast(id as string) id_str  from pica.ods_pica_p_doctor a where a.delete_flag = 1 and to_date(a.creat_time) <= '${created_day}') AS b on ss.user_id = b.id_str
+         |""".stripMargin
+    val userIdDF: DataFrame = sparkSQLSession.sql(USER_ID_INT_SQL)
+    userIdDF
+  }
+}
+case class SessionLog(pseudo_session: String,
+                      user_id: String,
+                      mobile: String,
+                      device_token: String,
+                      user_token: String,
+                      view_class: String,
+                      view_path: String,
+                      action_type: String,
+                      component_tag: String,
+                      menu_code: String,
+                      action_code: String,
+                      position: String,
+                      label_value: String,
+                      label_class: String,
+                      module_class1: String,
+                      module_class2: String,
+                      app_version: String,
+                      device_type: String,
+                      device_brand: String,
+                      device_model: String,
+                      device_system: String,
+                      net_type: String,
+                      created_time: String,
+                      date_time: String)
+
+

src/main/scala/com/aliyun/odps/spark/sparksql/SessionProcessTerm.scala

+package com.aliyun.odps.spark.sparksql
+
+import config.MyConfigSession
+
+
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types.{IntegerType, LongType, StringType, StructField, StructType}
+import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
+import org.apache.spark.storage.StorageLevel
+import utils.{DateUtils, StringUtils, UseUtil}
+
+import scala.collection.mutable
+import scala.collection.mutable.ListBuffer
+import scala.util.control.Breaks.{break, breakable}
+
+
+/**
+ * 处理昨天的数据,用于计算时长以及访问路径，结果导入： dw_fact_log_session_term
+ * 注意点：
+ * 1.只保留service_name='trace2'(native)埋点数据，（h5埋点数据也会走native上报一份）
+ * 2.去掉ACTION_EQUIP_INFO类数据
+ * 1.首页加载前的引导页(android不埋 )不计入首页流量统计
+ * 2.对于ACTION_VIEW类型事件,用页面的resume以及stop事件计算访问时差
+ * 3.对于ACTION_HEART_BEAT事件中的back类型或者是BACKGROUND类事件，将menu_code定位back，
+ * 取连续backgroud的末次back的时间以及首次back时间差作为后台执行时长
+ * 4.如果menu_code值为空或值不符合规范，如果view_class有值取view_class代替
+ *
+ * @Author weicheng.mao
+ * @Date 2022/03/02 10:58
+ * @Version 1.0
+ */
+
+object SessionProcessTerm {
+  def apply(): SessionProcessTerm = new SessionProcessTerm()
+
+  def main(args: Array[String]): Unit = {
+
+    //设置同步数据的批次号,格式是2019-09-12
+    var scnData: String = DateUtils.getYesterdayDate
+    var elseFiler = " 1=1"
+    if (args.length >= 1) {
+      scnData = args(0)
+      if(args.length > 1 && args(1)!=""){
+        elseFiler = args(1)
+      }
+    }
+    println(s"scnData=${scnData}")
+    //设置任务开始时间,格式是2019-09-12 14:03:30
+    val startTime: String = DateUtils.getTodayTime
+    //存储SQL中的参数
+    val insertArr: Array[String] = Array[String](scnData, startTime)
+
+    val sessionProcessTerm: SessionProcessTerm = SessionProcessTerm()
+     try {
+      val sparkSession: SparkSession = sessionProcessTerm.getSparkSession("SessionProcessTerm")
+
+       //获取menu_code广播变量
+       val menuCodeBroad = UseUtil.getBroadcast(sparkSession, MyConfigSession.MENU_CODE_SQL, "view_path", "menu_code")
+       //    //获取actionCategory变量
+       val actionCategory = UseUtil.getBroadcast(sparkSession, MyConfigSession.ACTION_CATEGORY_SQL, "action_type", "action_category")
+       //获取position对应的label_value广播变量
+       val positionUrlLabelBroad = UseUtil.getBroadcast(sparkSession, MyConfigSession.ACTION_URLLABEL_SQL, "url_content", "label_value")
+       println(s"positionUrlLabelBroad=${positionUrlLabelBroad.value}")
+      var SOURCE_SQL_COMMON =
+        s"""
+           |select  pseudo_session,doctor_id,mobile,device_token,class_name,view_path,action,
+           |component_tag,app_version,device_type, cast(created as bigint) created,user_token_tourist,
+           |network_type ,device_brand,device_model,alternate_info,remark2 first_app_version,serviceName service_name
+           |from pica.picalog_trace_app_part
+           | where  pseudo_session is not null and pseudo_session !='' and pseudo_id !='' and extra_info !='com.picahealth.patient'
+           | and action!='ACTION_EQUIP_INFO'
+           | and action!='ACTION_WEB_AFTER'
+           |  and created is not null and created!='' and FROM_UNIXTIME(cast(substring(created,1,10) as bigint),'yyyy-MM-dd')='${scnData}'
+           |   and created_day='${scnData}' and ${elseFiler}
+           |""".stripMargin
+      var SOURCE_SQL_TERM = SOURCE_SQL_COMMON + " and  serviceName='trace2' "
+       println("SOURCE_SQL_TERM=="+SOURCE_SQL_TERM )
+       var sourceDF: DataFrame = sparkSession.sql(SOURCE_SQL_TERM )
+       /*
+        补充站外埋点数据
+        */
+       //part1.通过device_token补充,device_token不为空时对应的埋点只有trace1数据的需要加进来
+      val SOURCE_SQL_TERM_ADD =
+        s"""
+          |select  b.*  from (
+            | select device_token,   concat_ws( '_',collect_set(service_name) )  cws
+            | from  (${SOURCE_SQL_COMMON}) s group by device_token  having concat_ws( '_',collect_set(service_name)) ='trace1'
+            |  ) a
+          | join ( ${SOURCE_SQL_COMMON + " and  serviceName='trace1' "}) b on a.device_token=b.device_token
+          |""".stripMargin
+       println("SOURCE_SQL_TERM_ADD=="+SOURCE_SQL_TERM_ADD)
+       var sourceAddDF =  sparkSession.sql(SOURCE_SQL_TERM_ADD )
+       sourceAddDF.createOrReplaceTempView("source_add_table")
+       //part2.通过pseudo_session补充,device_token为空时,pseudo_session对应的埋点只有trace1数据且doctor_id不为空的需要加进来
+       val SOURCE_SQL_TERM_ADD2 =
+         s"""
+            |select  b.* from ( ${SOURCE_SQL_COMMON + " and  serviceName='trace1' "}) b where b.doctor_id in (
+            | select doctor_id
+            | from  (${SOURCE_SQL_COMMON +" and doctor_id not in ('','0') and doctor_id is not null" }) s group by doctor_id  having concat_ws( '_',collect_set(service_name)) ='trace1'
+            |  )
+            | and   b.pseudo_session not in (select  pseudo_session from source_add_table group by pseudo_session )
+            |""".stripMargin
+       println("SOURCE_SQL_TERM_ADD2=="+SOURCE_SQL_TERM_ADD2)
+       var sourceAdd2DF =  sparkSession.sql(SOURCE_SQL_TERM_ADD2 )
+      val sourceResDF = sourceDF.union(sourceAddDF).union(sourceAdd2DF)
+      println("sourceResDF.show==================")
+       sourceResDF.printSchema()
+      println("selectDF.count=========",sourceResDF.count())
+      var conditionGroup = List( "<='1'","between '2' and '3'","between '4' and '5'","between '6' and '7'","between '8' and '9'",
+          "between 'a' and 'b'","between 'c' and 'd'",">='e'" )
+      var dataCount = 0
+      var index = 0
+       sourceResDF.persist(StorageLevel.MEMORY_AND_DISK_SER)
+
+      for(condition <- conditionGroup){
+        index += 1
+        val slideDF = sourceResDF.where(s" SUBSTRING(pseudo_session,2,1)  ${condition}").repartition(100)
+        println(s"-----------------------------------compute refer columns,condition=${condition}-----------------------------------------")
+        println(s"-----------------------------------compute refer columns,condition_count=${slideDF.count()}-----------------------------------------")
+
+        val baseDF = sessionProcessTerm.offsetValues(menuCodeBroad,actionCategory,positionUrlLabelBroad,slideDF)
+        baseDF.repartition(120).persist(StorageLevel.MEMORY_AND_DISK_SER)
+        val referResultRdd = sessionProcessTerm.getReferColumns(baseDF)
+        val referResultDF: DataFrame = sparkSession.createDataFrame(referResultRdd, StructType(
+          List(StructField("pseudo_session", StringType, false),
+            StructField("session_id", StringType, false),
+            StructField("device_token", StringType, false),
+            StructField("user_id", IntegerType, false),
+            StructField("user_token", StringType, false),
+            StructField("mobile", StringType, false),
+            StructField("menu_code", StringType, false),
+            StructField("menu_begin_time", StringType, false),
+            StructField("action_code", StringType, false),
+            StructField("position", StringType, false),
+            StructField("label_value", StringType, false),
+            StructField("action", StringType, false),
+            StructField("action_type", StringType, false),
+            StructField("action_step", StringType, false),
+            StructField("device_type", StringType, false),
+            StructField("app_version", StringType, false),
+            StructField("created_time", LongType, false),
+            StructField("date_time", StringType, false),
+            StructField("pre_session_id", StringType, false),
+            //新增字段
+            StructField("label_class", StringType, true),
+            StructField("net_type", StringType, true),
+            StructField("module_class1", StringType, true),
+            StructField("module_class2", StringType, true),
+            StructField("device_brand", StringType, true),
+            StructField("device_model", StringType, true),
+            StructField("view_class", StringType, true),
+            StructField("view_path", StringType, true),
+            StructField("alternate_info", StringType, true),
+            StructField("first_app_version", StringType, true),
+            StructField("service_name", StringType, true),
+            StructField("tag8", StringType, true),
+            StructField("tag9", StringType, true),
+            StructField("component_tag", StringType, true)
+          )
+        ))
+        println("referResultDF.show()============'")
+        referResultDF.show()
+        referResultDF.printSchema()
+        referResultDF.persist(StorageLevel.MEMORY_AND_DISK_SER).createOrReplaceTempView("refer_result_table")
+        println("-----------------------------------compute menu_code term-----------------------------------------")
+        val getMenuTermSql =
+          """
+            |select  a.session_id,a.device_token,a.user_id,a.menu_code,
+            |lag(a.created_time) over(partition by a.session_id order by a.created_time desc ) menu_end_time,a.created_time
+            | from  refer_result_table a
+            | left  join(select session_id,min(app_version) min_version from refer_result_table group by session_id) b on a.session_id=b.session_id
+            |""".stripMargin
+        //处理session最小版本>='3.4.5'的session数据
+        val newVersionMenuDF = sparkSession.sql(s"${getMenuTermSql} where b.min_version>='3.4.5' and a.action in('ACTION_ACTIVITY_RESUME','ACTION_HEART_BEAT','ACTION_WEB_ENTER') ")
+        println("newVersionMenuDF,show()======")
+        val oldVersionMenuDF = sparkSession.sql(s"${getMenuTermSql} where b.min_version<'3.4.5' and a.action_type in ('ACTION_VIEW','ACTION_HEART')")
+        println("oldVersionMenuDF,show()======")
+        val referMenuDF = newVersionMenuDF.union(oldVersionMenuDF)
+        println("referMenuDF.show()=========")
+        referMenuDF.printSchema()
+        referMenuDF.createOrReplaceTempView("refer_menu_table")
+
+        println("-----------------------------------compute session_id end_time-----------------------------------------")
+        val sessionEndDF: DataFrame = sessionProcessTerm.getSessionTail(slideDF, sparkSession)
+        println("------sessionEndDF.show()-------------")
+        sessionEndDF.printSchema()
+        sessionEndDF.createOrReplaceTempView("session_end_table")
+        val getReferSql =
+          s"""
+             |select
+             |a.session_id,
+             |a.device_token,
+             |a.user_id,
+             |a.user_token,
+             |a.mobile,
+             |a.menu_code,
+             |a.action_code,
+             |a.position,
+             |a.label_value,
+             |a.label_class,
+             |a.module_class1,
+             |a.module_class2,
+             |a.action_type,
+             |a.menu_begin_time,
+             | case when  a.action_type in('ACTION_VIEW','ACTION_HEART') and  b.menu_end_time is null  then c.session_end_time else b.menu_end_time end menu_end_time,
+             |(cast((case when  a.action_type in('ACTION_VIEW','ACTION_HEART') and  b.menu_end_time is null  then c.session_end_time else b.menu_end_time end) as bigint)-cast(a.menu_begin_time as bigint))/1000 menu_time_diff,
+             |a.action_step,
+             |a.device_type,
+             |a.device_brand,
+             |a.device_model,
+             |a.app_version,
+             |a.net_type,
+             |a.created_time,
+             |a.date_time,
+             |c.session_begin_time,
+             |c.session_end_time,
+             |(cast(c.session_end_time as bigint)-cast(c.session_begin_time as bigint))/1000 session_time_diff,
+             |a.pre_session_id,
+             |case when a.user_id=0 then a.device_token else a.user_id end user_identity_id,
+             |a.view_class,a.view_path, a.alternate_info, a.first_app_version,a.service_name,a.tag8,a.tag9,a.component_tag
+             |from refer_result_table a
+             |left join refer_menu_table b on a.session_id=b.session_id and a.device_token=b.device_token and a.user_id=b.user_id and a.menu_code=b.menu_code  and a.created_time=b.created_time and a.action_type in('ACTION_VIEW','ACTION_HEART')
+             |left join session_end_table c on a.session_id = c.session_id
+             | distribute by rand()
+             |""".stripMargin
+        val menuCodeAddDF = sparkSession.sql(getReferSql).repartition(100)
+        println("menuCodeAddDF.show=======")
+        menuCodeAddDF.printSchema()
+        menuCodeAddDF.persist(StorageLevel.MEMORY_AND_DISK_SER)
+        println("-------------------------------match user_id 逻辑-------------------------------------------------")
+        val resultDF: DataFrame = sessionProcessTerm.matchUserId(menuCodeAddDF, sparkSession, scnData).repartition(5)
+        println("dwFactLogSession.show=======>")
+        resultDF.printSchema()
+        println("-----------------------------------load data to pica_dw.dw_fact_log_session_term-----------------")
+        sessionProcessTerm.loadData(resultDF, sparkSession, scnData,index,dataCount)
+        val resCount = resultDF.count().toInt
+        println(s"${condition}的结果==${resCount}")
+        dataCount += resCount
+      }
+      println("----------------------------------update task record table---------------------------------------")
+      //任务执行成功,更新 Mysql record 配置表
+      val updateSQL: String =
+        s"""
+           |update ${MyConfigSession.JDBC_TABLE} set status=?,end_time=?,data_count=? where job_name='${MyConfigSession.HIVE_TABLE4}' and start_time='${startTime}'
+				""".stripMargin
+
+      sparkSession.stop()
+    } catch {
+      case e: Exception => {
+        println("-----------------------------------任务异常---------------------------------------------------")
+        e.printStackTrace()
+        val exceptionSQL: String =
+          s"""
+             |update ${MyConfigSession.JDBC_TABLE} set status=?,exception=?,end_time=? where job_name='${MyConfigSession.HIVE_TABLE4}' and start_time='${startTime}'
+					""".stripMargin
+
+      }
+    }
+  }
+}
+
+class SessionProcessTerm {
+
+  def getSparkSession(appName: String): SparkSession = {
+//    val conf: SparkConf = new SparkConf().setAppName(appName)
+//    UseUtil.setConfigure(conf)
+//    val sparkSession: SparkSession = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate()
+//    sparkSession
+
+  val sparkSession = SparkSession
+    .builder()
+    .config("spark.hadoop.odps.project.name", "pica")
+    .appName("SessionProcessTerm")
+    .getOrCreate()
+    sparkSession
+  }
+
+  def offsetValues(menuCodeBroad:Broadcast[Map[String,String]], actionCategory:Broadcast[Map[String,String]],
+                   positionUrlLabelBroad:Broadcast[Map[String,String]],dataFrame: DataFrame): DataFrame = {
+    val path_menu: Map[String, String] = menuCodeBroad.value //关联到menu_code的映射表广播变量
+    val actionCategoryMap: Map[String, String] = actionCategory.value //关联到action_category的映射表广播变量
+    val positionLabelMap: Map[String, String] = positionUrlLabelBroad.value
+
+    val groupRdd = dataFrame.rdd.groupBy(r => r.getAs[String]("pseudo_session"))
+
+    val baseRdd = groupRdd.flatMap(g => {
+      val pseudo_session = g._1
+      val resList: ListBuffer[SessionTerm] = new ListBuffer[SessionTerm]()
+      var rowList = g._2
+      rowList = rowList.toList.sortWith((x, y) => x.getAs[Long]("created") > y.getAs[Long]("created")) //按created由大到小排序
+      var thisDeviceToken = ""
+      var thisDoctorId = "0"
+      var thisMobile = ""
+      rowList.foreach(row => {
+        var deviceToken = row.getAs[String]("device_token")
+        var doctorId = row.getAs[String]("doctor_id")
+        var mobile = row.getAs[String]("mobile")
+        if (deviceToken != null && !deviceToken.equals("")) {
+          thisDeviceToken = deviceToken
+        } else {
+          deviceToken = thisDeviceToken
+        }
+         if (doctorId != null && !doctorId.equals("") && !doctorId.equals("0")) {
+          thisDoctorId = doctorId
+        } else {
+          doctorId = thisDoctorId
+        }
+        if (mobile != null && !mobile.equals("")) {
+          thisMobile = mobile
+        } else {
+          mobile = thisMobile
+        }
+        //1.获取网络类型
+        //2G,3G,4G,2G/3G/4G,WIFI,WLAN,或者为空字符串
+        val net_type = UseUtil.netTypeMatch(StringUtils.getNotNullString(row.getAs[String]("network_type")))
+        //2.修改action类型,保留原始字段
+        val action = row.getAs[String]("action")
+        var action_type: String = ""
+        if (action != null) {
+          action_type = actionCategoryMap.getOrElse(action, "ACTION_UNKNOWN")
+          if(action=="ACTION_HEART_BEAT"){
+            action_type="ACTION_HEART"
+          }
+        }
+        //3.拆分 component_tag字段
+        val component_tag: String = StringUtils.getNotNullString(row.getAs[String]("component_tag"))
+        val view_path = StringUtils.getNotNullString(row.getAs[String]("view_path"))
+        val tagArr = Array("menu_code", "action_code", "position", "label_value", "label_class", "module_class1", "module_class2", "tag8", "tag9")
+        val tagMap = mutable.Map[String, String]()
+        tagArr.foreach(r => tagMap.put(r, ""))
+        //针对特定类型事件,将符合要求的component_tag进行切割,获取 aciton_code,label_value
+        if(List("ACTION_VIEW", "ACTION_CLICK", "ACTION_EXPOSE").contains(action_type)){
+          if (component_tag.contains("#")) {//默认是
+            //按照#号切割
+            val strs: Array[String] = component_tag.split("#")
+            var index = 0
+            for (value <- strs) {
+              val filedName = tagArr.apply(index)
+              if ("label_value".equals(filedName)) {
+                tagMap.put(filedName, value.substring(0, math.min(250, strs(3).length)))
+              } else {
+                tagMap.put(filedName, value)
+              }
+              index += 1
+            }
+          }else if(component_tag.length<5  && StringUtils.isNumeric(component_tag) && component_tag.toInt>0  ){ //如果component_tag不为0,且长度在合理区间,则作为menu_code值
+            tagMap.put("menu_code",component_tag)
+          }
+        }
+        var menu_code_new = tagMap("menu_code")
+        if ("MenuCode_081".equals(menu_code_new)) {
+          menu_code_new = "081" //针对异常menu_code值单独处理
+        }
+        //匹配menu_code:如果上述截取出来的menu_code为(''||null||0||length(menu_code)>3  && !component_tag.contains("#") ) and action is ACTION_VIEW
+        if ((menu_code_new.equals("") || menu_code_new.equals("null") || menu_code_new.equals("0") || (menu_code_new.length > 3  && !component_tag.contains("#")) )
+          && action_type.equals("ACTION_VIEW")) {
+          menu_code_new = "0" //关联不上的显示为0
+          import scala.util.control.Breaks._
+          breakable {
+            //利用menu_code映射表匹配
+            for (tuple <- path_menu) {
+              //源数据view_path的字符串包含映射表view_path的字符串
+              if (view_path.contains(tuple._1)) {
+                //满足条件后,修改源数据的menu_code
+                menu_code_new = tuple._2
+                println("--------------------menu_code match successfully-----------------------")
+                //结束遍历
+                break()
+              }
+            }
+          }
+        }
+        //解析(5分钟|健康知识)详情页的id到position
+        if(action_type.equals("ACTION_VIEW")  && List("425","015").contains(menu_code_new) && view_path.contains("?")){
+          val kvs=view_path.split("\\?").apply(1).split("&")
+          for(kv <- kvs){
+            val k = kv.split("=").apply(0)
+            if(menu_code_new=="425" && k=="id"){ //五分钟详情页
+              tagMap("position")= kv.split("=").apply(1)
+            }
+            if(menu_code_new=="015" && k=="eduComId"){ //健康知识详情页
+              tagMap("position")= kv.split("=").apply(1)
+            }
+          }
+        }
+        //
+        if(List("930000","930001","930002" ).contains(tagMap("action_code"))
+            && "ACTION_CLICK".equals(action_type) && "930"== menu_code_new){
+          println("------------------------------------单独计算label_value----------------------------------------------")
+          //"menu_code = '930' and  action_code IN ( '930000', '930001', '930002' ) and action_type = 'ACTION_CLICK'
+          breakable {
+            //利用position url_content映射表匹配
+            for (tuple <- positionLabelMap) {
+              if (StringUtils.getNotNullString(tagMap("position")).contains(tuple._1) && tuple._2!="" && tuple._2 != null) {
+                //满足条件后,修改源数据的label_value
+                tagMap("label_value") = tuple._2
+                println("--------------------menu_code match successfully-----------------------")
+                //结束遍历
+                break()
+              }
+            }
+          }
+        }
+        //针对menu_code缺失情况补偿处理
+        var menu_code_offset = menu_code_new
+        if(component_tag=="back"){
+          menu_code_offset = "back"
+        }
+        val view_class = StringUtils.getNotNullString(row.getAs[String]("class_name"))
+        if( List("0","null","").contains(menu_code_new) &&  !List("0","null","").contains(view_class)){
+          menu_code_offset = view_class
+        }
+        var user_id  = 0
+        if(doctorId != null && !doctorId.equals("") && !doctorId.equals("0")){
+          user_id = doctorId.toInt
+        }
+        var app_version = StringUtils.getNotNullString(row.getAs[String]("app_version"))
+        var is_need = true //添加一个字段,下一步计算时长使用,
+        // 针对3.4.5之后的版本,使用ACTION_ACTIVITY_RESUME计算页面起止
+        if(app_version.length<8 && app_version>="3.4.5" && action=="ACTION_ACTIVITY_CREATE" && menu_code_new!="017" ){
+          is_need =  false
+        }
+        resList += SessionTerm(pseudo_session,
+          user_id,
+          mobile,
+          deviceToken,
+          StringUtils.getNotNullString(row.getAs[String]("user_token_tourist")),
+          view_class,
+          StringUtils.getNotNullString(row.getAs[String]("view_path")),
+          action, action_type, component_tag, tagMap("menu_code"), menu_code_new,menu_code_offset, tagMap("action_code"),
+          tagMap("position"), tagMap("label_value"), tagMap("label_class"), tagMap("module_class1"), tagMap("module_class2"),
+          app_version,
+          StringUtils.getNotNullString(row.getAs[String]("device_type")),
+          StringUtils.getNotNullString(row.getAs[String]("device_brand")),
+          StringUtils.getNotNullString(row.getAs[String]("device_model")),
+          net_type, row.getAs[Long]("created"),
+          DateUtils.milliSecondsFormatTime(row.getAs[Long]("created") + ""),
+          StringUtils.getNotNullString(row.getAs[String]("alternate_info")),
+          StringUtils.getNotNullString(row.getAs[String]("first_app_version")),
+          StringUtils.getNotNullString(row.getAs[String]("service_name")),
+          tagMap("tag8"), tagMap("tag9"), is_need
+        )
+      })
+      resList.iterator
+    })
+    import dataFrame.sparkSession.implicits._
+    var baseDF = baseRdd.toDF("pseudo_session", "user_id", "mobile", "device_token", "user_token", "view_class", "view_path", "action", "action_type", "component_tag",
+      "menu_code", "menu_code_new","menu_code_offset", "action_code", "position", "label_value", "label_class", "module_class1", "module_class2", "app_version", "device_type", "device_brand", "device_model",
+      "net_type", "created_time", "date_time", "alternate_info",  "first_app_version", "service_name", "tag8", "tag9",  "is_need")
+    println("baseDF.show=======>")
+    baseDF.where("menu_code_new ='017'").show()
+    baseDF.printSchema()
+    baseDF
+  }
+  /**
+   * @Description 匹配user_id,补全数据中的user_id字段
+   * @param dataFrame       筛选后的数据
+   * @param sparkSQLSession SparkSession 环境
+   * @param created_day     当前数据的日期,格式 "2020-03-01"
+   * @return org.apache.spark.sql.Dataset<org.apache.spark.sql.Row>
+   **/
+  def matchUserId(dataFrame: DataFrame, sparkSQLSession: SparkSession, created_day: String): DataFrame = {
+    //追加:将dataFrame与pica_ds.pica_doctor根据user_id进行匹配,匹配不上的user_id置为'0'
+    println("matchUserId开始执行-----------------------------------")
+    dataFrame.withColumnRenamed("user_id", "user_id_ods").createOrReplaceTempView(MyConfigSession.VIEW_SESSION_ODS)
+    val INIT_USER_ID_SQL_PREF =
+      s"""
+         |SELECT t.*,t.user_id_ods user_id_old, COALESCE(cast(b.id as string),'0') AS user_id  from ${MyConfigSession.VIEW_SESSION_ODS} as t
+         |left join  (select  id,cast(id as string) id_str  from pica.ods_pica_p_doctor a where  to_date(a.creat_time) <= '${created_day}') AS b on t.user_id_ods = cast(b.id as string)
+            """.stripMargin
+    val DF = sparkSQLSession.sql(INIT_USER_ID_SQL_PREF).drop("user_id_ods")
+    /*
+        以下的所有逻辑是为了补全user_id字段
+     */
+    //第一步：首先筛选出不符合的use_id数据,将这些user_id置为字符串'0'
+    val noMatchUserIdDF: Dataset[Row] = DF.where("user_id ='' OR user_id = '0' OR LENGTH(user_id) = 24")
+    noMatchUserIdDF.drop("user_id_old").withColumnRenamed("user_id", "user_id_old")
+      .createOrReplaceTempView(MyConfigSession.VIEW_SESSION_NO_MATCH)
+    //1.筛选出上一步没有匹配到的user_id,先按照mobile_phone进行匹配
+    val MOBILE_PHONE_SQL_PREF: String =
+      s"""
+         |SELECT ss.*,COALESCE(cast(b.id as string),'0') AS user_id  from ${MyConfigSession.VIEW_SESSION_NO_MATCH}  AS ss
+         |left join (select distinct id,mobile_phone from pica.ods_pica_p_doctor where  delete_flag=1 and mobile_phone!='' and mobile_phone!='XK0HdMN6dAfOlYPOFHHL0A==') AS b on ss.mobile = b.mobile_phone
+         """.stripMargin
+    val mobilePhoneDF: DataFrame = sparkSQLSession.sql(MOBILE_PHONE_SQL_PREF)
+    //2.使用临时表equiment,筛选出为1的那条最新数据
+    var equipmentInfoSql = MyConfigSession.EQUIPMENT_INFO_SQL
+    if (!created_day.equals(DateUtils.getYesterdayDate)) { //如果不是跑昨天的数据,使用equipment拉链表
+      equipmentInfoSql = MyConfigSession.EQUIPMENT_INFO_SQL_ARGS + s"'${created_day}'"
+    }
+    println(s"equipmentInfoSql=${equipmentInfoSql}")
+    val equipmentDF: DataFrame = sparkSQLSession.sql(equipmentInfoSql).where("row_d =1")
+    equipmentDF.createOrReplaceTempView(MyConfigSession.VIEW_EQUIPMENT_INFO)
+    mobilePhoneDF.drop("user_id_old").withColumnRenamed("user_id", "user_id_old")
+      .createOrReplaceTempView(MyConfigSession.VIEW_MOBILE_PHONE)
+    val DEVICE_TOKEN_SQL_PREF: String =
+      s""" SELECT t.*,COALESCE(cast(b.user_id as string),'0') AS user_id
+         | from (select * from  ${MyConfigSession.VIEW_MOBILE_PHONE}  a where a.user_id_old= '0' ) as t
+         |left join ${MyConfigSession.VIEW_EQUIPMENT_INFO} as b on t.device_token = b.device_token
+            """.stripMargin
+    println(s"DEVICE_TOKEN_SQL_PREF=${DEVICE_TOKEN_SQL_PREF}")
+    //3.将第2步筛选出来的数据按照device_token进行匹配,获得user_id
+    val deviceTokenDF: DataFrame = sparkSQLSession.sql(DEVICE_TOKEN_SQL_PREF)
+
+    //4.将上述三者union,最终导入表中的数据
+    val rightUserIdDF: Dataset[Row] = DF.where("user_id !='' and user_id != '0' and LENGTH(user_id) !=24")
+    val mobilePhoneResDF: Dataset[Row] = mobilePhoneDF.where("user_id !='0'")
+    println("rightUserIdDF/mobilePhoneResDF/deviceTokenDF.schema===========")
+    rightUserIdDF.printSchema()
+    mobilePhoneResDF.printSchema()
+    deviceTokenDF.printSchema()
+    val dwFactLogSession: Dataset[Row] = rightUserIdDF.union(mobilePhoneResDF).union(deviceTokenDF)
+    dwFactLogSession.createOrReplaceTempView(MyConfigSession.VIEW_DEVICE_TOKEN)
+    dwFactLogSession
+  }
+
+  /**
+   * @Description 获取需要的字段的refer字段以及对pseudo_session进行拆分
+   *              https://www.tapd.cn/64812329/prong/stories/view/1164812329001012031需求上线版本为(Android:3.4.5,iOS:3.4.4,之后的版本统一到3.4.6)
+   *              计算sessionId,按版本号区分数据,之前的作为老数据处理,之后的作为新数据处理,老数据对pseudo_session进行拆分,时间最大间隔30分钟
+   *              新数据不拆分session,按ACTION_HEART_BEAT区分session活跃时间(新增ACTION_HEART_BEAT事件的版本是3.3.2)
+   * @param sourceDF 源数据
+   * @return org.apache.spark.sql.Dataset<org.apache.spark.sql.Row>
+   **/
+  def getReferColumns(sourceDF: DataFrame): RDD[Row] = {
+    val actions = List("'ACTION_VIEW'", "'ACTION_CLICK'", "'ACTION_EXPOSE'","'ACTION_START'") //高版本使用"'ACTION_HEART_BEAT'"
+    val actionCommonDF = sourceDF.where(s" ( action_type in (${actions.mkString(",")}) or (action_type='ACTION_HEART' and menu_code_offset='back' )) ")
+        .filter("is_need")
+
+    println("actionCommonDF.show()===========")
+    actionCommonDF.filter("menu_code='017'").show()
+    actionCommonDF.printSchema()
+    //计算session中menu_code的起始时间,对于ACTION_HEART_BEAT的埋点记录剔除冗余数据
+    val groupRdd = actionCommonDF.rdd.groupBy(r => r.getAs[String]("pseudo_session"))//.repartition(100)
+    println("开始执行groupRdd.flatMap=========")
+    val resRdd = groupRdd.flatMap(g => {
+      val pseudo_session = g._1
+      val resList: ListBuffer[Row] = new ListBuffer[Row]()
+      var rowList = g._2
+      rowList = rowList.toList.sortWith((x, y) => x.getAs[Long]("created_time") < y.getAs[Long]("created_time")) //按created由小到小排序
+      var actionStep = "0_0"
+      var thisMenuCode = ""
+      var menuBeginTime = ""
+      var prefCreatedTime = ""
+      var prefActionType = ""
+      var count = 0
+
+      for (row <- rowList) {
+        var isSessionNew = false
+        var appVersion = row.getAs[String]("app_version")
+        //如果版本号低于3.3.2,则没有ACTION_HEART_BEAT事件,需要根据两次事件之间的时差对session进行切分,超过30分钟则拆分出一个session
+        var createdTime = row.getAs[Long]("created_time")
+        if (appVersion < "3.3.2") { //版本号低于3.3.2,需要根据时差判断是否 拆分session
+          if (prefCreatedTime != "" && (createdTime.toLong - prefCreatedTime.toLong) > MyConfigSession.SESSION_GAP) { //本次action事件事件比
+            count += 1
+            isSessionNew = true
+          }
+        }
+        var menuCode = row.getAs[String]("menu_code_offset")
+        val action = row.getAs[String]("action")
+        val actionType = row.getAs[String]("action_type")
+        var sessionId = pseudo_session + count
+        var preSessionId = ""
+        if (count > 0) {
+          preSessionId = pseudo_session + (count - 1)
+        }
+        //使用版本以及action_type限制是否入库
+        var needPut = true
+        if ("ACTION_VIEW".equals(actionType) || "ACTION_HEART".equals(actionType)) {
+          if (appVersion >= "3.4.5") { //针对3.4.5之后的版本单独处理
+            //如果本条记录为ACTION_ACTIVITY_RESUME类型或者ACTION_HEART类型或者menu_code为017表示外部方式启动app,则入库
+            if ("ACTION_ACTIVITY_RESUME".equals(action) || "ACTION_HEART".equals(actionType) ||"ACTION_WEB_ENTER".equals(action)|| "017".equals(menuCode)) {//有些h5页面没有对应的ACTION_ACTIVITY_RESUME,需要补进来
+              needPut = true
+            }else{
+              needPut = false
+            }
+            if ( thisMenuCode.equals(menuCode) && ("ACTION_HEART"==prefActionType  || "ACTION_VIEW" == prefActionType)){
+              needPut = false
+            }
+          }else{
+            if ( thisMenuCode.equals(menuCode) && ("ACTION_HEART"==prefActionType  || "ACTION_VIEW" == prefActionType) && !isSessionNew ) {
+              needPut = false
+            }
+          }
+          if(needPut){
+            menuBeginTime = createdTime.toString
+          }
+        }
+        //如果menuCode发生改变,或者session改变,重置本轮menuCode
+        if (!thisMenuCode.equals(menuCode) || isSessionNew) {
+          menuBeginTime = createdTime.toString
+          if(needPut ){//只有在确定该menu_code对应的记录入库后,才更新thisMenuCode以及对应的menu_code访问顺序
+            thisMenuCode = menuCode
+            actionStep = (actionStep.split("_")(0).toInt + 1).toString + "_0"
+          }
+        } else {//如果menuCode与session都不变, actionType改变或者actionType为非浏览行为,则调整actionStep
+          if (!(prefActionType.equals(actionType) && "ACTION_VIEW".equals(actionType))) {
+            actionStep = (actionStep.split("_")(0)) + "_" + (actionStep.split("_")(1).toInt + 1).toString
+          }
+        }
+//        println(s"created_time=${createdTime},thisMenuCode=${thisMenuCode},actionType=${actionType},action=${action},isMenuChange=${!thisMenuCode.equals(menuCode)},prefActionType=${prefActionType},needPut=${needPut}" )
+        prefCreatedTime = createdTime.toString
+        prefActionType = actionType
+        if (needPut) {
+          resList += (Row( pseudo_session,sessionId,
+            row.getAs[String]("device_token"),
+            row.getAs[Integer]("user_id"),
+            row.getAs[String]("user_token"),
+            row.getAs[String]("mobile"),
+            thisMenuCode,
+            menuBeginTime,
+            row.getAs[String]("action_code"),
+            row.getAs[String]("position"),
+            row.getAs[String]("label_value"),
+            action, actionType, actionStep,
+            row.getAs[String]("device_type"),
+            row.getAs[String]("app_version"),
+            createdTime,
+            row.getAs[String]("date_time"),
+            preSessionId,
+            row.getAs[String]("label_class"),
+            row.getAs[String]("net_type"),
+            row.getAs[String]("module_class1"),
+            row.getAs[String]("module_class2"),
+            row.getAs[String]("device_brand"),
+            row.getAs[String]("device_model"),
+            row.getAs[String]("view_class"),
+            row.getAs[String]("view_path"),
+            row.getAs[String]("alternate_info"),
+            row.getAs[String]("first_app_version"),
+            row.getAs[String]("service_name"),
+            row.getAs[String]("tag8"),
+            row.getAs[String]("tag9"),
+            row.getAs[String]("component_tag")
+          ))
+        }
+      }
+      resList.iterator
+    })
+    resRdd
+  }
+
+
+  /**
+   * @Description 计算session_end_time,分两张情况:
+   *             1.如果是低版本数据,session有拆分记录,则取session_id的首条记录时间以及末次记录时间作为session起始结束时间
+   *             2.如果是高版本数据,session无拆分记录,则取session_id对应的pseudo_session首次以及末次出现的记录时间作为session起止时间
+   * @param sourceDF 源数据
+   * @return
+   */
+  def getSessionTail(sourceDF: DataFrame, sparkSession: SparkSession): DataFrame = {
+    sourceDF.createOrReplaceTempView("select_res_table")
+    val sourceSql =
+      """
+        |select session_id,min(created_time) session_begin_time,max(created_time) session_end_time from refer_result_table
+        | group by  session_id
+        |""".stripMargin
+    val firstSessionEndDF = sparkSession.sql(sourceSql)
+    println("firstSessionEndDF.show()=========")
+    firstSessionEndDF
+  }
+  def loadData(dataFrame: DataFrame, sparkSession: SparkSession, partitionDay: String,index:Integer,count:Integer): Unit = {
+    dataFrame.createOrReplaceTempView("result_view")
+    var insertSql = "insert overwrite"
+    if(index!=1){
+      insertSql = "insert into"
+    }
+    //补充新生session超过10个之后的session的session_begin_time与session_end_time
+    val HIVE_TABLE4 = "pica_dw.dw_fact_log_session_term_tmp"
+    val loadDataSql =
+      s"""
+         |${insertSql} table ${MyConfigSession.HIVE_TABLE4} partition(created_day='${partitionDay}')
+         | select concat(regexp_replace( '${partitionDay}','-','') ,cast( (row_number() over(partition by 1 order by created_time)  )+${count}  as string)) id,
+         | t1.session_id,device_token,user_id,user_token,mobile,menu_code,action_code,position,label_value,label_class,module_class1,module_class2,action_type,
+         | cast(menu_begin_time as string),   cast(menu_end_time as string),  menu_time_diff,
+         | action_step,device_type,device_brand,device_model, app_version, net_type ,created_time,date_time,
+         | cast(session_begin_time as string) ,  cast(session_end_time as string), session_time_diff,
+         | pre_session_id, user_identity_id,view_class,view_path,alternate_info,first_app_version,service_name,tag8,tag9,component_tag
+         | from result_view t1
+         """.stripMargin
+    sparkSession.sql(loadDataSql)
+  }
+  def strToInt(str: String): Int = {
+    val regex = """([0-9]+)""".r
+    val res = str match{
+      case regex(num) => num
+      case _ => "0"
+    }
+    val resInt = Integer.parseInt(res)
+    resInt
+  }
+
+}
+case class SessionTerm(pseudo_session: String,
+                       user_id: Integer,
+                       mobile: String,
+                       device_token: String,
+                       user_token: String,
+                       view_class: String,
+                       view_path: String,
+                       action: String,
+                       action_type: String,
+                       component_tag: String,
+                       menu_code: String,
+                       menu_code_new: String,
+                       menu_code_offset:String,
+                       action_code: String,
+                       position: String,
+                       label_value: String,
+                       label_class: String,
+                       module_class1: String,
+                       module_class2: String,
+                       app_version: String,
+                       device_type: String,
+                       device_brand: String,
+                       device_model: String,
+                       net_type: String,
+                       created_time: Long,
+                       date_time: String,
+                       alternate_info: String,
+                       first_app_version: String,
+                       service_name: String,
+                       tag8: String, tag9: String,
+                       is_need:Boolean)

src/main/scala/com/aliyun/odps/spark/sparksql/SparkSQL.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.sparksql
+
+import org.apache.spark.sql.{SaveMode, SparkSession}
+
+object SparkSQL {
+  def main(args: Array[String]) {
+    val spark = SparkSession
+      .builder()
+      .appName("SparkSQL-on-MaxCompute")
+      .config("spark.sql.broadcastTimeout", 20 * 60)
+      .config("spark.sql.crossJoin.enabled", true)
+      .config("odps.exec.dynamic.partition.mode", "nonstrict")
+      .getOrCreate()
+
+    // val project = spark.conf.get("odps.project.name")
+
+    import spark._
+    import sqlContext.implicits._
+    val tableName = "mc_test_table"
+    val ptTableName = "mc_test_pt_table"
+    // Drop Create
+    sql(s"DROP TABLE IF EXISTS ${tableName}")
+    sql(s"DROP TABLE IF EXISTS ${ptTableName}")
+
+    sql(s"CREATE TABLE ${tableName} (name STRING, num BIGINT)")
+    sql(s"CREATE TABLE ${ptTableName} (name STRING, num BIGINT) PARTITIONED BY (pt1 STRING, pt2 STRING)")
+
+    val df = spark.sparkContext.parallelize(0 to 99, 2).map(f => {
+      (s"name-$f", f)
+    }).toDF("name", "num")
+
+    val ptDf = spark.sparkContext.parallelize(0 to 99, 2).map(f => {
+      (s"name-$f", f, "2018", "0601")
+    }).toDF("name", "num", "pt1", "pt2")
+
+    // 写 普通表
+    df.write.insertInto(tableName) // insertInto语义
+    df.write.mode("overwrite").insertInto(tableName) // insertOverwrite语义
+
+    // 写 分区表
+    // DataFrameWriter 无法指定分区写入 需要通过临时表再用SQL写入特定分区
+    df.createOrReplaceTempView(s"${ptTableName}_tmp_view")
+    sql(s"insert into table ${ptTableName} partition (pt1='2018', pt2='0601') select * from ${ptTableName}_tmp_view")
+    sql(s"insert overwrite table ${ptTableName} partition (pt1='2018', pt2='0601') select * from ${ptTableName}_tmp_view")
+
+    ptDf.write.insertInto(ptTableName) // 动态分区 insertInto语义
+    ptDf.write.mode("overwrite").insertInto(ptTableName) // 动态分区 insertOverwrite语义
+
+    // 读 普通表
+    val rdf = sql(s"select name, num from $tableName")
+    println(s"rdf count, ${rdf.count()}")
+    rdf.printSchema()
+
+    // 读 分区表
+    val rptdf = sql(s"select name, num, pt1, pt2 from $ptTableName where pt1 = '2018' and pt2 = '0601'")
+    println(s"rptdf count, ${rptdf.count()}")
+    rptdf.printSchema()
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/sparksql/profile/Business_profile.scala

+package com.aliyun.odps.spark.sparksql.profile
+
+import com.alibaba.fastjson.JSONObject
+import org.apache.spark.SparkConf
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Row, SparkSession}
+import java.{sql, util}
+import java.sql.{PreparedStatement, Timestamp}
+
+import config.MyConfigSession
+import utils.{DateUtils, MySqlUtil, StringUtils}
+
+object Business_profile {
+  def apply(): Business_profile = new Business_profile()
+  def main(args: Array[String]): Unit = {
+    //1.执行任务之前先往record表记录
+//    val insertSQL: String =
+//      s"""
+//               			   |insert into ${MyConfigSession.DATA_BASE}.${MyConfigSession.JDBC_TABLE} (job_id,job_name,job_type,job_scn,status,start_time)
+//               			   |values(0,'Business_profile','3',?,'0',?)
+//		""".stripMargin
+    //设置同步数据的批次号,格式是2019-09-12
+    var scnData: String = DateUtils.getTodayDate
+    var first_lable_id = ""
+    var tbname = ""
+    var list =
+
+    if (args.length >= 1) {
+      scnData = args(0)
+      if (args.length > 1 && args(1) != "") {
+        first_lable_id = args(1)
+      }
+      if (args.length > 2 && args(2) != "") {
+        tbname = args(2)
+      }
+    }
+    println(s"scnData=${scnData}")
+    //设置任务开始时间,格式是2019-09-12 14:03:30
+    val startTime: String = DateUtils.getTodayTime
+    //存储SQL中的参数
+    val insertArr: Array[String] = Array[String](scnData, startTime)
+    //获取MYSQL连接
+//    val connSql: sql.Connection = JDBCUtil.getConnection()
+    //向 record 表插入数据
+//    val flag: Int = JDBCUtil.insertRecord(connSql, insertSQL, insertArr)
+    val business_profile: Business_profile = Business_profile()
+    try {
+      val sparkSession: SparkSession = business_profile.getSparkSession("Business_profile")
+
+      var conditionGroup = List("between 7693 and 7718",
+                                  "between 7719 and 8031",
+                                  "between 8032 and 8083",
+                                  "between 8084 and 8149",
+                                  "between 8150 and 8195",
+                                  "between 8196 and 8362",
+                                   "between 8363 and 8377",
+                                    "between 8378 and 9531",
+                                    "between 9532 and 9568",
+                                    "between 9569 and 9653"
+      )
+      var index = 0  //插入新增标识
+      for(condition <- conditionGroup){
+        index += 1
+        println("查询标签的sql"+s"select * from ods_pica_bi_user_profile where sql_string <>'' and first_lable_id in (${first_lable_id}) and  third_label_id ${condition}    order by third_label_id ")
+        val list_row: util.List[Row] = sparkSession.sql(
+          s"""
+             |
+             |select * from ods_pica_bi_user_profile
+             |where sql_string <>''
+             |and first_lable_id in (${first_lable_id})
+             |and  third_label_id ${condition}
+             |order by third_label_id
+             |""".stripMargin).collectAsList()
+
+//      val profileListSQL=s"select * from user_profile where sql_string <>'' and first_lable_id in (${first_lable_id}) and  third_label_id ${condition}    order by third_label_id "
+//      val MYSQL_URL = MyConfigSession.mysql_url_user_profile
+//      val profileList: util.List[JSONObject] = MySqlUtil.queryList(profileListSQL,MYSQL_URL)
+      import collection.JavaConverters._
+      val profileListScala = list_row.asScala
+      val tbbuffer = new StringBuilder
+      val partitionDay = DateUtils.getTodayDate
+      for(profile <- profileListScala){
+       val first_lable_id= profile.getAs[String]("first_lable_id")
+       val first_lable_name= profile.getAs[String]("first_lable_name")
+       val secondary_label_id= profile.getAs[String]("secondary_label_id")
+       val secondary_label_name =  profile.getAs[String]("secondary_label_name")
+       val third_label_id = profile.getAs[String]("third_label_id")
+       val third_label_name = profile.getAs[String]("third_label_name")
+        val sql_string = profile.getAs[String]("sql_string")
+        val Stringstr = s"""
+             |select ${first_lable_id} as first_lable_id,'${first_lable_name}' as first_lable_name
+             |       ,${secondary_label_id} as secondary_label_id,'${secondary_label_name}' as secondary_label_name
+             |       ,${third_label_id} as third_label_id,'${third_label_name}' as third_label_name,A.user_id,A.value,A.acct_id,A.profile_id
+             |from ${first_lable_id}_${secondary_label_id}_${third_label_id} A
+             |""".stripMargin
+
+        println("计算的sql拼接："+Stringstr)
+
+        val df = sparkSession.sql(sql_string).repartition(400)
+        df.createOrReplaceTempView(s"${first_lable_id}_${secondary_label_id}_${third_label_id}")
+        sparkSession.sql(
+            s"""
+                |select ${first_lable_id} as first_lable_id,'${first_lable_name}' as first_lable_name
+                |       ,${secondary_label_id} as secondary_label_id,'${secondary_label_name}' as secondary_label_name
+                |       ,${third_label_id} as third_label_id,'${third_label_name}' as third_label_name,user_id,value,acct_id,profile_id
+                |from ${first_lable_id}_${secondary_label_id}_${third_label_id}
+                |""".stripMargin).createOrReplaceTempView(s"temp_${first_lable_id}_${secondary_label_id}_${third_label_id}")
+
+
+
+
+        if(StringUtils.isEmpty(tbbuffer.toString())){
+          var insertSql = "insert overwrite"
+          if(index!=1){
+            insertSql = "insert into"
+          }
+            tbbuffer.append(
+              s"""
+                 |${insertSql} table ${tbname}  partition(created_day='${scnData}')
+                 | select user_id,cast(value as string),CAST(acct_id AS STRING),CAST(profile_id AS STRING),first_lable_id,first_lable_name,secondary_label_id,secondary_label_name,third_label_id,third_label_name
+                 | FROM temp_${first_lable_id}_${secondary_label_id}_${third_label_id}
+                 |""".stripMargin)
+          }else{
+            tbbuffer.append(
+              s"""
+                 |union all
+                 |select user_id,cast(value as string),CAST(acct_id AS STRING),CAST(profile_id AS STRING),first_lable_id,first_lable_name,secondary_label_id,secondary_label_name,third_label_id,third_label_name
+                 |FROM temp_${first_lable_id}_${secondary_label_id}_${third_label_id}
+                 |""".stripMargin
+            )
+        }
+      }
+        println("------------------------------------------------开始最后的union拼接sql------------------------------------------------------------"+tbbuffer.toString())
+        val resulteDf = sparkSession.sql(tbbuffer.toString())
+        println("------------------------------------------------success------------------------------------------------------------"+tbbuffer.toString())
+
+      }
+      sparkSession.stop()
+
+
+//      //任务执行成功,更新 Mysql record 配置表
+//      val updateSQL: String =
+//        s"""
+//           |update ${MyConfigSession.JDBC_TABLE} set status=?,end_time=? where job_name='Business_profile' and start_time='${startTime}'
+//				""".stripMargin
+//      val upreSta: PreparedStatement = connSql.prepareStatement(updateSQL)
+//      upreSta.setString(1, "1")
+//      upreSta.setString(2, DateUtils.getTodayTime)
+//
+//      //更新表数据
+//      upreSta.executeUpdate()
+//      //关闭连接
+////      JDBCUtil.close(connSql, upreSta)
+      sparkSession.stop()
+
+    } catch {
+      case e: Exception => {
+        println("-----------------------------------任务异常---------------------------------------------------")
+        e.printStackTrace()
+        throw new Exception
+
+//        val exceptionSQL: String =
+//          s"""
+//             |update ${MyConfigSession.JDBC_TABLE} set status=?,exception=?,end_time=? where job_name='Business_profile' and start_time='${startTime}'
+//					""".stripMargin
+//        val errorArr = Array[String]("2", e.getMessage, DateUtils.getTodayTime)
+//        JDBCUtil.insertRecord(connSql, exceptionSQL, errorArr)
+//        connSql.close()
+
+      }
+
+    }
+  }
+
+
+
+
+}
+
+
+class Business_profile {
+  def getSparkSession(appName: String): SparkSession = {
+//        val sparkSession = SparkSession
+//          .builder()
+//          .appName("SparkPi")
+//          .config("spark.master", "local[1]") // 需设置spark.master为local[N]才能直接运行，N为并发数。
+//          .config("spark.hadoop.odps.project.name", "pica")
+//          .config("spark.hadoop.odps.access.id", "LTAI5tQEnb9VREcPdDAR32bg")
+//          .config("spark.hadoop.odps.access.key", "RYazt1JIYZnRcEdlntarZ1Ov21zoNi")
+//          .config("spark.hadoop.odps.end.point", "http://service.cn.maxcompute.aliyun.com/api")
+//          .config("spark.sql.catalogImplementation", "odps")
+//          .getOrCreate()
+
+    val sparkSession = SparkSession
+      .builder()
+      .config("spark.hadoop.odps.project.name", "pica")
+      .appName("SessionProcess")
+      .getOrCreate()
+
+    sparkSession
+  }
+}
+case class profile(
+                             first_lable_id:Int,
+                             first_lable_name:String,
+                             secondary_label_id:Int,
+                             secondary_label_name:String,
+                             third_label_id:Int,
+                             third_label_name:String,
+                             load_date:Timestamp,
+                             sql_string:String
+                           )
\ No newline at end of file

src/main/scala/com/aliyun/odps/spark/streaming/common/SparkSessionSingleton.scala

+package com.aliyun.odps.spark.streaming.common
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.SparkSession
+
+object SparkSessionSingleton {
+  @transient  private var instance: SparkSession = _
+
+  def getInstance(sparkConf: SparkConf): SparkSession = {
+    if (instance == null) {
+      instance = SparkSession
+        .builder
+        .config(sparkConf)
+        .getOrCreate()
+    }
+    instance
+  }
+}

src/main/scala/com/aliyun/odps/spark/streaming/datahub/DataHub2OdpsDemo.scala

+package com.aliyun.odps.spark.streaming.datahub
+
+import com.aliyun.datahub.model.RecordEntry
+import com.aliyun.odps.spark.streaming.common.SparkSessionSingleton
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+import org.apache.spark.streaming.aliyun.datahub.DatahubUtils
+
+object DataHub2OdpsDemo {
+
+  def transferFunc(record: RecordEntry): String = {
+    // 这个转化函数目前只支持把DataHub Record转成String
+    // 如果是需要多个字段的话, 那么需要处理一下拼接的逻辑
+    record.getString(1)
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName("DataHubStreamingDemo")
+      .config("spark.hadoop.fs.oss.credentials.provider", "org.apache.hadoop.fs.aliyun.oss.AliyunStsTokenCredentialsProvider")
+      .config("spark.hadoop.fs.oss.ststoken.roleArn", "acs:ram::****:role/aliyunodpsdefaultrole")
+      .config("spark.hadoop.fs.oss.endpoint", "oss-cn-hangzhou-zmf.aliyuncs.com")
+      .getOrCreate()
+
+    // 设置Batch间隔时间
+    val ssc = new StreamingContext(spark.sparkContext, Seconds(10))
+
+    // checkpoint dir to oss
+    ssc.checkpoint("oss://bucket/inputdata/")
+
+    val dataStream = DatahubUtils.createStream(
+      ssc,
+      "projectName",
+      "topic",
+      "subId",
+      "accessId",
+      "accessKey",
+      "endPoint",
+      transferFunc(_),
+      StorageLevel.MEMORY_AND_DISK
+    )
+
+    dataStream.map(x => new String(x)).foreachRDD(rdd => {
+      val spark = SparkSessionSingleton.getInstance(rdd.sparkContext.getConf)
+      import spark.implicits._
+
+      rdd.toDF("id").write.mode("append").saveAsTable("test_table")
+    })
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}

src/main/scala/com/aliyun/odps/spark/streaming/datahub/DataHubStreamingDemo.scala

+package com.aliyun.odps.spark.streaming.datahub
+
+import com.aliyun.datahub.model.RecordEntry
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.aliyun.datahub.DatahubUtils
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+
+object DataHubStreamingDemo {
+
+  def transferFunc(record: RecordEntry): String = {
+    // 这个转化函数目前只支持把DataHub Record转成String
+    // 如果是需要多个字段的话, 那么需要处理一下拼接的逻辑
+    record.getString(1)
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName("DataHubStreamingDemo")
+      .config("spark.hadoop.fs.oss.credentials.provider", "org.apache.hadoop.fs.aliyun.oss.AliyunStsTokenCredentialsProvider")
+      .config("spark.hadoop.fs.oss.ststoken.roleArn", "acs:ram::****:role/aliyunodpsdefaultrole")
+      .config("spark.hadoop.fs.oss.endpoint", "oss-cn-hangzhou-zmf.aliyuncs.com")
+      .getOrCreate()
+
+    // 设置Batch间隔时间
+    val ssc = new StreamingContext(spark.sparkContext, Seconds(5))
+
+    // checkpoint dir to oss
+    ssc.checkpoint("oss://bucket/inputdata/")
+
+    val dataStream = DatahubUtils.createStream(
+      ssc,
+      "projectName",
+      "topic",
+      "subId",
+      "accessId",
+      "accessKey",
+      "endPoint",
+      transferFunc(_),
+      StorageLevel.MEMORY_AND_DISK
+    )
+
+    dataStream.count().print()
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}

src/main/scala/com/aliyun/odps/spark/streaming/kafka/Kafka2OdpsDemo.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.streaming.kafka
+
+import com.aliyun.odps.spark.streaming.common.SparkSessionSingleton
+import org.apache.kafka.clients.consumer.ConsumerRecord
+import org.apache.kafka.common.serialization.StringDeserializer
+import org.apache.spark.SparkConf
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+import org.apache.spark.streaming.dstream.{DStream, InputDStream}
+import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
+
+object Kafka2OdpsDemo {
+  def main(args: Array[String]): Unit = {
+    val sparkConf = new SparkConf().setAppName("test")
+    val ssc = new StreamingContext(sparkConf, Seconds(10))
+
+    // 请使用OSS作为Checkpoint存储,修改为有效OSS路径。OSS访问文档请参考 https://github.com/aliyun/MaxCompute-Spark/wiki/08.-Oss-Access%E6%96%87%E6%A1%A3%E8%AF%B4%E6%98%8E
+    ssc.checkpoint("oss://bucket/checkpointdir")
+
+    // kafka配置参数
+    val kafkaParams = Map[String, Object](
+      "bootstrap.servers" -> "localhost:9092",
+      "key.deserializer" -> classOf[StringDeserializer],
+      "value.deserializer" -> classOf[StringDeserializer],
+      "group.id" -> "testGroupId",
+      "auto.offset.reset" -> "latest",
+      "enable.auto.commit" -> (false: java.lang.Boolean)
+    )
+
+    // 创建kafka dstream
+    val topics = Set("test")
+    val recordDstream: InputDStream[ConsumerRecord[String, String]] =
+      KafkaUtils.createDirectStream[String, String](
+        ssc,
+        LocationStrategies.PreferConsistent,
+        ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)
+      )
+    val dstream = recordDstream.map(f => (f.key(), f.value()))
+    // 解析kafka数据并写入odps
+    val data: DStream[String] = dstream.map(_._2)
+    val wordsDStream: DStream[String] = data.flatMap(_.split(" "))
+    wordsDStream.foreachRDD(rdd => {
+      val spark = SparkSessionSingleton.getInstance(rdd.sparkContext.getConf)
+      import spark.implicits._
+
+      rdd.toDF("id").write.mode("append").saveAsTable("test_table")
+    })
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/streaming/kafka/KafkaStreamingDemo.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.streaming.kafka
+
+import org.apache.kafka.clients.consumer.ConsumerRecord
+import org.apache.kafka.common.serialization.StringDeserializer
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.streaming.dstream.{DStream, InputDStream}
+import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+
+object KafkaStreamingDemo {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName("KafkaStreamingDemo")
+      .getOrCreate()
+
+    val ssc = new StreamingContext(spark.sparkContext, Seconds(5))
+
+    // 请使用OSS作为Checkpoint存储
+    ssc.checkpoint("oss://bucket/checkpointDir/")
+
+    // kafka配置参数
+    val kafkaParams = Map[String, Object](
+      "bootstrap.servers" -> "192.168.1.1:9200,192.168.1.2:9200,192.168.1.3:9200",
+      "key.deserializer" -> classOf[StringDeserializer],
+      "value.deserializer" -> classOf[StringDeserializer],
+      "group.id" -> "testGroupId",
+      "auto.offset.reset" -> "latest",
+      "enable.auto.commit" -> (false: java.lang.Boolean)
+    )
+
+    val topics = Set("event_topic")
+    val recordDstream: InputDStream[ConsumerRecord[String, String]] =
+      KafkaUtils.createDirectStream[String, String](
+        ssc,
+        LocationStrategies.PreferConsistent,
+        ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)
+      )
+
+
+    val dstream = recordDstream.map(f => (f.key(), f.value()))
+    val data: DStream[String] = dstream.map(_._2)
+    val wordsDStream: DStream[String] = data.flatMap(_.split(" "))
+    val wordAndOneDstream: DStream[(String, Int)] = wordsDStream.map((_, 1))
+    val result: DStream[(String, Int)] = wordAndOneDstream.reduceByKey(_ + _)
+    result.print()
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/streaming/loghub/LogHub2OdpsDemo.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.streaming.loghub
+
+import com.aliyun.odps.spark.streaming.common.SparkSessionSingleton
+import com.aliyun.openservices.loghub.client.config.LogHubCursorPosition
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{Durations, StreamingContext}
+import org.apache.spark.streaming.loghub.{LoghubUtils, StreamingParam}
+
+object LogHub2OdpsDemo {
+
+  def buildParam(conf: SparkConf): StreamingParam = {
+    val sp = new StreamingParam()
+    sp.setId(conf.get("spark.logservice.accessKeyId"))
+    sp.setSecret(conf.get("spark.logservice.accessKeySecret"))
+    sp.setEndpoint(conf.get("spark.logservice.endpoint"))
+    sp.setProject(conf.get("spark.logservice.project"))
+    sp.setLogstore(conf.get("spark.logservice.logstore"))
+    sp.setCursor(LogHubCursorPosition.END_CURSOR)
+    sp.setGroup("test")
+    sp.setLevel(StorageLevel.MEMORY_AND_DISK)
+
+    sp
+  }
+
+  def main(args: Array[String]) {
+    val conf = new SparkConf(true).setAppName("LogHubStreamingDemo")
+    val sc = new SparkContext(conf)
+
+    val ssc = new StreamingContext(sc, Durations.seconds(5))
+
+    val lines = LoghubUtils.createStream(ssc, buildParam(conf), 1).map(line => {
+        val str = new String(line)
+        str
+      })
+
+    val words = lines.flatMap(_.split(" "))
+    words.foreachRDD(rdd => {
+      val spark = SparkSessionSingleton.getInstance(rdd.sparkContext.getConf)
+      import spark.implicits._
+
+      rdd.toDF("id").write.mode("append").saveAsTable("test_table")
+    })
+
+    ssc.start()             // Start the computation
+    ssc.awaitTermination()  // Wait for the computation to terminate
+  }
+}

src/main/scala/com/aliyun/odps/spark/streaming/loghub/LogHubStreamingDemo.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.streaming.loghub
+
+import com.aliyun.openservices.loghub.client.config.LogHubCursorPosition
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.loghub.{LoghubUtils, StreamingParam}
+import org.apache.spark.streaming.{Durations, StreamingContext}
+import org.apache.spark.{SparkConf, SparkContext}
+
+object LogHubStreamingDemo {
+
+  def buildParam(conf: SparkConf): StreamingParam = {
+    val sp = new StreamingParam()
+    sp.setId(conf.get("spark.logservice.accessKeyId"))
+    sp.setSecret(conf.get("spark.logservice.accessKeySecret"))
+    sp.setEndpoint(conf.get("spark.logservice.endpoint"))
+    sp.setProject(conf.get("spark.logservice.project"))
+    sp.setLogstore(conf.get("spark.logservice.logstore"))
+    sp.setCursor(LogHubCursorPosition.END_CURSOR)
+    sp.setGroup("test")
+    sp.setLevel(StorageLevel.MEMORY_AND_DISK)
+
+    sp
+  }
+
+  def main(args: Array[String]) {
+    val conf = new SparkConf(true).setAppName("LogHubStreamingDemo")
+    val sc = new SparkContext(conf)
+
+    val ssc = new StreamingContext(sc, Durations.seconds(5))
+
+    val lines = LoghubUtils.createStream(ssc, buildParam(conf), 1).map(line => {
+        val str = new String(line)
+        str
+      })
+
+    val words = lines.flatMap(_.split(" "))
+    val pairs = words.map(word => (word, 1))
+    val wordCounts = pairs.reduceByKey(_ + _)
+
+    // Print the first ten elements of each RDD generated in this DStream to the console
+    wordCounts.print()
+
+    ssc.start()             // Start the computation
+    ssc.awaitTermination()  // Wait for the computation to terminate
+  }
+}

src/main/scala/com/aliyun/odps/spark/structuredStreaming/datahub/DatahubStructuredStreamingDemo.scala

+package com.aliyun.odps.spark.structuredStreaming.datahub
+
+import org.apache.spark.sql.SparkSession
+
+object DatahubStructuredStreamingDemo {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName("DatahubStructuredStreamingDemo")
+      .getOrCreate()
+
+    import spark.implicits._
+
+    val df = spark
+      .readStream
+      .format("datahub")
+      .option("datahub.endpoint", "http://dh-cn-beijing.aliyun-inc.com")
+      .option("datahub.project", "zkytest")
+      .option("datahub.topic", "zkytest")
+      .option("datahub.AccessId", "******")
+      .option("datahub.AccessKey", "******")
+      .option("StartingOffsets", "earliest")
+      .load()
+
+    /** *
+     * WordCount Demo
+     */
+    // 请使用OSS作为Checkpoint存储
+    val checkpointLocation = "oss://bucket/checkpoint/"
+    val lines = df.select($"id").as[String]
+    val wordCounts = lines.flatMap(_.split(" ")).toDF("word").groupBy("word").count()
+
+    val query = wordCounts.writeStream
+      .outputMode("complete")
+      .format("console")
+      .option("checkpointLocation", checkpointLocation)
+      .start()
+
+    query.awaitTermination()
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/structuredStreaming/kafka/KafkaStructuredStreamingDemo.scala

+package com.aliyun.odps.spark.structuredStreaming.kafka
+
+import java.sql.Timestamp
+
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.functions.window
+
+object KafkaStructuredStreamingDemo{
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName("KafkaStreamingDemo")
+      .getOrCreate()
+
+    import spark.implicits._
+
+    val df = spark
+      .readStream
+      .format("kafka")
+      .option("kafka.bootstrap.servers", "localhost:9092")
+      .option("subscribe", "topic")
+      .load()
+
+    /** *
+     * WordCount Demo
+     */
+    // 请使用OSS作为Checkpoint存储
+    val checkpointLocation = "oss://bucket/checkpoint/"
+    val lines = df.selectExpr("cast(value as string)").as[String]
+    val wordCounts = lines.flatMap(_.split(" ")).toDF("word").groupBy("word").count()
+
+    val query = wordCounts.writeStream
+      .outputMode("complete")
+      .format("console")
+      .option("checkpointLocation", checkpointLocation)
+      .option("path", "query1")
+      .start()
+
+    query.awaitTermination()
+
+    /** *
+     * Windowed WordCount Demo
+     */
+    val wordsWithTimestamp = df.selectExpr("cast(value as string)").as[String]
+      .flatMap(x => {
+        val Array(ts, data) = x.split(",")
+        data.split(" ").map((new Timestamp(ts.toLong), _))
+      }).as[(Timestamp, String)].toDF("timestamp", "word")
+
+    // 请使用OSS作为Checkpoint存储
+    val checkpointLocation2 = "oss://bucket/checkpoint2/"
+    val windowedCounts = wordsWithTimestamp
+      .groupBy(
+        window($"timestamp", "10 seconds", "5 seconds"),
+        $"word"
+      ).count()
+
+    val query2 = windowedCounts.writeStream
+      .outputMode("complete")
+      .format("console")
+      .option("checkpointLocation", checkpointLocation2)
+      .start()
+
+    query2.awaitTermination()
+
+    /** *
+     * Windowed WordCount with Watermark Demo
+     */
+    // 请使用OSS作为Checkpoint存储
+    val checkpointLocation3 = "oss://bucket/checkpoint3/"
+
+    val windowedCountsWithWatermark = wordsWithTimestamp
+      .withWatermark("timestamp", "5 seconds")
+      .groupBy(
+        window($"timestamp", "6 seconds", "3 seconds"),
+        $"word"
+      ).count()
+
+    val query3 = windowedCountsWithWatermark.writeStream
+      .outputMode("append")
+      .format("console")
+      .option("checkpointLocation", checkpointLocation3)
+      .start()
+
+    query3.awaitTermination()
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/structuredStreaming/loghub/LoghubStructuredStreamingDemo.scala

+package com.aliyun.odps.spark.structuredStreaming.loghub
+
+import org.apache.spark.sql.SparkSession
+
+object LoghubStructuredStreamingDemo {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .appName("LoghubStructuredStreamingDemo")
+      .getOrCreate()
+
+    import spark.implicits._
+
+    val df = spark
+      .readStream
+      .format("loghub")
+      .option("Loghub.Endpoint", "cn-beijing-intranet.log.aliyuncs.com")
+      .option("Loghub.Project", "zkytest")
+      .option("Loghub.AccessId", "******")
+      .option("Loghub.AccessKey", "******")
+      .option("Loghub.Logstores", "zkytest")
+      .option("StartingOffsets", "latest")
+      .load()
+
+    /** *
+     * WordCount Demo
+     */
+    // 请使用OSS作为Checkpoint存储
+    val checkpointLocation = "oss://bucket/checkpoint"
+    val lines = df.select($"contents").as[String]
+    val wordCounts = lines.flatMap(_.split(" ")).toDF("word").groupBy("word").count()
+
+    val query = wordCounts.writeStream
+      .outputMode("complete")
+      .format("console")
+      .option("checkpointLocation", checkpointLocation)
+      .start()
+
+    query.awaitTermination()
+  }
+}
+

src/main/scala/com/aliyun/odps/spark/zeppelin/ZeppelinServer.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.zeppelin
+
+import java.io.{File, FilenameFilter, PrintWriter}
+import java.net.{Inet4Address, InetAddress, NetworkInterface, ServerSocket}
+import java.util.concurrent.{ScheduledExecutorService, ScheduledThreadPoolExecutor, TimeUnit}
+
+import com.aliyun.odps.cupid.CupidSession
+import com.aliyun.odps.cupid.requestcupid.CupidProxyTokenUtil
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.log4j.Logger
+import org.apache.spark.sql.SparkSession
+import org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer
+
+import scala.collection.JavaConverters._
+import scala.io.Source
+import scala.sys.process.Process
+
+object ZeppelinServer {
+
+  def newDaemonSingleThreadScheduledExecutor(threadName: String): ScheduledExecutorService = {
+    val threadFactory = new ThreadFactoryBuilder().setDaemon(true).setNameFormat(threadName).build()
+    val executor = new ScheduledThreadPoolExecutor(1, threadFactory)
+    // By default, a cancelled task is not automatically removed from the work queue until its delay
+    // elapses. We have to enable it manually.
+    executor.setRemoveOnCancelPolicy(true)
+    executor
+  }
+
+  private val LOG = Logger.getLogger(ZeppelinServer.getClass)
+  // zeppelin package location
+  val zeppelinHome = s"${new File(".").getCanonicalPath}/" +
+    s"public.zeppelin-0.8.1-bin-netinst.tar.gz/" +
+    s"zeppelin-0.8.1-bin-netinst/"
+  val fixedReplOutputDir = "/home/admin/replClassOutputDir"
+  val pool = newDaemonSingleThreadScheduledExecutor("link4replClassOutputDir")
+
+  def getRandomPort(): Int = {
+    val socket = new ServerSocket(0)
+    val port = socket.getLocalPort
+    socket.close()
+    port
+  }
+
+
+  /**
+    * COPY from spark source code.
+    * @return
+    */
+  def findLocalInetAddress(): InetAddress = {
+    val defaultIpOverride = System.getenv("SPARK_LOCAL_IP")
+    if (defaultIpOverride != null) {
+      InetAddress.getByName(defaultIpOverride)
+    } else {
+      val address = InetAddress.getLocalHost
+      if (address.isLoopbackAddress) {
+        // Address resolves to something like 127.0.1.1, which happens on Debian; try to find
+        // a better address using the local network interfaces
+        // getNetworkInterfaces returns ifs in reverse order compared to ifconfig output order
+        // on unix-like system. On windows, it returns in index order.
+        // It's more proper to pick ip address following system output order.
+        val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.asScala.toSeq
+        val reOrderedNetworkIFs = activeNetworkIFs.reverse
+
+        for (ni <- reOrderedNetworkIFs) {
+          val addresses = ni.getInetAddresses.asScala
+            .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress).toSeq
+          if (addresses.nonEmpty) {
+            val addr = addresses.find(_.isInstanceOf[Inet4Address]).getOrElse(addresses.head)
+            // because of Inet6Address.toHostName may add interface at the end if it knows about it
+            val strippedAddress = InetAddress.getByAddress(addr.getAddress)
+            // We've found an address that looks reasonable!
+            LOG.warn("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" +
+              " a loopback address: " + address.getHostAddress + "; using " +
+              strippedAddress.getHostAddress + " instead (on interface " + ni.getName + ")")
+            LOG.warn("Set SPARK_LOCAL_IP if you need to bind to another address")
+            return strippedAddress
+          }
+        }
+        LOG.warn("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" +
+          " a loopback address: " + address.getHostAddress + ", but we couldn't find any" +
+          " external IP address!")
+        LOG.warn("Set SPARK_LOCAL_IP if you need to bind to another address")
+      }
+      address
+    }
+  }
+
+  def replaceZeppelinCoreSite(zeppelinPort: Int): Unit = {
+    Process(s"cp -f ${zeppelinHome}/conf/zeppelin-site.xml.todo ${zeppelinHome}/conf/zeppelin-site.xml").!
+
+    val file = new File(s"${zeppelinHome}/conf/zeppelin-site.xml")
+    val replaceStr = "##zeppelin_port##"
+    val replaceContent = Source.fromFile(file).mkString.replace(replaceStr, zeppelinPort.toString)
+    val writer = new PrintWriter(file)
+    writer.write(replaceContent)
+    writer.close()
+  }
+
+  def replaceZeppelinInterpreterJson(interpreterPort: Int): Unit = {
+    Process(s"cp -f ${zeppelinHome}/conf/interpreter.json.todo ${zeppelinHome}/conf/interpreter.json").!
+
+    val file = new File(s"${zeppelinHome}/conf/interpreter.json")
+    val replaceStr = "##interpreter_port##"
+    val replaceContent = Source.fromFile(file).mkString.replace(replaceStr, interpreterPort.toString)
+    val writer = new PrintWriter(file)
+    writer.write(replaceContent)
+    writer.close()
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .config("spark.repl.class.outputDir", fixedReplOutputDir)
+      .appName("ZeppelinServer")
+      .getOrCreate()
+
+    pool.scheduleWithFixedDelay(
+      new Runnable {
+        override def run(): Unit = {
+          val linkDir = new File(fixedReplOutputDir)
+          if (linkDir.exists()) {
+            return
+          } else {
+            val tmpDir = new File(System.getProperty("java.io.tmpdir"))
+            val dirs = tmpDir.list()
+            for (dir <- dirs) {
+              if (dir.startsWith("spark")) {
+                // old interp like this spark-fdf1027a-a006-476c-9abb-c32b9fd784d7
+                // new interp like this spark8819436304609717444
+                val tmpDir2 = new File(s"${tmpDir.getAbsolutePath}/${dir}/")
+                val lineCount = tmpDir2.list(new FilenameFilter {
+                  override def accept(dir: File, name: String): Boolean = {
+                    if (name.startsWith("$line")) {
+                      true
+                    } else {
+                      false
+                    }
+                  }
+                }).length
+
+                if (lineCount > 0) {
+                  val cmd = s"ln -s ${tmpDir.getCanonicalPath}/${dir} ${fixedReplOutputDir}"
+                  LOG.info(s"find repl dir and do link with cmd ${cmd}")
+                  Process(cmd).!
+                }
+              }
+            }
+          }
+        }
+      },
+      0,
+      1,
+      TimeUnit.SECONDS
+    )
+
+    val interpreterServerPort = getRandomPort
+    val zeppelinPort = getRandomPort
+    LOG.info(s"interpreterServerPort: ${interpreterServerPort}, zeppelinPort: ${zeppelinPort}")
+
+    replaceZeppelinCoreSite(zeppelinPort)
+    replaceZeppelinInterpreterJson(interpreterServerPort)
+
+    val zeppelinAppName = spark.sparkContext.getConf.get("spark.zeppelin.appName", "zeppelin")
+
+    // prepare zeppelin end point
+    val address = findLocalInetAddress()
+    val zeppelinEndpoint = s"http://${address.getHostAddress}:${zeppelinPort}"
+    LOG.info(s"zeppelinEndpoint: ${zeppelinEndpoint}")
+
+    // write CUPID appName meta info
+    val metaPanguPath = System.getenv("FUXI_JOB_TEMP_ROOT") + s"../../AppAddresses/${zeppelinAppName}"
+    val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration)
+    LOG.info(s"metaPanguPath: ${metaPanguPath}")
+    val out = fs.create(new Path(metaPanguPath))
+    out.write(zeppelinEndpoint.getBytes)
+    out.close()
+
+    // generate external url for end user
+    val cupidProxyEndPoint = spark.sparkContext.getConf
+      .get("spark.hadoop.odps.cupid.proxy.end.point", "cupid.aliyun-inc.com")
+    val zeppelinExpiredInHours = spark.sparkContext.getConf
+      .get("spark.zeppelin.expired.in.hours", "144")
+    val cupidToken = CupidProxyTokenUtil.getProxyToken(CupidSession.get().getJobLookupName,
+      zeppelinAppName,
+      zeppelinExpiredInHours.toInt,
+      CupidSession.get())
+    val externalUrl = s"http://${cupidToken}.${cupidProxyEndPoint}"
+    println(s"Please visit the following url for zeppelin interaction.\n${externalUrl}")
+
+    // start zeppelin
+    // have to reset CLASSPATH to get rid of cupid runtime classpath
+    // update PATH for hostname sleep nice nohup echo system command
+    val pathEnv = System.getenv("PATH")
+    val startZeppelinCmd = s"${zeppelinHome}/bin/zeppelin-daemon.sh start"
+    Process(startZeppelinCmd, None,
+      "CLASSPATH" -> "",
+      "PATH" -> s"${zeppelinHome}/extraPath:${pathEnv}",
+      "ZEPPELIN_HOME" -> zeppelinHome).!
+
+    val interpreter = new RemoteInterpreterServer(null, interpreterServerPort, null)
+    interpreter.start()
+    interpreter.join()
+    System.exit(0)
+  }
+
+}

src/main/scala/com/aliyun/odps/spark/zeppelin/ZeppelinServerPublic.scala

+/**
+  * Licensed to the Apache Software Foundation (ASF) under one
+  * or more contributor license agreements.  See the NOTICE file
+  * distributed with this work for additional information
+  * regarding copyright ownership.  The ASF licenses this file
+  * to you under the Apache License, Version 2.0 (the
+  * "License"); you may not use this file except in compliance
+  * with the License.  You may obtain a copy of the License at
+  * <p>
+  * http://www.apache.org/licenses/LICENSE-2.0
+  * <p>
+  * Unless required by applicable law or agreed to in writing, software
+  * distributed under the License is distributed on an "AS IS" BASIS,
+  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  * See the License for the specific language governing permissions and
+  * limitations under the License.
+  */
+
+package com.aliyun.odps.spark.zeppelin
+
+import java.io.{File, FilenameFilter, PrintWriter}
+import java.net.{Inet4Address, InetAddress, NetworkInterface, ServerSocket}
+import java.util.concurrent.{ScheduledExecutorService, ScheduledThreadPoolExecutor, TimeUnit}
+
+import com.aliyun.odps.cupid.CupidSession
+import com.aliyun.odps.cupid.requestcupid.CupidProxyTokenUtil
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.log4j.Logger
+import org.apache.spark.sql.SparkSession
+import org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer
+
+import scala.collection.JavaConverters._
+import scala.io.Source
+import scala.sys.process.Process
+
+object ZeppelinServerPublic {
+
+  def newDaemonSingleThreadScheduledExecutor(threadName: String): ScheduledExecutorService = {
+    val threadFactory = new ThreadFactoryBuilder().setDaemon(true).setNameFormat(threadName).build()
+    val executor = new ScheduledThreadPoolExecutor(1, threadFactory)
+    // By default, a cancelled task is not automatically removed from the work queue until its delay
+    // elapses. We have to enable it manually.
+    executor.setRemoveOnCancelPolicy(true)
+    executor
+  }
+
+  private val LOG = Logger.getLogger(ZeppelinServer.getClass)
+  // zeppelin package location
+  val zeppelinHome = s"${new File(".").getCanonicalPath}/" +
+    s"public.zeppelin-0.8.1-bin-netinst.tar.gz/" +
+    s"zeppelin-0.8.1-bin-netinst/"
+  val fixedReplOutputDir = "/home/admin/replClassOutputDir"
+  val pool = newDaemonSingleThreadScheduledExecutor("link4replClassOutputDir")
+
+  def getRandomPort(): Int = {
+    val socket = new ServerSocket(0)
+    val port = socket.getLocalPort
+    socket.close()
+    port
+  }
+
+
+  /**
+    * COPY from spark source code.
+    * @return
+    */
+  def findLocalInetAddress(): InetAddress = {
+    val defaultIpOverride = System.getenv("SPARK_LOCAL_IP")
+    if (defaultIpOverride != null) {
+      InetAddress.getByName(defaultIpOverride)
+    } else {
+      val address = InetAddress.getLocalHost
+      if (address.isLoopbackAddress) {
+        // Address resolves to something like 127.0.1.1, which happens on Debian; try to find
+        // a better address using the local network interfaces
+        // getNetworkInterfaces returns ifs in reverse order compared to ifconfig output order
+        // on unix-like system. On windows, it returns in index order.
+        // It's more proper to pick ip address following system output order.
+        val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.asScala.toSeq
+        val reOrderedNetworkIFs = activeNetworkIFs.reverse
+
+        for (ni <- reOrderedNetworkIFs) {
+          val addresses = ni.getInetAddresses.asScala
+            .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress).toSeq
+          if (addresses.nonEmpty) {
+            val addr = addresses.find(_.isInstanceOf[Inet4Address]).getOrElse(addresses.head)
+            // because of Inet6Address.toHostName may add interface at the end if it knows about it
+            val strippedAddress = InetAddress.getByAddress(addr.getAddress)
+            // We've found an address that looks reasonable!
+            LOG.warn("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" +
+              " a loopback address: " + address.getHostAddress + "; using " +
+              strippedAddress.getHostAddress + " instead (on interface " + ni.getName + ")")
+            LOG.warn("Set SPARK_LOCAL_IP if you need to bind to another address")
+            return strippedAddress
+          }
+        }
+        LOG.warn("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" +
+          " a loopback address: " + address.getHostAddress + ", but we couldn't find any" +
+          " external IP address!")
+        LOG.warn("Set SPARK_LOCAL_IP if you need to bind to another address")
+      }
+      address
+    }
+  }
+
+  def replaceZeppelinCoreSite(zeppelinPort: Int): Unit = {
+    Process(s"cp -f ${zeppelinHome}/conf/zeppelin-site.xml.todo ${zeppelinHome}/conf/zeppelin-site.xml").!
+
+    val file = new File(s"${zeppelinHome}/conf/zeppelin-site.xml")
+    val replaceStr = "##zeppelin_port##"
+    val replaceContent = Source.fromFile(file).mkString.replace(replaceStr, zeppelinPort.toString)
+    val writer = new PrintWriter(file)
+    writer.write(replaceContent)
+    writer.close()
+  }
+
+  def replaceZeppelinInterpreterJson(interpreterPort: Int): Unit = {
+    Process(s"cp -f ${zeppelinHome}/conf/interpreter.json.todo ${zeppelinHome}/conf/interpreter.json").!
+
+    val file = new File(s"${zeppelinHome}/conf/interpreter.json")
+    val replaceStr = "##interpreter_port##"
+    val replaceContent = Source.fromFile(file).mkString.replace(replaceStr, interpreterPort.toString)
+    val writer = new PrintWriter(file)
+    writer.write(replaceContent)
+    writer.close()
+  }
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder()
+      .config("spark.repl.class.outputDir", fixedReplOutputDir)
+      .appName("ZeppelinServer")
+      .getOrCreate()
+
+    pool.scheduleWithFixedDelay(
+      new Runnable {
+        override def run(): Unit = {
+          val linkDir = new File(fixedReplOutputDir)
+          if (linkDir.exists()) {
+            return
+          } else {
+            val tmpDir = new File(System.getProperty("java.io.tmpdir"))
+            val dirs = tmpDir.list()
+            for (dir <- dirs) {
+              if (dir.startsWith("spark")) {
+                // old interp like this spark-fdf1027a-a006-476c-9abb-c32b9fd784d7
+                // new interp like this spark8819436304609717444
+                val tmpDir2 = new File(s"${tmpDir.getAbsolutePath}/${dir}/")
+                val lineCount = tmpDir2.list(new FilenameFilter {
+                  override def accept(dir: File, name: String): Boolean = {
+                    if (name.startsWith("$line")) {
+                      true
+                    } else {
+                      false
+                    }
+                  }
+                }).length
+
+                if (lineCount > 0) {
+                  val cmd = s"ln -s ${tmpDir.getCanonicalPath}/${dir} ${fixedReplOutputDir}"
+                  LOG.info(s"find repl dir and do link with cmd ${cmd}")
+                  Process(cmd).!
+                }
+              }
+            }
+          }
+        }
+      },
+      0,
+      1,
+      TimeUnit.SECONDS
+    )
+
+    val interpreterServerPort = 12345
+    val zeppelinPort = 54321
+    LOG.info(s"interpreterServerPort: ${interpreterServerPort}, zeppelinPort: ${zeppelinPort}")
+
+    // replaceZeppelinCoreSite(zeppelinPort)
+    // replaceZeppelinInterpreterJson(interpreterServerPort)
+
+    val zeppelinAppName = spark.sparkContext.getConf.get("spark.zeppelin.appName", "zeppelin")
+
+    // prepare zeppelin end point
+    // val address = findLocalInetAddress()
+    // val zeppelinEndpoint = s"http://${address.getHostAddress}:${zeppelinPort}"
+    // LOG.info(s"zeppelinEndpoint: ${zeppelinEndpoint}")
+
+    // write CUPID appName meta info
+    // val metaPanguPath = System.getenv("FUXI_JOB_TEMP_ROOT") + s"../../AppAddresses/${zeppelinAppName}"
+    // val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration)
+    // LOG.info(s"metaPanguPath: ${metaPanguPath}")
+    // val out = fs.create(new Path(metaPanguPath))
+    // out.write(zeppelinEndpoint.getBytes)
+    // out.close()
+
+    // generate external url for end user
+    val commonProxy = spark.sparkContext.getConf
+      .get("spark.hadoop.odps.cupid.common.proxy",
+      "open.maxcompute.aliyun.com")
+    val externalUrl = s"http://${System.getenv("META_LOOKUP_NAME")}-zeppelin.${commonProxy}"
+    println(s"Please visit the following url for zeppelin interaction.\n${externalUrl}")
+
+    Process(s"cp -r $zeppelinHome/conf /worker/zeppelin_conf").!
+    Process(s"cp -r $zeppelinHome/notebook /worker/local/notebook").!
+    Process(s"mv /worker/zeppelin_conf/interpreter.json.public /worker/zeppelin_conf/interpreter.json").!
+    Process(s"mv /worker/zeppelin_conf/zeppelin-site.xml.public /worker/zeppelin_conf/zeppelin-site.xml").!
+
+    // start zeppelin
+    // have to reset CLASSPATH to get rid of cupid runtime classpath
+    // update PATH for hostname sleep nice nohup echo system command
+    val pathEnv = System.getenv("PATH")
+    val startZeppelinCmd = s"${zeppelinHome}/bin/zeppelin-daemon.sh --config /worker/zeppelin_conf start"
+    Process(startZeppelinCmd, None,
+      "CLASSPATH" -> "",
+      "PATH" -> s"${zeppelinHome}/extraPath:${pathEnv}",
+      "ZEPPELIN_HOME" -> zeppelinHome,
+      "ZEPPELIN_LOG_DIR" -> "/worker/zeppelin_logs/",
+      "ZEPPELIN_PID_DIR" -> "/worker/zeppelin_pids/"
+    ).!
+
+    val interpreter = new RemoteInterpreterServer(null, interpreterServerPort, null)
+    interpreter.start()
+    interpreter.join()
+    System.exit(0)
+  }
+
+}

src/main/scala/config/MyConfigSession.scala

+package config
+
+/**
+ *
+ * 埋点流量相关常量配置
+ * @Author zhenxin.ma
+ * @Date 2019/8/20 9:43
+ * @Version 1.0
+ */
+object MyConfigSession {
+
+    //从源表pica_log.picalog_trace_app_part中执行SQL获取源数据,这里获取昨天的
+    final val SOURCE_SQL: String =
+        """
+          		  |select pseudo_session,doctor_id,mobile,device_token,user_token_tourist,class_name,view_path,action,
+          		  |component_tag,app_version,device_type,device_brand,device_model,network_type,created from pica.picalog_trace_app_part
+          		  | where  pseudo_session is not null and pseudo_session !=''
+          		  | and pseudo_id !='' and extra_info !='com.picahealth.patient' and serviceName != 'trace3'
+                | and action!='ACTION_WEB_AFTER'
+                | and FROM_UNIXTIME(cast(substring(created,1,10) as bigint),'yyyy-MM-dd')=created_day and created is not null and created!=''
+        		""".stripMargin //and `action`!='ACTION_EQUIP_INFO'
+
+
+    //Hive的DW层流量表
+    final val HIVE_TABLE0: String = "pica_ods.ods_log_session_pref"
+    final val HIVE_TABLE1_TMP: String = "pica_dw.dw_fact_log_session_tmp"
+    final val HIVE_TABLE2_TMP: String = "pica_dw.dw_fact_log_session_path_tmp"
+    final val HIVE_TABLE1: String = "pica.dwd_fact_log_session"
+    final val HIVE_TABLE2: String = "pica_dw.dw_fact_log_session_path"
+    final val HIVE_TABLE3: String = "pica_dw.dw_fact_log_session_heart"
+    final val HIVE_TABLE4: String = "pica.dwd_fact_log_session_term"
+    final val HIVE_TABLE4_MID: String = "pica_dw.dw_fact_log_session_term_mid"
+    final val HIVE_TABLE5: String = "pica_dw.pathconvertconfig"
+    final val HIVE_TABLE6: String = "pica_dw.dw_fact_log_session_path_convert"
+
+
+    //写入的文件路径
+    final val PARQUET_PATH: String = "hdfs://bi-name1:8020/tmp/output/"
+
+
+    //流量表中的临时视图
+    final val VIEW_SESSION_ODS: String = "ods_session"
+    final val VIEW_SESSION_NO_MATCH: String = "ods_session_no_user_id"
+    final val VIEW_MOBILE_PHONE: String = "mobile_phone_match"
+    final val VIEW_EQUIPMENT_INFO: String = "equipment_info"
+    final val VIEW_DEVICE_TOKEN: String = "device_token_match"
+    //流量表中使用的三个字典表作为过滤数据条件
+    final val ACTION_TYPE_SQL: String = "select action_type,'1' as is_valid from pica.dwd_log_action_type where is_valid=1"
+    final val ACTION_TYPE_SQL_HEART: String = "select action_type,'1' as is_valid from pica.dwd_log_action_type where  action_type='ACTION_HEART_BEAT'"
+    final val CLASS_NAME_SQL: String = "select class_name, '0' as is_valid from pica.dwd_log_class_name where is_valid=0"
+    final val MENU_CODE_SQL: String = "select distinct view_path,  menu_code from pica.dwd_log_menu_class_code where view_path is not Null"
+    //流量表中根据action_type获取对应的action_category类型
+    final val ACTION_CATEGORY_SQL: String = "select action_type,action_category from pica.dwd_log_action_type where is_valid=1"
+    final val ACTION_CATEGORY_SQL_HEART: String = "select action_type,action_category from pica_dw.dw_dim_log_action_type  where  action_type='ACTION_HEART_BEAT'"
+    final val ACTION_URLLABEL_SQL:String = "select url_content,label_value from pica.dwd_log_action_urllabel"
+
+    //从源表pica_log.picalog_trace_app_part中执行SQL获取源数据,这里获取昨天的
+    final val SOURCE_SQL_PREF: String =
+        """
+          		  |select device_token,pseudo_session,class_name,action,view_path,component_tag,created,mobile,doctor_id,device_brand,device_model,app_version,
+          |device_type,web_data,web_data_type,alternate_info,network_type,remark1 login_state,remark2 first_app_version,remark3 ,user_token_tourist,serviceName
+          | from pica_log.picalog_trace_app_part
+          		  | where  pseudo_session is not null and pseudo_session !=''
+          		  | and pseudo_id !='' and extra_info !='com.picahealth.patient' and serviceName != 'trace3'
+          | and created is not null and created!='' and FROM_UNIXTIME(cast(substring(created,1,10) as bigint),'yyyy-MM-dd')=created_day
+        		""".stripMargin
+
+
+
+    //从源表pica_log.picalog_trace_app_part中执行SQL获取源数据,输入指定日期参数时执行的SQL
+    final val SOURCE_SQL_FROM_PREF: String =
+        s"""
+           |select pseudo_session,cast(user_id_int as string) user_id,mobile,device_token,user_token  , view_class  ,view_path,action,action_type,
+           |component_tag,menu_code,action_code,`position`,label_value,label_class,app_version,device_type,device_brand,device_model,"" device_system,
+           |net_type,created_time,date_time from ${HIVE_TABLE0}
+           | where 1=1  and `action`!='ACTION_EQUIP_INFO'
+        """.stripMargin
+
+
+    //从dw_fact_log_session表中筛选数据
+    final val SOURCE_SQL_PATH: String =
+        s"""
+           |select id log_session_id, session_id,user_id_int user_id,device_token,action_type,user_token,menu_code,action_code,position,label_value,label_class,
+           |app_version,device_type,created_time,date_time,module_class1,module_class2 from ${MyConfigSession.HIVE_TABLE1}
+           | where  app_version >= '3.1.7'
+           | AND ((action_type ='ACTION_CLICK'  and action_code != 'null' ) OR  action_type ='ACTION_VIEW' )
+           | and (menu_code != '0' and menu_code !='null' and menu_code !='' and (length(menu_code) <= 3 or length(menu_code)=7) )
+        """.stripMargin
+
+    //从dw_fact_log_session_TERM表中筛选数据
+    final val SOURCE_SQL_PATH_NEW: String =
+        s"""
+           |select id log_session_id, session_id,  user_id,device_token,action_type,user_token,menu_code,action_code,position,label_value,label_class,action_step,
+           |app_version,device_type,device_brand,device_model,net_type,created_time,date_time,module_class1,module_class2 from ${MyConfigSession.HIVE_TABLE4}
+           | where  app_version >= '3.1.7'
+           | AND ((action_type ='ACTION_CLICK'  and action_code != 'null' )   OR  action_type ='ACTION_VIEW'   )
+           | and (menu_code != '0' and menu_code !='null' and menu_code !='' and (length(menu_code) <= 3 or length(menu_code)=7) and cast(menu_code as int ) is not null)
+          """.stripMargin
+
+    //从pica_dw.pathconvertconfig表中筛选数据
+    final val SOURCE_SQL_CONFIG: String =
+        s"""
+           |select
+           |projectid,
+           |nodeid,
+           |nodename,
+           |menucode,
+           |actioncode,
+           |uv,
+           |refer_menu_code,
+           |refer_action_code,
+           |starttime,
+           |endtime
+           |from ${MyConfigSession.HIVE_TABLE5}
+          """.stripMargin
+
+    //从pica_dw.dw_fact_log_session_path_convert表中筛选数据
+    final val SOURCE_SQL_PATH_CONVERT: String =
+        s"""
+           |select
+           |log_session_id,session_id,user_id,action_type,
+           |user_token,menu_code,action_code,position,
+           |label_value,label_class,refer_log_session_id,
+           |refer_menu_code,refer_action_code,refer_position,
+           |refer_label_value,refer_time_diff,refer_created,step_id,
+           |app_version,device_type,device_brand,
+           |device_model,net_type,created_time,
+           |date_time,module_class1,module_class2,
+           |user_identity_id,created_day
+           |from ${MyConfigSession.HIVE_TABLE6}
+          """.stripMargin
+
+
+
+
+
+    //从dw_fact_log_session_TERM表中筛选数据
+    final val SOURCE_PATH_CONVERT: String =
+        s"""
+           |select id log_session_id, session_id,  user_id,device_token,action_type,user_token,menu_code,action_code,position,label_value,label_class,action_step,
+           |app_version,device_type,device_brand,device_model,net_type,created_time,date_time,module_class1,module_class2,view_path from ${MyConfigSession.HIVE_TABLE4}
+           | where  app_version >= '3.1.7'
+           | AND ((action_type ='ACTION_CLICK'  and action_code != 'null' )   OR  action_type ='ACTION_VIEW'   )
+           | and (menu_code != '0' and menu_code !='null' and menu_code !='' and (length(menu_code) <= 3 or length(menu_code)=7) and cast(menu_code as int ) is not null)
+           | and menu_code !='499'
+          """.stripMargin
+
+
+
+
+    //匹配user_id的条件
+    //0.使用pica_ds.pica_doctor表匹配,匹配不上的user_id值为'0'
+    final val INIT_USER_ID_SQL =
+    s"""
+       |SELECT t.session_id, COALESCE(cast(b.id as string),'0') AS user_id, t.mobile, t.device_token, t.user_token,
+       |t.view_class,t.view_path,t.action_type,t.component_tag, t.menu_code,
+       |t.action_code, t.position, t.label_value,t.label_class,t.module_class1,t.module_class2,t.app_version,t.device_type,
+       |t.device_brand, t.device_model, t.device_system,t.net_type,t.created_time,
+       |t.date_time from ${MyConfigSession.VIEW_SESSION_ODS} as t
+       |left join pica.ods_pica_p_doctor as b on t.user_id = cast(b.id as string)
+  """.stripMargin
+    final val INIT_USER_ID_SQL_PREF =
+        s"""
+           |SELECT t.pseudo_session, COALESCE(cast(b.id as string),'0') AS user_id, t.mobile, t.device_token, t.user_token,
+           |t.view_class,t.view_path,t.action, t.component_tag, t.menu_code,
+           |t.action_code, t.position, t.label_value,t.label_class,t.app_version,t.device_type,
+           |t.device_brand, t.device_model, t.net_type,t.created_time,t.date_time,
+           |t.web_data,t.web_data_type,t.alternate_info,t.login_state,t.first_app_version,t.serviceName
+           | from ${MyConfigSession.VIEW_SESSION_ODS} as t
+           |left join pica_ds.pica_doctor as b on t.user_id = cast(b.id as string)
+  """.stripMargin
+    //1.针对没有匹配到的user_id,先使用 mobile_phone 进行匹配,得到 user_id 匹配,'0',XK0HdMN6dAfOlYPOFHHL0A==表示''加密之后的mobile
+    final val MOBILE_PHONE_SQL: String =
+        s"""
+           |SELECT ss.session_id, COALESCE(cast(b.id as string),'0') AS user_id, ss.mobile, ss.device_token, ss.user_token,
+           |ss.view_class,ss.view_path,ss.action_type,ss.component_tag, ss.menu_code,
+           |ss.action_code, ss.position,ss.label_value,ss.label_class,ss.module_class1,ss.module_class2,ss.app_version, ss.device_type,
+           |ss.device_brand, ss.device_model,ss.device_system, ss.net_type,ss.created_time,
+           |ss.date_time from ${MyConfigSession.VIEW_SESSION_NO_MATCH}  AS ss
+           |left join (select distinct id,mobile_phone from pica.ods_pica_p_doctor where ods_pica_p_doctor.delete_flag = 1 and mobile_phone!='' and mobile_phone!='XK0HdMN6dAfOlYPOFHHL0A==' ) AS b on ss.mobile = b.mobile_phone
+  """.stripMargin
+    final val MOBILE_PHONE_SQL_PREF: String =
+        s"""
+           |SELECT ss.pseudo_session,COALESCE(cast(b.id as string),'0') AS user_id,ss.mobile,ss.device_token,ss.user_token,
+           |ss.view_class,ss.view_path,ss.action, ss.component_tag,ss.menu_code,
+           |ss.action_code,ss.position,ss.label_value,ss.label_class,ss.app_version,ss.device_type,
+           |ss.device_brand,ss.device_model, ss.net_type,ss.created_time,
+           |ss.date_time,ss.web_data,ss.web_data_type,ss.alternate_info,ss.login_state,ss.first_app_version,ss.serviceName
+           | from ${MyConfigSession.VIEW_SESSION_NO_MATCH}  AS ss
+           |left join (select distinct id,mobile_phone from pica_ds.pica_doctor where pica_doctor.delete_flag = 1 ) AS b on ss.mobile = b.mobile_phone
+  """.stripMargin
+    //2.使用equipment表匹配,默认是昨天的
+    final val EQUIPMENT_INFO_SQL: String =
+        """
+          |SELECT a.user_id,a.device_token ,ROW_NUMBER() OVER ( PARTITION BY a.device_token ORDER BY a.creat_time DESC ) row_d
+          |from (select e1.user_id,e1.device_token,e1.creat_time,e1.modify_time,e1.delete_flag from  pica.ods_picamessage_p_equipment_info e1  union
+          | select e2.user_id,e2.device_token,e2.creat_time, e2.modify_time,e2.delete_flag  from  pica.ods_picamessage_p_equipment_info_shadow  e2 )  a
+          | where a.user_id IS NOT NULL and a.user_id>0 and a.delete_flag=1 and device_token is not null
+          | AND (to_date(a.creat_time) = date_sub(current_date(),1) OR to_date(a.modify_time) = date_sub(current_date(),1))
+    """.stripMargin
+
+    //注意:1.使用equipment表匹配,如果匹配的是历史指定日期的数据,那么需要使用不同的表和下面这个SQL表,限定stat_date日期
+    final val EQUIPMENT_INFO_SQL_ARGS: String =
+        """
+      		|SELECT a.doctor_id as user_id, a.device_token, ROW_NUMBER() OVER (PARTITION BY a.device_token ORDER BY a.creat_time DESC) row_d
+      		| from pica.dwd_study_zipper_picams_equipment_info AS a where a.doctor_id IS NOT NULL AND a.stat_date=
+    """.stripMargin
+
+
+    //先和equiment表进行匹配,使用 device_token 匹配,得到 user_id 匹配,'0'
+    final val DEVICE_TOKEN_SQL: String =
+        s"""
+           |SELECT t.session_id, COALESCE(cast(b.user_id as string),'0') AS user_id, t.mobile, t.device_token, t.user_token,
+           |t.view_class,t.view_path,t.action_type,t.component_tag, t.menu_code,
+           |t.action_code, t.position, t.label_value,t.label_class,t.module_class1,t.module_class2,t.app_version,t.device_type,
+           |t.device_brand, t.device_model, t.device_system,t.net_type,t.created_time,
+           |t.date_time from (select * from  ${MyConfigSession.VIEW_MOBILE_PHONE}  a where a.user_id= '0' ) as t
+           |left join ${MyConfigSession.VIEW_EQUIPMENT_INFO} as b on t.device_token = b.device_token
+  """.stripMargin
+    final val DEVICE_TOKEN_SQL_PREF: String =
+        s"""
+	   |SELECT t.pseudo_session,COALESCE(cast(b.user_id as string),'0') AS user_id,t.mobile,t.device_token,t.user_token,
+	   |t.view_class,t.view_path,t.action ,t.component_tag,t.menu_code,
+	   |t.action_code,t.position,t.label_value,t.label_class,t.app_version,t.device_type,
+	   |t.device_brand,t.device_model, t.net_type,t.created_time,
+	   |t.date_time,t.web_data,t.web_data_type,t.alternate_info,t.login_state,t.first_app_version,t.serviceName
+	   | from (select * from  ${MyConfigSession.VIEW_MOBILE_PHONE}  a where a.user_id= '0' ) as t
+	   |left join ${MyConfigSession.VIEW_EQUIPMENT_INFO} as b on t.device_token = b.device_token
+""".stripMargin
+
+
+
+
+    //sessionGap时间,切割 session 的依据,30 minutes
+    final val SESSION_GAP: Long = 30 * 60 * 1000
+    //分隔符
+    final val DELIMITER: String = "\001"
+
+    //hdfs127 Mysql配置,记录任务的执行情况
+    final val JDBC_DRIVER = "com.mysql.jdbc.Driver"
+    final val DATA_BASE = "pica_job"
+    final val JDBC_URL = s"jdbc:mysql://hdfs127:3306/${DATA_BASE}?useTimezone=true&serverTimezone=GMT%2B8"
+    final val JDBC_USERNAME = "pica_spider"
+    final val JDBC_PSSWORD = "5$7FXgz#e5JWP08e"
+    final val JDBC_TABLE = "schedule_job_record"
+
+    final val mysql_url_user_profile="jdbc:mysql://hdfs127:3306/user_profile?useTimezone=true&serverTimezone=GMT%2B8"
+    final val mysql_username_user_profile="bi_admin"
+    final val mysql_password_user_profile="5$7FXgz#e5JWP08e"
+
+
+    final val MENU_CODE_VIEW_PATH_SQL: String = "select distinct full_path view_path, menu_code from pica_ds.pica_bi_bi_menu_code_h5 where full_path is not Null"
+
+
+
+}

src/main/scala/utils/DateUtils.java

+package utils;
+
+import java.text.ParseException;
+import java.text.SimpleDateFormat;
+import java.util.Calendar;
+import java.util.Date;
+
+/**
+ * 日期时间工具类
+ * @Author 	zhenxin.ma
+ * @Date 	2019/8/29 17:28
+ * @Version 1.0
+ */
+public class DateUtils {
+	public static final SimpleDateFormat TIME_FORMAT =
+			new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
+	public static final SimpleDateFormat DATE_FORMAT =
+			new SimpleDateFormat("yyyy-MM-dd");
+	public static ThreadLocal<SimpleDateFormat> simpleDateFormatThreadLocal=new ThreadLocal<SimpleDateFormat>(){
+		@Override
+		protected SimpleDateFormat initialValue() {
+			return new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
+		}
+	};
+
+	/**
+	 * 判断一个时间是否在另一个时间之前
+	 * @param time1 第一个时间
+	 * @param time2 第二个时间
+	 * @return 判断结果
+	 */
+	public static boolean before(String time1, String time2) {
+		try {
+			Date dateTime1 = TIME_FORMAT.parse(time1);
+			Date dateTime2 = TIME_FORMAT.parse(time2);
+
+			if(dateTime1.before(dateTime2)) {
+				return true;
+			}
+		} catch (Exception e) {
+			e.printStackTrace();
+		}
+		return false;
+	}
+
+	/**
+	 * 判断一个时间是否在另一个时间之后
+	 * @param time1 第一个时间
+	 * @param time2 第二个时间
+	 * @return 判断结果
+	 */
+	public static boolean after(String time1, String time2) {
+		try {
+			Date dateTime1 = TIME_FORMAT.parse(time1);
+			Date dateTime2 = TIME_FORMAT.parse(time2);
+
+			if(dateTime1.after(dateTime2)) {
+				return true;
+			}
+		} catch (Exception e) {
+			e.printStackTrace();
+		}
+		return false;
+	}
+
+
+	/**
+	 * 计算时间差值（单位为秒）
+	 * @param time1 时间1
+	 * @param time2 时间2
+	 * @return 差值
+	 */
+	public static int minus(String time1, String time2) {
+		try {
+			Date datetime1 = TIME_FORMAT.parse(time1);
+			Date datetime2 = TIME_FORMAT.parse(time2);
+
+			long millisecond = datetime1.getTime() - datetime2.getTime();
+
+			return Integer.valueOf(String.valueOf(millisecond / 1000));
+		} catch (Exception e) {
+			e.printStackTrace();
+		}
+		return 0;
+	}
+
+	/**
+	 * 获取年月日和小时
+	 * @param datetime 时间（yyyy-MM-dd HH:mm:ss）
+	 * @return 结果
+	 */
+	public static String getDateHour(String datetime) {
+		String date = datetime.split(" ")[0];
+		String hourMinuteSecond = datetime.split(" ")[1];
+		String hour = hourMinuteSecond.split(":")[0];
+		return date + "_" + hour;
+	}
+
+
+
+	/**
+	 * @Description 毫秒转换成标准时间 yyyy-MM-dd HH:mm:ss
+	 * @param time 传入的参数,毫秒时间
+	 * @return java.lang.String
+	 **/
+	public static String milliSecondsFormatTime(String time){
+		if (time == null || time.isEmpty()) {
+			return "";
+		}
+		Date tmpDate = new Date();
+		tmpDate.setTime(Long.parseLong(time));
+		String dtime = simpleDateFormatThreadLocal.get().format(tmpDate);
+		return dtime;
+	}
+
+
+	/**
+	 * 获取昨天的日期（yyyy-MM-dd）
+	 * @return 昨天的日期
+	 */
+	public static String getYesterdayDate() {
+		Calendar cal = Calendar.getInstance();
+		cal.setTime(new Date());
+		cal.add(Calendar.DAY_OF_YEAR, -1);
+
+		Date date = cal.getTime();
+
+		return DATE_FORMAT.format(date);
+	}
+
+	/**
+	 * 获取当天日期（yyyy-MM-dd）
+	 * @return 当天日期
+	 */
+	public static String getTodayDate() {
+		return DATE_FORMAT.format(new Date());
+	}
+
+
+
+	/**
+	 * @Description 获取当天的时间:yyyy-MM-dd HH:mm:ss
+	 * @param
+	 * @return java.lang.String
+	 **/
+	public static String getTodayTime() {
+		return simpleDateFormatThreadLocal.get().format(new Date());
+	}
+
+
+//	/**
+//	 * @Description		获取昨天的日期:yyyy-MM-dd HH:mm:ss
+//	 * @param gapDay
+//	 * @return java.lang.String
+//	 **/
+//	public static String getYesterdayTime(int gapDay){
+//		Calendar calendar=Calendar.getInstance();
+//		calendar.set(Calendar.HOUR_OF_DAY,24*gapDay);
+//		String yesterdayDate=simpleDateFormatThreadLocal.get().format(calendar.getTime());
+//		return  yesterdayDate;
+//	}
+
+
+	/**
+	 * 格式化日期（yyyy-MM-dd）
+	 * @param date Date对象
+	 * @return 格式化后的日期
+	 */
+	public static String formatDate(Date date) {
+		return DATE_FORMAT.format(date);
+	}
+
+	/**
+	 * 格式化时间（yyyy-MM-dd HH:mm:ss）
+	 * @param date Date对象
+	 * @return 格式化后的时间
+	 */
+	public static String formatTime(Date date) {
+		return TIME_FORMAT.format(date);
+	}
+
+
+	/**
+	 * @Description		日期解析
+	 * @param time		格式化的时间(yyyy-MM-dd HH:mm:ss)
+	 * @return java.util.Date
+	 **/
+	public static Date parseTime(String time)
+	{
+		try {
+			Date result=simpleDateFormatThreadLocal.get().parse(time);
+			simpleDateFormatThreadLocal.remove();
+			return result;
+		} catch (ParseException e) {
+			e.printStackTrace();
+		}
+		return null;
+	}
+
+
+	public static void main(String[] args) {
+		final Date date = new Date();
+		System.out.println(date);
+		System.out.println(parseTime("2020-03-30 11:45:18"));
+		System.out.println(formatDate(date));
+		System.out.println(formatTime(date));
+		System.out.println(getYesterdayDate());
+
+		String com = "005#";
+		final String[] strs = com.split("#");
+		System.out.println("length is "+strs.length);
+		System.out.println("0位数据是: "+strs[0]);
+	}
+
+}
+

src/main/scala/utils/MyBeanUtils.scala

+package utils
+
+import java.lang.reflect.{Field, Modifier}
+import java.text.SimpleDateFormat
+import java.util.Locale
+
+import scala.util.control.Breaks._
+
+object MyBeanUtils {
+  /**
+    * 对象相同属性copy
+    *
+    * @param obj
+    * @param toResult
+    * @return
+    * @throws Exception
+    * 转换报错
+    */
+  def copyProperties(obj: Object, toResult: Object): Unit = {
+    if (obj == null) {
+      return
+    }
+    try {
+      val fields = toResult.getClass.getDeclaredFields
+      for (field <- fields) {
+        breakable {
+          field.setAccessible(true) //修改访问权限
+          if (Modifier.isFinal(field.getModifiers()))
+            break
+          if (isWrapType(field)) {
+            val getMethodName = field.getName()
+            val setMethodName = field.getName()+"_$eq"
+            val getMethod = {
+              try {
+                obj.getClass().getMethod(getMethodName)
+              } catch {
+                case ex: Exception =>
+                  //  println(ex)
+                  break
+              }
+            }
+
+            //从源对象获取get方法
+            val setMethod =
+              toResult.getClass.getMethod(setMethodName, field.getType)
+            //从目标对象获取set方法
+            val value = {
+              val objValue = getMethod.invoke(obj) // get 获取的是源对象的值
+              if (objValue != null && objValue.isInstanceOf[java.util.Date]) {
+                // GMT时间转时间戳
+                val format =
+                  new SimpleDateFormat("yyyy-MM-dd HH:mm:ss", Locale.ENGLISH)
+                val formatValue = format.format(objValue)
+                java.lang.Long.valueOf(format.parse(formatValue).getTime)
+              } else
+                objValue
+            }
+            setMethod.invoke(toResult, value)
+          }
+        }
+      }
+    } catch {
+      case ex: Exception => throw ex
+    }
+  }
+
+  /**
+    * 是否是基本类型、包装类型、String类型
+    */
+  def isWrapType(field: Field): Boolean = {
+    val typeList = List[String]("java.lang.Integer"
+      , "java.lang.Double", "java.lang.Float", "java.lang.Long", "java.util.Optional"
+      , "java.lang.Short", "java.lang.Byte", "java.lang.Boolean", "java.lang.Char"
+      , "java.lang.String", "int", "double", "long"
+      , "short", "byte", "boolean", "char", "float")
+    if (typeList.contains(field.getType().getName())) true else false
+  }
+
+
+  def getV(ref:AnyRef, name: String): Any = ref.getClass.getMethods.find(_.getName == name).get.invoke(ref)
+
+  def setV(ref:AnyRef,name: String, value: Any): Unit = {
+    ref.getClass.getMethods.find(_.getName == name + "_$eq").get.invoke(ref, value.asInstanceOf[AnyRef])
+  }
+
+
+}

src/main/scala/utils/MySqlUtil.scala

+package utils
+
+import java.lang.reflect.Field
+import java.sql.{Connection, DriverManager, PreparedStatement, ResultSet, ResultSetMetaData, Statement}
+
+import com.alibaba.fastjson.JSONObject
+import com.google.common.base.CaseFormat
+import config.MyConfigSession
+
+import scala.collection.mutable.ListBuffer
+
+object MySqlUtil {
+
+
+  def main(args: Array[String]): Unit = {
+//     val mysql_tablename = "r_project_doctor_certificate_log"
+//      val hive_table="pica.pica_project_x_gpr_doctor_certificate"
+//      val mc_table="pica.dws_gpranking_x_gpr_doctor_certificate"
+//    val list: java.util.List[JSONObject] = queryList(s"show create table ${mysql_tablename}"
+//      ,MyConfigSession.mysql_url_user_profile)
+//    import collection.JavaConverters._
+//    val profileListScala = list.asScala
+//    println("profileListScala"+profileListScala)
+////    var index = 0  //插入新增标识
+////    val tbbuffer = new StringBuilder
+////    val tbbuffer2 = new StringBuilder
+////    tbbuffer.append("where ")
+////    for(profile <- profileListScala) {
+////      val s: JSONObject = profile
+////
+////      if (!profile.getString("COLUMN_NAME").equals("id") && !profile.getString("COLUMN_NAME").contains("log_date") && !profile.getString("COLUMN_NAME").contains("times")
+////        && !profile.getString("COLUMN_NAME").contains("delete_flag") && !profile.getString("COLUMN_NAME").contains("created_id")
+////        && !profile.getString("COLUMN_NAME").contains("created_time") && !profile.getString("COLUMN_NAME").contains("modified_id")
+////        && !profile.getString("COLUMN_NAME").contains("modified_time")) {
+////          if (index != 0) {
+////            tbbuffer.append("\n")
+////            tbbuffer.append("or  ")
+////          }
+////          if (profile.getString("COLUMN_TYPE").contains("varchar")) {
+////            tbbuffer.append(s"NVL(A.${profile.getString("COLUMN_NAME")}, '') != NVL(B.${profile.getString("COLUMN_NAME")}, '')")
+////          } else if (profile.getString("COLUMN_TYPE").contains("int")) {
+////            tbbuffer.append(s"NVL(A.${profile.getString("COLUMN_NAME")}, -1) != NVL(B.${profile.getString("COLUMN_NAME")}, -1)")
+////          } else {
+////            tbbuffer.append(s"NVL(A.${profile.getString("COLUMN_NAME")}, '') != NVL(B.${profile.getString("COLUMN_NAME")}, '')")
+////          }
+////          tbbuffer2.append(s"case when NVL(A.${profile.getString("COLUMN_NAME")}, '') != NVL(B.${profile.getString("COLUMN_NAME")}, '') THEN concat(NVL(A.${profile.getString("COLUMN_NAME")}, 'null'),'##',NVL(B.${profile.getString("COLUMN_NAME")}, 'null'))  END AS ${profile.getString("COLUMN_NAME")} , \n")
+////
+////          index += 1
+////        }
+////
+////    }
+//////    println(tbbuffer)
+//////    println(tbbuffer2)
+////
+////    val tbbuffer3 = new StringBuilder
+////    tbbuffer3.append("select ").append(tbbuffer2.toString())
+////      .append(s"from ${hive_table} as A \n join ${mc_table} as b \n")
+////      .append(tbbuffer)
+////    println(tbbuffer3)
+
+
+
+
+
+    }
+
+//  private val properties: Properties = MyPropertiesUtil.load("myconfig.properties")
+
+
+  val MYSQL_URL = MyConfigSession.mysql_url_user_profile
+  val MYSQL_USERNAME = MyConfigSession.mysql_username_user_profile
+  val MYSQL_PASSWORD = MyConfigSession.mysql_password_user_profile
+
+//  val MYSQL_URL = "jdbc:mysql://rm-nf9p0m7e89sji2n8ijo.mysql.rds.aliyuncs.com:3306/pica_report?useTimezone=true&serverTimezone=GMT%2B8"
+//    val MYSQL_USERNAME = "pica_report_w"
+//    val MYSQL_PASSWORD = "lsHB!%@cPGJOfw0E"
+
+
+  def queryList(sql: String,mySqlUrl:String): java.util.List[JSONObject] = {
+    Class.forName("com.mysql.jdbc.Driver")
+    //创建结果列表
+    val resultList: java.util.List[JSONObject] = new java.util.ArrayList[JSONObject]()
+
+    println("mySqlUrl:"+mySqlUrl)
+    println("MYSQL_USERNAME:"+MYSQL_USERNAME)
+    println("MYSQL_PASSWORD:"+MYSQL_PASSWORD)
+
+
+    //创建连接
+    val conn: Connection = DriverManager.getConnection(mySqlUrl, MYSQL_USERNAME, MYSQL_PASSWORD)
+    //创建会话
+    val stat: Statement = conn.createStatement
+//    println(sql)
+    //提交sql 返回结果
+    val rs: ResultSet = stat.executeQuery(sql)
+    //为了获得列名 要取得元数据
+    val md: ResultSetMetaData = rs.getMetaData
+    while (rs.next) {
+      val rowData = new JSONObject();
+      for (i <- 1 to md.getColumnCount) {
+        // 根据下标得到对应元数据中的字段名，以及结果中值
+        rowData.put(md.getColumnName(i), rs.getObject(i))
+      }
+      resultList.add(rowData)
+    }
+
+    stat.close()
+    conn.close()
+    resultList
+  }
+
+
+  def queryList[T<:AnyRef](sql: String, clazz: Class[T], underScoreToCamel: Boolean):List[T]={
+
+    Class.forName("com.mysql.jdbc.Driver");
+    val resultList: ListBuffer[T] = new ListBuffer[T]();
+    val connection: Connection = DriverManager.getConnection(MYSQL_URL, MYSQL_USERNAME, MYSQL_PASSWORD)
+    val stat: Statement = connection.createStatement();
+    val rs: ResultSet = stat.executeQuery(sql);
+    val md: ResultSetMetaData = rs.getMetaData();
+    while (rs.next()) {
+      val obj: T = clazz.newInstance()
+      for (i <- 1 to md.getColumnCount) {
+        var propertyName: String = md.getColumnLabel(i)
+
+        if (underScoreToCamel) {
+          propertyName = CaseFormat.LOWER_UNDERSCORE.to(CaseFormat.LOWER_CAMEL, md.getColumnLabel(i))
+        }
+        MyBeanUtils.setV(obj,propertyName,rs.getObject(i))
+
+      }
+      resultList.append(obj);
+    }
+    stat.close()
+    connection.close()
+    resultList.toList
+
+  }
+
+  def queryOne[T<:AnyRef](sql: String, clazz: Class[T] ,
+                          underScoreToCamel: Boolean,mySql_URL:String): Option[T] ={
+    println(sql)
+    Class.forName("com.mysql.jdbc.Driver");
+    val resultList: ListBuffer[T] = new ListBuffer[T]();
+    val connection: Connection =
+      DriverManager.getConnection(mySql_URL, MYSQL_USERNAME, MYSQL_PASSWORD)
+    val stat: Statement = connection.createStatement();
+    val rs: ResultSet = stat.executeQuery(sql);
+    val md: ResultSetMetaData = rs.getMetaData();
+    while (rs.next()) {
+      val obj: T = clazz.newInstance()
+
+
+      for (i <- 1 to md.getColumnCount) {
+        var propertyName: String = md.getColumnName(i)
+        if (underScoreToCamel) {
+          propertyName =
+            CaseFormat.LOWER_UNDERSCORE.to(CaseFormat.LOWER_CAMEL, md.getColumnName(i))
+        }
+        MyBeanUtils.setV(obj,propertyName,rs.getObject(i))
+      }
+      resultList.append(obj);
+    }
+    stat.close()
+    connection.close()
+    if(resultList!=null){
+      Some(resultList(0))
+    }else{
+      None
+    }
+
+  }
+
+
+  def queryOne[T<:AnyRef](sql: String,mySql_URL:String): Option[JSONObject] ={
+    Class.forName("com.mysql.jdbc.Driver")
+    //创建结果列表
+    val resultList: java.util.List[JSONObject] = new java.util.ArrayList[JSONObject]()
+    //创建连接
+    val conn: Connection = DriverManager.getConnection(mySql_URL, MYSQL_USERNAME, MYSQL_PASSWORD)
+    //创建会话
+    val stat: Statement = conn.createStatement
+    println(sql)
+    //提交sql 返回结果
+    val rs: ResultSet = stat.executeQuery(sql)
+    //为了获得列名 要取得元数据
+    val md: ResultSetMetaData = rs.getMetaData
+    while (rs.next) {
+      val rowData = new JSONObject();
+      for (i <- 1 to md.getColumnCount) {
+        // 根据下标得到对应元数据中的字段名，以及结果中值
+        rowData.put(md.getColumnName(i), rs.getObject(i))
+      }
+      resultList.add(rowData)
+    }
+
+    stat.close()
+    conn.close()
+
+    if(resultList!=null){
+      Some(resultList.get(0))
+    }else{
+      None
+    }
+
+
+
+  }
+
+  def insertOne[T](sql: String, obj:T ): Unit ={
+    Class.forName("com.mysql.jdbc.Driver");
+    val resultList: ListBuffer[T] = new ListBuffer[T]();
+    val connection: Connection =
+      DriverManager.getConnection(MYSQL_URL, MYSQL_USERNAME, MYSQL_PASSWORD)
+    val pstat: PreparedStatement = connection.prepareStatement(sql)
+    val fields: Array[Field] = obj.getClass.getDeclaredFields
+
+    for(i<- 1 to fields.size){
+      val field:Field = fields(i-1);
+      field.setAccessible(true)
+      val value: AnyRef = field.get(obj)
+      pstat.setObject(i,value)
+    }
+    val resInsert = pstat.execute()
+    println("插入数据，返回值=>" + resInsert)
+
+  }
+
+
+
+}

src/main/scala/utils/NumberUtils.java

+package utils;
+
+import java.math.BigDecimal;
+
+/**
+ * 数字格工具类
+ * @Author zhenxin.ma
+ * @Date 2020/3/30 14:05
+ * @Version 1.0
+ */
+public class NumberUtils {
+
+    /**
+     * 格式化小数
+     * @param num 数字
+     * @param scale 四舍五入的位数
+     * @return 格式化小数
+     */
+    public static double formatDouble(double num, int scale) {
+        BigDecimal bd = new BigDecimal(num);
+        return bd.setScale(scale, BigDecimal.ROUND_HALF_UP).doubleValue();
+    }
+
+}
+

src/main/scala/utils/ParamUtils.java

+package utils;
+
+import com.alibaba.fastjson.JSONArray;
+import com.alibaba.fastjson.JSONObject;
+
+/**
+ * 参数工具类
+ * @Author zhenxin.ma
+ * @Date 2020/3/30 14:09
+ * @Version 1.0
+ */
+public class ParamUtils {
+    /**
+     * 从命令行参数中提取任务id
+     * @param args 命令行参数
+     * @return 任务id
+     */
+    public static Long getTaskIdFromArgs(String[] args) {
+        try {
+            if(args != null && args.length > 0) {
+                return Long.valueOf(args[0]);
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+        return null;
+    }
+
+    /**
+     * 从JSON对象中提取参数
+     * @param jsonObject JSON对象
+     * @return 参数
+     */
+    public static String getParam(JSONObject jsonObject, String field) {
+        JSONArray jsonArray = jsonObject.getJSONArray(field);
+        if(jsonArray != null && jsonArray.size() > 0) {
+            return jsonArray.getString(0);
+        }
+        return null;
+    }
+
+
+}
+

src/main/scala/utils/PropertiesUtil.java

+package utils;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FSDataInputStream;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+
+import java.io.BufferedReader;
+import java.io.InputStreamReader;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * 读取HDFS配置文件
+ * @Author zhenxin.ma
+ * @Date 2020/1/2 14:50
+ * @Version 1.0
+ */
+public class PropertiesUtil {
+	public static Map<String, String> propertiesMap = new HashMap();
+
+	static {
+		//HDFS配置文件路径
+		String hdfsPropertiesPath = "/user/big-data/script/config2/config.properties";
+		System.out.println("hdfsPropertiesPath is :" +hdfsPropertiesPath);
+		BufferedReader br =null;
+		try {
+			Configuration conf = new Configuration();
+			FileSystem fs = FileSystem.get(conf);
+			Path path = new Path(hdfsPropertiesPath);
+			FSDataInputStream fsDataInputStream = fs.open(path);
+			br = new BufferedReader(new InputStreamReader(fsDataInputStream));
+			String line = null;
+			while ((line = br.readLine()) != null) {
+				if(line.contains("=") && !line.trim().startsWith("#")){
+					//只替换第一个"="号
+					String rline =line.replaceFirst("="," ");
+					propertiesMap.put(rline.split(" ")[0].trim(),rline.split(" ")[1].trim());
+				}
+			}
+			//判断运行环境
+			if("online".equals(propertiesMap.get("spark.model"))){
+				System.out.println("propertiesMap:" + propertiesMap);
+			}
+		} catch (Exception e){
+			e.printStackTrace();
+		} finally {
+			try {
+				if (br !=null) {
+					br.close();
+				}
+			} catch (Exception e) {
+				e.printStackTrace();
+			}
+		}
+	}
+
+}
+

src/main/scala/utils/StringUtils.java

+package utils;
+
+import java.math.BigDecimal;
+
+/**
+ * 字符串工具类
+ * @Author zhenxin.ma
+ * @Date 2020/3/30 15:35
+ * @Version 1.0
+ */
+public class StringUtils {
+
+    /**
+     * 判断字符串是否为空
+     * @param str 字符串
+     * @return 是否为空
+     */
+    public static boolean isEmpty(String str) {
+        return str == null || "".equals(str);
+    }
+
+    /**
+     * 判断字符串是否为数值
+     * @param str 字符串
+     * @return 是否数值
+     */
+    public static boolean isNumeric(String str) {
+        String bigStr;
+        try {
+            bigStr = new BigDecimal(str).toString();
+        } catch (Exception e) {
+            return false;//异常 说明包含非数字。
+        }
+        return true;
+    }
+    /**
+     * 判断字符串是否不为空
+     * @param str 字符串
+     * @return 是否不为空
+     */
+    public static boolean isNotEmpty(String str) {
+        return str != null && !"".equals(str);
+    }
+
+
+    /**
+     * 判断字符串是否为null,如果是,那么给字符串设置默认值"";否则返回原值
+     * @param str   字符串
+     * @return java.lang.String 返回字符串值
+     **/
+    public static String getNotNullString(String str) {
+        if (isEmpty(str)) {
+            return "";
+        }
+        return str;
+    }
+
+
+    /**
+     * 截断字符串两侧的逗号
+     * @param str 字符串
+     * @return 字符串
+     */
+    public static String trimComma(String str) {
+        if(str.startsWith(",")) {
+            str = str.substring(1);
+        }
+        if(str.endsWith(",")) {
+            str = str.substring(0, str.length() - 1);
+        }
+        return str;
+    }
+
+    /**
+     * 补全两位数字
+     * @param str
+     * @return
+     */
+    public static String fulfuill(String str) {
+        if(str.length() == 2) {
+            return str;
+        } else {
+            return "0" + str;
+        }
+    }
+
+    /**
+     * 从拼接的字符串中提取字段
+     * @param str 字符串
+     * @param delimiter 分隔符
+     * @param field 字段
+     * @return 字段值
+     */
+    public static String getFieldFromConcatString(String str,
+                                                  String delimiter, String field) {
+        String[] fields = str.split(delimiter);
+        for(String concatField : fields) {
+            if(concatField.split("=").length==2) {
+                String fieldName = concatField.split("=")[0];
+                String fieldValue = concatField.split("=")[1];
+                if (fieldName.equals(field)) {
+                    return fieldValue;
+                }
+            }
+        }
+        return null;
+    }
+
+    /**
+     * 从拼接的字符串中给字段设置值
+     * @param str 字符串
+     * @param delimiter 分隔符
+     * @param field 字段名
+     * @param newFieldValue 新的field值
+     * @return 字段值
+     */
+    public static String setFieldInConcatString(String str,
+                                                String delimiter, String field, String newFieldValue) {
+        String[] fields = str.split(delimiter);
+
+        for(int i = 0; i < fields.length; i++) {
+            String fieldName = fields[i].split("=")[0];
+            if(fieldName.equals(field)) {
+                String concatField = fieldName + "=" + newFieldValue;
+                fields[i] = concatField;
+                break;
+            }
+        }
+
+        StringBuffer buffer = new StringBuffer("");
+        for(int i = 0; i < fields.length; i++) {
+            buffer.append(fields[i]);
+            if(i < fields.length - 1) {
+                buffer.append("|");
+            }
+        }
+
+        return buffer.toString();
+    }
+
+}
+

src/main/scala/utils/UseUtil.scala

+package utils
+
+import java.sql.{Connection, DriverManager}
+import java.util.{Properties, UUID}
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.serializer.KryoSerializer
+import org.apache.spark.sql.{Column, DataFrame, Row, SaveMode, SparkSession}
+import org.apache.spark.{SparkConf, SparkContext}
+
+
+/**
+  * @Author zhenxin.ma
+  * @Date 2019/8/30 11:59
+  * @Version 1.0
+  */
+object UseUtil {
+	def getUUID(): String = {
+		val uuid: String = UUID.randomUUID().toString().replaceAll("-", "").toLowerCase
+		uuid
+	}
+
+
+
+	//给DF增加列
+	def addDataFrameColumn(df:DataFrame , map: Map[String,Column]): DataFrame={
+		var result: DataFrame = df
+		map.foreach(it => {
+			result = result.withColumn(it._1,it._2)
+		})
+		result
+	}
+
+
+	/**
+	 * @Description //TODO
+	 * @param net NotNull
+	 * @return java.lang.String
+	 **/
+	def netTypeMatch(net: String): String = {
+		var net_type: String = null
+		net match {
+			case "2G" => net_type = "2"
+			case "3G" => net_type = "3"
+			case "4G" => net_type = "4"
+			case "5G" => net_type = "5"
+			case "6G" => net_type = "6"
+			case "2G/3G/4G" => net_type = "4"
+			case "2G/3G/4G/5G" => net_type = "5"
+			case "2G/3G/4G/5G/6G" => net_type = "6"
+			case "WIFI" => net_type = "1"
+			case "WLAN" => net_type = "1"
+			case _:Any => net_type = "0"
+		}
+		net_type
+	}
+
+
+	//Spark 任务设置配置
+	def setConfigure(conf: SparkConf): Unit = {
+		conf.set("spark.serializer", classOf[KryoSerializer].getName)
+		// 序列化时使用的内存缓冲区大小
+		conf.set("spark.kryoserializer.buffer.max", "128m")
+		// 启用rdd压缩
+		conf.set("spark.rdd.compress", "true")
+		// 设置压缩格式为snappy, 默认也就是lz4, 这种压缩格式压缩比高, 速度快, 但是耗费的内存相对也多一些
+		conf.set("spark.io.compression.codec", "snappy")
+		// 设置压缩时使用的内存缓冲区大小
+		conf.set("spark.io.compression.snappy.blockSize", "64k")
+		// spark sql 在shuffle时产生的partition数量, 默认是200
+		conf.set("spark.sql.shuffle.partitions", "200")
+		// SortShuffleManager开启by-pass(不需要排序)模式的阈值, 默认为200, 在partition数量小于这个值时会开启by-pass模式
+		conf.set("spark.shuffle.sort.bypassMergeThreshold", "210")
+		//调节持久化的内存比例
+		conf.set("spark.memory.useLegacyMode", "true")
+		//设置shuffle过程中一个task拉取到上个stage的task的输出后，进行聚合操作时能够使用的Executor内存的比例，默认是0.2
+		//shuffle操作比较多时,适当增加这个值,增加task执行需要的内存
+		conf.set("spark.shuffle.memoryFraction","0.5")
+		// rdd默认的并行度
+		conf.set("spark.default.parallelism", "200")
+		// shuffle溢写缓冲区, 默认32k, 在内存充足的情况下可以适当增加
+		conf.set("spark.shuffle.file.buffer", "64k")
+		// shuffle read task的buffer缓冲大小, 这个缓冲区大小决定了read task每次能拉取的数据量, 在内存充足的情况下可以适当增加
+		conf.set("spark.reducer.maxSizeInFlight", "96m")
+		//设置字符串
+		conf.set("spark.debug.maxToStringFields","100")
+		//启用自动设置 Shuffle Reducer,默认false
+		conf.set("spark.sql.adaptive.enabled","true")
+	}
+
+
+	/**
+	 * @Description	读取配置表,筛选符合的action_type类型
+	 * @param action
+	 * @return java.lang.String
+	 **/
+	def getActionType(action:String): String = {
+		var actionType: String = action
+		actionType match {
+			case "ACTION_APP_BACKGROUND" => actionType = "ACTION_BACKGROUND"
+			case "ACTION_CLICK" => actionType = "ACTION_CLICK"
+			case "ACTION_WEB_CLICK" => actionType = "ACTION_CLICK"
+			case "WEB_ACTION_CLICK" => actionType = "ACTION_CLICK"
+			case "ACTION_EQUIP_INFO" => actionType = "ACTION_EQUIP_INFO"
+			case "ACTION_EXIT_APP" => actionType = "ACTION_EXIT"
+			case "ACTION_PAGE_EXPOSE" => actionType = "ACTION_EXPOSE"
+			case "ACTION_RECENT_APPS" => actionType = "ACTION_RECENT"
+			case "ACTION_APP_START" => actionType = "ACTION_START"
+			case "ACTION_ACTIVITY_CREATE" => actionType = "ACTION_VIEW"
+			case "ACTION_LOAD_URL" => actionType = "ACTION_VIEW"
+			case "ACTION_WEB_ENTER" => actionType = "ACTION_VIEW"
+			case "ACTION_WEB_PAGE_IN" => actionType = "ACTION_VIEW"
+			case _ => actionType =" "
+		}
+		actionType
+	}
+
+
+	/**
+	 * @Description 根据SQL获取广播变量
+	 * @param sparkSession
+	 * @param sourSql
+	 * @param colName1
+	 * @param colName2
+	 * @return org.apache.spark.broadcast.Broadcast<scala.collection.immutable.Map<java.lang.String,java.lang.Object>>
+	 **/
+	def getBroadcast(sparkSession: SparkSession,sourSql: String,colName1: String,colName2: String):Broadcast[Map[String,String]] = {
+		import sparkSession.implicits._
+		val df: DataFrame = sparkSession.sql(sourSql)
+		val map: Map[String, String] = df.map(row => {
+			(row.getAs[String](colName1), row.getAs[String](colName2))
+		}).collect().toMap
+		val broad: Broadcast[Map[String, String]] = sparkSession.sparkContext.broadcast(map)
+		broad
+	}
+
+
+
+	/**
+	  * 将DataFrame保存为Mysql表
+	  * @param dataFrame 需要保存的dataFrame
+	  * @param tableName 保存的mysql 表名
+	  * @param saveMode  保存的模式 ：Append、Overwrite、ErrorIfExists、Ignore
+	  * @param proPath   配置文件的路径
+	  */
+	def saveASMysqlTable(dataFrame: DataFrame, tableName: String, saveMode: SaveMode, proPath: String) = {
+		var table = tableName
+		val properties: Properties = getProPerties(proPath)
+		val prop = new Properties //配置文件中的key 与 spark 中的 key 不同 所以创建prop 按照spark 的格式 进行配置数据库
+		prop.setProperty("user", properties.getProperty("mysql.username"))
+		prop.setProperty("password", properties.getProperty("mysql.password"))
+		prop.setProperty("driver", properties.getProperty("mysql.driver"))
+		prop.setProperty("url", properties.getProperty("mysql.url"))
+		if (saveMode == SaveMode.Overwrite) {
+			var conn: Connection = null
+			try {
+				conn = DriverManager.getConnection(
+					prop.getProperty("url"),
+					prop.getProperty("user"),
+					prop.getProperty("password")
+				)
+				val stmt = conn.createStatement
+				table = table.toUpperCase
+				stmt.execute(s"truncate table $table") //此操作的目的是在覆盖的时候不删除原来的表，避免数据的类型全部变为TEXT类型
+				conn.close()
+			}
+			catch {
+				case e: Exception =>
+					println("MySQL Error:")
+					e.printStackTrace()
+			}
+		}
+		dataFrame.write.mode(SaveMode.Append).jdbc(prop.getProperty("url"), tableName, prop)
+	}
+
+	/**
+	  * 获取配置文件
+	  * @param proPath	配置文件路径,放在resources目录下,路径为: /myconfig.properties
+	  * @return
+	  */
+	def getProPerties(proPath: String): Properties = {
+		val properties: Properties = new Properties()
+		properties.load(this.getClass.getResourceAsStream(proPath))
+		properties
+	}
+
+
+}

src/main/scala/utils/ValidUtils.java

+package utils;
+
+/**
+ * @Author zhenxin.ma
+ * @Date 2020/3/30 15:37
+ * @Version 1.0
+ */
+public class ValidUtils {
+
+    /**
+     * 校验数据中的指定字段，是否在指定范围内
+     * @param data 数据
+     * @param dataField 数据字段
+     * @param parameter 参数
+     * @param startParamField 起始参数字段
+     * @param endParamField 结束参数字段
+     * @return 校验结果
+     */
+    public static boolean between(String data, String dataField,
+                                  String parameter, String startParamField, String endParamField) {
+        String startParamFieldStr = StringUtils.getFieldFromConcatString(
+                parameter, "\\|", startParamField);
+        String endParamFieldStr = StringUtils.getFieldFromConcatString(
+                parameter, "\\|", endParamField);
+        if(startParamFieldStr == null || endParamFieldStr == null) {
+            return true;
+        }
+
+        int startParamFieldValue = Integer.valueOf(startParamFieldStr);
+        int endParamFieldValue = Integer.valueOf(endParamFieldStr);
+
+        String dataFieldStr = StringUtils.getFieldFromConcatString(
+                data, "\\|", dataField);
+        if(dataFieldStr != null) {
+            int dataFieldValue = Integer.valueOf(dataFieldStr);
+            if(dataFieldValue >= startParamFieldValue &&
+                    dataFieldValue <= endParamFieldValue) {
+                return true;
+            } else {
+                return false;
+            }
+        }
+
+        return false;
+    }
+
+    /**
+     * 校验数据中的指定字段，是否有值与参数字段的值相同
+     * @param data 数据
+     * @param dataField 数据字段
+     * @param parameter 参数
+     * @param paramField 参数字段
+     * @return 校验结果
+     */
+    public static boolean in(String data, String dataField,
+                             String parameter, String paramField) {
+        String paramFieldValue = StringUtils.getFieldFromConcatString(
+                parameter, "\\|", paramField);
+        if(paramFieldValue == null) {
+            return true;
+        }
+        String[] paramFieldValueSplited = paramFieldValue.split(",");
+
+        String dataFieldValue = StringUtils.getFieldFromConcatString(
+                data, "\\|", dataField);
+        if(dataFieldValue != null) {
+            String[] dataFieldValueSplited = dataFieldValue.split(",");
+
+            for(String singleDataFieldValue : dataFieldValueSplited) {
+                for(String singleParamFieldValue : paramFieldValueSplited) {
+                    if(singleDataFieldValue.equals(singleParamFieldValue)) {
+                        return true;
+                    }
+                }
+            }
+        }
+
+        return false;
+    }
+
+    /**
+     * 校验数据中的指定字段，是否在指定范围内
+     * @param data 数据
+     * @param dataField 数据字段
+     * @param parameter 参数
+     * @param paramField 参数字段
+     * @return 校验结果
+     */
+    public static boolean equal(String data, String dataField,
+                                String parameter, String paramField) {
+        String paramFieldValue = StringUtils.getFieldFromConcatString(
+                parameter, "\\|", paramField);
+        if(paramFieldValue == null) {
+            return true;
+        }
+
+        String dataFieldValue = StringUtils.getFieldFromConcatString(
+                data, "\\|", dataField);
+        if(dataFieldValue != null) {
+            if(dataFieldValue.equals(paramFieldValue)) {
+                return true;
+            }
+        }
+
+        return false;
+    }
+
+}
+

src/main/scala/utils/commonUtil.java

+package utils;
+
+import java.lang.reflect.Array;
+import java.util.Collection;
+import java.util.Map;
+
+/**
+ * 通用工具类
+ * @Author 	zhenxin.ma
+ * @Date 	2019/9/18 15:16
+ * @Version 1.0
+ */
+public class commonUtil {
+	/**
+	 * 判断是否为空，支持字符串、map、集合、数组和其它对象
+	 *
+	 * @param obj				判断的对象
+	 * @return 					为空返回True，不为空返回False
+	 */
+	public static boolean isEmpty(Object obj){
+		if(obj == null){
+			return true;
+		}else if (obj instanceof String){
+			return "".equals(String.valueOf(obj).trim());
+		}else if (obj instanceof Map<?,?>){
+			return ((Map<?,?>) obj).isEmpty();
+		}else if (obj instanceof Collection<?>){
+			return ((Collection<?>) obj).isEmpty();
+		}else if (obj.getClass().isArray()){
+			return Array.getLength(obj) == 0;
+		}
+		return false;
+	}
+
+	/**
+	 * 判断是否为空，支持字符串、map、集合、数组和其它对象
+	 *
+	 * @param obj				判断的对象
+	 * @return 					为空返回False，不为空返回True
+	 */
+	public static boolean isNotEmpty(Object obj){
+		return !isEmpty(obj);
+	}
+
+
+}
+

target/classes.1507859400.timestamp

+.
\ No newline at end of file

target/maven-archiver/pom.properties

+#Generated by Maven
+#Wed Jun 08 16:07:15 CST 2022
+version=1.0.0-SNAPSHOT
+groupId=com.aliyun.odps
+artifactId=spark-session_2.11

target/maven-status/maven-compiler-plugin/compile/default-compile/inputFiles.lst

+D:\Users\Pica\worker\MaxCompute-Spark\spark-2.x\src\main\java\com\aliyun\odps\spark\sparksql\JavaSparkSQL.java
+D:\Users\Pica\worker\MaxCompute-Spark\spark-2.x\src\main\java\com\aliyun\odps\spark\utils\ConfigLog4j2.java