hive on spark怎样编译

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前置条件说明

Hive on Spark是Hive跑在Spark上，用的是Spark执行引擎，而不是MapReduce，和Hive on Tez的道理一样。
从Hive 1.1版本开始，Hive on Spark已经成为Hive代码的一部分了，并且在spark分支上面。

源码下载

git clone https://github.com/apache/hive.git hive_on_spark

编译

 cd hive_on_spark/ git branch -r  origin/HEAD -> origin/master  origin/HIVE-4115  origin/HIVE-8065  origin/beeline-cli  origin/branch-0.10  origin/branch-0.11  origin/branch-0.12  origin/branch-0.13  origin/branch-0.14  origin/branch-0.2  origin/branch-0.3  origin/branch-0.4  origin/branch-0.5  origin/branch-0.6  origin/branch-0.7  origin/branch-0.8  origin/branch-0.8-r2  origin/branch-0.9  origin/branch-1  origin/branch-1.0  origin/branch-1.0.1  origin/branch-1.1  origin/branch-1.1.1  origin/branch-1.2  origin/cbo  origin/hbase-metastore  origin/llap  origin/master  origin/maven  origin/next  origin/parquet  origin/ptf-windowing  origin/release-1.1  origin/spark  origin/spark-new  origin/spark2  origin/tez  origin/vectorization git checkout origin/spark git branch* （分离自 origin/spark）  master123456789101112131415161718192021222324252627282930313233343536373839404142434445

修改$HIVE_ON_SPARK/pom.xml
spark版本改成spark1.4.1

 1.4.11

hadoop版本改成2.3.0-cdh6.1.0

2.3.0-cdh6.1.01

编译命令

export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"mvn clean package -Phadoop-2 -DskipTests12

添加Spark的依赖到Hive的方法

spark home:/home/cluster/apps/spark/spark-1.4.1
hive home:/home/cluster/apps/hive_on_spark

1.set the property 'spark.home' to point to the Spark installation:

hive> set spark.home=/home/cluster/apps/spark/spark-1.4.1;  1

1. Define the SPARK_HOME environment variable before starting Hive CLI/HiveServer2:

export SPARK_HOME=/home/cluster/apps/spark/spark-1.4.11

3.Set the spark-assembly jar on the Hive auxpath:

hive --auxpath /home/cluster/apps/spark/spark-1.4.1/lib/spark-assembly-*.jar1

1. Add the spark-assembly jar for the current user session:

hive> add jar /home/cluster/apps/spark/spark-1.4.1/lib/spark-assembly-*.jar;1

1. Link the spark-assembly jar to $HIVE_HOME/lib.

启动Hive过程中可能出现的错误：

[ERROR] Terminal initialization failed; falling back to unsupportedjava.lang.IncompatibleClassChangeError: Found class jline.Terminal, but interface was expected        at jline.TerminalFactory.create(TerminalFactory.java:101)        at jline.TerminalFactory.get(TerminalFactory.java:158)        at jline.console.ConsoleReader.(ConsoleReader.java:229)        at jline.console.ConsoleReader.(ConsoleReader.java:221)        at jline.console.ConsoleReader.(ConsoleReader.java:209)        at org.apache.hadoop.hive.cli.CliDriver.getConsoleReader(CliDriver.java:773)        at org.apache.hadoop.hive.cli.CliDriver.executeDriver(CliDriver.java:715)        at org.apache.hadoop.hive.cli.CliDriver.run(CliDriver.java:675)        at org.apache.hadoop.hive.cli.CliDriver.main(CliDriver.java:615)        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)        at java.lang.reflect.Method.invoke(Method.java:606)        at org.apache.hadoop.util.RunJar.main(RunJar.java:212)Exception in thread "main" java.lang.IncompatibleClassChangeError: Found class jline.Terminal, but interface was expected123456789101112131415161718

解决方法：export HADOOP_USER_CLASSPATH_FIRST=true

其他场景的错误解决方法参见：https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started

需要设置spark.eventLog.dir参数，比如：

set spark.eventLog.dir= hdfs://master:8020/directory
否则查询会报错，否则一直报错:/tmp/spark-event类似的文件夹不存在

启动hive后设置执行引擎为spark：

hive> set hive.execution.engine=spark;1

设置spark的运行模式：

hive> set spark.master=spark://master:70771

或者yarn：spark.master=yarn

Configure Spark-application configs for Hive

可以配置在spark-defaults.conf或者hive-site.xml

spark.master=spark.eventLog.enabled=true;            spark.executor.memory=512m;             spark.serializer=org.apache.spark.serializer.KryoSerializer;spark.executor.memory=...  #Amount of memory to use per executor process.spark.executor.cores=...  #Number of cores per executor.spark.yarn.executor.memoryOverhead=...spark.executor.instances=...  #The number of executors assigned to each application.spark.driver.memory=...  #The amount of memory assigned to the Remote Spark Context (RSC). We recommend 4GB.spark.yarn.driver.memoryOverhead=...  #We recommend 400 (MB).12345678910

参数配置详见文档：https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started

执行sql语句后可以在监控页面查看job/stages等信息

hive (default)> select city_id, count(*) c from city_info group by city_id order by c desc limit 5;Query ID = spark_20150309173838_444cb5b1-b72e-4fc3-87db-4162e364cb1eTotal jobs = 1Launching Job 1 out of 1In order to change the average load for a reducer (in bytes):  set hive.exec.reducers.bytes.per.reducer=In order to limit the maximum number of reducers:  set hive.exec.reducers.max=In order to set a constant number of reducers:  set mapreduce.job.reduces=state = SENTstate = STARTEDstate = STARTEDstate = STARTEDstate = STARTEDQuery Hive on Spark job[0] stages:1Status: Running (Hive on Spark job[0])Job Progress FormatCurrentTime StageId_StageAttemptId: SucceededTasksCount(+RunningTasksCount-FailedTasksCount)/TotalTasksCount [StageCost]2015-03-09 17:38:11,822 Stage-0_0: 0(+1)/1      Stage-1_0: 0/1  Stage-2_0: 0/1state = STARTEDstate = STARTEDstate = STARTED2015-03-09 17:38:14,845 Stage-0_0: 0(+1)/1      Stage-1_0: 0/1  Stage-2_0: 0/1state = STARTEDstate = STARTED2015-03-09 17:38:16,861 Stage-0_0: 1/1 Finished Stage-1_0: 0(+1)/1      Stage-2_0: 0/1state = SUCCEEDED2015-03-09 17:38:17,867 Stage-0_0: 1/1 Finished Stage-1_0: 1/1 Finished Stage-2_0: 1/1 FinishedStatus: Finished successfully in 10.07 secondsOKcity_id c-1000   22826-10     17294-20     10608-1      6186    4158Time taken: 18.417 seconds, Fetched: 5 row(s)

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