Spark源码编译与环境搭建
Note that you must have a version of Spark which does not include the Hive jars;
Spark编译:
git clone https://github.com/apache/spark.git spark_srccd spark_srcexport MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"./make-distribution.sh --name "spark-without-hive" --tgz -Phadoop-2.4 -Dhadoop.version=2.5.0-cdh5.3.1 -Pyarn -DskipTests package
Spark搭建：见Spark环境搭建章节
Hive源码编译与环境搭建
Hive编译：
git clone https://github.com/apache/hive.git hive_on_sparkgit checkout sparkcd hive_on_sparkmvn clean install -Phadoop-2,dist -DskipTests
编译完成后，hive安装包的位置： /packaging/target/apache-hive-1.2.0-SNAPSHOT-bin.tar.gz
注意pom.xml中spark.version要和spark的版本号对应
1.3.0
Hive安装：见Hive环境搭建章节
本案例中Spark和Hive的安装路径如下：
Spark安装目录：/home/spark/app/spark-1.3.0-bin-spark-without-hive
Hive安装目录：/home/spark/app/apache-hive-1.2.0-SNAPSHOT-bin
添加Spark的依赖到Hive的方法
方式一: Set the property 'spark.home' to point to the Spark installation:
hive> set spark.home=/home/spark/app/spark-1.3.0-bin-spark-without-hive;
方式二: Define the SPARK_HOME environment variable before starting Hive CLI/HiveServer2:
export SPARK_HOME=/home/spark/app/spark-1.3.0-bin-spark-without-hive
方式三: Set the spark-assembly jar on the Hive auxpath:
hive --auxpath /home/spark/app/spark-1.3.0-bin-spark-without-hive/lib/spark-assembly-*.jar
方式四: Add the spark-assembly jar for the current user session:
hive> add jar /home/spark/app/spark-1.3.0-bin-spark-without-hive/lib/spark-assembly-*.jar;
方式五: 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 expected
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
还有一个坑：需要设置spark.eventLog.dir参数，比如：
set spark.eventLog.dir= hdfs://hadoop000:8020/directory
否则查询会报错，这个坑深啊。。。。。。，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;
设置spark的运行模式：
hive> set spark.master=spark://hadoop000:7077
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).
参数配置详见文档：
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 page_views 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:012Status: 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 6186237 4158Time taken: 18.417 seconds, Fetched: 5 row(s)
