spark on yarn 安装配置

张映 发表于 2019-01-02

分类目录: hadoop/spark

标签:, ,

sppark支持三种分布式部署方式,分别是standalone、spark on mesos和 spark on yarn。


spark on mesos官方推荐这种模式(当然,原因之一是血缘关系)。正是由于spark开发之初就考虑到支持Mesos,Spark运行在Mesos上会比运行在yarn上更加灵活,更加自然。

spark on yarn这是一种最有前景的部署模式。但限于yarn自身的发展,目前仅支持粗粒度模式(Coarse-grained Mode)。这是由于yarn上的Container资源是不可以动态伸缩的,一旦Container启动之后,可使用的资源不能再发生变化,不过这个已经在yarn计划中了

1,spark on yarn 下载

# wget
# tar zxvf spark-2.4.0-bin-hadoop2.7.tgz
# mv spark-2.4.0-bin-hadoop2.7 /bigdata/spark


# echo "export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-" >> ~/.bashrc
# echo "export PATH=/bigdata/hadoop/bin:$PATH" >> ~/.bashrc
# echo "export HADOOP_HOME=/bigdata/hadoop" >> ~/.bashrc
# echo "export SPARK_HOME=/bigdata/spark" >> ~/.bashrc
# echo "export LD_LIBRARY_PATH=/bigdata/hadoop/lib/native/" >> ~/.bashrc
# source ~/.bashrc



# hdfs dfs -mkdir /spark
# hdfs dfs -mkdir /spark/logs
# hdfs dfs -mkdir /spark/jars

# cd /bigdata/spark/
# hdfs dfs -put jars/* /spark/jars/


2018-12-29 18:17:22 INFO Client:54 - Source and destination file systems are the same. Not copying hdfs://bigserver1:9000/spark/jars/JavaEWAH-0.3.2.jar
2018-12-29 18:17:22 INFO Client:54 - Source and destination file systems are the same. Not copying hdfs://bigserver1:9000/spark/jars/RoaringBitmap-0.5.11.jar
2018-12-29 18:17:22 INFO Client:54 - Source and destination file systems are the same. Not copying hdfs://bigserver1:9000/spark/jars/ST4-4.0.4.jar


# cp  //添加以下内容

export SPARK_CONF_DIR=/bigdata/spark/conf
export HADOOP_CONF_DIR=/bigdata/hadoop/etc/hadoop
export YARN_CONF_DIR=/bigdata/hadoop/etc/hadoop
export SPARK_HISTORY_OPTS="-Dspark.history.retainedApplications=3 -Dspark.history.fs.logDirectory=hdfs://bigserver1:9000/spark/logs


# cp spark-defaults.conf.template spark-defaults.conf  //添加以下内容

spark.master                     yarn
spark.eventLog.enabled           true
spark.eventLog.dir               hdfs://bigserver1:9000/spark/logs
spark.driver.cores               1
spark.driver.memory              512m
spark.executor.cores             1
spark.executor.memory            512m
spark.executor.instances         1
spark.submit.deployMode          client
spark.yarn.jars                  hdfs://bigserver1:9000/spark/jars/*
spark.serializer                 org.apache.spark.serializer.KryoSerializer


# cp slaves.template slaves //添加以下内容 bigserver1 bigserver2 bigserver3

7,scp -r 同步/bigdata/spark文件夹到所有节点


# cd /bigdata/spark
# ./bin/run-example SparkPi 10 //输出结果中,有以下内容,说明测试OK

Pi is roughly 3.1451911451911454


2018-12-29 17:28:28 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform...


# echo "export LD_LIBRARY_PATH=$HADOOP_HOME/lib/native/" >> ~/.bashrc
# source ~/.bashrc
spark on yarn 测试

spark on yarn 测试

spark log

spark log

9,spark-submit 提交任务

# ./bin/spark-submit \
 --class org.apache.spark.examples.SparkPi \
 --master yarn \
 --deploy-mode cluster \
 examples/jars/spark-examples_2.11-2.4.0.jar \

10,spark-shell 提交任务

# ./spark-shell --master yarn
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://bigserver1:4040
Spark context available as 'sc' (master = yarn, app id = application_1546074832851_0007).
Spark session available as 'spark'.
Welcome to
 ____ __
 / __/__ ___ _____/ /__
 _\ \/ _ \/ _ `/ __/ '_/
 /___/ .__/\_,_/_/ /_/\_\ version 2.4.0

Using Scala version 2.11.12 (OpenJDK 64-Bit Server VM, Java 1.8.0_191)
Type in expressions to have them evaluated.
Type :help for more information.

scala> :help
All commands can be abbreviated, e.g., :he instead of :help.
:edit <id>|<line> edit history
:help [command] print this summary or command-specific help
:history [num] show the history (optional num is commands to show)
:h? <string> search the history
:imports [name name ...] show import history, identifying sources of names
:implicits [-v] show the implicits in scope
:javap <path|class> disassemble a file or class name
:line <id>|<line> place line(s) at the end of history
:load <path> interpret lines in a file
:paste [-raw] [path] enter paste mode or paste a file
:power enable power user mode
:quit exit the interpreter
:replay [options] reset the repl and replay all previous commands
:require <path> add a jar to the classpath
:reset [options] reset the repl to its initial state, forgetting all session entries
:save <path> save replayable session to a file
:sh <command line> run a shell command (result is implicitly => List[String])
:settings <options> update compiler options, if possible; see reset
:silent disable/enable automatic printing of results
:type [-v] <expr> display the type of an expression without evaluating it
:kind [-v] <expr> display the kind of expression's type
:warnings show the suppressed warnings from the most recent line which had any

我没有装scala,但交互模式里的scala是可以用的,有可能新spark on yarn集成了。


# cd /bigdata/spark
# cd ./sbin/
spark 任务 history

spark 任务 history




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