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Shark集群搭建配置

一、Shark简单介绍

Shark是基于Spark与Hive之上的一种SQL查询引擎，官网的架构图及性能測试图例如以下：(Ps:本人也做了一个性能測试见Shark性能測试报告)

Shark集群搭建配置-LMLPHP

我们涉及到了2个依赖组件，1是Apache Spark， 另外一个是AMPLAB的Hive0.11.

这里注意版本号的选择。一定要选择官方的推荐版本号：

Spark0.91 + AMPLAB Hive0.11 + Shark0.91

一定要自己编译好它们，适用于自己的集群。

二、Shark集群搭建

1. 搭建Spark集群。这个能够參照：Spark集群搭建。

2. 编译AMPLAB的Hive0.11， 进入到根文件夹下直接 ant package.

3.编译Shark，这个步骤和编译Spark是一样的。和HDFS的版本号记得兼容即可，改动project以下的SharkBuild.scala里面的Hadoop版本号号。然后sbt/sbt assembly.

三、启动Spark + Shark

首先。启动Spark，这里要改动spark的配置文件,在Spark-env.sh里面配置：

HADOOP_CONF_DIR=/home/hadoop/src/hadoop/conf

SPARK_CLASSPATH=/home/hadoop/src/hadoop/lib/:/app/hadoop/shengli/sharklib/*

SPARK_LOCAL_DIRS=/app/hadoop/shengli/spark/data

SPARK_MASTER_IP=10.1.8.210

SPARK_MASTER_WEBUI_PORT=7078

接着，配置Spark的spark-defaults.conf

spark.master            spark://10.1.8.210:7077

spark.executor.memory   32g

spark.shuffle.spill  true

java.library.path    /usr/local/lib

spark.shuffle.consolidateFiles true

# spark.eventLog.enabled  true

# spark.eventLog.dir      hdfs://namenode:8021/directory

# spark.serializer        org.apache.spark.serializer.KryoSerializer

接着配置slaves：

10.1.8.210  #这里master节点不会做cache

10.1.8.211

10.1.8.212

10.1.8.213

最后启动集群，sbin/start-all.sh，至此Spark集群配置完成。

Shark有依赖的Jar包。我们集中将其复制到一个目录内：

#!/bin/bash

for jar in `find /home/hadoop/shengli/shark/lib -name '*jar'`; do

      cp $jar /home/hadoop/shengli/sharklib/

done

for jar in `find /home/hadoop/shengli/shark/lib_managed/jars -name '*jar'`; do

      cp $jar /home/hadoop/shengli/sharklib/

done

for jar in `find /home/hadoop/shengli/shark/lib_managed/bundles -name '*jar'`; do

  cp $jar /home/hadoop/shengli/sharklib/

done

配置Shark，在shark/conf/shark-env.sh中配置

# format as the JVM's -Xmx option, e.g. 300m or 1g.

export JAVA_HOME=/usr/java/jdk1.7.0_25

# (Required) Set the master program's memory

#export SHARK_MASTER_MEM=1g

# (Optional) Specify the location of Hive's configuration directory. By default,

# Shark run scripts will point it to $SHARK_HOME/conf

#export HIVE_CONF_DIR=""

export HADOOP_HOME=/home/hadoop/src/hadoop

# For running Shark in distributed mode, set the following:

export SHARK_MASTER_MEM=1g

export HADOOP_HOME=$HADOOP_HOME

export SPARK_HOME=/app/hadoop/shengli/spark

export SPARK_MASTER_IP=10.1.8.210

export MASTER=spark://10.1.8.210:7077

# Only required if using Mesos:

#export MESOS_NATIVE_LIBRARY=/usr/local/lib/libmesos.so

# Only required if run shark with spark on yarn

#export SHARK_EXEC_MODE=yarn

#export SPARK_ASSEMBLY_JAR=

#export SHARK_ASSEMBLY_JAR=

# (Optional) Extra classpath

#export SPARK_LIBRARY_PATH=""

# Java options

# On EC2, change the local.dir to /mnt/tmp

# (Optional) Tachyon Related Configuration

#export TACHYON_MASTER=""                     # e.g. "localhost:19998"

#export TACHYON_WAREHOUSE_PATH=/sharktables   # Could be any valid path name

#export HIVE_HOME=/home/hadoop/shengli/hive/build/dest

export HIVE_CONF_DIR=/app/hadoop/shengli/hive/conf

export CLASSPATH=$CLASSPATH:/home/hadoop/src/hadoop/lib:home/hadoop/src/hadoop/lib/native:/app/hadoop/shengli/sharklib/*

export SCALA_HOME=/app/hadoop/shengli/scala-2.10.3

#export SPARK_LIBRARY_PATH=/home/hadoop/src/hadoop/lib/native/Linux-amd64-64

#export LD_LIBRARY_PATH=/home/hadoop/src/hadoop/lib/native/Linux-amd64-64

#spark conf copy here

SPARK_JAVA_OPTS=" -Dspark.cores.max=8 -Dspark.local.dir=/app/hadoop/shengli/spark/data -Dspark.deploy.defaultCores=2 -Dspark.executor.memory=24g -Dspark.shuffle.spill=true -Djava.library.path=/usr/local/lib "

SPARK_JAVA_OPTS+="-Xmx4g -Xms4g -verbose:gc -XX:-PrintGCDetails -XX:+PrintGCTimeStamps -XX:+UseCompressedOops "

export SPARK_JAVA_OPTS

接下来配置Shark的集群了，我们要将编译好的Spark，Shark。Hive所有都分发到各个节点。保持同步更新rsync。

rsync --update -pav --progress /app/hadoop/shengli/spark/ [email protected]:/app/hadoop/shengli/spark/

......

rsync --update -pav --progress /app/hadoop/shengli/shark/ [email protected]:/app/hadoop/shengli/shark/

......

rsync --update -pav --progress /app/hadoop/shengli/hive/ [email protected]:/app/hadoop/shengli/hive/

......

rsync --update -pav --progress /app/hadoop/shengli/sharklib/ [email protected]:/app/hadoop/shengli/sharklib/

......

rsync --update -pav --progress /usr/java/jdk1.7.0_25/ [email protected]:/usr/java/jdk1.7.0_25/

......

启动Shark，能够在WEBUI上查看集群状态（上面配置的是WEB UI PORT 7078）

进入到SHARK_HOME/bin

drwxr-xr-x  4 hadoop games 4.0K Jun 12 10:01 .

drwxr-xr-x 13 hadoop games 4.0K Jun 16 16:59 ..

-rwxr-xr-x  1 hadoop games  882 Apr 10 19:18 beeline

drwxr-xr-x  2 hadoop games 4.0K Jun 12 10:01 dev

drwxr-xr-x  2 hadoop games 4.0K Jun 12 10:01 ext

-rwxr-xr-x  1 hadoop games 1.4K Apr 10 19:18 shark

-rwxr-xr-x  1 hadoop games  730 Apr 10 19:18 shark-shell

-rwxr-xr-x  1 hadoop games  840 Apr 10 19:18 shark-withdebug

-rwxr-xr-x  1 hadoop games  838 Apr 10 19:18 shark-withinfo

Shark集群搭建配置-LMLPHP

这里shark是直接执行shark

shark-shell类似spark-shell

shark-withdebug是在执行中以DEBUG的log4J模式进入，适合排查错误和理解执行。

shark-withinfo同上。

shark还提供了一种shark-server共享Application中Cacahed RDD概念。

bin/shark -h 10.1.8.210 -p 7100
-h 10.1.8.210 -p 7100

Starting the Shark Command Line Client

Logging initialized using configuration in jar:file:/app/hadoop/shengli/sharklib/hive-common-0.11.0-shark-0.9.1.jar!/hive-log4j.properties

Hive history file=/tmp/root/hive_job_log_root_25876@wh-8-210_201406171640_1172020906.txt

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in [jar:file:/app/hadoop/shengli/sharklib/slf4j-log4j12-1.7.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/app/hadoop/shengli/sharklib/shark-assembly-0.9.1-hadoop0.20.2-cdh3u5.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/app/hadoop/shengli/shark/lib_managed/jars/org.slf4j/slf4j-log4j12/slf4j-log4j12-1.7.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

2.870: [GC 262208K->21869K(1004928K), 0.0274310 secs]

[10.1.8.210:7100] shark>

这样就能够用多个client连接这个port了。

bin/shark -h 10.1.8.210 -p 7100

-h 10.1.8.210 -p 7100

Starting the Shark Command Line Client

Logging initialized using configuration in jar:file:/app/hadoop/shengli/sharklib/hive-common-0.11.0-shark-0.9.1.jar!/hive-log4j.properties

Hive history file=/tmp/hadoop/hive_job_log_hadoop_28486@wh-8-210_201406171719_457245737.txt

SLF4J: Class path contains multiple SLF4J bindings.

SLF4J: Found binding in [jar:file:/app/hadoop/shengli/sharklib/slf4j-log4j12-1.7.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/app/hadoop/shengli/sharklib/shark-assembly-0.9.1-hadoop0.20.2-cdh3u5.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: Found binding in [jar:file:/app/hadoop/shengli/shark/lib_managed/jars/org.slf4j/slf4j-log4j12/slf4j-log4j12-1.7.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]

SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.

SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

show ta3.050: [GC 262208K->22324K(1004928K), 0.0240010 secs]

ble[10.1.8.210:7100] shark> show tables;

Time taken (including network latency): 0.072 seconds

至此，shark启动完成。

3、測试

来做一个简单的測试，看是否可用，处理一个21g的文件。

[hadoop@wh-8-210 shark]$ hadoop dfs -ls /user/hive/warehouse/log/

Found 1 items

-rw-r--r--   3 hadoop supergroup 22499035249 2014-06-16 18:32 /user/hive/warehouse/log/21gfile
create table log

(

  c1 string,

  c2 string,

  c3 string,

  c4 string,

  c5 string,

  c6 string,

  c7 string,

  c8 string,

  c9 string,

  c10 string,

  c11 string,

  c12 string,

  c13 string

) row format delimited fields terminated by '\t' stored as textfile; 
load data inpath '/user/hive/warehouse/log/21gfile' into table log;

count一下log表：

[10.1.8.210:7100] shark> select count(1) from log > ;

171802086

Time taken (including network latency): 33.753 seconds

用时33秒。

将log表所有装在至内存，count一下log_cached：

CREATE TABLE log_cached TBLPROPERTIES ("shark.cache" = "true") AS SELECT * from log;

Time taken (including network latency): 481.96 seconds
shark> select count(1) from log_cached;

171802086

Time taken (including network latency): 6.051 seconds

用时6秒，速度提升了至少5倍。

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查看Executor以及Task存储状况：

Shark集群搭建配置-LMLPHP

查看存储状况Storage：

Shark集群搭建配置-LMLPHP

至此，Shark集群搭建和简单的測试已完毕。

兴许我会写篇环境搭建中常见的问题，以及更具体的Shark測试结论。

注： 原创文章。转载请注明出处。出自：http://blog.csdn.net/oopsoom/article/details/30513929

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04-13 14:41
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