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)
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/ root@10.1.8.211:/app/hadoop/shengli/spark/
......
rsync --update -pav --progress /app/hadoop/shengli/shark/ root@10.1.8.211:/app/hadoop/shengli/shark/
......
rsync --update -pav --progress /app/hadoop/shengli/hive/ root@10.1.8.211:/app/hadoop/shengli/hive/
......
rsync --update -pav --progress /app/hadoop/shengli/sharklib/ root@10.1.8.211:/app/hadoop/shengli/sharklib/
......
rsync --update -pav --progress /usr/java/jdk1.7.0_25/ root@10.1.8.211:/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
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：
查看存储状况Storage：
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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