一 前言:
1. 语术：
Hadoop 版本:
Version 2.7.1
HiBench 版本：
Version 6.0
Spark 版本：
Version 2.1.0
2. 本篇讲个啥子？
以pagerank为例子，讲述哪些参数跟performance相关，后续可以深入去看相关的代码。
同样的硬件配置和Hadoop软件配置，hadoop VS spark 的 pagerank 测试结果如下：
Type Date Time Input_data_size Duration(s) Throughput(bytes/s) Throughput/node HadoopPagerank 2018-05-18 17:06:47 1596962260
601.049
2656958 2656958
ScalaSparkPagerank 2018-05-28 16:19:28 1596962260
377.125
4234570 4234570
但hadoop VS spark 的Terasort 测试结果如下：
Type Date Time Input_data_size Duration(s) Throughput(bytes/s) Throughput/node
HadoopTerasort 2018-06-05 14:41:29 32000000000
288.466
110931617 110931617
ScalaSparkTerasort 2018-05-18 15:00:48 32000000000
326.053
98143553 98143553
二 、简述 HiBench/conf/spark.conf：
HiBench根据 HiBench/conf/spark.conf最终生成具体Test Case的conf文件，例如：~/HiBench/report/pagerank/spark/conf/sparkbench/spark.conf 。而$SPARK_HOME/conf/spark-defaults.conf配置文件，HiBench默认是不使用的，即不生效。这就是为什么在$SPARK_HOME/conf/spark-defaults.conf明明设置了spark.eventLog.enabled为true,
而访问不了History Sever, 18080端口
。为什么？
关键配置参数如下：
hibench.spark.home /home/xxx/Spark
# Spark master
# standalone mode: spark://xxx:7077
# YARN mode: yarn-client
hibench.spark.master yarn
方案一：
###
这里需要同时修改脚本 HiBench/bin/functions/workload_functions.sh
###HiBench6.0版本里是匹配$SPARK_MASTER” == yarn-
###diff workload_functions.sh workload_functions.sh.template
###207c207
###< if [[ “$SPARK_MASTER” == yarn* ]]; then
###—
###> if [[ “$SPARK_MASTER” == yarn-* ]]; then
方案二：不需要修改脚本 HiBench/bin/functions/workload_functions.sh，增加配置
spark.deploy.mode
spark.deploy.mode yarn-cluster
# executor number and cores when running on Yarn
hibench.yarn.executor.num 4
hibench.yarn.executor.cores 11
# executor and driver memory in standalone & YARN mode
spark.executor.memory 17g
spark.driver.memory 4g
###
注意2：下面黑色参数可以打开调试，绿色可以做性能调优
spark.eventLog.dir hdfs://localhost:9000/sparklogs
spark.eventLog.enabled true
spark.executor.extraJavaOptions -XX:+PrintGCDetails -XX:+PrintGCTimeStamps
-XX:NewRatio=1
spark.io.compression.lz4.blockSize 128k
spark.memory.fraction 0.8
spark.memory.storageFraction 0.2
spark.rdd.compress true
spark.reducer.maxSizeInFlight 272m
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.shuffle.service.index.cache.size 128m
三 配置参数说明:
1. hibench.yarn.executor.num：
这三哥俩是一个玩意,只是披了不同的马甲–num-executors(Number of executors to launch ，Spark/bin/spark-submit的参数，Default: 2，参考源码SparkSubmitArguments.scala) 、spark.executor.instances(可住在文件Spark/conf/spark-defautl.conf)、 hibench.yarn.executor.num(住在文件HiBench/conf/hibench.conf)
2. hibench.yarn.executor.cores：
这三哥类似（–executor-cores（Number of cores per executor. (Default: 1 in YARN mode, | or all available cores on the worker in standalone mode，参考源码SparkSubmitArguments.scala)、spark.executor.cores hibench.yarn.executor.cores
3. spark.executor.memory (Amount of memory to use per executor process )
–executor-memory、SPARK_EXECUTOR_MEMORY(spark-env.sh). 参考源码SparkSubmitArguments.scala, 取值的顺序如下：
executorMemory = Option(executorMemory)
.orElse(sparkProperties.get(“spark.executor.memory”))
.orElse(env.get(“SPARK_EXECUTOR_MEMORY”))
.orNull
4. hibench.default.shuffle.parallelism
因为pagerank会用到FPU（float point unit），所以值应该不少于CPU cores（这样才能充分利用FPU）。
5. SPARK_LOCAL_DIRS
export SPARK_LOCAL_DIRS=/home/xxx/data/tmp, /home/xxx/ssd2/tmp
6. Worker相关的参数：
# – SPARK_WORKER_CORES, to set the number of cores to use on this machine
# – SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)
# – SPARK_WORKER_INSTANCES, to set the number of worker processes per node
# – SPARK_WORKER_DIR, to set the working directory of worker processes
i.e:
export SCALA_HOME=/home/hadoopuser/.local/bin/scala-2.11.12
export JAVA_HOME=/home/hadoopuser/.local/bin/jdk8u-server-release-1708
export HADOOP_HOME=/home/hadoopuser/.local/bin/Hadoop
export HADOOP_CONF_DIR=${HADOOP_HOME}/etc/hadoop
export SPARK_HOME=/home/hadoopuser/.local/bin/Spark
export SPARK_LOCAL_DIRS=/home/hadoopuser/data/tmp,/home/hadoopuser/ssd2/tmp
export SPARK_MASTER_HOST=localhost
export SPARK_MASTER_PORT=7077
export SPARK_WORKER_CORES=46
export SPARK_WORKER_INSTANCES=2
export SPARK_WORKER_MEMORY=80g
export SPARK_WORKER_DIR=/home/hadoopuser/data/tmp/worker,/home/hadoopuser/ssd2/tmp/work
四 分析方法：
1. 更改为DEBUG log:
log4j.properties文件：log4j.rootCategory=INFO, console =〉log4j.rootCategory=DEBUG, console
2. 开启spark.eventLog.enable (
记得创建目录
：
hdfs://localhost:9000/sparklogs
)
这个页面可以参看配置参数的值和stage及消耗的时间
3. 分析Hadoop/logs/Userlogs/的application log.
五 参考：
http://spark.apache.org/docs/2.1.0/configuration.html