[Spark] 读取MySQL大Table的效能问题

在测试从 Spark Shell读取MySQL一张Large Table时，发生了Out of memory和connection timeout问题，记录一下处理的过程：

MySQL Table资料笔数：1400万笔左右
Spark Cluster配置：Master * 1，Slave * 3，皆为1 core 8G
Spark 版本：2.1.1

Spark Config配置：
spark-env.sh

SPARK_WORKER_MEMORY=6gSPARK_DRIVER_MEMORY=6gSPARK_EXECUTOR_MEMORY=2g

执行指令：

./bin/spark-shell --master spark://192.168.xx.xx:7077 --executor-memory 4g --packages mysql:mysql-connector-java:5.1.38val sqlContext = new org.apache.spark.sql.SQLContext(sc)val df = sqlContext.read.format("jdbc").option("url", "jdbc:mysql://192.168.x.x/test").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "test_table").option("user", "root").option("password", "test").load()df.createOrReplaceTempView("test_table")import org.apache.spark.storage.StorageLeveldf.persist(StorageLevel.MEMORY_AND_DISK)val sqlDf = sql("select * from test_table limit 10000")sqlDf.show()

一开始无论执行什么query，只要是query 这张大table都会出现OOM，重试几次甚至出现executor heartbeat timeout。

错误讯息：
Error Message: Executor heartbeat timeout

[Stage 0:=======================================================> (39 + 1) / 40]17/06/06 10:26:09 WARN HeartbeatReceiver: Removing executor 2 with no recent heartbeats: 147897 ms exceeds timeout 120000 ms17/06/06 10:26:09 ERROR TaskSchedulerImpl: Lost executor 2 on 192.168.1.181: Executor heartbeat timed out after 147897 ms17/06/06 10:26:09 WARN TaskSetManager: Lost task 39.0 in stage 0.0 (TID 39, 192.168.1.181, executor 2): ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Executor heartbeat timed out after 147897 ms...略

Error Message: OOM

[Stage 0:======================================================>  (27 + 1) / 28]17/06/06 10:14:25 WARN TaskSetManager: Lost task 27.0 in stage 0.0 (TID 27, 192.168.1.184, executor 0): java.lang.OutOfMemoryError: Java heap space        at com.mysql.jdbc.MysqlIO.nextRowFast(MysqlIO.java:2157)        at com.mysql.jdbc.MysqlIO.nextRow(MysqlIO.java:1964)        at com.mysql.jdbc.MysqlIO.readSingleRowSet(MysqlIO.java:3316)        at com.mysql.jdbc.MysqlIO.getResultSet(MysqlIO.java:463)        at com.mysql.jdbc.MysqlIO.readResultsForQueryOrUpdate(MysqlIO.java:3040)        at com.mysql.jdbc.MysqlIO.readAllResults(MysqlIO.java:2288)        at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2681)        at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2551)        at ...略

查了官方文件和这篇 spark read mysql 效能调校文章后，有几种作法来 tuning:

(1) 增加 partition 数
会比较慢，但是至少可以把Table读进来，Spark会将资料切成小块partition，分散到不同的 executor上。

(2) 设定spark mysql connection options

主要是这几个设定：partitionColumn, lowerBound, upperBound, numPartitions, fetchsize
PS 如果使用partitionColumn，则lowerBound,upperBound,numPartitions都需设定

partitionColumn: 用来决定partition切割的栏位，必须是numeric型态资料，不一定要唯一。

lowerBound \ upperBound: 决定要fetch的值range

会用这几个options来决定要query的dataset。
SELECT * FROM table WHERE partitionColumn BETWEEN lowerBound AND upperBound

(3) Spark记忆体管理：worker 本身会保留一部份的memory来做cache

spark 会将memory大致分成三种，user可使用的、资料shuffle用的，储存RDD的如果使用者程式要操作的物件很大，可以把快取RDD的大小和资料shuffle大小调低，以增加物件处理的效能

官方文件说明：

- spark.memory.fraction expresses the size of M as a fraction of the (JVM heap space - 300MB) (default 0.6). The rest of the space (40%) is reserved for user data structures, internal metadata in Spark, and safeguarding against OOM errors in the case of sparse and unusually large records.- spark.memory.storageFraction expresses the size of R as a fraction of M (default 0.5). R is the storage space within M where cached blocks immune to being evicted by execution.

---

这边使用到的是第2和第3个方法

首先在query mysql的指令加上options
val df = sqlContext.read.format("jdbc").option("url", "jdbc:mysql://192.168.x.x/test").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "test_table").option("user", "root").option("password", "test").option("numPartitions",30).option("partitionColumn","id").option("lowerBound", "0").option("upperBound","20000").load()

并在 spark-default.conf 加入下面设定

# for heartbeat timeoutspark.network.timeout            10000000spark.executor.heartbeatInterval 10000000# for OOMspark.memory.fraction            0.75spark.storage.memoryFraction     0.45

再执行以下query，都可以顺利读入了

val sqlDf = sql("select count(*) from test_table where dt >= '2017-05-01'")sqlDf: org.apache.spark.sql.DataFrame = [count(1): bigint]scala> sqlDf.show()+--------+|count(1)|+--------+|  222166|+--------+val sqlDf = sql("select count(*) from test_table")sqlDf: org.apache.spark.sql.DataFrame = [count(1): bigint]scala> sqlDf.show()+--------+|count(1)|+--------+|14665557|+--------+

不确定这样是不是最佳解法，不过至少解决掉问题了@@
如果不需要读整张Table，其实可以把dbtable的value改成SQL query

原本是这样，直接写Table名称，就会读整张Table
.option("dbtable", "test_table")
可以改写成：
.option("dbtable", "(select * from test_table where dt >= '2017-05-01') as T")
PS 记得一定要用左右括号包起来，因为dbtable的value会被当成一张table作查询，mysql connector会自动dbtable后面加上 where 1=1，如果没包起来就会出现SQL Syntax Error之类的错误