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
星火配置配置:
spark-env.sh
SPARK_WORKER_MEMORY=6g
SPARK_DRIVER_MEMORY=6g
SPARK_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:5.1.38
val 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.StorageLevel
df.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 ms
17/06/06 10:26:09 ERROR TaskSchedulerImpl: Lost executor 2 on 192.168.1.181: Executor heartbeat timed out after 147897 ms
17/06/06 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
错误信息: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)设定火花MySQL连接选项
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
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optionsval 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 timeout
spark.network.timeout 10000000
spark.executor.heartbeatInterval 10000000
# for OOM
spark.memory.fraction 0.75
spark.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
原文http://ithelp.ithome.com.tw/articles/10189787?sc=rss.qu