将mysql表转换为parquet时发生spark异常

svdrlsy4  于 2021-05-27  发布在  Spark
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我正在尝试使用spark 1.6.2将mysql远程表转换为Parquet文件。
该过程运行10分钟,填满内存,然后从以下消息开始:

WARN NettyRpcEndpointRef: Error sending message [message = Heartbeat(driver,[Lscala.Tuple2;@dac44da,BlockManagerId(driver, localhost, 46158))] in 1 attempts
org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [10 seconds]. This timeout is controlled by spark.executor.heartbeatInterval

最后失败并出现以下错误:

ERROR ActorSystemImpl: Uncaught fatal error from thread [sparkDriverActorSystem-scheduler-1] shutting down ActorSystem [sparkDriverActorSystem]
java.lang.OutOfMemoryError: GC overhead limit exceeded

我用这些命令在spark shell中运行它:

spark-shell --packages mysql:mysql-connector-java:5.1.26 org.slf4j:slf4j-simple:1.7.21 --driver-memory 12G

val dataframe_mysql = sqlContext.read.format("jdbc").option("url", "jdbc:mysql://.../table").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "...").option("user", "...").option("password", "...").load()

dataframe_mysql.saveAsParquetFile("name.parquet")

我的最大执行器内存限制为12g。有没有办法强制将Parquet文件写成“小”块来释放内存?

camsedfj

camsedfj1#

问题似乎是在使用jdbc连接器读取数据时没有定义分区。
默认情况下,从jdbc读取不是分布式的,因此要启用分布式,必须设置手动分区。您需要一个列,它是一个很好的分区键,并且您必须预先了解分布情况。
很明显,你的数据是这样的:

root 
|-- id: long (nullable = false) 
|-- order_year: string (nullable = false) 
|-- order_number: string (nullable = false) 
|-- row_number: integer (nullable = false) 
|-- product_code: string (nullable = false) 
|-- name: string (nullable = false) 
|-- quantity: integer (nullable = false) 
|-- price: double (nullable = false) 
|-- price_vat: double (nullable = false) 
|-- created_at: timestamp (nullable = true) 
|-- updated_at: timestamp (nullable = true)
``` `order_year` 在我看来是个不错的候选人(根据你的评论,你似乎有20年的时间)

import org.apache.spark.sql.SQLContext

val sqlContext: SQLContext = ???

val driver: String = ???
val connectionUrl: String = ???
val query: String = ???
val userName: String = ???
val password: String = ???

// Manual partitioning
val partitionColumn: String = "order_year"

val options: Map[String, String] = Map("driver" -> driver,
"url" -> connectionUrl,
"dbtable" -> query,
"user" -> userName,
"password" -> password,
"partitionColumn" -> partitionColumn,
"lowerBound" -> "0",
"upperBound" -> "3000",
"numPartitions" -> "300"
)

val df = sqlContext.read.format("jdbc").options(options).load()

附言: `partitionColumn` ,  `lowerBound` ,  `upperBound` ,  `numPartitions` :如果指定了任何选项,则必须全部指定这些选项。
现在你可以省钱了 `DataFrame` Parquet地板。

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