将MySQL表转换为镶木地板时出现Spark异常

时间:2016-10-27 17:08:17

标签: apache-spark apache-spark-sql parquet

我尝试使用spark 1.6.2将MySQL远程表转换为镶木地板文件。

该过程运行10分钟,填满内存,而不是从这些消息开始:

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

最后因此错误而失败:

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

我使用这些命令在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")

我将最大执行程序内存限制为12G。有没有办法强制在"小"大块释放记忆?

1 个答案:

答案 0 :(得分:3)

似乎问题是当您使用jdbc连接器读取数据时没有定义分区。

默认情况下,从JDBC读取并不是分布式的,因此要启用分发,您必须设置手动分区。您需要一个列是一个很好的分区键,您必须事先了解分发。

这显然是您的数据:

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对我来说似乎是一个很好的候选人。 (根据你的评论,你好像有20年了)

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()

PS: partitionColumnlowerBoundupperBoundnumPartitions: 如果指定了这些选项,则必须全部指定这些选项。

现在您可以将DataFrame保存到镶木地板。