| title | RpcTimeoutException for Apache Spark thrift - Azure HDInsight |
|---|---|
| description | You see 502 errors when processing large data sets using Apache Spark thrift server |
| ms.service | azure-hdinsight |
| ms.topic | troubleshooting |
| author | abhishjain002 |
| ms.author | abhishjain |
| ms.reviewer | sairamyeturi |
| ms.date | 09/06/2024 |
This article describes troubleshooting steps and possible resolutions for issues when using Apache Spark components in Azure HDInsight clusters.
Spark application fails with a org.apache.spark.rpc.RpcTimeoutException exception and a message: Futures timed out, as in the following example:
org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [120 seconds]. This timeout is controlled by spark.rpc.askTimeout
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
OutOfMemoryError and overhead limit exceeded errors may also appear in the sparkthriftdriver.log as in the following example:
WARN [rpc-server-3-4] server.TransportChannelHandler: Exception in connection from /10.0.0.17:53218
java.lang.OutOfMemoryError: GC overhead limit exceeded
These errors are caused by a lack of memory resources during data processing. If the Java garbage collection process starts, it could lead to the Spark application to stop responding. Queries will begin to time out and stop processing. The Futures timed out error indicates a cluster under severe stress.
Increase the cluster size by adding more worker nodes or increasing the memory capacity of the existing cluster nodes. You can also adjust the data pipeline to reduce the amount of data being processed at once.
The spark.network.timeout controls the timeout for all network connections. Increasing the network timeout may allow more time for some critical operations to finish, but this will not resolve the issue completely.
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