Revision history [back]

There could be several reasons for the Spark Executor to become unresponsive during remote fetches of ShuffleBlockFetcherIterator. Some of the common ones are:

1. Network congestion: If the network between the Executor and the remote nodes is congested, it can cause delays or timeouts during the fetch process.

2. Resource contention: If the Executor is running other tasks or services that are consuming a lot of resources (CPU, memory, disk), it may not have enough resources available to handle the fetch requests.

3. Slow remote nodes: If the remote nodes are slow or overloaded, it can cause delays or timeouts during the fetch process.

4. Garbage collection: If the Executor is doing a lot of garbage collection, it can cause pauses in the JVM that may affect the responsiveness of the fetch process.

5. Bugs or configuration problems: Finally, there could be bugs or misconfigurations in the Spark, Hadoop or network configurations that could cause the fetch process to fail or become unresponsive.