How to tune "spark.rpc.askTimeout"?

Question

We have a spark 1.6.1 application, which takes input from two kafka topics and writes the result to another kafka topic. The application receives some large (approximately 1MB) files in the first input topic and some simple conditions from the second input topic. If the condition is satisfied, the file is written to output topic else held in state (we use mapWithState).

The logic works fine for less (few hundred) number of input files, but fails with org.apache.spark.rpc.RpcTimeoutException and recommendation is to increase spark.rpc.askTimeout. After increasing from default (120s) to 300s the ran fine longer but crashed with the same error after 1 hour. After changing the value to 500s, the job ran fine for more than 2 hours.

Note: We are running the spark job in local mode and kafka is also running locally in the machine. Also, some time I see warning "[2016-09-06 17:36:05,491] [WARN] - [org.apache.spark.storage.MemoryStore] - Not enough space to cache rdd_2123_0 in memory! (computed 2.6 GB so far)"

Now, 300s seemed large enough a timeout considering all local configuration. But any idea, how to come up to an ideal timeout value instead of just using 500s or higher based on testing, as I see crashed cases using 800s and cases suggesting to use 60000s?

Answer 1

I was facing the same problem, I found this page saying that under heavy workloads it is wise to set spark.network.timeout(which controls all the timeouts, also the RPC one) to 800. At the moment it solved my problem.