Hopefully this is not off topic for this list, but I am hoping to reach some people who have used Kafka + Spark before.
We are new to Spark and are setting up our first production environment and hitting a speed issue that maybe configuration related - and we have little experience in configuring Spark environments. So we've got a Spark streaming job that seems to take an inordinate amount of time to process. I realize that without specifics, it is difficult to trace - however the most basic primitives in Spark are performing horribly. The lazy nature of Spark is making it difficult for me to understand what is happening - any suggestions are very much appreciated. Environment is MBP 2.2 i7. Spark master is "local[*]". We are using Kafka and PostgreSQL, both local. The job is designed to: a) grab some data from Kafka b) correlate with existing data in PostgreSQL c) output data to Kafka I am isolating timings by calling System.nanoTime() before and after something that forces calculation, for example .count() on a DataFrame. It seems like every operation has a MASSIVE fixed overhead and that is stacking up making each iteration on the RDD extremely slow. Slow operations include pulling a single item from the Kafka queue, running a simple query against PostgresSQL, and running a Spark aggregation on a RDD with a handful of rows. The machine is not maxing out on memory, disk or CPU. The machine seems to be doing nothing for a high percentage of the execution time. We have reproduced this behavior on two other machines. So we're suspecting a configuration issue As a concrete example, we have a DataFrame produced by running a JDBC query by mapping over an RDD from Kafka. Calling count() (I guess forcing execution) on this DataFrame when there is *1* item/row (Note: SQL database is EMPTY at this point so this is not a factor) takes 4.5 seconds, calling count when there are 10,000 items takes 7 seconds. Can anybody offer experience of something like this happening for them? Any suggestions on how to understand what is going wrong? I have tried tuning the number of Kafka partitions - increasing this seems to increase the concurrency and ultimately number of things processed per minute, but to get something half decent, I'm going to need running with 1024 or more partitions. Is 1024 partitions a reasonable number? What do you use in you environments? I've tried different options for batchDuration. The calculation seems to be batchDuration * Kafka partitions for number of items per iteration, but this is always still extremely slow (many per iteration vs. very few doesn't seem to really improve things). Can you suggest a list of the Spark configuration parameters related to speed that you think are key - preferably with the values you use for those parameters? I'd really really appreciate any help or suggestions as I've been working on this speed issue for 3 days without success and my head is starting to hurt. Thanks in advance. Thanks, -- Malcolm Lockyer --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
