Spark - Shuffle Read Blocked Time

8
votes

Lately I've been tuning the performance of some large, shuffle heavy jobs. Looking at the spark UI, I noticed an option called "Shuffle Read Blocked Time" under the additional metrics section.

This "Shuffle Read Blocked Time" seems to account for upwards of 50% of the task duration for a large swath of tasks.

While I can intuit some possibilities for what this means, I can't find any documentation that explains what it actually represents. Needless to say, I also haven't been able to find any resources on mitigation strategies.

Can anyone provide some insight into how I might reduce Shuffle Read Blocked Time?

1
apache-sparkpysparkapache-spark-sql

1 Answers

4
votes

"Shuffle Read Blocked Time" is the time that tasks spent blocked waiting for shuffle data to be read from remote machines. The exact metric it feeds from is shuffleReadMetrics.fetchWaitTime.

Hard to give input into a strategy to mitigate it without actually knowing what data you're trying to read or what sort of remote machines you're reading from. However, consider the following:

  1. Check your connection to the remote machines from which you're reading data.
  2. Check your code/jobs to ensure that you're only reading data that you absolutely need to read to finish your job.
  3. In some cases, you could consider splitting your job into multiple jobs that run in parallel, so long as they are independent of each other.
  4. Perhaps you could upgrade your cluster to have more nodes so you can split the workload to be more granular and thus have an overall smaller wait time.

As to the metrics, this documentation should shed some light on them: https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-webui-StagePage.html

Lastly, i did also find it hard to find information on Shuffle Read Blocked Time, but if you put in quotes like: "Shuffle Read Blocked Time" in a google search, you'll find some decent results.