From 4be53d0395d3c7f61eef6b7d72db078e2e1199a7 Mon Sep 17 00:00:00 2001 From: Sean Owen <sowen@cloudera.com> Date: Fri, 19 Jun 2015 11:03:04 -0700 Subject: [PATCH] [SPARK-5836] [DOCS] [STREAMING] Clarify what may cause long-running Spark apps to preserve shuffle files Clarify what may cause long-running Spark apps to preserve shuffle files Author: Sean Owen <sowen@cloudera.com> Closes #6901 from srowen/SPARK-5836 and squashes the following commits: a9faef0 [Sean Owen] Clarify what may cause long-running Spark apps to preserve shuffle files --- docs/programming-guide.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/programming-guide.md b/docs/programming-guide.md index d5ff416fe8..ae712d6274 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -1144,9 +1144,11 @@ generate these on the reduce side. When data does not fit in memory Spark will s to disk, incurring the additional overhead of disk I/O and increased garbage collection. Shuffle also generates a large number of intermediate files on disk. As of Spark 1.3, these files -are not cleaned up from Spark's temporary storage until Spark is stopped, which means that -long-running Spark jobs may consume available disk space. This is done so the shuffle doesn't need -to be re-computed if the lineage is re-computed. The temporary storage directory is specified by the +are preserved until the corresponding RDDs are no longer used and are garbage collected. +This is done so the shuffle files don't need to be re-created if the lineage is re-computed. +Garbage collection may happen only after a long period time, if the application retains references +to these RDDs or if GC does not kick in frequently. This means that long-running Spark jobs may +consume a large amount of disk space. The temporary storage directory is specified by the `spark.local.dir` configuration parameter when configuring the Spark context. Shuffle behavior can be tuned by adjusting a variety of configuration parameters. See the -- GitLab