Ryan Blue created SPARK-25004: --------------------------------- Summary: Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS Key: SPARK-25004 URL: https://issues.apache.org/jira/browse/SPARK-25004 Project: Spark Issue Type: Bug Components: PySpark Affects Versions: 2.3.0 Reporter: Ryan Blue
Some platforms support limiting Python's addressable memory space by limiting [{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS]. We've found that adding a limit is very useful when running in YARN because when Python doesn't know about memory constraints, it doesn't know when to garbage collect and will continue using memory when it doesn't need to. Adding a limit reduces PySpark memory consumption and avoids YARN killing containers because Python hasn't cleaned up memory. This also improves error messages for users, allowing them to see when Python is allocating too much memory instead of YARN killing the container: {code:lang=python} File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in fe_engineer fe_eval_rec.update(f(src_rec_prep, mat_rec_prep)) File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item, []), mat_rec_prep.get(item, [])) File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in leven_list_compare permutations = sorted(permutations, reverse=True) MemoryError {code} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org