have you deactivated the spark.ui ?
I have read several thread explaining the ui can lead to OOM because it
stores 1000 dags by default
On Sun, Oct 20, 2019 at 03:18:20AM -0700, Paul Wais wrote:
> Dear List,
>
> I've observed some sort of memory leak when using pyspark to run ~100
> jobs in local mode. Each job is essentially a create RDD -> create DF
> -> write DF sort of flow. The RDD and DFs go out of scope after each
> job completes, hence I call this issue a "memory leak." Here's
> pseudocode:
>
> ```
> row_rdds = []
> for i in range(100):
> row_rdd = spark.sparkContext.parallelize([{'a': i} for i in range(1000)])
> row_rdds.append(row_rdd)
>
> for row_rdd in row_rdds:
> df = spark.createDataFrame(row_rdd)
> df.persist()
> print(df.count())
> df.write.save(...) # Save parquet
> df.unpersist()
>
> # Does not help:
> # del df
> # del row_rdd
> ```
>
> In my real application:
> * rows are much larger, perhaps 1MB each
> * row_rdds are sized to fit available RAM
>
> I observe that after 100 or so iterations of the second loop (each of
> which creates a "job" in the Spark WebUI), the following happens:
> * pyspark workers have fairly stable resident and virtual RAM usage
> * java process eventually approaches resident RAM cap (8GB standard)
> but virtual RAM usage keeps ballooning.
>
> Eventually the machine runs out of RAM and the linux OOM killer kills
> the java process, resulting in an "IndexError: pop from an empty
> deque" error from py4j/java_gateway.py .
>
>
> Does anybody have any ideas about what's going on? Note that this is
> local mode. I have personally run standalone masters and submitted a
> ton of jobs and never seen something like this over time. Those were
> very different jobs, but perhaps this issue is bespoke to local mode?
>
> Emphasis: I did try to del the pyspark objects and run python GC.
> That didn't help at all.
>
> pyspark 2.4.4 on java 1.8 on ubuntu bionic (tensorflow docker image)
>
> 12-core i7 with 16GB of ram and 22GB swap file (swap is *on*).
>
> Cheers,
> -Paul
>
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--
nicolas
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