Thank you very much for your help and your inputs. I understood some stuff but I finally understood my issue. In this case my main issue was a virtualization problem my vm was running on a small hypervysor, I split them on multiple hypervisor and the application now scale properly with the number of core and processing uncompressed data is indeed faster. My bottleneck seems to be the compression.
Thank you all and have a merry chrismas De: "ayan guha" <guha.a...@gmail.com> À: "Enrico Minack" <m...@enrico.minack.dev> Cc: "Antoine DUBOIS" <antoine.dub...@cc.in2p3.fr>, "Chris Teoh" <chris.t...@gmail.com>, user@spark.apache.org Envoyé: Vendredi 20 Décembre 2019 09:39:49 Objet: Re: Identify bottleneck Cool, thanks! Very helpful On Fri, 20 Dec 2019 at 6:53 pm, Enrico Minack < [ mailto:m...@enrico.minack.dev | m...@enrico.minack.dev ] > wrote: The issue is explained in depth here: [ https://medium.com/@manuzhang/the-hidden-cost-of-spark-withcolumn-8ffea517c015 | https://medium.com/@manuzhang/the-hidden-cost-of-spark-withcolumn-8ffea517c015 ] Am 19.12.19 um 23:33 schrieb Chris Teoh: BQ_BEGIN As far as I'm aware it isn't any better. The logic all gets processed by the same engine so to confirm, compare the DAGs generated from both approaches and see if they're identical. On Fri, 20 Dec 2019, 8:56 am ayan guha, < [ mailto:guha.a...@gmail.com | guha.a...@gmail.com ] > wrote: BQ_BEGIN Quick question: Why is it better to use one sql vs multiple withColumn? isnt everything eventually rewritten by catalyst? On Wed, 18 Dec 2019 at 9:14 pm, Enrico Minack < [ mailto:m...@enrico.minack.dev | m...@enrico.minack.dev ] > wrote: BQ_BEGIN How many withColumn statements do you have? Note that it is better to use a single select, rather than lots of withColumn. This also makes drops redundant. Reading 25m CSV lines and writing to Parquet in 5 minutes on 32 cores is really slow. Can you try this on a single machine, i.e. run wit "local[*]". Can you rule out the writing part by counting the rows? I presume this all happens in a single stage. Enrico Am 18.12.19 um 10:56 schrieb Antoine DUBOIS: BQ_BEGIN Hello I'm working on an ETL based on csv describing file systems to transform it into parquet so I can work on them easily to extract informations. I'm using Mr. Powers framework Daria to do so. I've quiet different input and a lot of transformation and the framework helps organize the code. I have a stand-alone cluster v2.3.2 composed of 4 node with 8 cores and 32GB of memory each. The storage is handle by a CephFS volume mounted on all nodes. First a small description of my algorithm (it's quiet simple): BQ_BEGIN Use SparkContext to load the csv.bz2 file, Chain a lot of withColumn() statement, Drop all unnecessary columns, Write parquet file to CephFS This treatment can take several hours depending on how much lines the CSV is and I wanted to identify if bz2 or network could be an issue so I run the following test (several time with consistent result) : I tried the following scenario with 20 cores and 2 core per task: * Read the csv.bz2 from CephFS with connection with 1Gb/s for each node: ~5 minutes. * Read the csv.bz2 from TMPFS(setup to look like a shared storage space): ~5 minutes. * From the 2 previous tests I concluded that uncompressing the file was part of the bottleneck so I decided to uncompress the file and store it in TMPFS as well, result: ~5.9 minutes. The test file has 25'833'369 lines and is 370MB compressed and 3700MB uncompressed. Those results have been reproduced several time each. My question here is by what am I bottleneck in this case ? I though that the uncompressed file in RAM would be the fastest. Is it possible that my program is suboptimal reading the CSV ? In the execution logs on the cluster I have 5 to 10 seconds GC time max, and timeline shows mainly CPU time (no shuffling, no randomization overload either). I also noticed that memory storage is never used during the execution. I know from several hours of research that bz2 is the only real compression algorithm usable as an input in spark for parallelization reasons. Do you have any idea of why such a behaviour ? and do you have any idea on how to improve such treatment ? Cheers Antoine BQ_END BQ_END -- Best Regards, Ayan Guha BQ_END BQ_END BQ_END -- Best Regards, Ayan Guha
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