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):
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
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