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:
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
