Good points, but single-line CSV files are splitable (not multi-line CSV
though), especially in the mentioned size. And bz2 is also splitable,
though reading speed is much slower than uncompressed csv.
If your csv.bz2 files are not splittable then repartitioning does not
improve the situation much because reading happens through one worker
first before repartitioning happens.
Besides checking the Spark UI SQL tab you can check that your stage has
multiple tasks, ideally 200, at least 32 to fully employ your cluster.
Am 18.12.19 um 13:33 schrieb Chris Teoh:
Please look at the spark UI and confirm you are indeed getting more
than 1 partition in your dataframe. Text files are usually not
splittable so you may just be doing all the work in a single partition.
If that is the case, It may be worthwhile considering calling the
repartition method to distribute your data across multiple partitions
so you get more parallelism.
On Wed, 18 Dec 2019, 9:35 pm Antoine DUBOIS,
<antoine.dub...@cc.in2p3.fr <mailto:antoine.dub...@cc.in2p3.fr>> wrote:
There's 15 withColumn Statement and one drop at the end to remove
old column.
I which I could write it as a single sql statement, but it's not
reasonable for maintaining purpose.
I will try on a local instance and let you know.
Thanks for the help.
------------------------------------------------------------------------
*De: *"Enrico Minack" <m...@enrico.minack.dev
<mailto:m...@enrico.minack.dev>>
*À: *user@spark.apache.org <mailto:user@spark.apache.org>,
"Antoine DUBOIS" <antoine.dub...@cc.in2p3.fr
<mailto:antoine.dub...@cc.in2p3.fr>>
*Envoyé: *Mercredi 18 Décembre 2019 11:13:38
*Objet: *Re: Identify bottleneck
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
