If you're inferring the schema, that also incurs an overhead whilst the data is being read into dataframe.
Are you observing data skew? Perhaps some nodes are busier than others. Look at the average task time compared to the lowest and highest times. At 20 cores, 2 cores each executor, 10 executors, how many tasks/partitions in the dataframe? Your executors should be able to process 2 tasks in parallel each. Additionally, you should aim to have number of input partitions % total cores =0 so you don't have idle executors. On Thu, 19 Dec 2019, 1:11 am Antoine DUBOIS, <antoine.dub...@cc.in2p3.fr> wrote: > Also, > the framework allow to execute all the modification at the same time as > one big request (but i wont paste it here, it would not be really relevant > > > ------------------------------ > *De: *"Antoine DUBOIS" <antoine.dub...@cc.in2p3.fr> > *À: *"Enrico Minack" <m...@enrico.minack.dev> > *Cc: *"Chris Teoh" <chris.t...@gmail.com>, "user @spark" < > user@spark.apache.org> > *Envoyé: *Mercredi 18 Décembre 2019 14:59:12 > *Objet: *Re: Identify bottleneck > > > I can confirm that the job is able to use multiple cores on multiple nodes > at the same time and that I have several task running at the same time. > Depending on my csv it take from 5 part up to several hundred part. > Regarding the job running locally on one node I took more than 20 minutes, > ans didn't had time to let it finish. > > > > ------------------------------ > *De: *"Enrico Minack" <m...@enrico.minack.dev> > *À: *"Chris Teoh" <chris.t...@gmail.com>, "Antoine DUBOIS" < > antoine.dub...@cc.in2p3.fr> > *Cc: *"user @spark" <user@spark.apache.org> > *Envoyé: *Mercredi 18 Décembre 2019 14:29:07 > *Objet: *Re: Identify bottleneck > > 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> > 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> >> *À: *user@spark.apache.org, "Antoine DUBOIS" <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 >> >> >> >> > >
