Re: [PySpark] Releasing memory after a spark job is finished
Hi, Responding to your queries: I am using Spark 2.2.1.I have tried with both dynamic resource allocation turned on and off and have encountered the same behaviour. The way data is being read is that filepaths (for each independent data set) are passed to a method, then the method does the processing for those particular files and writes the result. So, even that doesn't seem to release memory. There are multiple independent data sets (for which the method is called sequentially). While doing this, memory consumption just keeps stacking up. You can replicate this behaviour in spark-shell (pyspark: %SPARK_HOME%/bin/pyspark) by: 1. Creating a method that reads data from filepaths passed to it as arguments and creates a dataframe on top of that 2. Doing some processing (filter etc) on that dataframe 3. Write the results to a target (can be passed to the method) 4. Try running this method again and again (either by providing different target paths/deleting target folder before calling the method again) -> to replicate behaviour of multiple datasets [OR you can provide different data sets altogether for each run of the method] You will notice that the memory consumption for that particular JVM started by spark shell will continuously increase (observe from Task Manager). Maybe, Jon is right. Probably I need to run different spark-submit for different data sets (as they are completely independent). Any other advice would also be really appreciated. On Tue, 5 Jun 2018 at 10:46, Jörn Franke [via Apache Spark User List] < ml+s1001560n3246...@n3.nabble.com> wrote: > Additionally I meant with modularization that jobs that have really > nothing to do with each other should be in separate python programs > > On 5. Jun 2018, at 04:50, Thakrar, Jayesh <[hidden email] > <http:///user/SendEmail.jtp?type=node=32465=0>> wrote: > > Disclaimer - I use Spark with Scala and not Python. > > > > But I am guessing that Jorn's reference to modularization is to ensure > that you do the processing inside methods/functions and call those methods > sequentially. > > I believe that as long as an RDD/dataset variable is in scope, its memory > may not be getting released. > > By having functions, they will get out of scope and their memory can be > released. > > > > Also, assuming that the variables are not daisy-chained/inter-related as > that too will not make it easy. > > > > > > *From: *Jay <[hidden email] > <http:///user/SendEmail.jtp?type=node=32465=1>> > *Date: *Monday, June 4, 2018 at 9:41 PM > *To: *Shuporno Choudhury <[hidden email] > <http:///user/SendEmail.jtp?type=node=32465=2>> > *Cc: *"Jörn Franke [via Apache Spark User List]" <[hidden email] > <http:///user/SendEmail.jtp?type=node=32465=3>>, <[hidden email] > <http:///user/SendEmail.jtp?type=node=32465=4>> > *Subject: *Re: [PySpark] Releasing memory after a spark job is finished > > > > Can you tell us what version of Spark you are using and if Dynamic > Allocation is enabled ? > > > > Also, how are the files being read ? Is it a single read of all files > using a file matching regex or are you running different threads in the > same pyspark job? > > > > > > On Mon 4 Jun, 2018, 1:27 PM Shuporno Choudhury, <[hidden email] > <http:///user/SendEmail.jtp?type=node=32465=5>> wrote: > > Thanks a lot for the insight. > > Actually I have the exact same transformations for all the datasets, hence > only 1 python code. > > Now, do you suggest that I run different spark-submit for all the > different datasets given that I have the exact same transformations? > > > > On Tue 5 Jun, 2018, 1:48 AM Jörn Franke [via Apache Spark User List], <[hidden > email] <http:///user/SendEmail.jtp?type=node=32465=6>> wrote: > > Yes if they are independent with different transformations then I would > create a separate python program. Especially for big data processing > frameworks one should avoid to put everything in one big monotholic > applications. > > > > > On 4. Jun 2018, at 22:02, Shuporno Choudhury <[hidden email] > <http://user/SendEmail.jtp?type=node=32458=0>> wrote: > > Hi, > > > > Thanks for the input. > > I was trying to get the functionality first, hence I was using local mode. > I will be running on a cluster definitely but later. > > > > Sorry for my naivety, but can you please elaborate on the modularity > concept that you mentioned and how it will affect whatever I am already > doing? > > Do you mean running a different spark-submit for each different dataset > when you say 'an independent python program for each process '? > > > > On Tue, 5 Jun 2018 at 01:12,
Re: [PySpark] Releasing memory after a spark job is finished
Additionally I meant with modularization that jobs that have really nothing to do with each other should be in separate python programs > On 5. Jun 2018, at 04:50, Thakrar, Jayesh > wrote: > > Disclaimer - I use Spark with Scala and not Python. > > But I am guessing that Jorn's reference to modularization is to ensure that > you do the processing inside methods/functions and call those methods > sequentially. > I believe that as long as an RDD/dataset variable is in scope, its memory may > not be getting released. > By having functions, they will get out of scope and their memory can be > released. > > Also, assuming that the variables are not daisy-chained/inter-related as that > too will not make it easy. > > > From: Jay > Date: Monday, June 4, 2018 at 9:41 PM > To: Shuporno Choudhury > Cc: "Jörn Franke [via Apache Spark User List]" > , > Subject: Re: [PySpark] Releasing memory after a spark job is finished > > Can you tell us what version of Spark you are using and if Dynamic Allocation > is enabled ? > > Also, how are the files being read ? Is it a single read of all files using a > file matching regex or are you running different threads in the same pyspark > job? > > > > On Mon 4 Jun, 2018, 1:27 PM Shuporno Choudhury, > wrote: > Thanks a lot for the insight. > Actually I have the exact same transformations for all the datasets, hence > only 1 python code. > Now, do you suggest that I run different spark-submit for all the different > datasets given that I have the exact same transformations? > > On Tue 5 Jun, 2018, 1:48 AM Jörn Franke [via Apache Spark User List], > wrote: > Yes if they are independent with different transformations then I would > create a separate python program. Especially for big data processing > frameworks one should avoid to put everything in one big monotholic > applications. > > > On 4. Jun 2018, at 22:02, Shuporno Choudhury <[hidden email]> wrote: > > Hi, > > Thanks for the input. > I was trying to get the functionality first, hence I was using local mode. I > will be running on a cluster definitely but later. > > Sorry for my naivety, but can you please elaborate on the modularity concept > that you mentioned and how it will affect whatever I am already doing? > Do you mean running a different spark-submit for each different dataset when > you say 'an independent python program for each process '? > > On Tue, 5 Jun 2018 at 01:12, Jörn Franke [via Apache Spark User List] > <[hidden email]> wrote: > Why don’t you modularize your code and write for each process an independent > python program that is submitted via Spark? > > Not sure though if Spark local make sense. If you don’t have a cluster then a > normal python program can be much better. > > On 4. Jun 2018, at 21:37, Shuporno Choudhury <[hidden email]> wrote: > > Hi everyone, > I am trying to run a pyspark code on some data sets sequentially [basically > 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. Write > modified data in parquet format to a target location] > Now, while running this pyspark code across multiple independent data sets > sequentially, the memory usage from the previous data set doesn't seem to get > released/cleared and hence spark's memory consumption (JVM memory consumption > from Task Manager) keeps on increasing till it fails at some data set. > So, is there a way to clear/remove dataframes that I know are not going to be > used later? > Basically, can I clear out some memory programmatically (in the pyspark code) > when processing for a particular data set ends? > At no point, I am caching any dataframe (so unpersist() is also not a > solution). > > I am running spark using local[*] as master. There is a single SparkSession > that is doing all the processing. > If it is not possible to clear out memory, what can be a better approach for > this problem? > > Can someone please help me with this and tell me if I am going wrong anywhere? > > --Thanks, > Shuporno Choudhury > > > If you reply to this email, your message will be added to the discussion > below: > http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32455.html > To start a new topic under Apache Spark User List, email [hidden email] > To unsubscribe from Apache Spark User List, click here. > NAML > > > -- > --Thanks, > Shuporno Choudhury > > > If you reply to this email, your message will be added to the discussion > below: > http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32458.html > To start a new topic under Apache Spark User List, email > ml+s1001560n1...@n3.nabble.com > To unsubscribe from Apache Spark User List, click here. > NAML
Re: [PySpark] Releasing memory after a spark job is finished
Disclaimer - I use Spark with Scala and not Python. But I am guessing that Jorn's reference to modularization is to ensure that you do the processing inside methods/functions and call those methods sequentially. I believe that as long as an RDD/dataset variable is in scope, its memory may not be getting released. By having functions, they will get out of scope and their memory can be released. Also, assuming that the variables are not daisy-chained/inter-related as that too will not make it easy. From: Jay Date: Monday, June 4, 2018 at 9:41 PM To: Shuporno Choudhury Cc: "Jörn Franke [via Apache Spark User List]" , Subject: Re: [PySpark] Releasing memory after a spark job is finished Can you tell us what version of Spark you are using and if Dynamic Allocation is enabled ? Also, how are the files being read ? Is it a single read of all files using a file matching regex or are you running different threads in the same pyspark job? On Mon 4 Jun, 2018, 1:27 PM Shuporno Choudhury, mailto:shuporno.choudh...@gmail.com>> wrote: Thanks a lot for the insight. Actually I have the exact same transformations for all the datasets, hence only 1 python code. Now, do you suggest that I run different spark-submit for all the different datasets given that I have the exact same transformations? On Tue 5 Jun, 2018, 1:48 AM Jörn Franke [via Apache Spark User List], mailto:ml%2bs1001560n32458...@n3.nabble.com>> wrote: Yes if they are independent with different transformations then I would create a separate python program. Especially for big data processing frameworks one should avoid to put everything in one big monotholic applications. On 4. Jun 2018, at 22:02, Shuporno Choudhury <[hidden email]<http://user/SendEmail.jtp?type=node=32458=0>> wrote: Hi, Thanks for the input. I was trying to get the functionality first, hence I was using local mode. I will be running on a cluster definitely but later. Sorry for my naivety, but can you please elaborate on the modularity concept that you mentioned and how it will affect whatever I am already doing? Do you mean running a different spark-submit for each different dataset when you say 'an independent python program for each process '? On Tue, 5 Jun 2018 at 01:12, Jörn Franke [via Apache Spark User List] <[hidden email]<http://user/SendEmail.jtp?type=node=32458=1>> wrote: Why don’t you modularize your code and write for each process an independent python program that is submitted via Spark? Not sure though if Spark local make sense. If you don’t have a cluster then a normal python program can be much better. On 4. Jun 2018, at 21:37, Shuporno Choudhury <[hidden email]<http://user/SendEmail.jtp?type=node=32455=0>> wrote: Hi everyone, I am trying to run a pyspark code on some data sets sequentially [basically 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. Write modified data in parquet format to a target location] Now, while running this pyspark code across multiple independent data sets sequentially, the memory usage from the previous data set doesn't seem to get released/cleared and hence spark's memory consumption (JVM memory consumption from Task Manager) keeps on increasing till it fails at some data set. So, is there a way to clear/remove dataframes that I know are not going to be used later? Basically, can I clear out some memory programmatically (in the pyspark code) when processing for a particular data set ends? At no point, I am caching any dataframe (so unpersist() is also not a solution). I am running spark using local[*] as master. There is a single SparkSession that is doing all the processing. If it is not possible to clear out memory, what can be a better approach for this problem? Can someone please help me with this and tell me if I am going wrong anywhere? --Thanks, Shuporno Choudhury If you reply to this email, your message will be added to the discussion below: http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32455.html To start a new topic under Apache Spark User List, email [hidden email]<http://user/SendEmail.jtp?type=node=32458=2> To unsubscribe from Apache Spark User List, click here. NAML<http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=macro_viewer=instant_html%21nabble%3Aemail.naml=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> -- --Thanks, Shuporno Choudhury If you reply to this email, your message will be added to the discussion below: http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p3
Re: [PySpark] Releasing memory after a spark job is finished
Can you tell us what version of Spark you are using and if Dynamic Allocation is enabled ? Also, how are the files being read ? Is it a single read of all files using a file matching regex or are you running different threads in the same pyspark job? On Mon 4 Jun, 2018, 1:27 PM Shuporno Choudhury, < shuporno.choudh...@gmail.com> wrote: > Thanks a lot for the insight. > Actually I have the exact same transformations for all the datasets, hence > only 1 python code. > Now, do you suggest that I run different spark-submit for all the > different datasets given that I have the exact same transformations? > > On Tue 5 Jun, 2018, 1:48 AM Jörn Franke [via Apache Spark User List], < > ml+s1001560n32458...@n3.nabble.com> wrote: > >> Yes if they are independent with different transformations then I would >> create a separate python program. Especially for big data processing >> frameworks one should avoid to put everything in one big monotholic >> applications. >> >> >> On 4. Jun 2018, at 22:02, Shuporno Choudhury <[hidden email] >> <http:///user/SendEmail.jtp?type=node=32458=0>> wrote: >> >> Hi, >> >> Thanks for the input. >> I was trying to get the functionality first, hence I was using local >> mode. I will be running on a cluster definitely but later. >> >> Sorry for my naivety, but can you please elaborate on the modularity >> concept that you mentioned and how it will affect whatever I am already >> doing? >> Do you mean running a different spark-submit for each different dataset >> when you say 'an independent python program for each process '? >> >> On Tue, 5 Jun 2018 at 01:12, Jörn Franke [via Apache Spark User List] >> <[hidden >> email] <http:///user/SendEmail.jtp?type=node=32458=1>> wrote: >> >> Why don’t you modularize your code and write for each process an >>> independent python program that is submitted via Spark? >>> >>> Not sure though if Spark local make sense. If you don’t have a cluster >>> then a normal python program can be much better. >>> >>> On 4. Jun 2018, at 21:37, Shuporno Choudhury <[hidden email] >>> <http:///user/SendEmail.jtp?type=node=32455=0>> wrote: >>> >>> Hi everyone, >>> I am trying to run a pyspark code on some data sets sequentially [basically >>> 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. >>> Write modified data in parquet format to a target location] >>> Now, while running this pyspark code across *multiple independent data >>> sets sequentially*, the memory usage from the previous data set doesn't >>> seem to get released/cleared and hence spark's memory consumption (JVM >>> memory consumption from Task Manager) keeps on increasing till it fails at >>> some data set. >>> So, is there a way to clear/remove dataframes that I know are not going >>> to be used later? >>> Basically, can I clear out some memory programmatically (in the pyspark >>> code) when processing for a particular data set ends? >>> At no point, I am caching any dataframe (so unpersist() is also not a >>> solution). >>> >>> I am running spark using local[*] as master. There is a single >>> SparkSession that is doing all the processing. >>> If it is not possible to clear out memory, what can be a better approach >>> for this problem? >>> >>> Can someone please help me with this and tell me if I am going wrong >>> anywhere? >>> >>> --Thanks, >>> Shuporno Choudhury >>> >>> >>> >>> -- >>> If you reply to this email, your message will be added to the discussion >>> below: >>> >>> http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32455.html >>> >> To start a new topic under Apache Spark User List, email [hidden email] >>> <http:///user/SendEmail.jtp?type=node=32458=2> >>> >> To unsubscribe from Apache Spark User List, click here. >>> NAML >>> <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=macro_viewer=instant_html%21nabble%3Aemail.naml=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >>> >> >> >> -- >> --Thanks, >> Shuporno Choudhury >> >>
Re: [PySpark] Releasing memory after a spark job is finished
Thanks a lot for the insight. Actually I have the exact same transformations for all the datasets, hence only 1 python code. Now, do you suggest that I run different spark-submit for all the different datasets given that I have the exact same transformations? On Tue 5 Jun, 2018, 1:48 AM Jörn Franke [via Apache Spark User List], < ml+s1001560n32458...@n3.nabble.com> wrote: > Yes if they are independent with different transformations then I would > create a separate python program. Especially for big data processing > frameworks one should avoid to put everything in one big monotholic > applications. > > > On 4. Jun 2018, at 22:02, Shuporno Choudhury <[hidden email] > <http:///user/SendEmail.jtp?type=node=32458=0>> wrote: > > Hi, > > Thanks for the input. > I was trying to get the functionality first, hence I was using local mode. > I will be running on a cluster definitely but later. > > Sorry for my naivety, but can you please elaborate on the modularity > concept that you mentioned and how it will affect whatever I am already > doing? > Do you mean running a different spark-submit for each different dataset > when you say 'an independent python program for each process '? > > On Tue, 5 Jun 2018 at 01:12, Jörn Franke [via Apache Spark User List] <[hidden > email] <http:///user/SendEmail.jtp?type=node=32458=1>> wrote: > >> Why don’t you modularize your code and write for each process an >> independent python program that is submitted via Spark? >> >> Not sure though if Spark local make sense. If you don’t have a cluster >> then a normal python program can be much better. >> >> On 4. Jun 2018, at 21:37, Shuporno Choudhury <[hidden email] >> <http:///user/SendEmail.jtp?type=node=32455=0>> wrote: >> >> Hi everyone, >> I am trying to run a pyspark code on some data sets sequentially [basically >> 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. >> Write modified data in parquet format to a target location] >> Now, while running this pyspark code across *multiple independent data >> sets sequentially*, the memory usage from the previous data set doesn't >> seem to get released/cleared and hence spark's memory consumption (JVM >> memory consumption from Task Manager) keeps on increasing till it fails at >> some data set. >> So, is there a way to clear/remove dataframes that I know are not going >> to be used later? >> Basically, can I clear out some memory programmatically (in the pyspark >> code) when processing for a particular data set ends? >> At no point, I am caching any dataframe (so unpersist() is also not a >> solution). >> >> I am running spark using local[*] as master. There is a single >> SparkSession that is doing all the processing. >> If it is not possible to clear out memory, what can be a better approach >> for this problem? >> >> Can someone please help me with this and tell me if I am going wrong >> anywhere? >> >> --Thanks, >> Shuporno Choudhury >> >> >> >> -- >> If you reply to this email, your message will be added to the discussion >> below: >> >> http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32455.html >> To start a new topic under Apache Spark User List, email [hidden email] >> <http:///user/SendEmail.jtp?type=node=32458=2> >> To unsubscribe from Apache Spark User List, click here. >> NAML >> <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=macro_viewer=instant_html%21nabble%3Aemail.naml=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >> > > > -- > --Thanks, > Shuporno Choudhury > > > > -- > If you reply to this email, your message will be added to the discussion > below: > > http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32458.html > To start a new topic under Apache Spark User List, email > ml+s1001560n1...@n3.nabble.com > To unsubscribe from Apache Spark User List, click here > <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code=1=c2h1cG9ybm8uY2hvdWRodXJ5QGdtYWlsLmNvbXwxfC0xODI0MTU0MzQ0> > . > NAML > <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=macro_viewer=instant_html%21nabble%3Aemail.naml=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >
Re: [PySpark] Releasing memory after a spark job is finished
Yes if they are independent with different transformations then I would create a separate python program. Especially for big data processing frameworks one should avoid to put everything in one big monotholic applications. > On 4. Jun 2018, at 22:02, Shuporno Choudhury > wrote: > > Hi, > > Thanks for the input. > I was trying to get the functionality first, hence I was using local mode. I > will be running on a cluster definitely but later. > > Sorry for my naivety, but can you please elaborate on the modularity concept > that you mentioned and how it will affect whatever I am already doing? > Do you mean running a different spark-submit for each different dataset when > you say 'an independent python program for each process '? > >> On Tue, 5 Jun 2018 at 01:12, Jörn Franke [via Apache Spark User List] >> wrote: >> Why don’t you modularize your code and write for each process an independent >> python program that is submitted via Spark? >> >> Not sure though if Spark local make sense. If you don’t have a cluster then >> a normal python program can be much better. >> >>> On 4. Jun 2018, at 21:37, Shuporno Choudhury <[hidden email]> wrote: >>> >>> Hi everyone, >>> I am trying to run a pyspark code on some data sets sequentially [basically >>> 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. >>> Write modified data in parquet format to a target location] >>> Now, while running this pyspark code across multiple independent data sets >>> sequentially, the memory usage from the previous data set doesn't seem to >>> get released/cleared and hence spark's memory consumption (JVM memory >>> consumption from Task Manager) keeps on increasing till it fails at some >>> data set. >>> So, is there a way to clear/remove dataframes that I know are not going to >>> be used later? >>> Basically, can I clear out some memory programmatically (in the pyspark >>> code) when processing for a particular data set ends? >>> At no point, I am caching any dataframe (so unpersist() is also not a >>> solution). >>> >>> I am running spark using local[*] as master. There is a single SparkSession >>> that is doing all the processing. >>> If it is not possible to clear out memory, what can be a better approach >>> for this problem? >>> >>> Can someone please help me with this and tell me if I am going wrong >>> anywhere? >>> >>> --Thanks, >>> Shuporno Choudhury >> >> >> If you reply to this email, your message will be added to the discussion >> below: >> http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32455.html >> To start a new topic under Apache Spark User List, email >> ml+s1001560n1...@n3.nabble.com >> To unsubscribe from Apache Spark User List, click here. >> NAML > > > -- > --Thanks, > Shuporno Choudhury
Re: [PySpark] Releasing memory after a spark job is finished
Hi, Thanks for the input. I was trying to get the functionality first, hence I was using local mode. I will be running on a cluster definitely but later. Sorry for my naivety, but can you please elaborate on the modularity concept that you mentioned and how it will affect whatever I am already doing? Do you mean running a different spark-submit for each different dataset when you say 'an independent python program for each process '? On Tue, 5 Jun 2018 at 01:12, Jörn Franke [via Apache Spark User List] < ml+s1001560n32455...@n3.nabble.com> wrote: > Why don’t you modularize your code and write for each process an > independent python program that is submitted via Spark? > > Not sure though if Spark local make sense. If you don’t have a cluster > then a normal python program can be much better. > > On 4. Jun 2018, at 21:37, Shuporno Choudhury <[hidden email] > <http:///user/SendEmail.jtp?type=node=32455=0>> wrote: > > Hi everyone, > I am trying to run a pyspark code on some data sets sequentially [basically > 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. > Write modified data in parquet format to a target location] > Now, while running this pyspark code across *multiple independent data > sets sequentially*, the memory usage from the previous data set doesn't > seem to get released/cleared and hence spark's memory consumption (JVM > memory consumption from Task Manager) keeps on increasing till it fails at > some data set. > So, is there a way to clear/remove dataframes that I know are not going to > be used later? > Basically, can I clear out some memory programmatically (in the pyspark > code) when processing for a particular data set ends? > At no point, I am caching any dataframe (so unpersist() is also not a > solution). > > I am running spark using local[*] as master. There is a single > SparkSession that is doing all the processing. > If it is not possible to clear out memory, what can be a better approach > for this problem? > > Can someone please help me with this and tell me if I am going wrong > anywhere? > > --Thanks, > Shuporno Choudhury > > > > -- > If you reply to this email, your message will be added to the discussion > below: > > http://apache-spark-user-list.1001560.n3.nabble.com/PySpark-Releasing-memory-after-a-spark-job-is-finished-tp32454p32455.html > To start a new topic under Apache Spark User List, email > ml+s1001560n1...@n3.nabble.com > To unsubscribe from Apache Spark User List, click here > <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code=1=c2h1cG9ybm8uY2hvdWRodXJ5QGdtYWlsLmNvbXwxfC0xODI0MTU0MzQ0> > . > NAML > <http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=macro_viewer=instant_html%21nabble%3Aemail.naml=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> > -- --Thanks, Shuporno Choudhury
Re: [PySpark] Releasing memory after a spark job is finished
Why don’t you modularize your code and write for each process an independent python program that is submitted via Spark? Not sure though if Spark local make sense. If you don’t have a cluster then a normal python program can be much better. > On 4. Jun 2018, at 21:37, Shuporno Choudhury > wrote: > > Hi everyone, > I am trying to run a pyspark code on some data sets sequentially [basically > 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. Write > modified data in parquet format to a target location] > Now, while running this pyspark code across multiple independent data sets > sequentially, the memory usage from the previous data set doesn't seem to get > released/cleared and hence spark's memory consumption (JVM memory consumption > from Task Manager) keeps on increasing till it fails at some data set. > So, is there a way to clear/remove dataframes that I know are not going to be > used later? > Basically, can I clear out some memory programmatically (in the pyspark code) > when processing for a particular data set ends? > At no point, I am caching any dataframe (so unpersist() is also not a > solution). > > I am running spark using local[*] as master. There is a single SparkSession > that is doing all the processing. > If it is not possible to clear out memory, what can be a better approach for > this problem? > > Can someone please help me with this and tell me if I am going wrong anywhere? > > --Thanks, > Shuporno Choudhury
[PySpark] Releasing memory after a spark job is finished
Hi everyone, I am trying to run a pyspark code on some data sets sequentially [basically 1. Read data into a dataframe 2.Perform some join/filter/aggregation 3. Write modified data in parquet format to a target location] Now, while running this pyspark code across *multiple independent data sets sequentially*, the memory usage from the previous data set doesn't seem to get released/cleared and hence spark's memory consumption (JVM memory consumption from Task Manager) keeps on increasing till it fails at some data set. So, is there a way to clear/remove dataframes that I know are not going to be used later? Basically, can I clear out some memory programmatically (in the pyspark code) when processing for a particular data set ends? At no point, I am caching any dataframe (so unpersist() is also not a solution). I am running spark using local[*] as master. There is a single SparkSession that is doing all the processing. If it is not possible to clear out memory, what can be a better approach for this problem? Can someone please help me with this and tell me if I am going wrong anywhere? --Thanks, Shuporno Choudhury
