Re: SPARK Issue in Standalone cluster
Hi everyone, I have a huge dataframe with 1 billion rows and each row is a nested list. That being said, I want to train some ML models on this df but due to the huge size, I get out memory error on one of my nodes when I run fit function. currently, my configuration is: 144 cores, 16 cores for each of the 8 nodes. 100gb of ram for each slave and 100gb of ram for the driver. I set the maxResultSize to be 20gb. Do you have any suggestion so far? I can think of splitting the data to multiple dataframes and then training the model on each individually but besides the longer runtime, I learned that fit function overwrites the previous model each time I call it. Isn't there a way to get the fit function to train the new model with regard to the previously trained model? Thanks On Sun, Aug 6, 2017 at 11:04 PM, Gourav Sengupta wrote: > Hi Marco, > > thanks a ton, I will surely use those alternatives. > > > Regards, > Gourav Sengupta > > On Sun, Aug 6, 2017 at 3:45 PM, Marco Mistroni > wrote: > >> Sengupta >> further to this, if you try the following notebook in databricks cloud, >> it will read a .csv file , write to a parquet file and read it again (just >> to count the number of rows stored) >> Please note that the path to the csv file might differ for you. >> So, what you will need todo is >> 1 - create an account to community.cloud.databricks.com >> 2 - upload the .csv file onto the Data of your databricks private cluster >> 3 - run the script. that will store the data on the distrubuted >> filesystem of the databricks cloudn (dbfs) >> >> It's worth investing in this free databricks cloud as it can create a >> cluster for you with minimal effort, and it's a very easy way to test your >> spark scripts on a real cluster >> >> hope this helps >> kr >> >> ## >> from pyspark.sql import SQLContext >> >> from random import randint >> from time import sleep >> from pyspark.sql.session import SparkSession >> import logging >> logger = logging.getLogger(__name__) >> logger.setLevel(logging.INFO) >> ch = logging.StreamHandler() >> logger.addHandler(ch) >> >> >> import sys >> >> def read_parquet_file(parquetFileName): >> logger.info('Reading now the parquet files we just created...:%s', >> parquetFileName) >> parquet_data = sqlContext.read.parquet(parquetFileName) >> logger.info('Parquet file has %s', parquet_data.count()) >> >> def dataprocessing(filePath, count, sqlContext): >> logger.info( 'Iter count is:%s' , count) >> if count == 0: >> print 'exiting' >> else: >> df_traffic_tmp = sqlContext.read.format("csv"). >> option("header",'true').load(filePath) >> logger.info( '#DataSet has:%s' , >> df_traffic_tmp.count()) >> logger.info('WRting to a parquet file') >> parquetFileName = "dbfs:/myParquetDf2.parquet" >> df_traffic_tmp.write.parquet(parquetFileName) >> sleepInterval = randint(10,100) >> logger.info( '#Sleeping for %s' , >> sleepInterval) >> sleep(sleepInterval) >> read_parquet_file(parquetFileName) >> dataprocessing(filePath, count-1, sqlContext) >> >> filename = '/FileStore/tables/wb4y1wrv1502027870004/tree_addhealth.csv'#This >> path might differ for you >> iterations = 1 >> logger.info('--') >> logger.info('Filename:%s', filename) >> logger.info('Iterations:%s', iterations ) >> logger.info('--') >> >> logger.info ('Initializing sqlContext') >> logger.info( 'Starting spark..Loading from%s for %s >> iterations' , filename, iterations) >> logger.info( 'Starting up') >> sc = SparkSession.builder.appName("Data Processsing").getOrCreate() >> logger.info ('Initializing sqlContext') >> sqlContext = SQLContext(sc) >> dataprocessing(filename, iterations, sqlContext) >> logger.info('Out of here..') >> ## >> >> >> On Sat, Aug 5, 2017 at 9:09 PM, Marco Mistroni >> wrote: >> >>> Uh believe me there are lots of ppl on this list who will send u code >>> snippets if u ask... 😀 >>> >>> Yes that is what Steve pointed out, suggesting also that for that simple >>> exercise you should perform all operations on a spark standalone instead >>> (or alt. Use an nfs on the cluster) >>> I'd agree with his suggestion >>> I suggest u another alternative: >>> https://community.cloud.databricks.com/ >>> >>> That's a ready made cluster and you can run your spark app as well store >>> data on the cluster (well I haven't tried myself but I assume it's >>> possible). Try that out... I will try ur script there as I have an >>> account there (though I guess I'll get there before me.) >>> >>> Try that out and let me know if u get stuck >>> Kr >>> >>> On Aug 5, 2017 8:40 PM, "Gourav Sengupta" >>> wrote: >>> Hi Marco, For the first time in several years FOR THE VERY FIRST TIME. I am seeing someone actually exec
Re: SPARK Issue in Standalone cluster
Hi Marco, thanks a ton, I will surely use those alternatives. Regards, Gourav Sengupta On Sun, Aug 6, 2017 at 3:45 PM, Marco Mistroni wrote: > Sengupta > further to this, if you try the following notebook in databricks cloud, > it will read a .csv file , write to a parquet file and read it again (just > to count the number of rows stored) > Please note that the path to the csv file might differ for you. > So, what you will need todo is > 1 - create an account to community.cloud.databricks.com > 2 - upload the .csv file onto the Data of your databricks private cluster > 3 - run the script. that will store the data on the distrubuted > filesystem of the databricks cloudn (dbfs) > > It's worth investing in this free databricks cloud as it can create a > cluster for you with minimal effort, and it's a very easy way to test your > spark scripts on a real cluster > > hope this helps > kr > > ## > from pyspark.sql import SQLContext > > from random import randint > from time import sleep > from pyspark.sql.session import SparkSession > import logging > logger = logging.getLogger(__name__) > logger.setLevel(logging.INFO) > ch = logging.StreamHandler() > logger.addHandler(ch) > > > import sys > > def read_parquet_file(parquetFileName): > logger.info('Reading now the parquet files we just created...:%s', > parquetFileName) > parquet_data = sqlContext.read.parquet(parquetFileName) > logger.info('Parquet file has %s', parquet_data.count()) > > def dataprocessing(filePath, count, sqlContext): > logger.info( 'Iter count is:%s' , count) > if count == 0: > print 'exiting' > else: > df_traffic_tmp = sqlContext.read.format("csv"). > option("header",'true').load(filePath) > logger.info( '#DataSet has:%s' , > df_traffic_tmp.count()) > logger.info('WRting to a parquet file') > parquetFileName = "dbfs:/myParquetDf2.parquet" > df_traffic_tmp.write.parquet(parquetFileName) > sleepInterval = randint(10,100) > logger.info( '#Sleeping for %s' , > sleepInterval) > sleep(sleepInterval) > read_parquet_file(parquetFileName) > dataprocessing(filePath, count-1, sqlContext) > > filename = '/FileStore/tables/wb4y1wrv1502027870004/tree_addhealth.csv'#This > path might differ for you > iterations = 1 > logger.info('--') > logger.info('Filename:%s', filename) > logger.info('Iterations:%s', iterations ) > logger.info('--') > > logger.info ('Initializing sqlContext') > logger.info( 'Starting spark..Loading from%s for %s > iterations' , filename, iterations) > logger.info( 'Starting up') > sc = SparkSession.builder.appName("Data Processsing").getOrCreate() > logger.info ('Initializing sqlContext') > sqlContext = SQLContext(sc) > dataprocessing(filename, iterations, sqlContext) > logger.info('Out of here..') > ## > > > On Sat, Aug 5, 2017 at 9:09 PM, Marco Mistroni > wrote: > >> Uh believe me there are lots of ppl on this list who will send u code >> snippets if u ask... 😀 >> >> Yes that is what Steve pointed out, suggesting also that for that simple >> exercise you should perform all operations on a spark standalone instead >> (or alt. Use an nfs on the cluster) >> I'd agree with his suggestion >> I suggest u another alternative: >> https://community.cloud.databricks.com/ >> >> That's a ready made cluster and you can run your spark app as well store >> data on the cluster (well I haven't tried myself but I assume it's >> possible). Try that out... I will try ur script there as I have an >> account there (though I guess I'll get there before me.) >> >> Try that out and let me know if u get stuck >> Kr >> >> On Aug 5, 2017 8:40 PM, "Gourav Sengupta" >> wrote: >> >>> Hi Marco, >>> >>> For the first time in several years FOR THE VERY FIRST TIME. I am seeing >>> someone actually executing code and providing response. It feel wonderful >>> that at least someone considered to respond back by executing code and just >>> did not filter out each and every technical details to brood only on my >>> superb social skills, while claiming the reason for ignoring technical >>> details is that it elementary. I think that Steve also is the first person >>> who could answer the WHY of an elementary question instead of saying that >>> is how it is and pointed out to the correct documentation. >>> >>> That code works fantastically. But the problem which I have tried to >>> find out is while writing out the data and not reading it. >>> >>> >>> So if you see try to read the data from the same folder which has the >>> same file across all the nodes then it will work fine. In fact that is what >>> should work. >>> >>> What does not work is that if you try to write back the file and then >>> read it once again from the location you have written that is when th
Re: SPARK Issue in Standalone cluster
Sengupta further to this, if you try the following notebook in databricks cloud, it will read a .csv file , write to a parquet file and read it again (just to count the number of rows stored) Please note that the path to the csv file might differ for you. So, what you will need todo is 1 - create an account to community.cloud.databricks.com 2 - upload the .csv file onto the Data of your databricks private cluster 3 - run the script. that will store the data on the distrubuted filesystem of the databricks cloudn (dbfs) It's worth investing in this free databricks cloud as it can create a cluster for you with minimal effort, and it's a very easy way to test your spark scripts on a real cluster hope this helps kr ## from pyspark.sql import SQLContext from random import randint from time import sleep from pyspark.sql.session import SparkSession import logging logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) ch = logging.StreamHandler() logger.addHandler(ch) import sys def read_parquet_file(parquetFileName): logger.info('Reading now the parquet files we just created...:%s', parquetFileName) parquet_data = sqlContext.read.parquet(parquetFileName) logger.info('Parquet file has %s', parquet_data.count()) def dataprocessing(filePath, count, sqlContext): logger.info( 'Iter count is:%s' , count) if count == 0: print 'exiting' else: df_traffic_tmp = sqlContext.read.format("csv").option("header",'true').load(filePath) logger.info( '#DataSet has:%s' , df_traffic_tmp.count()) logger.info('WRting to a parquet file') parquetFileName = "dbfs:/myParquetDf2.parquet" df_traffic_tmp.write.parquet(parquetFileName) sleepInterval = randint(10,100) logger.info( '#Sleeping for %s' , sleepInterval) sleep(sleepInterval) read_parquet_file(parquetFileName) dataprocessing(filePath, count-1, sqlContext) filename = '/FileStore/tables/wb4y1wrv1502027870004/tree_addhealth.csv'#This path might differ for you iterations = 1 logger.info('--') logger.info('Filename:%s', filename) logger.info('Iterations:%s', iterations ) logger.info('--') logger.info ('Initializing sqlContext') logger.info( 'Starting spark..Loading from%s for %s iterations' , filename, iterations) logger.info( 'Starting up') sc = SparkSession.builder.appName("Data Processsing").getOrCreate() logger.info ('Initializing sqlContext') sqlContext = SQLContext(sc) dataprocessing(filename, iterations, sqlContext) logger.info('Out of here..') ## On Sat, Aug 5, 2017 at 9:09 PM, Marco Mistroni wrote: > Uh believe me there are lots of ppl on this list who will send u code > snippets if u ask... 😀 > > Yes that is what Steve pointed out, suggesting also that for that simple > exercise you should perform all operations on a spark standalone instead > (or alt. Use an nfs on the cluster) > I'd agree with his suggestion > I suggest u another alternative: > https://community.cloud.databricks.com/ > > That's a ready made cluster and you can run your spark app as well store > data on the cluster (well I haven't tried myself but I assume it's > possible). Try that out... I will try ur script there as I have an > account there (though I guess I'll get there before me.) > > Try that out and let me know if u get stuck > Kr > > On Aug 5, 2017 8:40 PM, "Gourav Sengupta" > wrote: > >> Hi Marco, >> >> For the first time in several years FOR THE VERY FIRST TIME. I am seeing >> someone actually executing code and providing response. It feel wonderful >> that at least someone considered to respond back by executing code and just >> did not filter out each and every technical details to brood only on my >> superb social skills, while claiming the reason for ignoring technical >> details is that it elementary. I think that Steve also is the first person >> who could answer the WHY of an elementary question instead of saying that >> is how it is and pointed out to the correct documentation. >> >> That code works fantastically. But the problem which I have tried to find >> out is while writing out the data and not reading it. >> >> >> So if you see try to read the data from the same folder which has the >> same file across all the nodes then it will work fine. In fact that is what >> should work. >> >> What does not work is that if you try to write back the file and then >> read it once again from the location you have written that is when the >> issue starts happening. >> >> Therefore if in my code you were to save the pandas dataframe as a CSV >> file and then read it then you will find the following observations: >> >> FOLLOWING WILL FAIL SINCE THE FILE IS NOT IN ALL THE NODES >> >> -
Re: SPARK Issue in Standalone cluster
Uh believe me there are lots of ppl on this list who will send u code snippets if u ask... 😀 Yes that is what Steve pointed out, suggesting also that for that simple exercise you should perform all operations on a spark standalone instead (or alt. Use an nfs on the cluster) I'd agree with his suggestion I suggest u another alternative: https://community.cloud.databricks.com/ That's a ready made cluster and you can run your spark app as well store data on the cluster (well I haven't tried myself but I assume it's possible). Try that out... I will try ur script there as I have an account there (though I guess I'll get there before me.) Try that out and let me know if u get stuck Kr On Aug 5, 2017 8:40 PM, "Gourav Sengupta" wrote: > Hi Marco, > > For the first time in several years FOR THE VERY FIRST TIME. I am seeing > someone actually executing code and providing response. It feel wonderful > that at least someone considered to respond back by executing code and just > did not filter out each and every technical details to brood only on my > superb social skills, while claiming the reason for ignoring technical > details is that it elementary. I think that Steve also is the first person > who could answer the WHY of an elementary question instead of saying that > is how it is and pointed out to the correct documentation. > > That code works fantastically. But the problem which I have tried to find > out is while writing out the data and not reading it. > > > So if you see try to read the data from the same folder which has the same > file across all the nodes then it will work fine. In fact that is what > should work. > > What does not work is that if you try to write back the file and then read > it once again from the location you have written that is when the issue > starts happening. > > Therefore if in my code you were to save the pandas dataframe as a CSV > file and then read it then you will find the following observations: > > FOLLOWING WILL FAIL SINCE THE FILE IS NOT IN ALL THE NODES > > > > --- > pandasdf = pandas.DataFrame(numpy.random.randn(1, 4), > columns=list('ABCD')) > pandasdf.to_csv("/Users/gouravsengupta/Development/spark/sparkdata/testdir/test.csv", > header=True, sep=",", index=0) > testdf = spark.read.load("/Users/gouravsengupta/Development/spark/ > sparkdata/testdir/") > testdf.cache() > testdf.count() > > > > --- > > > FOLLOWING WILL WORK BUT THE PROCESS WILL NOT AT ALL USE THE NODE IN WHICH > THE DATA DOES NOT EXISTS > > > > --- > pandasdf = pandas.DataFrame(numpy.random.randn(1, 4), > columns=list('ABCD')) > pandasdf.to_csv("/Users/gouravsengupta/Development/spark/sparkdata/testdir/test.csv", > header=True, sep=",", index=0) > testdf = spark.read.load("file:///Users/gouravsengupta/ > Development/spark/sparkdata/testdir/") > testdf.cache() > testdf.count() > > > > --- > > > if you execute my code then also you will surprisingly see that the writes > in the nodes which is not the master node does not complete moving the > files from the _temporary folder to the main one. > > > Regards, > Gourav Sengupta > > > > On Fri, Aug 4, 2017 at 9:45 PM, Marco Mistroni > wrote: > >> Hello >> please have a look at this. it'sa simple script that just read a >> dataframe for n time, sleeping at random interval. i used it to test memory >> issues that another user was experiencing on a spark cluster >> >> you should run it like this e.g >> spark-submit dataprocessing_Sample.-2py > of iterations> >> >> i ran it on the cluster like this >> >> ./spark-submit --master spark://ec2-54-218-113-119.us- >> west-2.compute.amazonaws.com:7077 >> /root/pyscripts/dataprocessing_Sample-2.py >> file:///root/pyscripts/tree_addhealth.csv >> >> hth, ping me back if you have issues >> i do agree with Steve's comments if you want to test your spark >> script s just for playing, do it on a standaone server on your localhost. >> Moving to a c luster is just a matter of deploying your script and mke sure >> you have a common place where to read and store the data. SysAdmin >> should give you this when they setup the cluster... >> >> kr >> >> >> >> >> On Fri, Aug 4, 20
Re: SPARK Issue in Standalone cluster
Hi Marco, For the first time in several years FOR THE VERY FIRST TIME. I am seeing someone actually executing code and providing response. It feel wonderful that at least someone considered to respond back by executing code and just did not filter out each and every technical details to brood only on my superb social skills, while claiming the reason for ignoring technical details is that it elementary. I think that Steve also is the first person who could answer the WHY of an elementary question instead of saying that is how it is and pointed out to the correct documentation. That code works fantastically. But the problem which I have tried to find out is while writing out the data and not reading it. So if you see try to read the data from the same folder which has the same file across all the nodes then it will work fine. In fact that is what should work. What does not work is that if you try to write back the file and then read it once again from the location you have written that is when the issue starts happening. Therefore if in my code you were to save the pandas dataframe as a CSV file and then read it then you will find the following observations: FOLLOWING WILL FAIL SINCE THE FILE IS NOT IN ALL THE NODES --- pandasdf = pandas.DataFrame(numpy.random.randn(1, 4), columns=list('ABCD')) pandasdf.to_csv("/Users/gouravsengupta/Development/spark/sparkdata/testdir/test.csv", header=True, sep=",", index=0) testdf = spark.read.load("/Users/gouravsengupta/Development/ spark/sparkdata/testdir/") testdf.cache() testdf.count() -- - FOLLOWING WILL WORK BUT THE PROCESS WILL NOT AT ALL USE THE NODE IN WHICH THE DATA DOES NOT EXISTS --- pandasdf = pandas.DataFrame(numpy.random.randn(1, 4), columns=list('ABCD')) pandasdf.to_csv("/Users/gouravsengupta/Development/spark/sparkdata/testdir/test.csv", header=True, sep=",", index=0) testdf = spark.read.load("file:///Users/gouravsengupta/Development/ spark/sparkdata/testdir/") testdf.cache() testdf.count() -- - if you execute my code then also you will surprisingly see that the writes in the nodes which is not the master node does not complete moving the files from the _temporary folder to the main one. Regards, Gourav Sengupta On Fri, Aug 4, 2017 at 9:45 PM, Marco Mistroni wrote: > Hello > please have a look at this. it'sa simple script that just read a > dataframe for n time, sleeping at random interval. i used it to test memory > issues that another user was experiencing on a spark cluster > > you should run it like this e.g > spark-submit dataprocessing_Sample.-2py of iterations> > > i ran it on the cluster like this > > ./spark-submit --master spark://ec2-54-218-113-119.us- > west-2.compute.amazonaws.com:7077 /root/pyscripts/dataprocessing_Sample-2.py > file:///root/pyscripts/tree_addhealth.csv > > hth, ping me back if you have issues > i do agree with Steve's comments if you want to test your spark > script s just for playing, do it on a standaone server on your localhost. > Moving to a c luster is just a matter of deploying your script and mke sure > you have a common place where to read and store the data. SysAdmin > should give you this when they setup the cluster... > > kr > > > > > On Fri, Aug 4, 2017 at 4:50 PM, Gourav Sengupta > wrote: > >> Hi Marco, >> >> I am sincerely obliged for your kind time and response. Can you please >> try the solution that you have so kindly suggested? >> >> It will be a lot of help if you could kindly execute the code that I have >> given. I dont think that anyone has yet. >> >> There are lots of fine responses to my question here, but if you read the >> last response from Simon, it comes the closest to being satisfactory. I am >> sure even he did not execute the code, but at least he came quite close to >> understanding what the problem is. >> >> >> Regards, >> Gourav Sengupta >> >> >> On Thu, Aug 3, 2017 at 7:59 PM, Marco Mistroni >> wrote: >> >>> Hello >>> my 2 cents here, hope it helps >>> If you want to just to play around with Spark, i'd leave Hadoop out, >>> it's an unnecessary dependency that you dont need for just running a python >>> script >>> Instead do the following: >>> -
Re: SPARK Issue in Standalone cluster
Hi Marco, I am sincerely obliged for your kind time and response. Can you please try the solution that you have so kindly suggested? It will be a lot of help if you could kindly execute the code that I have given. I dont think that anyone has yet. There are lots of fine responses to my question here, but if you read the last response from Simon, it comes the closest to being satisfactory. I am sure even he did not execute the code, but at least he came quite close to understanding what the problem is. Regards, Gourav Sengupta On Thu, Aug 3, 2017 at 7:59 PM, Marco Mistroni wrote: > Hello > my 2 cents here, hope it helps > If you want to just to play around with Spark, i'd leave Hadoop out, it's > an unnecessary dependency that you dont need for just running a python > script > Instead do the following: > - got to the root of our master / slave node. create a directory > /root/pyscripts > - place your csv file there as well as the python script > - run the script to replicate the whole directory across the cluster (i > believe it's called copy-script.sh) > - then run your spark-submit , it will be something lke > ./spark-submit /root/pyscripts/mysparkscripts.py > file:///root/pyscripts/tree_addhealth.csv 10 --master > spark://ip-172-31-44-155.us-west-2.compute.internal:7077 > - in your python script, as part of your processing, write the parquet > file in directory /root/pyscripts > > If you have an AWS account and you are versatile with that - you need to > setup bucket permissions etc - , you can just > - store your file in one of your S3 bucket > - create an EMR cluster > - connect to master or slave > - run your scritp that reads from the s3 bucket and write to the same s3 > bucket > > > Feel free to mail me privately, i have a working script i have used to > test some code on spark standalone cluster > hth > > > > > > > > > > > On Thu, Aug 3, 2017 at 10:30 AM, Gourav Sengupta < > gourav.sengu...@gmail.com> wrote: > >> Hi Steve, >> >> I love you mate, thanks a ton once again for ACTUALLY RESPONDING. >> >> I am now going through the documentation (https://github.com/stevelough >> ran/hadoop/blob/s3guard/HADOOP-13786-committer/hadoop- >> tools/hadoop-aws/src/site/markdown/tools/hadoop-aws/s3a_ >> committer_architecture.md) and it makes much much more sense now. >> >> Regards, >> Gourav Sengupta >> >> On Thu, Aug 3, 2017 at 10:09 AM, Steve Loughran >> wrote: >> >>> >>> On 2 Aug 2017, at 20:05, Gourav Sengupta >>> wrote: >>> >>> Hi Steve, >>> >>> I have written a sincere note of apology to everyone in a separate >>> email. I sincerely request your kind forgiveness before hand if anything >>> does sound impolite in my emails, in advance. >>> >>> Let me first start by thanking you. >>> >>> I know it looks like I formed all my opinion based on that document, but >>> that is not the case at all. If you or anyone tries to execute the code >>> that I have given then they will see what I mean. Code speaks louder and >>> better than words for me. >>> >>> So I am not saying you are wrong. I am asking verify and expecting >>> someone will be able to correct a set of understanding that a moron like >>> me has gained after long hours of not having anything better to do. >>> >>> >>> SCENARIO: there are two files file1.csv and file2.csv stored in HDFS >>> with replication 2 and there is a HADOOP cluster of three nodes. All these >>> nodes have SPARK workers (executors) running in them. Both are stored in >>> the following way: >>> - >>> | SYSTEM 1 | SYSTEM 2 | SYSTEM 3 | >>> | (worker1) | (worker2)| (worker3) | >>> | (master) | || >>> - >>> | file1.csv | | file1.csv | >>> - >>> || file2.csv | file2.csv | >>> - >>> | file3.csv | file3.csv | | >>> - >>> >>> >>> >>> >>> >>> CONSIDERATION BASED ON WHICH ABOVE SCENARIO HAS BEEN DRAWN: >>> HDFS replication does not store the same file in all the nodes in the >>> cluster. So if I have three nodes and the replication is two then the same >>> file will be stored physically in two nodes in the cluster. Does that sound >>> right? >>> >>> >>> HDFS breaks files up into blocks (default = 128MB). If a .csv file is > >>> 128 then it will be broken up into blocks >>> >>> file1.cvs -> [block0001, block002, block0003] >>> >>> and each block will be replicated. With replication = 2 there will be >>> two copies of each block, but the file itself can span > 2 hosts. >>> >>> >>> ASSUMPTION (STEVE PLEASE CLARIFY THIS): >>> If SPARK is trying to process to the records then I am expecting that >>> WORKER2 should not be processing file1.csv, and similary WORKER 1 should >>> not be p
Re: SPARK Issue in Standalone cluster
I use CIFS and it works reasonably well and easily cross platform, well documented... > On Aug 4, 2017, at 6:50 AM, Steve Loughran wrote: > > >> On 3 Aug 2017, at 19:59, Marco Mistroni wrote: >> >> Hello >> my 2 cents here, hope it helps >> If you want to just to play around with Spark, i'd leave Hadoop out, it's an >> unnecessary dependency that you dont need for just running a python script >> Instead do the following: >> - got to the root of our master / slave node. create a directory >> /root/pyscripts >> - place your csv file there as well as the python script >> - run the script to replicate the whole directory across the cluster (i >> believe it's called copy-script.sh) >> - then run your spark-submit , it will be something lke >>./spark-submit /root/pyscripts/mysparkscripts.py >> file:///root/pyscripts/tree_addhealth.csv 10 --master >> spark://ip-172-31-44-155.us-west-2.compute.internal:7077 >> - in your python script, as part of your processing, write the parquet file >> in directory /root/pyscripts >> > > That's going to hit the commit problem discussed: only the spark driver > executes the final commit process; the output from the other servers doesn't > get picked up and promoted. You need a shared stpre (NFS is the easy one) > > >> If you have an AWS account and you are versatile with that - you need to >> setup bucket permissions etc - , you can just >> - store your file in one of your S3 bucket >> - create an EMR cluster >> - connect to master or slave >> - run your scritp that reads from the s3 bucket and write to the same s3 >> bucket > > > Aah, and now we are into the problem of implementing a safe commit protocol > for an inconsistent filesystem > > My current stance there is out-the-box S3 isn't safe to use as the direct > output of work, Azure is. It mostly works for a small experiment, but I > wouldn't use it in production. > > Simplest: work on one machine, if you go to 2-3 for exploratory work: NFS > > > - > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > - To unsubscribe e-mail: user-unsubscr...@spark.apache.org
Re: SPARK Issue in Standalone cluster
> On 3 Aug 2017, at 19:59, Marco Mistroni wrote: > > Hello > my 2 cents here, hope it helps > If you want to just to play around with Spark, i'd leave Hadoop out, it's an > unnecessary dependency that you dont need for just running a python script > Instead do the following: > - got to the root of our master / slave node. create a directory > /root/pyscripts > - place your csv file there as well as the python script > - run the script to replicate the whole directory across the cluster (i > believe it's called copy-script.sh) > - then run your spark-submit , it will be something lke > ./spark-submit /root/pyscripts/mysparkscripts.py > file:///root/pyscripts/tree_addhealth.csv 10 --master > spark://ip-172-31-44-155.us-west-2.compute.internal:7077 > - in your python script, as part of your processing, write the parquet file > in directory /root/pyscripts > That's going to hit the commit problem discussed: only the spark driver executes the final commit process; the output from the other servers doesn't get picked up and promoted. You need a shared stpre (NFS is the easy one) > If you have an AWS account and you are versatile with that - you need to > setup bucket permissions etc - , you can just > - store your file in one of your S3 bucket > - create an EMR cluster > - connect to master or slave > - run your scritp that reads from the s3 bucket and write to the same s3 > bucket Aah, and now we are into the problem of implementing a safe commit protocol for an inconsistent filesystem My current stance there is out-the-box S3 isn't safe to use as the direct output of work, Azure is. It mostly works for a small experiment, but I wouldn't use it in production. Simplest: work on one machine, if you go to 2-3 for exploratory work: NFS - To unsubscribe e-mail: user-unsubscr...@spark.apache.org
Re: SPARK Issue in Standalone cluster
Hello my 2 cents here, hope it helps If you want to just to play around with Spark, i'd leave Hadoop out, it's an unnecessary dependency that you dont need for just running a python script Instead do the following: - got to the root of our master / slave node. create a directory /root/pyscripts - place your csv file there as well as the python script - run the script to replicate the whole directory across the cluster (i believe it's called copy-script.sh) - then run your spark-submit , it will be something lke ./spark-submit /root/pyscripts/mysparkscripts.py file:///root/pyscripts/tree_addhealth.csv 10 --master spark://ip-172-31-44-155.us-west-2.compute.internal:7077 - in your python script, as part of your processing, write the parquet file in directory /root/pyscripts If you have an AWS account and you are versatile with that - you need to setup bucket permissions etc - , you can just - store your file in one of your S3 bucket - create an EMR cluster - connect to master or slave - run your scritp that reads from the s3 bucket and write to the same s3 bucket Feel free to mail me privately, i have a working script i have used to test some code on spark standalone cluster hth On Thu, Aug 3, 2017 at 10:30 AM, Gourav Sengupta wrote: > Hi Steve, > > I love you mate, thanks a ton once again for ACTUALLY RESPONDING. > > I am now going through the documentation (https://github.com/ > steveloughran/hadoop/blob/s3guard/HADOOP-13786- > committer/hadoop-tools/hadoop-aws/src/site/markdown/tools/ > hadoop-aws/s3a_committer_architecture.md) and it makes much much more > sense now. > > Regards, > Gourav Sengupta > > On Thu, Aug 3, 2017 at 10:09 AM, Steve Loughran > wrote: > >> >> On 2 Aug 2017, at 20:05, Gourav Sengupta >> wrote: >> >> Hi Steve, >> >> I have written a sincere note of apology to everyone in a separate email. >> I sincerely request your kind forgiveness before hand if anything does >> sound impolite in my emails, in advance. >> >> Let me first start by thanking you. >> >> I know it looks like I formed all my opinion based on that document, but >> that is not the case at all. If you or anyone tries to execute the code >> that I have given then they will see what I mean. Code speaks louder and >> better than words for me. >> >> So I am not saying you are wrong. I am asking verify and expecting >> someone will be able to correct a set of understanding that a moron like >> me has gained after long hours of not having anything better to do. >> >> >> SCENARIO: there are two files file1.csv and file2.csv stored in HDFS with >> replication 2 and there is a HADOOP cluster of three nodes. All these nodes >> have SPARK workers (executors) running in them. Both are stored in the >> following way: >> - >> | SYSTEM 1 | SYSTEM 2 | SYSTEM 3 | >> | (worker1) | (worker2)| (worker3) | >> | (master) | || >> - >> | file1.csv | | file1.csv | >> - >> || file2.csv | file2.csv | >> - >> | file3.csv | file3.csv | | >> - >> >> >> >> >> >> CONSIDERATION BASED ON WHICH ABOVE SCENARIO HAS BEEN DRAWN: >> HDFS replication does not store the same file in all the nodes in the >> cluster. So if I have three nodes and the replication is two then the same >> file will be stored physically in two nodes in the cluster. Does that sound >> right? >> >> >> HDFS breaks files up into blocks (default = 128MB). If a .csv file is > >> 128 then it will be broken up into blocks >> >> file1.cvs -> [block0001, block002, block0003] >> >> and each block will be replicated. With replication = 2 there will be two >> copies of each block, but the file itself can span > 2 hosts. >> >> >> ASSUMPTION (STEVE PLEASE CLARIFY THIS): >> If SPARK is trying to process to the records then I am expecting that >> WORKER2 should not be processing file1.csv, and similary WORKER 1 should >> not be processing file2.csv and WORKER3 should not be processing file3.csv. >> Because in case WORKER2 was trying to process file1.csv then it will >> actually causing network transmission of the file unnecessarily. >> >> >> Spark prefers to schedule work locally, so as to save on network traffic, >> but it schedules for execution time over waiting for workers free on the >> node with the data. IF a block is on nodes 2 and 3 but there is only a free >> thread on node 1, then node 1 gets the work >> >> There's details on whether/how work across blocks takes place which I'm >> avoiding. For now know those formats which are "splittable" will have work >> scheduled by block. If you use Parquet/ORC/avro for your data and compress >> with snappy, it will be split. This give
Re: SPARK Issue in Standalone cluster
Hi Steve, I love you mate, thanks a ton once again for ACTUALLY RESPONDING. I am now going through the documentation ( https://github.com/steveloughran/hadoop/blob/s3guard/HADOOP-13786-committer/hadoop-tools/hadoop-aws/src/site/markdown/tools/hadoop-aws/s3a_committer_architecture.md) and it makes much much more sense now. Regards, Gourav Sengupta On Thu, Aug 3, 2017 at 10:09 AM, Steve Loughran wrote: > > On 2 Aug 2017, at 20:05, Gourav Sengupta > wrote: > > Hi Steve, > > I have written a sincere note of apology to everyone in a separate email. > I sincerely request your kind forgiveness before hand if anything does > sound impolite in my emails, in advance. > > Let me first start by thanking you. > > I know it looks like I formed all my opinion based on that document, but > that is not the case at all. If you or anyone tries to execute the code > that I have given then they will see what I mean. Code speaks louder and > better than words for me. > > So I am not saying you are wrong. I am asking verify and expecting someone > will be able to correct a set of understanding that a moron like me has > gained after long hours of not having anything better to do. > > > SCENARIO: there are two files file1.csv and file2.csv stored in HDFS with > replication 2 and there is a HADOOP cluster of three nodes. All these nodes > have SPARK workers (executors) running in them. Both are stored in the > following way: > - > | SYSTEM 1 | SYSTEM 2 | SYSTEM 3 | > | (worker1) | (worker2)| (worker3) | > | (master) | || > - > | file1.csv | | file1.csv | > - > || file2.csv | file2.csv | > - > | file3.csv | file3.csv | | > - > > > > > > CONSIDERATION BASED ON WHICH ABOVE SCENARIO HAS BEEN DRAWN: > HDFS replication does not store the same file in all the nodes in the > cluster. So if I have three nodes and the replication is two then the same > file will be stored physically in two nodes in the cluster. Does that sound > right? > > > HDFS breaks files up into blocks (default = 128MB). If a .csv file is > > 128 then it will be broken up into blocks > > file1.cvs -> [block0001, block002, block0003] > > and each block will be replicated. With replication = 2 there will be two > copies of each block, but the file itself can span > 2 hosts. > > > ASSUMPTION (STEVE PLEASE CLARIFY THIS): > If SPARK is trying to process to the records then I am expecting that > WORKER2 should not be processing file1.csv, and similary WORKER 1 should > not be processing file2.csv and WORKER3 should not be processing file3.csv. > Because in case WORKER2 was trying to process file1.csv then it will > actually causing network transmission of the file unnecessarily. > > > Spark prefers to schedule work locally, so as to save on network traffic, > but it schedules for execution time over waiting for workers free on the > node with the data. IF a block is on nodes 2 and 3 but there is only a free > thread on node 1, then node 1 gets the work > > There's details on whether/how work across blocks takes place which I'm > avoiding. For now know those formats which are "splittable" will have work > scheduled by block. If you use Parquet/ORC/avro for your data and compress > with snappy, it will be split. This gives you maximum performance as >1 > thread can work on different blocks. That is, if file1 is split into three > blocks, three worker threads can process it. > > > ASSUMPTION BASED ON ABOVE ASSUMPTION (STEVE ONCE AGAIN, PLEASE CLARIFY > THIS): > if WORKER 2 is not processing file1.csv then how does it matter whether > the file is there or not at all in the system? Should not SPARK just ask > the workers to process the files which are avialable in the worker nodes? > In case both WORKER2 and WORKER3 fails and are not available then file2.csv > will not be processed at all. > > > locality is best-effort, not guaranteed. > > > ALSO I DID POST THE CODE AND I GENUINELY THINK THAT THE CODE SHOULD BE > EXECUTED (Its been pointed out that I am learning SPARK, and even I did not > take more than 13 mins to set up the cluster and run the code). > > Once you execute the code then you will find that: > 1. if the path starts with file:/// while reading back then there is no > error reported, but the number of records reported back are only those > records in the worker which also has the server. > 2. also you will notice that once you cache the file before writing the > partitions are ditributed nicely across the workers, and while writing > back, the dataframe partitions does write properly to the worker node in > the Master, but the workers in the other system have t
Re: SPARK Issue in Standalone cluster
On 2 Aug 2017, at 20:05, Gourav Sengupta mailto:gourav.sengu...@gmail.com>> wrote: Hi Steve, I have written a sincere note of apology to everyone in a separate email. I sincerely request your kind forgiveness before hand if anything does sound impolite in my emails, in advance. Let me first start by thanking you. I know it looks like I formed all my opinion based on that document, but that is not the case at all. If you or anyone tries to execute the code that I have given then they will see what I mean. Code speaks louder and better than words for me. So I am not saying you are wrong. I am asking verify and expecting someone will be able to correct a set of understanding that a moron like me has gained after long hours of not having anything better to do. SCENARIO: there are two files file1.csv and file2.csv stored in HDFS with replication 2 and there is a HADOOP cluster of three nodes. All these nodes have SPARK workers (executors) running in them. Both are stored in the following way: - | SYSTEM 1 | SYSTEM 2 | SYSTEM 3 | | (worker1) | (worker2)| (worker3) | | (master) | || - | file1.csv | | file1.csv | - || file2.csv | file2.csv | - | file3.csv | file3.csv | | - CONSIDERATION BASED ON WHICH ABOVE SCENARIO HAS BEEN DRAWN: HDFS replication does not store the same file in all the nodes in the cluster. So if I have three nodes and the replication is two then the same file will be stored physically in two nodes in the cluster. Does that sound right? HDFS breaks files up into blocks (default = 128MB). If a .csv file is > 128 then it will be broken up into blocks file1.cvs -> [block0001, block002, block0003] and each block will be replicated. With replication = 2 there will be two copies of each block, but the file itself can span > 2 hosts. ASSUMPTION (STEVE PLEASE CLARIFY THIS): If SPARK is trying to process to the records then I am expecting that WORKER2 should not be processing file1.csv, and similary WORKER 1 should not be processing file2.csv and WORKER3 should not be processing file3.csv. Because in case WORKER2 was trying to process file1.csv then it will actually causing network transmission of the file unnecessarily. Spark prefers to schedule work locally, so as to save on network traffic, but it schedules for execution time over waiting for workers free on the node with the data. IF a block is on nodes 2 and 3 but there is only a free thread on node 1, then node 1 gets the work There's details on whether/how work across blocks takes place which I'm avoiding. For now know those formats which are "splittable" will have work scheduled by block. If you use Parquet/ORC/avro for your data and compress with snappy, it will be split. This gives you maximum performance as >1 thread can work on different blocks. That is, if file1 is split into three blocks, three worker threads can process it. ASSUMPTION BASED ON ABOVE ASSUMPTION (STEVE ONCE AGAIN, PLEASE CLARIFY THIS): if WORKER 2 is not processing file1.csv then how does it matter whether the file is there or not at all in the system? Should not SPARK just ask the workers to process the files which are avialable in the worker nodes? In case both WORKER2 and WORKER3 fails and are not available then file2.csv will not be processed at all. locality is best-effort, not guaranteed. ALSO I DID POST THE CODE AND I GENUINELY THINK THAT THE CODE SHOULD BE EXECUTED (Its been pointed out that I am learning SPARK, and even I did not take more than 13 mins to set up the cluster and run the code). Once you execute the code then you will find that: 1. if the path starts with file:/// while reading back then there is no error reported, but the number of records reported back are only those records in the worker which also has the server. 2. also you will notice that once you cache the file before writing the partitions are ditributed nicely across the workers, and while writing back, the dataframe partitions does write properly to the worker node in the Master, but the workers in the other system have the files written in _temporary folder which does not get copied back to the main folder. Inspite of this the job is not reported as failed in SPARK. This gets into the "commit protocol". You don't want to know all the dirty details (*) but essentially its this 1. Every worker writes its output to a directory under the destination directory, something like '$dest/_temporary/$appAttemptId/_temporary/$taskAttemptID' 2. it is the spark driver which "commits" the job by moving the output from the individual
Re: SPARK Issue in Standalone cluster
o my query do need to go and check this link > https://spark.apache.org/docs/2.2.0/spark-standalone. > html#spark-standalone-mode. > > > I see. Well, we shall have to edit that document to make clear something > which had been omitted: > > *in order for multiple spark workers to process data, they must have a > shared store for that data, one with read/write access for all workers. > This is must be provided by a shared filesystem: HDFS, network-mounted NFS, > Glusterfs, through an object store (S3, Azure WASB, ...), or through > alternative datastores implementing the Hadoop Filesystem API (example: > Apache Cassandra).* > > n your case, for a small cluster of 1-3 machines, especially if you are > just learning to play with spark, I'd start with an NFS mounted disk > accessible on the same path on all machines. If you aren't willing to set > that up, stick to spark standalone on a single machine first. You don't > need a shared cluster to use spark standalone. > > Personally, I'd recommend downloading apache zeppelin and running it > locally as the simplest out-the-box experience. > > > It does mention that SPARK standalone cluster can have multiple machines > running as slaves. > > > Clearly it omits the small detail about the requirement for a shared store. > > The general idea of writing to the user group is that people who know > should answer, and not those who do not know. > > > Agreed, but if the answer doesn't appear to be correct to you, do consider > that there may be some detail that hasn't been mentioned, rather than > immediately concluding that the person replying is wrong. > > -Steve > > > > > > Regards, > Gourav Sengupta > > On Tue, Aug 1, 2017 at 4:50 AM, Mahesh Sawaiker < > mahesh_sawai...@persistent.com> wrote: > >> Gourav, >> >> Riccardo’s answer is spot on. >> >> What is happening is one node of spark is writing to its own directory >> and telling a slave to read the data from there, when the slave goes to >> read it, the part is not found. >> >> >> >> Check the folder Users/gouravsengupta/Developme >> nt/spark/sparkdata/test1/part-1-e79273b5-9b4e-4037-92f3- >> 2e52f523dfdf-c000.snappy.parquet on the slave. >> >> The reason it ran on spark 1.5 may have been because the executor ran on >> the driver itself. There is not much use to a set up where you don’t have >> some kind of distributed file system, so I would encourage you to use hdfs, >> or a mounted file system shared by all nodes. >> >> >> >> Regards, >> >> Mahesh >> >> >> >> >> >> *From:* Gourav Sengupta [mailto:gourav.sengu...@gmail.com] >> *Sent:* Monday, July 31, 2017 9:54 PM >> *To:* Riccardo Ferrari >> *Cc:* user >> *Subject:* Re: SPARK Issue in Standalone cluster >> >> >> >> Hi Riccardo, >> >> >> >> I am grateful for your kind response. >> >> >> >> Also I am sure that your answer is completely wrong and errorneous. SPARK >> must be having a method so that different executors do not pick up the same >> files to process. You also did not answer the question why was the >> processing successful in SPARK 1.5 and not in SPARK 2.2. >> >> >> >> Also the exact same directory is is present across in both the nodes. >> >> >> >> I feel quite facinated when individuals respond before even understanding >> the issue, or trying out the code. >> >> >> >> It will be of great help if someone could kindly read my email and help >> me figure out the issue. >> >> >> >> >> >> Regards, >> >> Gourav Sengupta >> >> >> >> >> >> >> >> On Mon, Jul 31, 2017 at 9:27 AM, Riccardo Ferrari >> wrote: >> >> Hi Gourav, >> >> >> >> The issue here is the location where you're trying to write/read from : >> /Users/gouravsengupta/Development/spark/sparkdata/test1/p... >> >> When dealing with clusters all the paths and resources should be >> available to all executors (and driver), and that is reason why you >> generally use HDFS, S3, NFS or any shared file system. >> >> >> >> Spark assumes your data is generally available to all nodes and does not >> tries to pick up the data from a selected node, it rather tries to >> write/read in parallel from the executor nodes. Also given its control >> logic there is no way (read. you should not care) to know what executor is >> doing what task.
Re: SPARK Issue in Standalone cluster
> The general idea of writing to the user group is that people who know should > answer, and not those who do not know. I’d also add that if you’re going to write to the user group, you should be polite to people who try to answer your queries, even if you think they’re wrong. This is especially true if the people you think are wrong are actually correct. Frank Austin Nothaft fnoth...@berkeley.edu fnoth...@eecs.berkeley.edu 202-340-0466 > On Aug 2, 2017, at 6:25 AM, Gourav Sengupta wrote: > > Hi, > > I am definitely sure that at this point of time everyone who has kindly cared > to respond to my query do need to go and check this link > https://spark.apache.org/docs/2.2.0/spark-standalone.html#spark-standalone-mode > > <https://spark.apache.org/docs/2.2.0/spark-standalone.html#spark-standalone-mode>. > > > It does mention that SPARK standalone cluster can have multiple machines > running as slaves. > > The general idea of writing to the user group is that people who know should > answer, and not those who do not know. > > > > Regards, > Gourav Sengupta > > On Tue, Aug 1, 2017 at 4:50 AM, Mahesh Sawaiker > mailto:mahesh_sawai...@persistent.com>> > wrote: > Gourav, > > Riccardo’s answer is spot on. > > What is happening is one node of spark is writing to its own directory and > telling a slave to read the data from there, when the slave goes to read it, > the part is not found. > > > > Check the folder > Users/gouravsengupta/Development/spark/sparkdata/test1/part-1-e79273b5-9b4e-4037-92f3-2e52f523dfdf-c000.snappy.parquet > on the slave. > > The reason it ran on spark 1.5 may have been because the executor ran on the > driver itself. There is not much use to a set up where you don’t have some > kind of distributed file system, so I would encourage you to use hdfs, or a > mounted file system shared by all nodes. > > > > Regards, > > Mahesh > > > > > > From: Gourav Sengupta [mailto:gourav.sengu...@gmail.com > <mailto:gourav.sengu...@gmail.com>] > Sent: Monday, July 31, 2017 9:54 PM > To: Riccardo Ferrari > Cc: user > Subject: Re: SPARK Issue in Standalone cluster > > > > Hi Riccardo, > > > > I am grateful for your kind response. > > > > Also I am sure that your answer is completely wrong and errorneous. SPARK > must be having a method so that different executors do not pick up the same > files to process. You also did not answer the question why was the processing > successful in SPARK 1.5 and not in SPARK 2.2. > > > > Also the exact same directory is is present across in both the nodes. > > > > I feel quite facinated when individuals respond before even understanding the > issue, or trying out the code. > > > > It will be of great help if someone could kindly read my email and help me > figure out the issue. > > > > > > Regards, > > Gourav Sengupta > > > > > > > > On Mon, Jul 31, 2017 at 9:27 AM, Riccardo Ferrari <mailto:ferra...@gmail.com>> wrote: > > Hi Gourav, > > > > The issue here is the location where you're trying to write/read from > :/Users/gouravsengupta/Development/spark/sparkdata/test1/p... > > When dealing with clusters all the paths and resources should be available to > all executors (and driver), and that is reason why you generally use HDFS, > S3, NFS or any shared file system. > > > > Spark assumes your data is generally available to all nodes and does not > tries to pick up the data from a selected node, it rather tries to write/read > in parallel from the executor nodes. Also given its control logic there is no > way (read. you should not care) to know what executor is doing what task. > > > > Hope it helps, > > Riccardo > > > > On Mon, Jul 31, 2017 at 2:14 AM, Gourav Sengupta <mailto:gourav.sengu...@gmail.com>> wrote: > > Hi, > > > > I am working by creating a native SPARK standalone cluster > (https://spark.apache.org/docs/2.2.0/spark-standalone.html > <https://spark.apache.org/docs/2.2.0/spark-standalone.html>) > > > > Therefore I do not have a HDFS. > > > > > > EXERCISE: > > Its the most fundamental and simple exercise. Create a sample SPARK dataframe > and then write it to a location and then read it back. > > > > SETTINGS: > > So after I have installed SPARK in two physical systems with the same: > > 1. SPARK version, > > 2. JAVA version, > > 3. PYTHON_PATH > > 4. SP
Re: SPARK Issue in Standalone cluster
On 2 Aug 2017, at 14:25, Gourav Sengupta mailto:gourav.sengu...@gmail.com>> wrote: Hi, I am definitely sure that at this point of time everyone who has kindly cared to respond to my query do need to go and check this link https://spark.apache.org/docs/2.2.0/spark-standalone.html#spark-standalone-mode. I see. Well, we shall have to edit that document to make clear something which had been omitted: in order for multiple spark workers to process data, they must have a shared store for that data, one with read/write access for all workers. This is must be provided by a shared filesystem: HDFS, network-mounted NFS, Glusterfs, through an object store (S3, Azure WASB, ...), or through alternative datastores implementing the Hadoop Filesystem API (example: Apache Cassandra). n your case, for a small cluster of 1-3 machines, especially if you are just learning to play with spark, I'd start with an NFS mounted disk accessible on the same path on all machines. If you aren't willing to set that up, stick to spark standalone on a single machine first. You don't need a shared cluster to use spark standalone. Personally, I'd recommend downloading apache zeppelin and running it locally as the simplest out-the-box experience. It does mention that SPARK standalone cluster can have multiple machines running as slaves. Clearly it omits the small detail about the requirement for a shared store. The general idea of writing to the user group is that people who know should answer, and not those who do not know. Agreed, but if the answer doesn't appear to be correct to you, do consider that there may be some detail that hasn't been mentioned, rather than immediately concluding that the person replying is wrong. -Steve Regards, Gourav Sengupta On Tue, Aug 1, 2017 at 4:50 AM, Mahesh Sawaiker mailto:mahesh_sawai...@persistent.com>> wrote: Gourav, Riccardo’s answer is spot on. What is happening is one node of spark is writing to its own directory and telling a slave to read the data from there, when the slave goes to read it, the part is not found. Check the folder Users/gouravsengupta/Development/spark/sparkdata/test1/part-1-e79273b5-9b4e-4037-92f3-2e52f523dfdf-c000.snappy.parquet on the slave. The reason it ran on spark 1.5 may have been because the executor ran on the driver itself. There is not much use to a set up where you don’t have some kind of distributed file system, so I would encourage you to use hdfs, or a mounted file system shared by all nodes. Regards, Mahesh From: Gourav Sengupta [mailto:gourav.sengu...@gmail.com<mailto:gourav.sengu...@gmail.com>] Sent: Monday, July 31, 2017 9:54 PM To: Riccardo Ferrari Cc: user Subject: Re: SPARK Issue in Standalone cluster Hi Riccardo, I am grateful for your kind response. Also I am sure that your answer is completely wrong and errorneous. SPARK must be having a method so that different executors do not pick up the same files to process. You also did not answer the question why was the processing successful in SPARK 1.5 and not in SPARK 2.2. Also the exact same directory is is present across in both the nodes. I feel quite facinated when individuals respond before even understanding the issue, or trying out the code. It will be of great help if someone could kindly read my email and help me figure out the issue. Regards, Gourav Sengupta On Mon, Jul 31, 2017 at 9:27 AM, Riccardo Ferrari mailto:ferra...@gmail.com>> wrote: Hi Gourav, The issue here is the location where you're trying to write/read from :/Users/gouravsengupta/Development/spark/sparkdata/test1/p... When dealing with clusters all the paths and resources should be available to all executors (and driver), and that is reason why you generally use HDFS, S3, NFS or any shared file system. Spark assumes your data is generally available to all nodes and does not tries to pick up the data from a selected node, it rather tries to write/read in parallel from the executor nodes. Also given its control logic there is no way (read. you should not care) to know what executor is doing what task. Hope it helps, Riccardo On Mon, Jul 31, 2017 at 2:14 AM, Gourav Sengupta mailto:gourav.sengu...@gmail.com>> wrote: Hi, I am working by creating a native SPARK standalone cluster (https://spark.apache.org/docs/2.2.0/spark-standalone.html) Therefore I do not have a HDFS. EXERCISE: Its the most fundamental and simple exercise. Create a sample SPARK dataframe and then write it to a location and then read it back. SETTINGS: So after I have installed SPARK in two physical systems with the same: 1. SPARK version, 2. JAVA version, 3. PYTHON_PATH 4. SPARK_HOME 5. PYSPARK_PYTHON the user in both the systems is the root user therefore there are no permission issues anywhere. I am able to start: 1. ./spark-2.2.0-bin-hadoop2.7/sbin/start-master.sh 2. ./spark-2.2.0-bin-hadoop2.7/sbin/start-slave.sh (f
Re: SPARK Issue in Standalone cluster
Hi, I am definitely sure that at this point of time everyone who has kindly cared to respond to my query do need to go and check this link https://spark.apache.org/docs/2.2.0/spark-standalone.html#spark-standalone-mode . It does mention that SPARK standalone cluster can have multiple machines running as slaves. The general idea of writing to the user group is that people who know should answer, and not those who do not know. Regards, Gourav Sengupta On Tue, Aug 1, 2017 at 4:50 AM, Mahesh Sawaiker < mahesh_sawai...@persistent.com> wrote: > Gourav, > > Riccardo’s answer is spot on. > > What is happening is one node of spark is writing to its own directory and > telling a slave to read the data from there, when the slave goes to read > it, the part is not found. > > > > Check the folder Users/gouravsengupta/Development/spark/sparkdata/ > test1/part-1-e79273b5-9b4e-4037-92f3-2e52f523dfdf-c000.snappy.parquet on > the slave. > > The reason it ran on spark 1.5 may have been because the executor ran on > the driver itself. There is not much use to a set up where you don’t have > some kind of distributed file system, so I would encourage you to use hdfs, > or a mounted file system shared by all nodes. > > > > Regards, > > Mahesh > > > > > > *From:* Gourav Sengupta [mailto:gourav.sengu...@gmail.com] > *Sent:* Monday, July 31, 2017 9:54 PM > *To:* Riccardo Ferrari > *Cc:* user > *Subject:* Re: SPARK Issue in Standalone cluster > > > > Hi Riccardo, > > > > I am grateful for your kind response. > > > > Also I am sure that your answer is completely wrong and errorneous. SPARK > must be having a method so that different executors do not pick up the same > files to process. You also did not answer the question why was the > processing successful in SPARK 1.5 and not in SPARK 2.2. > > > > Also the exact same directory is is present across in both the nodes. > > > > I feel quite facinated when individuals respond before even understanding > the issue, or trying out the code. > > > > It will be of great help if someone could kindly read my email and help me > figure out the issue. > > > > > > Regards, > > Gourav Sengupta > > > > > > > > On Mon, Jul 31, 2017 at 9:27 AM, Riccardo Ferrari > wrote: > > Hi Gourav, > > > > The issue here is the location where you're trying to write/read from : > /Users/gouravsengupta/Development/spark/sparkdata/test1/p... > > When dealing with clusters all the paths and resources should be available > to all executors (and driver), and that is reason why you generally use > HDFS, S3, NFS or any shared file system. > > > > Spark assumes your data is generally available to all nodes and does not > tries to pick up the data from a selected node, it rather tries to > write/read in parallel from the executor nodes. Also given its control > logic there is no way (read. you should not care) to know what executor is > doing what task. > > > > Hope it helps, > > Riccardo > > > > On Mon, Jul 31, 2017 at 2:14 AM, Gourav Sengupta < > gourav.sengu...@gmail.com> wrote: > > Hi, > > > > I am working by creating a native SPARK standalone cluster ( > https://spark.apache.org/docs/2.2.0/spark-standalone.html) > > > > Therefore I do not have a HDFS. > > > > > > EXERCISE: > > Its the most fundamental and simple exercise. Create a sample SPARK > dataframe and then write it to a location and then read it back. > > > > SETTINGS: > > So after I have installed SPARK in two physical systems with the same: > > 1. SPARK version, > > 2. JAVA version, > > 3. PYTHON_PATH > > 4. SPARK_HOME > > 5. PYSPARK_PYTHON > > the user in both the systems is the root user therefore there are no > permission issues anywhere. > > > > I am able to start: > > 1. ./spark-2.2.0-bin-hadoop2.7/sbin/start-master.sh > > 2. ./spark-2.2.0-bin-hadoop2.7/sbin/start-slave.sh (from two separate > computers) > > > > After that I can see in the spark UI (at port 8080) two workers. > > > > > > CODE: > > Then I run the following code: > > > > == > > import findspark > > import os > > os.environ["SPARK_HOME"] = '/Users/gouravsengupta/ > Development/spark/spark/' > > findspark.init() > > import pyspark > > from pyspark.sql import SparkSession > > spark = (SparkSession.builder > > .master("spark://mastersystem.local:7077") > > .appName("gouravtest") >
RE: SPARK Issue in Standalone cluster
Gourav, Riccardo’s answer is spot on. What is happening is one node of spark is writing to its own directory and telling a slave to read the data from there, when the slave goes to read it, the part is not found. Check the folder Users/gouravsengupta/Development/spark/sparkdata/test1/part-1-e79273b5-9b4e-4037-92f3-2e52f523dfdf-c000.snappy.parquet on the slave. The reason it ran on spark 1.5 may have been because the executor ran on the driver itself. There is not much use to a set up where you don’t have some kind of distributed file system, so I would encourage you to use hdfs, or a mounted file system shared by all nodes. Regards, Mahesh From: Gourav Sengupta [mailto:gourav.sengu...@gmail.com] Sent: Monday, July 31, 2017 9:54 PM To: Riccardo Ferrari Cc: user Subject: Re: SPARK Issue in Standalone cluster Hi Riccardo, I am grateful for your kind response. Also I am sure that your answer is completely wrong and errorneous. SPARK must be having a method so that different executors do not pick up the same files to process. You also did not answer the question why was the processing successful in SPARK 1.5 and not in SPARK 2.2. Also the exact same directory is is present across in both the nodes. I feel quite facinated when individuals respond before even understanding the issue, or trying out the code. It will be of great help if someone could kindly read my email and help me figure out the issue. Regards, Gourav Sengupta On Mon, Jul 31, 2017 at 9:27 AM, Riccardo Ferrari mailto:ferra...@gmail.com>> wrote: Hi Gourav, The issue here is the location where you're trying to write/read from :/Users/gouravsengupta/Development/spark/sparkdata/test1/p... When dealing with clusters all the paths and resources should be available to all executors (and driver), and that is reason why you generally use HDFS, S3, NFS or any shared file system. Spark assumes your data is generally available to all nodes and does not tries to pick up the data from a selected node, it rather tries to write/read in parallel from the executor nodes. Also given its control logic there is no way (read. you should not care) to know what executor is doing what task. Hope it helps, Riccardo On Mon, Jul 31, 2017 at 2:14 AM, Gourav Sengupta mailto:gourav.sengu...@gmail.com>> wrote: Hi, I am working by creating a native SPARK standalone cluster (https://spark.apache.org/docs/2.2.0/spark-standalone.html) Therefore I do not have a HDFS. EXERCISE: Its the most fundamental and simple exercise. Create a sample SPARK dataframe and then write it to a location and then read it back. SETTINGS: So after I have installed SPARK in two physical systems with the same: 1. SPARK version, 2. JAVA version, 3. PYTHON_PATH 4. SPARK_HOME 5. PYSPARK_PYTHON the user in both the systems is the root user therefore there are no permission issues anywhere. I am able to start: 1. ./spark-2.2.0-bin-hadoop2.7/sbin/start-master.sh 2. ./spark-2.2.0-bin-hadoop2.7/sbin/start-slave.sh (from two separate computers) After that I can see in the spark UI (at port 8080) two workers. CODE: Then I run the following code: == import findspark import os os.environ["SPARK_HOME"] = '/Users/gouravsengupta/Development/spark/spark/' findspark.init() import pyspark from pyspark.sql import SparkSession spark = (SparkSession.builder .master("spark://mastersystem.local:7077") .appName("gouravtest") .enableHiveSupport() .getOrCreate()) import pandas, numpy testdf = spark.createDataFrame(pandas.DataFrame(numpy.random.randn(1, 4), columns=list('ABCD'))) testdf.cache() testdf.count() testdf.write.save("/Users/gouravsengupta/Development/spark/sparkdata/test2") spark.read.load("/Users/gouravsengupta/Development/spark/sparkdata/test2").count() == ERROR I (in above code): ERROR in line: testdf.write.save("/Users/gouravsengupta/Development/spark/sparkdata/test2") This line does not fail or report any error. But when I am looking at the stage in spark Application UI the error reported for one of the slave node which is not in the same system as the master node is mentioned below. The writing on the slave node which is in the same physical system as the Master happens correctly. (NOTE: slave node basically the worker and master node the driver) -- 0 (TID 41). 2060 bytes result sent to driver 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_06_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_06 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_
Re: SPARK Issue in Standalone cluster
Hi Riccardo, I am grateful for your kind response. Also I am sure that your answer is completely wrong and errorneous. SPARK must be having a method so that different executors do not pick up the same files to process. You also did not answer the question why was the processing successful in SPARK 1.5 and not in SPARK 2.2. Also the exact same directory is is present across in both the nodes. I feel quite facinated when individuals respond before even understanding the issue, or trying out the code. It will be of great help if someone could kindly read my email and help me figure out the issue. Regards, Gourav Sengupta On Mon, Jul 31, 2017 at 9:27 AM, Riccardo Ferrari wrote: > Hi Gourav, > > The issue here is the location where you're trying to write/read from : > /Users/gouravsengupta/Development/spark/sparkdata/test1/p... > When dealing with clusters all the paths and resources should be available > to all executors (and driver), and that is reason why you generally use > HDFS, S3, NFS or any shared file system. > > Spark assumes your data is generally available to all nodes and does not > tries to pick up the data from a selected node, it rather tries to > write/read in parallel from the executor nodes. Also given its control > logic there is no way (read. you should not care) to know what executor is > doing what task. > > Hope it helps, > Riccardo > > On Mon, Jul 31, 2017 at 2:14 AM, Gourav Sengupta < > gourav.sengu...@gmail.com> wrote: > >> Hi, >> >> I am working by creating a native SPARK standalone cluster ( >> https://spark.apache.org/docs/2.2.0/spark-standalone.html) >> >> Therefore I do not have a HDFS. >> >> >> EXERCISE: >> Its the most fundamental and simple exercise. Create a sample SPARK >> dataframe and then write it to a location and then read it back. >> >> SETTINGS: >> So after I have installed SPARK in two physical systems with the same: >> 1. SPARK version, >> 2. JAVA version, >> 3. PYTHON_PATH >> 4. SPARK_HOME >> 5. PYSPARK_PYTHON >> the user in both the systems is the root user therefore there are no >> permission issues anywhere. >> >> I am able to start: >> 1. ./spark-2.2.0-bin-hadoop2.7/sbin/start-master.sh >> 2. ./spark-2.2.0-bin-hadoop2.7/sbin/start-slave.sh (from two separate >> computers) >> >> After that I can see in the spark UI (at port 8080) two workers. >> >> >> CODE: >> Then I run the following code: >> >> == >> import findspark >> import os >> os.environ["SPARK_HOME"] = '/Users/gouravsengupta/Develop >> ment/spark/spark/' >> findspark.init() >> import pyspark >> from pyspark.sql import SparkSession >> spark = (SparkSession.builder >> .master("spark://mastersystem.local:7077") >> .appName("gouravtest") >> .enableHiveSupport() >> .getOrCreate()) >> import pandas, numpy >> testdf = spark.createDataFrame(pandas.DataFrame(numpy.random.randn(1, >> 4), columns=list('ABCD'))) >> testdf.cache() >> testdf.count() >> testdf.write.save("/Users/gouravsengupta/Development/spark/ >> sparkdata/test2") >> spark.read.load("/Users/gouravsengupta/Development/spark/ >> sparkdata/test2").count() >> == >> >> >> ERROR I (in above code): >> ERROR in line: testdf.write.save("/Users/gour >> avsengupta/Development/spark/sparkdata/test2") >> This line does not fail or report any error. But when I am looking at the >> stage in spark Application UI the error reported for one of the slave node >> which is not in the same system as the master node is mentioned below. The >> writing on the slave node which is in the same physical system as the >> Master happens correctly. (NOTE: slave node basically the worker and master >> node the driver) >> >> -- >> >> 0 (TID 41). 2060 bytes result sent to driver >> 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task >> 'attempt_20170731001928_0002_m_06_0' to >> file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_06 >> 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: >> attempt_20170731001928_0002_m_06_0: Committed >> 17/07/31 00:19:29 INFO Executor: Finished task 31.0 in stage 2.0 (TID 64). >> 2060 bytes result sent to driver >> 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task >> 'attempt_20170731001928_0002_m_28_0' to >> file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_28 >> 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: >> attempt_20170731001928_0002_m_28_0: Committed >> 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task >> 'attempt_20170731001928_0002_m_21_0' to >> file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_21 >> 17/07/31 00:19:29 INFO SparkHadoopMapRed
Re: SPARK Issue in Standalone cluster
Hi Gourav, The issue here is the location where you're trying to write/read from : /Users/gouravsengupta/Development/spark/sparkdata/test1/p... When dealing with clusters all the paths and resources should be available to all executors (and driver), and that is reason why you generally use HDFS, S3, NFS or any shared file system. Spark assumes your data is generally available to all nodes and does not tries to pick up the data from a selected node, it rather tries to write/read in parallel from the executor nodes. Also given its control logic there is no way (read. you should not care) to know what executor is doing what task. Hope it helps, Riccardo On Mon, Jul 31, 2017 at 2:14 AM, Gourav Sengupta wrote: > Hi, > > I am working by creating a native SPARK standalone cluster ( > https://spark.apache.org/docs/2.2.0/spark-standalone.html) > > Therefore I do not have a HDFS. > > > EXERCISE: > Its the most fundamental and simple exercise. Create a sample SPARK > dataframe and then write it to a location and then read it back. > > SETTINGS: > So after I have installed SPARK in two physical systems with the same: > 1. SPARK version, > 2. JAVA version, > 3. PYTHON_PATH > 4. SPARK_HOME > 5. PYSPARK_PYTHON > the user in both the systems is the root user therefore there are no > permission issues anywhere. > > I am able to start: > 1. ./spark-2.2.0-bin-hadoop2.7/sbin/start-master.sh > 2. ./spark-2.2.0-bin-hadoop2.7/sbin/start-slave.sh (from two separate > computers) > > After that I can see in the spark UI (at port 8080) two workers. > > > CODE: > Then I run the following code: > > == > import findspark > import os > os.environ["SPARK_HOME"] = '/Users/gouravsengupta/ > Development/spark/spark/' > findspark.init() > import pyspark > from pyspark.sql import SparkSession > spark = (SparkSession.builder > .master("spark://mastersystem.local:7077") > .appName("gouravtest") > .enableHiveSupport() > .getOrCreate()) > import pandas, numpy > testdf = spark.createDataFrame(pandas.DataFrame(numpy.random.randn(1, > 4), columns=list('ABCD'))) > testdf.cache() > testdf.count() > testdf.write.save("/Users/gouravsengupta/Development/ > spark/sparkdata/test2") > spark.read.load("/Users/gouravsengupta/Development/ > spark/sparkdata/test2").count() > == > > > ERROR I (in above code): > ERROR in line: testdf.write.save("/Users/gouravsengupta/Development/ > spark/sparkdata/test2") > This line does not fail or report any error. But when I am looking at the > stage in spark Application UI the error reported for one of the slave node > which is not in the same system as the master node is mentioned below. The > writing on the slave node which is in the same physical system as the > Master happens correctly. (NOTE: slave node basically the worker and master > node the driver) > > -- > > 0 (TID 41). 2060 bytes result sent to driver > 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task > 'attempt_20170731001928_0002_m_06_0' to > file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_06 > 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: > attempt_20170731001928_0002_m_06_0: Committed > 17/07/31 00:19:29 INFO Executor: Finished task 31.0 in stage 2.0 (TID 64). > 2060 bytes result sent to driver > 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task > 'attempt_20170731001928_0002_m_28_0' to > file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_28 > 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: > attempt_20170731001928_0002_m_28_0: Committed > 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task > 'attempt_20170731001928_0002_m_21_0' to > file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_21 > 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: > attempt_20170731001928_0002_m_21_0: Committed > 17/07/31 00:19:29 INFO Executor: Finished task 12.0 in stage 2.0 (TID 45). > 2103 bytes result sent to driver > 17/07/31 00:19:29 INFO Executor: Finished task 4.0 in stage 2.0 (TID 37). > 2060 bytes result sent to driver > 17/07/31 00:19:29 INFO Executor: Finished task 6.0 in stage 2.0 (TID 39). > 2060 bytes result sent to driver > 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task > 'attempt_20170731001928_0002_m_18_0' to > file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_18 > 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: > attempt_20170731001928_0002_m_18_0: Committed > 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task > 'attempt_20170731001928_0002_m_29_0' to > file:/
SPARK Issue in Standalone cluster
Hi, I am working by creating a native SPARK standalone cluster ( https://spark.apache.org/docs/2.2.0/spark-standalone.html) Therefore I do not have a HDFS. EXERCISE: Its the most fundamental and simple exercise. Create a sample SPARK dataframe and then write it to a location and then read it back. SETTINGS: So after I have installed SPARK in two physical systems with the same: 1. SPARK version, 2. JAVA version, 3. PYTHON_PATH 4. SPARK_HOME 5. PYSPARK_PYTHON the user in both the systems is the root user therefore there are no permission issues anywhere. I am able to start: 1. ./spark-2.2.0-bin-hadoop2.7/sbin/start-master.sh 2. ./spark-2.2.0-bin-hadoop2.7/sbin/start-slave.sh (from two separate computers) After that I can see in the spark UI (at port 8080) two workers. CODE: Then I run the following code: == import findspark import os os.environ["SPARK_HOME"] = '/Users/gouravsengupta/Development/spark/spark/' findspark.init() import pyspark from pyspark.sql import SparkSession spark = (SparkSession.builder .master("spark://mastersystem.local:7077") .appName("gouravtest") .enableHiveSupport() .getOrCreate()) import pandas, numpy testdf = spark.createDataFrame(pandas.DataFrame(numpy.random.randn(1, 4), columns=list('ABCD'))) testdf.cache() testdf.count() testdf.write.save("/Users/gouravsengupta/Development/spark/sparkdata/test2") spark.read.load("/Users/gouravsengupta/Development/spark/sparkdata/test2").count() == ERROR I (in above code): ERROR in line: testdf.write.save("/Users/gouravsengupta/Development/spark/sparkdata/test2") This line does not fail or report any error. But when I am looking at the stage in spark Application UI the error reported for one of the slave node which is not in the same system as the master node is mentioned below. The writing on the slave node which is in the same physical system as the Master happens correctly. (NOTE: slave node basically the worker and master node the driver) -- 0 (TID 41). 2060 bytes result sent to driver 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_06_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_06 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_06_0: Committed 17/07/31 00:19:29 INFO Executor: Finished task 31.0 in stage 2.0 (TID 64). 2060 bytes result sent to driver 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_28_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_28 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_28_0: Committed 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_21_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_21 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_21_0: Committed 17/07/31 00:19:29 INFO Executor: Finished task 12.0 in stage 2.0 (TID 45). 2103 bytes result sent to driver 17/07/31 00:19:29 INFO Executor: Finished task 4.0 in stage 2.0 (TID 37). 2060 bytes result sent to driver 17/07/31 00:19:29 INFO Executor: Finished task 6.0 in stage 2.0 (TID 39). 2060 bytes result sent to driver 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_18_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_18 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_18_0: Committed 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_29_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_29 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_29_0: Committed 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_27_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_27 17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_27_0: Committed 17/07/31 00:19:29 INFO Executor: Finished task 21.0 in stage 2.0 (TID 54). 2060 bytes result sent to driver 17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_10_0' to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_10 17/07/31 00:19:29 INFO Executor: Finished task 19.0 in stage 2.0 (TID 52). 2060 bytes result sent t