Hi Sabarish
Thanks for the suggestion. I did not know about wholeTextFiles()
By the way once your suggestion about repartitioning was spot on!. My run
time for count() when from elapsed time:0:56:42.902407 to elapsed
time:0:00:03.215143 on a data set of about 34M of 4720 records.
Andy
From: Sabarish Sasidharan <sabarish.sasidha...@manthan.com>
Date: Monday, November 23, 2015 at 7:57 PM
To: Andrew Davidson <a...@santacruzintegration.com>
Cc: Xiao Li <gatorsm...@gmail.com>, "user @spark" <user@spark.apache.org>
Subject: Re: newbie : why are thousands of empty files being created on
HDFS?
>
> Hi Andy
>
> You can try sc.wholeTextFiles() instead of sc.textFile()
>
> Regards
> Sab
>
> On 24-Nov-2015 4:01 am, "Andy Davidson" <a...@santacruzintegration.com> wrote:
>> Hi Xiao and Sabarish
>>
>> Using the Stage tab on the UI. It turns out you can see how many
>> partitions there are. If I did nothing I would have 228155 partition.
>> (This confirms what Sabarish said). I tried coalesce(3). RDD.count()
>> fails. I though given I have 3 workers and 1/3 of the data would easily
>> fit into memory this would be a good choice.
>>
>> If I use coalesce(30) count works. How ever it still seems slow. It took
>> 2.42 min to read 4720 records. My total data set size is 34M.
>>
>> Any suggestions how to choose the number of partitions.?
>>
>> ('spark.executor.memory', '2G¹) ('spark.driver.memory', '2G')
>>
>>
>> The data was originally collected using spark stream. I noticed that the
>> number of default partitions == the number of files create on hdfs. I bet
>> each file is one spark streaming mini-batchI suspect if I concatenate
>> these into a small number of files things will run much faster. I suspect
>> I would not need to call coalesce() and that coalesce() is taking a lot of
>> time. Any suggestions how to choose the file number of files.
>>
>> Kind regards
>>
>> Andy
>>
>>
>> From: Xiao Li <gatorsm...@gmail.com>
>> Date: Monday, November 23, 2015 at 12:21 PM
>> To: Andrew Davidson <a...@santacruzintegration.com>
>> Cc: Sabarish Sasidharan <sabarish.sasidha...@manthan.com>, "user @spark"
>> <user@spark.apache.org>
>> Subject: Re: newbie : why are thousands of empty files being created on
>> HDFS?
>>
>>
>>> >In your case, maybe you can try to call the function coalesce?
>>> >Good luck,
>>> >
>>> >Xiao Li
>>> >
>>> >2015-11-23 12:15 GMT-08:00 Andy Davidson <a...@santacruzintegration.com>:
>>> >
>>> >Hi Sabarish
>>> >
>>> >I am but a simple padawan :-) I do not understand your answer. Why would
>>> >Spark be creating so many empty partitions? My real problem is my
>>> >application is very slow. I happened to notice thousands of empty files
>>> >being created. I thought this is a hint to why my app is slow.
>>> >
>>> >My program calls sample( 0.01).filter(not null).saveAsTextFile(). This
>>> >takes about 35 min, to scan 500,000 JSON strings and write 5000 to disk.
>>> >The total data writing in 38M.
>>> >
>>> >The data is read from HDFS. My understanding is Spark can not know in
>>> >advance how HDFS partitioned the data. Spark knows I have a master and 3
>>> >slaves machines. It knows how many works/executors are assigned to my
>>> >Job. I would expect spark would be smart enough not create more
>>> >partitions than I have worker machines?
>>> >
>>> >Also given I am not using any key/value operations like Join() or doing
>>> >multiple scans I would assume my app would not benefit from partitioning.
>>> >
>>> >
>>> >Kind regards
>>> >
>>> >Andy
>>> >
>>> >
>>> >From: Sabarish Sasidharan <sabarish.sasidha...@manthan.com>
>>> >Date: Saturday, November 21, 2015 at 7:20 PM
>>> >To: Andrew Davidson <a...@santacruzintegration.com>
>>> >Cc: "user @spark" <user@spark.apache.org>
>>> >Subject: Re: newbie : why are thousands of empty files being created on
>>> >HDFS?
>>> >
>>> >
>>> >
>>> >Those are empty partitions. I don't see the number of partitions
>>> >specified in code. That then implies the default parallelism config is
>>> >being used and is set to a very high number, the sum of empty + non empty
>>> >files.
>>> >Regards
>>> >Sab
>>> >On 21-Nov-2015 11:59 pm, "Andy Davidson" <a...@santacruzintegration.com>
>>> >wrote:
>>> >
>>> >I start working on a very simple ETL pipeline for a POC. It reads a in a
>>> >data set of tweets stored as JSON strings on in HDFS and randomly selects
>>> >1% of the observations and writes them to HDFS. It seems to run very
>>> >slowly. E.G. To write 4720 observations takes 1:06:46.577795. I
>>> >Also noticed that RDD saveAsTextFile is creating thousands of empty
>>> >files.
>>> >
>>> >I assume creating all these empty files must be slowing down the system.
>>> >Any idea why this is happening? Do I have write a script to periodical
>>> >remove empty files?
>>> >
>>> >
>>> >Kind regards
>>> >
>>> >Andy
>>> >
>>> >tweetStrings = sc.textFile(inputDataURL)
>>> >
>>> >
>>> >def removeEmptyLines(line) :
>>> > if line:
>>> > return True
>>> > else :
>>> > emptyLineCount.add(1);
>>> > return False
>>> >
>>> >emptyLineCount = sc.accumulator(0)
>>> >sample = (tweetStrings.filter(removeEmptyLines)
>>> > .sample(withReplacement=False, fraction=0.01, seed=345678))
>>> >
>>> >
>>> >startTime = datetime.datetime.now()
>>> >sample.saveAsTextFile(saveDataURL)
>>> >
>>> >endTime = datetime.datetime.now()
>>> >print("elapsed time:%s" % (datetime.datetime.now() - startTime))
>>> >
>>> >
>>> >elapsed time:1:06:46.577795
>>> >
>>> >Total number of empty files$ hadoop fs -du {saveDataURL} | grep '^0' | wc
>>> >l223515
>>> >Total number of files with data$ hadoop fs -du {saveDataURL} | grep v
>>> >'^0' | wc l4642
>>> >
>>> >I randomly pick a part file. It¹s size is 9251
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>
>>
