You refer to df.write.partitionBy, which creates for each value of "col"
a directory, and in worst-case writes one file per DataFrame partition.
So the number of output files is controlled by cardinality of "col",
which is your data and hence out of control, and the number of
partitions of
Hi all,
Is there a way to use dataframe.partitionBy("col") and control the number
of output files without doing a full repartition? The thing is some
partitions have more data while some have less. Doing a .repartition is a
costly operation. We want to control the size of the output files. Is it
Yes,
It created a list of records separated by , and it was created faster as
well.
On Wed, 27 Apr 2022, 13:42 Gourav Sengupta,
wrote:
> Hi,
> did that result in valid JSON in the output file?
>
> Regards,
> Gourav Sengupta
>
> On Tue, Apr 26, 2022 at 8:18 PM Sid wrote:
>
>> I have .txt
Hi,
did that result in valid JSON in the output file?
Regards,
Gourav Sengupta
On Tue, Apr 26, 2022 at 8:18 PM Sid wrote:
> I have .txt files with JSON inside it. It is generated by some API calls
> by the Client.
>
> On Wed, Apr 27, 2022 at 12:39 AM Bjørn Jørgensen
> wrote:
>
>> What is that
I have .txt files with JSON inside it. It is generated by some API calls by
the Client.
On Wed, Apr 27, 2022 at 12:39 AM Bjørn Jørgensen
wrote:
> What is that you have? Is it txt files or json files?
> Or do you have txt files with JSON inside?
>
>
>
> tir. 26. apr. 2022 kl. 20:41 skrev Sid :
>
What is that you have? Is it txt files or json files?
Or do you have txt files with JSON inside?
tir. 26. apr. 2022 kl. 20:41 skrev Sid :
> Thanks for your time, everyone :)
>
> Much appreciated.
>
> I solved it using jq utility since I was dealing with JSON. I have solved
> it using below
Thanks for your time, everyone :)
Much appreciated.
I solved it using jq utility since I was dealing with JSON. I have solved
it using below script:
find . -name '*.txt' -exec cat '{}' + | jq -s '.' > output.txt
Thanks,
Sid
On Tue, Apr 26, 2022 at 9:37 PM Bjørn Jørgensen
wrote:
> and the
and the bash script seems to read txt files not json
for f in Agent/*.txt; do cat ${f} >> merged.json;done;
tir. 26. apr. 2022 kl. 18:03 skrev Gourav Sengupta <
gourav.sengu...@gmail.com>:
> Hi,
>
> what is the version of spark are you using? And where is the data stored.
>
> I am not quite
Hi,
what is the version of spark are you using? And where is the data stored.
I am not quite sure that just using a bash script will help because
concatenating all the files into a single file creates a valid JSON.
Regards,
Gourav
On Tue, Apr 26, 2022 at 3:44 PM Sid wrote:
> Hello,
>
> Can
Most likely your JSON files are not formatted correctly. Please see the
Spark doc on specific formatting requirement for JSON data.
https://spark.apache.org/docs/latest/sql-data-sources-json.html.
On 4/26/22 10:43 AM, Sid wrote:
Hello,
Can somebody help me with the below problem?
df = spark.read.json("/*.json")
use the *.json
tir. 26. apr. 2022 kl. 16:44 skrev Sid :
> Hello,
>
> Can somebody help me with the below problem?
>
>
> https://stackoverflow.com/questions/72015557/dealing-with-large-number-of-small-json-files-using-pyspark
>
>
> Thanks,
> Sid
>
--
Bjørn
Hello,
Can somebody help me with the below problem?
https://stackoverflow.com/questions/72015557/dealing-with-large-number-of-small-json-files-using-pyspark
Thanks,
Sid
Hello Yann,
From my understanding, when reading small files Spark will group them and load
the content of each batch into the same partition so you won’t end up with 1
partition per file resulting in a huge number of very small partitions. This
behavior is controlled
Yes, it does bin-packing for small files which is a good thing so you avoid
having many small partitions especially if you’re writing this data back out
(e.g. it’s compacting as you read). The default partition size is 128MB with a
4MB “cost” for opening files. You can configure this using
Hello,
I'm using Spark 2.3.1.
I have a job that reads 5.000 small parquet files into s3.
When I do a mapPartitions followed by a collect, only *278* tasks are used
(I would have expected 5000). Does Spark group small files ? If yes, what
is the threshold for grouping ? Is it configurable ? Any
When I run spark.read.orc("hdfs://test").filter("conv_date = 20181025").count
with "spark.sql.orc.filterPushdown=true" I see below in executors logs.
Predicate push down is happening
18/11/01 17:31:17 INFO OrcInputFormat: ORC pushdown predicate: leaf-0 =
(IS_NULL conv_date)
leaf-1 = (EQUALS
A lot of small files is very inefficient itself and predicate push down will
not help you much there unless you merge them into one large file (one large
file can be much more efficiently processed).
How did you validate that predicate pushdown did not work on Hive? You Hive
Version is also
spark version 2.2.0
Hive version 1.1.0
There are lot of small files
Spark code :
"spark.sql.orc.enabled": "true",
"spark.sql.orc.filterPushdown": "true
val logs
=spark.read.schema(schema).orc("hdfs://test/date=201810").filter("date &
How large are they? A lot of (small) files will cause significant delay in
progressing - try to merge as much as possible into one file.
Can you please share full source code in Hive and Spark as well as the versions
you are using?
> Am 31.10.2018 um 18:23 schrieb gpatcham :
>
>
When reading large number of orc files from HDFS under a directory spark
doesn't launch any tasks until some amount of time and I don't see any tasks
running during that time. I'm using below command to read orc and spark.sql
configs.
What spark is doing under hoods when spark.read.orc is
. For instance, if your spark application
is working on 5 partitions, you can repartition to 1, this will again reduce
the number of files to 5x.
You can create staging to hold small files and once a decent amount of data is
accumulated you can prepare large files and load to your final hive table
Hi,
I am using spark streaming to write data back into hive with the below code
snippet
eventHubsWindowedStream.map(x => EventContent(new String(x)))
.foreachRDD(rdd => {
val sparkSession = SparkSession
.builder.enableHiveSupport.getOrCreate
import
mega file :) but i did not have to do it in my life, yet, so, maybe i am
wrong.
Please, take a look to this post and let us know about how you deal with it.
https://stuartsierra.com/2008/04/24/a-million-little-files
http://blog.cloudera.com/blog/2009/02/the-small-files-problem/
"One
Hi
Thanks for your mail. I have read few of those posts. But always I see
solutions assume data is on hdfs already. My problem is to get data on to
HDFS for the first time.
One way I can think of is to load small files on each cluster machines on
the same folder. For example load file 1-0.3 mil
That is a good question Ayan. A few searches on so returns me:
http://stackoverflow.com/questions/31009834/merge-multiple-small-files-in-to-few-larger-files-in-spark
http://stackoverflow.com/questions/29025147/how-can-i-merge-spark-results-files-without-repartition-and-copymerge
good luck
Hi
I have a general question: I have 1.6 mil small files, about 200G all put
together. I want to put them on hdfs for spark processing.
I know sequence file is the way to go because putting small files on hdfs
is not correct practice. Also, I can write a code to consolidate small
files to seq
vidson <a...@santacruzintegration.com>, Pedro Rodriguez
<ski.rodrig...@gmail.com>
Cc: "user @spark" <user@spark.apache.org>
Subject: use big files and read from HDFS was: performance problem when
reading lots of small files created by spark streaming.
> Hi Pedro
>
> I did some experi
Rodriguez <ski.rodrig...@gmail.com>
Cc: "user @spark" <user@spark.apache.org>
Subject: Re: performance problem when reading lots of small files created
by spark streaming.
> Hi Pedro
>
> Thanks for the explanation. I started watching your repo. In the short term I
> thi
There is an option to join small files up. If you are unable to find it
just let me know.
Regards,
Gourav
On Thu, Jul 28, 2016 at 4:58 PM, Andy Davidson <
a...@santacruzintegration.com> wrote:
> Hi Pedro
>
> Thanks for the explanation. I started watching your repo. In the short
Hi Pedro
Thanks for the explanation. I started watching your repo. In the short term
I think I am going to try concatenating my small files into 64MB and using
HDFS. My spark streaming app is implemented Java and uses data frames. It
writes to s3. My batch processing is written in python It reads
).textFileByPrefix("bucket", "file1",
"folder2").regularRDDOperationsHere or import implicits and do
sc.s3.textFileByPrefix
At present, I am battle testing and benchmarking it at my current job and
results are promising with significant improvements to jobs dealing with
many fil
the files to a normal file system and then using hadoop fs
put¹ to copy the files to hdfs how ever this takes several hours and is no
where near completion. It appears hdfs does not deal with small files well.
I am considering copying the files from s3 to a normal file system on one of
my workers
Hi,
This jira https://issues.apache.org/jira/browse/SPARK-8813 is fixed in spark
2.0.But resolution is not mentioned there.
In our use case, there are big as well as many small parquet files which are
being queried using spark sql.Can someone please explain what is the fix and
how I can use it
ropu
>
> On Fri, Jul 1, 2016 at 7:39 PM, kali.tumm...@gmail.com <
> kali.tumm...@gmail.com> wrote:
>
>> I found the jira for the issue will there be a fix in future ? or no fix ?
>>
>> https://issues.apache.org/jira/browse/SPARK-6221
>>
>>
>>
&
owse/SPARK-6221
>
>
>
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I found the jira for the issue will there be a fix in future ? or no fix ?
https://issues.apache.org/jira/browse/SPARK-6221
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t; Neelesh S. Salian
> Cloudera
>
>
> If you reply to this email, your message will be added to the discussion
> below:
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Thanks
Sri
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Many small files could cause technical issues in both hdfs and spark
though, they do not
generate many stages and tasks in the recent version of spark.
// maropu
On Fri, May 20, 2016 at 2:41 PM, Gavin Yue <yue.yuany...@gmail.com> wrote:
> For logs file I would suggest save as gziped
t files instead of
> keeping lots of small files in the HDFS. Please refer to [1] for more info.
>
> We also encountered the same issue with the slow query, and it was indeed
> caused by the many small parquet files. In our case, we were processing large
> data sets with batch jobs instea
IMO, it might be better to merge or compact the parquet files instead of
keeping lots of small files in the HDFS. Please refer to [1] for more info.
We also encountered the same issue with the slow query, and it was indeed
caused by the many small parquet files. In our case, we were processing
g message into parquet file
> every 10 mins.
> Now, when I query the parquet, it usually takes hundreds of thousands of
> stages to compute a single count.
> I looked into the parquet file’s path and find a great amount of small
> files.
>
> Do the small files caused the problem? Can I merg
I’m using a spark streaming program to store log message into parquet file
every 10 mins.
Now, when I query the parquet, it usually takes hundreds of thousands of stages
to compute a single count.
I looked into the parquet file’s path and find a great amount of small files.
Do the small files
will be much welcomed.
Thanks!
Lucas
From: ayan guha [mailto:guha.a...@gmail.com]
Sent: 24 September 2015 00:19
To: Tracewski, Lukasz (KFDB 3)
Cc: user@spark.apache.org
Subject: Re: Join over many small files
I think this can be a good case for using sequence file format to pack many
files to few
Hi all,
I would like you to ask for an advise on how to efficiently make a join
operation in Spark with tens of thousands of tiny files. A single file has a
few KB and ~50 rows. In another scenario they might have 200 KB and 2000 rows.
To give you impression how they look like:
File 01
ID |
I think this can be a good case for using sequence file format to pack many
files to few sequence files with file name as key andd content as value.
Then read it as RDD and produce tuples like you mentioned (key=fileno+id,
value=value). After that, it is a simple map operation to generate the diff
backed on S3 with large amount of small files
To: user@spark.apache.org
Hello Spark community,
I currently have a Spark 1.3.1 batch driver, deployed in YARN-cluster mode
on an EMR cluster (AMI 3.7.0) that reads input data through an HiveContext,
in particular SELECTing data from an EXTERNAL TABLE
Hello,
I would love to have hive merge the small files in my managed hive context
after every query. Right now, I am setting the hive configuration in my
Spark Job configuration but hive is not managing the files. Do I need to
set the hive fields in around place? How do you set Hive
This feature isn't currently supported.
On Wed, Aug 5, 2015 at 8:43 AM, Brandon White bwwintheho...@gmail.com
wrote:
Hello,
I would love to have hive merge the small files in my managed hive context
after every query. Right now, I am setting the hive configuration in my
Spark Job
would love to have hive merge the small files in my managed hive
context after every query. Right now, I am setting the hive configuration
in my Spark Job configuration but hive is not managing the files. Do I need
to set the hive fields in around place? How do you set Hive configurations
in Spark
Hello Spark community,
I currently have a Spark 1.3.1 batch driver, deployed in YARN-cluster mode
on an EMR cluster (AMI 3.7.0) that reads input data through an HiveContext,
in particular SELECTing data from an EXTERNAL TABLE backed on S3. Such
table has dynamic partitions and contains *hundreds
(list_of_filenames) appear to not perform well on small files,
why?
*sc.wholeTextFiles(path_to_files) performs better than sc.textfile, but does
not support bzipped files. However, also wholeTextFiles does not nearly
provide the speed of the Python script.
*The initialization of a Spark Context takes
of the memory cache which
could be much faster.
And, in general, small files hurt I/O performance.
On Tue, Feb 10, 2015 at 12:52 PM, Davies Liu dav...@databricks.com wrote:
Spark is an framework to do things in parallel very easy, it
definitely will help your cases.
def read_file(path
.
My preliminary findings and my questions:
*Even only counting the number of log lines with Spark is about 10 times
slower than the entire transformation done by the Python script.
*sc.textfile(list_of_filenames) appear to not perform well on small files,
why?
*sc.wholeTextFiles(path_to_files
started.
D
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$CombineTextFileRecordReader.init(Loaders.scala:31)
[info] ... 27 more
I saw that you tested with Spark 1.1.0 and but I am forced to use 1.0.2
currently. Perhaps that is the
source of the error.
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We encountered a problem of loading a huge number of small files (hundred
thousands of files) from HDFS in Spark. Our jobs were failed over time.
This one forced us to write own loader with combining by means of Hadoop
CombineFileInputFormat.
It significantly reduced number of mappers from 10
Hi all,
Spark is taking too much time to start the first stage with many small
files in HDFS.
I am reading a folder that contains RC files:
sc.hadoopFile(hdfs://hostname :8020/test_data2gb/,
classOf[RCFileInputFormat[LongWritable, BytesRefArrayWritable]],
classOf[LongWritable], classOf
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