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Date: Thu, 22 Jun 2017 23:18:00 +0000 (UTC)
From: =?utf-8?Q?Guillermo_Rodr=C3=ADguez_Cano_=28JIRA=29?= <jira@apache.org>
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Subject: [jira] [Comment Edited] (BEAM-2490) ReadFromText function is not
 taking all data with glob operator (*)
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archived-at: Thu, 22 Jun 2017 23:18:04 -0000


    [ https://issues.apache.org/jira/browse/BEAM-2490?page=3Dcom.atlassian.=
jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=3D16060=
177#comment-16060177 ]=20

Guillermo Rodr=C3=ADguez Cano edited comment on BEAM-2490 at 6/22/17 11:17 =
PM:
--------------------------------------------------------------------------

I am having a similar issue if not the same, although I am using the Direct=
Runner instead (but I believe that in a previous trial I was also using Dat=
aflowRunner and even gzip files).

In the following pipeline (I am trying to work with sessions):

{{
    with beam.Pipeline(options=3Dpipeline_options) as p:
        raw_events =3D p | 'Read input' >> ReadFromText(known_args.input)

        sessions =3D (raw_events
            | 'Extract event and timestamp' >> beam.ParDo(ExtractEventAndTi=
mestampDoFn())
            | 'Compute sessions window' >> WindowInto(Sessions(gap_size=3Dk=
nown_args.session_idle_gap))
            | 'Group by key' >> beam.GroupByKey()
            )

        output =3D sessions | 'Format output' >> beam.ParDo(FormatSessionOu=
tputDoFn())
        output | 'Write results' >> WriteToText(known_args.output)
}}

where the input is a list of uncompressed JSON files in the same directory,=
 I get the same output whether I use the glob operator (*) for all the file=
s or I set the first file in that list.

When running the pipeline like this (for files of about 200M size each):
{{python sessions_manager.py --input ./input/test/* --output ./output/ --ru=
nner DirectRunner}}

The following shows up as process:
{{python sessions_manager.py --input ./input/test/xaa.json ./input/test/xab=
.json ./input/test/xac.json ./input/test/xad.json ./input/test/xae.json ./i=
nput/test/xaf.json ./input/test/xag --output ./output/ --runner DirectRunne=
r}}

And if I run just this:=20
{{python sessions_manager.py --input ./input/test/xaa.json --output ./outpu=
t/ --runner DirectRunner}}
The output is precisely the same as the one with the glob operator (and qui=
te different if I merge the files into one and run again the pipeline with =
the merged files into one).


was (Author: wileeam):
I am having a similar issue if not the same, although I am using the Direct=
Runner instead (but I believe that in a previous trial I was also using Dat=
aflowRunner and even gzip files).

In the following pipeline (I am trying to work with sessions):

{{    with beam.Pipeline(options=3Dpipeline_options) as p:
        raw_events =3D p | 'Read input' >> ReadFromText(known_args.input)

        sessions =3D (raw_events
            | 'Extract event and timestamp' >> beam.ParDo(ExtractEventAndTi=
mestampDoFn())
            | 'Compute sessions window' >> WindowInto(Sessions(gap_size=3Dk=
nown_args.session_idle_gap))
            | 'Group by key' >> beam.GroupByKey()
            )

        output =3D sessions | 'Format output' >> beam.ParDo(FormatSessionOu=
tputDoFn())
        output | 'Write results' >> WriteToText(known_args.output)}}

where the input is a list of uncompressed JSON files in the same directory,=
 I get the same output whether I use the glob operator (*) for all the file=
s or I set the first file in that list.

When running the pipeline like this (for files of about 200M size each):
{{python sessions_manager.py --input ./input/test/* --output ./output/ --ru=
nner DirectRunner}}

The following shows up as process:
{{python sessions_manager.py --input ./input/test/xaa.json ./input/test/xab=
.json ./input/test/xac.json ./input/test/xad.json ./input/test/xae.json ./i=
nput/test/xaf.json ./input/test/xag --output ./output/ --runner DirectRunne=
r}}

And if I run just this:=20
{{python sessions_manager.py --input ./input/test/xaa.json --output ./outpu=
t/ --runner DirectRunner}}
The output is precisely the same as the one with the glob operator (and qui=
te different if I merge the files into one and run again the pipeline with =
the merged files into one).


> ReadFromText function is not taking all data with glob operator (*)=20
> --------------------------------------------------------------------
>
>                 Key: BEAM-2490
>                 URL: https://issues.apache.org/jira/browse/BEAM-2490
>             Project: Beam
>          Issue Type: Bug
>          Components: sdk-py
>    Affects Versions: 2.0.0
>         Environment: Usage with Google Cloud Platform: Dataflow runner
>            Reporter: Olivier NGUYEN QUOC
>            Assignee: Chamikara Jayalath
>             Fix For: 2.1.0
>
>
> I run a very simple pipeline:
> * Read my files from Google Cloud Storage
> * Split with '\n' char
> * Write in on a Google Cloud Storage
> I have 8 files that match with the pattern:
> * my_files_2016090116_20160902_060051_xxxxxxxxxx.csv.gz (229.25 MB)
> * my_files_2016090117_20160902_060051_xxxxxxxxxx.csv.gz (184.1 MB)
> * my_files_2016090118_20160902_060051_xxxxxxxxxx.csv.gz (171.73 MB)
> * my_files_2016090119_20160902_060051_xxxxxxxxxx.csv.gz (151.34 MB)
> * my_files_2016090120_20160902_060051_xxxxxxxxxx.csv.gz (129.69 MB)
> * my_files_2016090121_20160902_060051_xxxxxxxxxx.csv.gz (151.7 MB)
> * my_files_2016090122_20160902_060051_xxxxxxxxxx.csv.gz (346.46 MB)
> * my_files_2016090122_20160902_060051_xxxxxxxxxx.csv.gz (222.57 MB)
> This code should take them all:
> {code:python}
> beam.io.ReadFromText(
>       "gs://XXXX_folder1/my_files_20160901*.csv.gz",
>       skip_header_lines=3D1,
>       compression_type=3Dbeam.io.filesystem.CompressionTypes.GZIP
>       )
> {code}
> It runs well but there is only a 288.62 MB file in output of this pipelin=
e (instead of a 1.5 GB file).
> The whole pipeline code:
> {code:python}
> data =3D (p | 'ReadMyFiles' >> beam.io.ReadFromText(
>           "gs://XXXX_folder1/my_files_20160901*.csv.gz",
>           skip_header_lines=3D1,
>           compression_type=3Dbeam.io.filesystem.CompressionTypes.GZIP
>           )
>                        | 'SplitLines' >> beam.FlatMap(lambda x: x.split('=
\n'))
>                     )
> output =3D (
>           data| "Write" >> beam.io.WriteToText('gs://XXX_folder2/test.csv=
', num_shards=3D1)
>             )
> {code}
> Dataflow indicates me that the estimated size =09of the output after the =
ReadFromText step is 602.29 MB only, which not correspond to any unique inp=
ut file size nor the overall file size matching with the pattern.


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