[jira] [Work logged] (BEAM-6064) Python BigQuery performance much worse than Java

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/BEAM-6064?focusedWorklogId=467566&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-467566 ]

ASF GitHub Bot logged work on BEAM-6064:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 06/Aug/20 21:01
            Start Date: 06/Aug/20 21:01
    Worklog Time Spent: 10m 
      Work Description: pabloem commented on pull request #12485:
URL: https://github.com/apache/beam/pull/12485#issuecomment-670189538


   Run Python 3.8 PostCommit


----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

For queries about this service, please contact Infrastructure at:
users@infra.apache.org


Issue Time Tracking
-------------------

    Worklog Id:     (was: 467566)
    Time Spent: 20m  (was: 10m)

> Python BigQuery performance much worse than Java
> ------------------------------------------------
>
>                 Key: BEAM-6064
>                 URL: https://issues.apache.org/jira/browse/BEAM-6064
>             Project: Beam
>          Issue Type: Bug
>          Components: sdk-py-core
>    Affects Versions: 2.8.0
>            Reporter: Jan Kuipers
>            Assignee: Pablo Estrada
>            Priority: P2
>         Attachments: Screenshot from 2019-02-01 10-10-45.png, results-java.png, results-python.png
>
>          Time Spent: 20m
>  Remaining Estimate: 0h
>
> The performance of reading from BigQuery in Python seems to be much worse than the performance of it in Java.
> To reproduce this, I've run the following two programs on the Google Cloud, which basically read the weights from the public data set "natality" and outputs the top 100 largest weights.
> Python:
> {code:java}
> # <cut imports>
> options = PipelineOptions()
> options.view_as(StandardOptions).runner = 'DataflowRunner'
> # <cut more options>
> pipeline = Pipeline(options=options)
> (pipeline
>     | 'Read' >> beam.io.Read(beam.io.BigQuerySource(query='SELECT weight_pounds FROM [bigquery-public-data:samples.natality]'))
>     | 'MapToFloat' >> beam.Map(lambda elem: elem['weight_pounds'])
>     | 'Top' >> beam.combiners.Top.Largest(100)
>     | 'MapToString' >> beam.Map(lambda elem: str(elem))
>     | 'Write' >> beam.io.WriteToText("<output-file>"))
> pipeline.run()
> {code}
>  Java:
> {code:java}
> // <cut imports>
> public class Natality {
>     public static void main(String[] args) {
>         DataflowPipelineOptions options = PipelineOptionsFactory.create().as(DataflowPipelineOptions.class);
>         options.setRunner(DataflowRunner.class);
>         // <cut more options>
>         
>         Pipeline pipeline = Pipeline.create(options);
>         pipeline.apply("Read", BigQueryIO.readTableRows()
>             .fromQuery("SELECT weight_pounds FROM [bigquery-public-data:samples.natality]"))
>             .apply("MapToDouble", MapElements
>                 .into(TypeDescriptors.doubles())
>                 .via(row -> {
>                      Object obj = row.get("weight_pounds");
>                      return (obj == null ? 0.0 : (Double) obj);
>                 }))
>             .apply("Top", Top.largest(100))
>             .apply("MapToString", MapElements
>                 .into(TypeDescriptors.strings())
>                 .via(weight -> weight.toString()))
>             .apply("Write", TextIO.write().to("<output-file>"));
>         pipeline.run().waitUntilFinish();
>     }
> }
> {code}
> The "<cut more options>" are basic options like project, job name, temp location, etc. Both programs produce identical outputs.
> Running these programs launches a DataFlow job on the Google Cloud with the following results (data from the Google Cloud Platform web interface; screenshots attached).
> Python:
> {noformat}
> Read Succeeded 1 hr 40 min 40 sec
> MapToFloat Succeeded 2 min 43 sec
> Top Succeeded 5 min 25 sec
> MapToString Succeeded 0 sec
> Write Succeeded 3 sec{noformat}
> Java:
> {noformat}
> Read Succeeded 4 min 45 sec
> MapToDouble Succeeded 45 sec
> Top Succeeded 52 sec
> MapToString Succeeded 0 sec
> Write Succeeded 1 sec
> {noformat}
> As you can see, there is an enormous performance hit in Python w.r.t. the reading from BigQuery: 1h40m vs less than 5 minutes.
> Furthermore the other standard operations (like Top) are also much slower in Python than in Java.
>  



--
This message was sent by Atlassian Jira
(v8.3.4#803005)