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

["Chamikara Jayalath (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/BEAM-6064?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16739582#comment-16739582 ] 

Chamikara Jayalath commented on BEAM-6064:
------------------------------------------

Fastavro support for BigQuery for DataflowRunner is already available in the form of an experiment and will be made default in the future.

 

You can try running Dataflow pipelines with option "--experiment=use_fastavro" to enable this experiment. Please let me know if you run into any issues.

 

Closing this issue.

> 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: Chamikara Jayalath
>            Priority: Major
>         Attachments: results-java.png, results-python.png
>
>
> 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.
>  



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