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https://issues.apache.org/jira/browse/BEAM-8470?focusedWorklogId=340493&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-340493
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ASF GitHub Bot logged work on BEAM-8470:
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Author: ASF GitHub Bot
Created on: 08/Nov/19 13:42
Start Date: 08/Nov/19 13:42
Worklog Time Spent: 10m
Work Description: echauchot commented on issue #9866: [BEAM-8470] Create
a new Spark runner based on Spark Structured streaming framework
URL: https://github.com/apache/beam/pull/9866#issuecomment-551813147
@aromanenko-dev FYI it is normal the UTests are failing. I just figured that
the tests were not properly configured after the merge of the 2 modules (wrong
pipelineOptions used) + the changes on PipelineResult (no set of the testMode
to true in the pipelineOptions to wait for PAssert). As a consequence all
UTests passed no matter what. I fixed that with the last commit but
PipelineResults tests fail. @RyanSkraba who authored this part will take a look
at it when he has time.
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Issue Time Tracking
-------------------
Worklog Id: (was: 340493)
Time Spent: 6.5h (was: 6h 20m)
> Create a new Spark runner based on Spark Structured streaming framework
> -----------------------------------------------------------------------
>
> Key: BEAM-8470
> URL: https://issues.apache.org/jira/browse/BEAM-8470
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Reporter: Etienne Chauchot
> Assignee: Etienne Chauchot
> Priority: Major
> Time Spent: 6.5h
> Remaining Estimate: 0h
>
> h1. Why is it worth creating a new runner based on structured streaming:
> Because this new framework brings:
> * Unified batch and streaming semantics:
> * no more RDD/DStream distinction, as in Beam (only PCollection)
> * Better state management:
> * incremental state instead of saving all each time
> * No more synchronous saving delaying computation: per batch and partition
> delta file saved asynchronously + in-memory hashmap synchronous put/get
> * Schemas in datasets:
> * The dataset knows the structure of the data (fields) and can optimize
> later on
> * Schemas in PCollection in Beam
> * New Source API
> * Very close to Beam bounded source and unbounded sources
> h1. Why make a new runner from scratch?
> * Structured streaming framework is very different from the RDD/Dstream
> framework
> h1. We hope to gain
> * More up to date runner in terms of libraries: leverage new features
> * Leverage learnt practices from the previous runners
> * Better performance thanks to the DAG optimizer (catalyst) and by
> simplifying the code.
> * Simplify the code and ease the maintenance
>
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