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https://issues.apache.org/jira/browse/BEAM-8470?focusedWorklogId=349856&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-349856
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ASF GitHub Bot logged work on BEAM-8470:
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Author: ASF GitHub Bot
Created on: 26/Nov/19 15:48
Start Date: 26/Nov/19 15:48
Worklog Time Spent: 10m
Work Description: aromanenko-dev commented on issue #10221: [BEAM-8470]
Exclude failed ValidatesRunner tests
URL: https://github.com/apache/beam/pull/10221#issuecomment-558690828
> IMHO, I would prefer to keep it red until they all pass because it is the
essence of the tests.
We have disabled a bunch of other tests (that are failing) as well. Do we
need to enable them in this case?
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Issue Time Tracking
-------------------
Worklog Id: (was: 349856)
Time Spent: 15h (was: 14h 50m)
> 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: 15h
> 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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