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Posted to commits@beam.apache.org by "Kenneth Knowles (JIRA)" <ji...@apache.org> on 2016/03/15 05:33:33 UTC
[jira] [Updated] (BEAM-115) Beam Runner API
[ https://issues.apache.org/jira/browse/BEAM-115?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Kenneth Knowles updated BEAM-115:
---------------------------------
Description:
The PipelineRunner API from the SDK is not ideal for the Beam technical vision.
It has technical limitations:
- The user's DAG (even including library expansions) is never explicitly represented, so it cannot be analyzed except incrementally, and cannot necessarily be reconstructed (for example, to display it!).
- The flattened DAG of just primitive transforms isn't well-suited for display or transform override.
- The TransformHierarchy isn't well-suited for optimizations.
- The user must realistically pre-commit to a runner, and its configuration (batch vs streaming) prior to graph construction, since the runner will be modifying the graph as it is built.
- It is fairly language- and SDK-specific.
It has usability issues (these are not from intuition, but derived from actual cases of failure to use as design)
- The interleaving of apply() methods in PTransform/Pipeline/PipelineRunner is confusing.
- The TransformHierarchy, accessible only via visitor traversals, is cumbersome.
- The staging of construction-time vs run-time is not always obvious.
These are just examples. This ticket tracks designing, coming to consensus, and building an API that more simply and directly supports the technical vision.
was:
The PipelineRunner API from the SDK is not ideal for the Beam technical vision.
It has technical limitations:
- The user's DAG (even including library expansions) is never explicitly represented, so it cannot be analyzed except incrementally, and cannot necessarily be reconstructed (for example, to display it!).
- The flattened DAG of just primitive transforms isn't well-suited for display or transform override.
- The TransformHierarchy isn't well-suited for optimizations.
- The user must realistically pre-commit to a runner, and its configuration (batch vs streaming) prior to graph construction, since the runner will be modifying the graph as it is built.
- It is fairly language- and SDK-specific.
It has usability issues:
- The interleaving of apply() methods in PTransform/Pipeline/PipelineRunner is confusing.
- The TransformHierarchy, accessible only via visitor traversals, is cumbersome.
- The staging of construction-time vs run-time is not always obvious.
These are just examples. This ticket tracks designing, coming to consensus, and building an API that more simply and directly supports the technical vision.
> Beam Runner API
> ---------------
>
> Key: BEAM-115
> URL: https://issues.apache.org/jira/browse/BEAM-115
> Project: Beam
> Issue Type: Improvement
> Components: runner-core
> Reporter: Kenneth Knowles
> Assignee: Kenneth Knowles
>
> The PipelineRunner API from the SDK is not ideal for the Beam technical vision.
> It has technical limitations:
> - The user's DAG (even including library expansions) is never explicitly represented, so it cannot be analyzed except incrementally, and cannot necessarily be reconstructed (for example, to display it!).
> - The flattened DAG of just primitive transforms isn't well-suited for display or transform override.
> - The TransformHierarchy isn't well-suited for optimizations.
> - The user must realistically pre-commit to a runner, and its configuration (batch vs streaming) prior to graph construction, since the runner will be modifying the graph as it is built.
> - It is fairly language- and SDK-specific.
> It has usability issues (these are not from intuition, but derived from actual cases of failure to use as design)
> - The interleaving of apply() methods in PTransform/Pipeline/PipelineRunner is confusing.
> - The TransformHierarchy, accessible only via visitor traversals, is cumbersome.
> - The staging of construction-time vs run-time is not always obvious.
> These are just examples. This ticket tracks designing, coming to consensus, and building an API that more simply and directly supports the technical vision.
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