Offers new ways of computing bulk load plans #4898
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keith-turner
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Sep 18, 2024
ddanielr
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Sep 30, 2024
core/src/main/java/org/apache/accumulo/core/client/rfile/RFileWriter.java
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ddanielr
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core/src/test/java/org/apache/accumulo/core/data/LoadPlanTest.java
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core/src/test/java/org/apache/accumulo/core/data/LoadPlanTest.java
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Two new ways of computing bulk import load plans are offered in these changes. First the RFile API was modified to support computing a LoadPlan as the RFile is written. Second a new LoadPlan.compute() method was added that creates a LoadPlan from an existing RFile. In addition to these changes methods were added to LoadPlan that support serializing and deserializing load plans to/from json. All of these changes together support the use case of computing load plans in a distributed manner. For example, with a bulk import directory with N files the following use case is now supported. For eack file a task is spun up on a remote server that calls the new LoadPlan.compute() API to determine what tablets the file overlaps. Then the new LoadPlan.toJson() method is called to serialize the load plan and send it to a central place. All the load plans from the remote servers are deserialized calling the new LoadPlan.fromJson() method and merged into a single load plan that is used to do the bulk import. Another use case these new APIs could support is running this new code in the map reduce job that generates bulk import data. In each reducer as it writes to an rfile it could also be building a LoadPlan. A load plan can be obtained from the Rfile after closing it and serialized using LoadPlan.toJson() and the result saved to a file. So after the map reduce job completes each rfile would have corresponding file with a load plan for that file. Another process that runs after the map reduce job can load all the load plans from files and merge them using the new LoadPlan.fromJson() method. Then the merged LoadPlan can be used to do the bulk import. Both of these use cases avoid doing the analysis of files on a single machine doing the bulk import. Bulk import V1 had this functionality and would ask random tservers to do the file analysis. This could cause unexpected load on those tservers. Bulk V1 would interleave analyzing files and adding them to tablets. This could lead to odd situations where files are partially imported to some tablets and analysis fails, leaving the file partially imported. Bulk v2 does all analysis before any files are added to tablets, however it lacks this distributed analysis capability. These changes provide the building blocks to do the distributed analysis that bulk v1 did for bulk v2. Co-authored-by: Daniel Roberts <ddanielr@gmail.com>
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ctubbsii
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Jan 16, 2025
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Overall, looks good. I have minor quality suggestions. The main thing I'm concerned about is the semver breakage. I think it's probably okay in this case, but definitely should be called out as a semver violation in the release notes, in case anybody starts using this and wants to downgrade back to 2.1.3 for any reason.
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…WriterBuilder.java Co-authored-by: Christopher Tubbs <ctubbsii@apache.org>
…WriterBuilder.java Co-authored-by: Christopher Tubbs <ctubbsii@apache.org>
…Writer.java Co-authored-by: Christopher Tubbs <ctubbsii@apache.org>
Co-authored-by: Christopher Tubbs <ctubbsii@apache.org>
…s.java Co-authored-by: Christopher Tubbs <ctubbsii@apache.org>
Lowers the log level on a message from debug to trace to reduce logging spam. Adds a test case for LoadPlan.compute to ensure that the correct filesystem is being chosen for a given file URI.
ddanielr
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Jan 31, 2025
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Two new ways of computing bulk import load plans are offered in these change. First the RFile API was modified to support computing a LoadPlan as the RFile is written. Second a new LoadPlan.compute() method was added that creates a LoadPlan from an existing RFile. In addition to these changes methods were added to LoadPlan that support serializing and deserializing load plans to/from json.
All of these changes together support the use case of computing load plans in a distributed manner. For example, with a bulk import directory with N files the following use case is now supported.
Another use case these new APIs could support is running this new code in the map reduce job that generates bulk import data.
Both of these use cases avoid doing the analysis of files on a single machine doing the bulk import. Bulk import V1 had this functionality and would ask random tservers to do the file analysis. This could cause unexpected load on those tservers. Bulk V1 would interleave analyzing files and adding them to tablets. This could lead to odd situations where files are partially imported to some tablets and analysis fails, leaving the file partially imported. Bulk v2 does all analysis before any files are added to tablets, however it lacks this distributed analysis capability. These changes provide the building blocks to do the distributed analysis that bulk v1 did for bulk v2.