Offers new ways to compute bulk import load plans. #4933
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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.
1. 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.
2. 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.
1. 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.
2. 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.
BulkNewIT.testComputeLoadPlan() simulates this map reduce use case by
going through the steps in code that a map reduce job would. This tests
the new APIs and shows what using it would look like.
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.
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This PR squashed the changes from #4898 into 3.1 and resolved conflicts. |
keith-turner
commented
Sep 30, 2024
| @@ -92,12 +93,17 @@ public class RFileWriter implements AutoCloseable { | |||
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| private final FileSKVWriter writer; | |||
| private final LRUMap<ByteSequence,Boolean> validVisibilities; | |||
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| // TODO should be able to completely remove this as lower level code is already doing some things | |||
There was a problem hiding this comment.
I will remove this todo and open an issue before this PR is merged.
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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.