This document explains the mechanics of performing a rowset flush/compaction. For details explaining how compactions are selected, see compaction-policy.md. NOTE: this does not describe anything about flushing delta stores to delta files!
Goal: Take two or more RowSets with overlapping key ranges, and merge them into a new RowSet, while updates are concurrently being applied. The output RowSet should also garbage collect (i.e reclaim storage from) any rows which were deleted in the old RowSets.
Let's start with the simple example of compacting from 1 input rowset to 1 output rowset. This has the effect of removing GC-able data and applying updates. The compaction has two main phases:
"flush_snap" | | before v <----------| Phase 1: merging/flushing |-----------| Phase 2: migrate deltas |---------------| compaction complete |-----------> |-------------- time ----------------------------->
System steady state:
Transition into Phase 1:
Phase 1: merge/flush data:
Use the iterator created above to create a new set of data for the output RowSet. This will reflect any updates or deletes which arrived prior to the start of phase 1, but no updates or deletes which arrive during either phase of the compaction.
Any mutations which arrive during this phase are applied only to the input RowSets' delta tracking structures. Because the merge operates on a snapshot, it will not take these into account in the output RowSet.
Phase 2: migrate deltas from phase 1
Any mutations which arrive during this phase should be applied to both the input RowSet and the output RowSet. This is simple to do by duplicating the key lookup into the output RowSet's key column when the update arrives. This is implemented by swapping in a “DuplicatingRowSet” implementation which forwards updates to both the input and output rowsets.
Any reads during this phase must be served from the input RowSet, since the output RowSet is missing the deltas which arrived during the merge phase.
Because the merge output ignored any mutations which arrived during phase 1, we must now ‘migrate’ those mutations to the output RowSet. This can be done efficiently by collecting all of the deltas which were not included in the snapshot iterator, and applying them to the output rowset's delta tracker.
End of Phase 2: swap RowSets
The above algorithm can be extended to multiple RowSets equally well. At the beginning of the compaction, each RowSet is snapshotted, and a snapshot iterator created. A merge iterator then performs the merge of all of the snapshots in ascending key order.