Improve interaction between AAE and K/V deletion #477
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This change ensures that AAE does not attempt to continually repair deleted keys that an AAE hashtree believes still exist.
AAE already buffers writes and submits them as a single write-batch to LevelDB for better performance. Since deletes are also valid batch operations, this change buffers deletes alongside writes and submits all operations as a single write-batch as necessary. This change also fixes a potential bug in the previous approach that could reorder a write to happen after a delete, even if the delete occured after the write -- thus reviving a dead hash.
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Meant to create pull-request against 1.3 branch. Closing this one out. New pull-request: #478 |
| Dirty = bitarray_set(Segment, State3#state.dirty_segments), | ||
| State3#state{dirty_segments=Dirty}; | ||
| State2 = enqueue_action({put, HKey, ObjHash}, State), | ||
| %% Dirty = gb_sets:add_element(Segment, State2#state.dirty_segments), |
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This pull-request improves AAE's handling around object deletion.
First, the pull-request changes the rehash logic in
riak_kv_vnodeto delete AAE hashes if an object is found to be missing. Thus, if a K/V delete fails to update the relevant AAE hashtree, the hashtree will be fixed on the first repair. Without this change, AAE could continually attempt to repair keys that have been deleted without informing the relevant AAE hashtree. This is scenario is hard to trigger in practice, but it's better to be safe as the fix is easy and straightforward.Second, this pull-request changes the AAE hashtrees to buffer deletes using the same logic used for buffering writes. Thus, both deletes and writes are buffered and submitted to LevelDB as a single batch operation. As previously shown, write-batching is necessary to make AAE have low impact on the cluster, thus this change is also important for users that frequently delete data.