Details
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Improvement
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Status: Resolved
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Normal
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Resolution: Fixed
Description
I'm using the latest trunk (as of August 2016, which probably is going to be 3.10) to run some experiments on LeveledCompactionStrategy and noticed this inefficiency.
The test data is generated using cassandra-stress default parameters (keyspace1.standard1), so as you can imagine, it consists of a ton of newly inserted partitions that will never merge in compactions, which is probably the worst kind of workload for LCS (however, I'll detail later why this scenario should not be ignored as a corner case; for now, let's just assume we still want to handle this scenario efficiently).
After the compaction test is done, I scrubbed debug.log for patterns that match the "Compacted" summary so that I can see how long each individual compaction took and how many bytes they processed. The search pattern is like the following:
grep 'Compacted.*standard1' debug.log
Interestingly, I noticed a lot of the finished compactions are marked as having only one SSTable involved. With the workload mentioned above, the "single SSTable" compactions actually consist of the majority of all compactions (as shown below), so its efficiency can affect the overall compaction throughput quite a bit.
automaton@0ce59d338-1:~/cassandra-trunk/logs$ grep 'Compacted.*standard1' debug.log-test1 | wc -l 243 automaton@0ce59d338-1:~/cassandra-trunk/logs$ grep 'Compacted.*standard1' debug.log-test1 | grep ") 1 sstable" | wc -l 218
By looking at the code, it appears that there's a way to directly edit the level of a particular SSTable like the following:
sstable.descriptor.getMetadataSerializer().mutateLevel(sstable.descriptor, targetLevel); sstable.reloadSSTableMetadata();
To be exact, I summed up the time spent for these single-SSTable compactions (the total data size is 60GB) and found that if each compaction only needs to spend 100ms for only the metadata change (instead of the 10+ second they're doing now), it can already achieve 22.75% saving on total compaction time.
Compared to what we have now (reading the whole single-SSTable from old level and writing out the same single-SSTable at the new level), the only difference I could think of by using this approach is that the new SSTable will have the same file name (sequence number) as the old one's, which could break some assumptions on some other part of the code. However, not having to go through the full read/write IO, and not having to bear the overhead of cleaning up the old file, creating the new file, creating more churns in heap and file buffer, it seems the benefits outweigh the inconvenience. So I'd argue this JIRA belongs to LHF and should be made available in 3.0.x as well.
As mentioned in the 2nd paragraph, I'm also going to address why this kind of all-new-partition workload should not be ignored as a corner case. Basically, for the main use case of LCS where you need to frequently merge partitions to optimize read and eliminate tombstones and expired data sooner, LCS can be perfectly happy and efficiently perform the partition merge and tombstone elimination for a long time. However, as soon as the node becomes a bit unhealthy for various reasons (could be a bad disk so it's missing a whole bunch of mutations and need repair, could be the user chooses to ingest way more data than it usually takes and exceeds its capability, or god-forbidden, some DBA chooses to run offline sstablelevelreset), you will have to handle this kind of "all-new-partition with a lot of SSTables in L0" scenario, and once all L0 SSTables finally gets up-leveled to L1, you will likely see a lot of such single-SSTable compactions, which is the situation this JIRA is intended to address.
Actually, when I think more about this, to make this kind of single SSTable up-level more efficient will not only help the all-new-partition scenario, but also help in general any time when there is a big backlog of L0 SSTables due to too many flushes or excessive repair streaming with vnode. In those situations, by default STCS_in_L0 will be triggered, and you will end up getting a bunch of much bigger L0 SSTables after STCS is done. When it's time to up-level those much bigger L0 SSTables most likely they will overlap among themselves and you will add them all into your compaction session (along with all overlapped L1 SSTables). For these much bigger L0 SSTables, they have gone through a few rounds of STCS compactions, so if there's partition merge that needs to be done because fragments of the same partition are dispersed in smaller L0 SSTables earlier, after those STCS rounds, what you end up having in those much bigger L0 SSTables (generated by STCS) will not have much more opportunity for partition merge to happen, so we're in a scenario very similar to L0 data "consists of a ton of newly inserted partitions that will never merge in compactions" mentioned earlier.
Attachments
Issue Links
- depends upon
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CASSANDRA-14388 Fix setting min/max compaction threshold with LCS
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- Resolved
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