[
https://issues.apache.org/jira/browse/CASSANDRA-12526?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Deng updated CASSANDRA-12526:
---------------------------------
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:
{noformat}
grep 'Compacted.*standard1' debug.log
{noformat}
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.
{noformat}
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
{noformat}
By looking at the code, it appears that there's a way to directly edit the
level of a particular SSTable like the following:
{code}
sstable.descriptor.getMetadataSerializer().mutateLevel(sstable.descriptor,
targetLevel);
sstable.reloadSSTableMetadata();
{code}
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.
was:
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:
{noformat}
grep 'Compacted.*standard1' debug.log
{noformat}
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.
{noformat}
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
{noformat}
By looking at the code, it appears that there's a way to directly edit the
level of a particular SSTable like the following:
{code}
sstable.descriptor.getMetadataSerializer().mutateLevel(sstable.descriptor,
targetLevel);
sstable.reloadSSTableMetadata();
{code}
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.
> For LCS, single SSTable up-level is handled inefficiently
> ---------------------------------------------------------
>
> Key: CASSANDRA-12526
> URL: https://issues.apache.org/jira/browse/CASSANDRA-12526
> Project: Cassandra
> Issue Type: Bug
> Components: Compaction
> Reporter: Wei Deng
> Labels: compaction, lcs, performance
>
> 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:
> {noformat}
> grep 'Compacted.*standard1' debug.log
> {noformat}
> 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.
> {noformat}
> 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
> {noformat}
> By looking at the code, it appears that there's a way to directly edit the
> level of a particular SSTable like the following:
> {code}
> sstable.descriptor.getMetadataSerializer().mutateLevel(sstable.descriptor,
> targetLevel);
> sstable.reloadSSTableMetadata();
> {code}
> 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.
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