Samuel Klock created CASSANDRA-14415:
----------------------------------------
Summary: Performance regression in queries for distinct keys
Key: CASSANDRA-14415
URL: https://issues.apache.org/jira/browse/CASSANDRA-14415
Project: Cassandra
Issue Type: Improvement
Reporter: Samuel Klock
Assignee: Samuel Klock
Running Cassandra 3.0.16, we observed a major performance regression affecting
\{{SELECT DISTINCT keys}}-style queries against certain tables. Based on some
investigation (guided by some helpful feedback from Benjamin on the dev list),
we tracked the regression down to two problems.
* One is that Cassandra was reading more data from disk than was necessary to
satisfy the query. This was fixed under CASSANDRA-10657 in a later 3.x release.
* If the fix for CASSANDRA-10657 is incorporated, the other is this code
snippet in \{{RebufferingInputStream}}:
{code:java}
@Override
public int skipBytes(int n) throws IOException
{
if (n < 0)
return 0;
int requested = n;
int position = buffer.position(), limit = buffer.limit(), remaining;
while ((remaining = limit - position) < n)
{
n -= remaining;
buffer.position(limit);
reBuffer();
position = buffer.position();
limit = buffer.limit();
if (position == limit)
return requested - n;
}
buffer.position(position + n);
return requested;
}
{code}
The gist of it is that to skip bytes, the stream needs to read those bytes into
memory then throw them away. In our tests, we were spending a lot of time in
this method, so it looked like the chief drag on performance.
We noticed that the subclass of \{{RebufferingInputStream}} in use for our
queries, \{{RandomAccessReader}} (over compressed sstables), implements a
\{{seek()}} method. Overriding \{{skipBytes()}} in it to use \{{seek()}}
instead was sufficient to fix the performance regression.
The performance difference is significant for tables with large values. It's
straightforward to evaluate with very simple key-value tables, e.g.:
{\{CREATE TABLE testtable (key TEXT PRIMARY KEY, value BLOB);}}
We did some basic experimentation with the following variations (all in a
single-node 3.11.2 cluster with off-the-shelf settings running on a dev
workstation):
* small values (1 KB, 100,000 entries), somewhat larger values (25 KB, 10,000
entries), and much larger values (1 MB, 10,000 entries);
* compressible data (a single byte repeated) and uncompressible data (output
from \{{openssl rand $bytes}}); and
* with and without sstable compression. (With compression, we use Cassandra's
defaults.)
The difference is most conspicuous for tables with large, uncompressible data
and sstable decompression (which happens to describe the use case that
triggered our investigation). It is smaller but still readily apparent for
tables with effective compression. For uncompressible data without compression
enabled, there is no appreciable difference.
Here's what the performance looks like without our patch for the 1-MB entries
(times in seconds, five consecutive runs for each data set, all exhausting the
results from a \{{SELECT DISTINCT key FROM ...}} query with a page size of 24):
{noformat}
working on compressible
5.21180510521
5.10270500183
5.22311806679
4.6732840538
4.84219098091
working on uncompressible_uncompressed
55.0423607826
0.769015073776
0.850513935089
0.713396072388
0.62596988678
working on uncompressible
413.292617083
231.345913887
449.524993896
425.135111094
243.469946861
{noformat}
and without the fix:
{noformat}
working on compressible
2.86733293533
1.24895811081
1.108907938
1.12742400169
1.04647302628
working on uncompressible_uncompressed
56.4146180153
0.895509958267
0.922824144363
0.772884130478
0.731923818588
working on uncompressible
64.4587619305
1.81325793266
1.52577018738
1.41769099236
1.60442209244
{noformat}
The long initial runs for the uncompressible data presumably come from
repeatedly hitting the disk. In contrast to the runs without the fix, the
initial runs seem to be effective at warming the page cache (as lots of data is
skipped, so the data that's read can fit in memory), so subsequent runs are
faster.
For smaller data sets, \{{RandomAccessReader.seek()}} and
\{{RebufferingInputStream.skipBytes()}} are approximately equivalent in their
behavior (reducing to changing the position pointer of an in-memory buffer most
of the time), so there isn't much difference. Here's before the fix for the
1-KB entries:
{noformat}
working on small_compressible
8.34115099907
8.57280993462
8.3534219265
8.55130696297
8.17362189293
working on small_uncompressible_uncompressed
7.85155582428
7.54075288773
7.50106596947
7.39202189445
7.95735621452
working on small_uncompressible
7.89256501198
7.88875198364
7.9013261795
7.76551413536
7.84927678108
{noformat}
and after:
{noformat}
working on small_compressible
8.29225707054
7.57822394371
8.10092878342
8.21332192421
8.19347810745
working on small_uncompressible_uncompressed
7.74823594093
7.81218004227
7.68660092354
7.95432114601
7.77612304688
working on small_uncompressible
8.18260502815
8.21010804176
8.1233921051
7.31543707848
7.91079998016
{noformat}
The effect is similar for the 25-KB entries, which might enjoy a slight
performance benefit from the patch (perhaps because they're larger than the
default buffer size defined in \{{RandomAccessReader}}). Before:
{noformat}
working on medium_compressible
0.988080978394
1.02464294434
0.977658033371
1.02553391457
0.769363880157
working on medium_uncompressible_uncompressed
1.07718396187
1.08547902107
1.12398791313
1.10300898552
1.08757281303
working on medium_uncompressible
0.940990209579
0.917474985123
0.768013954163
0.871683835983
0.814841985703
{noformat}
and after:
{noformat}
working on medium_compressible
0.829009056091
0.705173015594
0.603646993637
0.820069074631
0.873830080032
working on medium_uncompressible_uncompressed
0.785156965256
0.808106184006
0.848286151886
0.857885837555
0.825689077377
working on medium_uncompressible
0.845101118088
0.913790941238
0.824147939682
0.849114894867
0.85981798172
{noformat}
In short, this looks like a pretty straightforward performance win with
negligible cost. (It's worth noting that for our use case, disabling sstable
compression is clearly the _best_ solution, but there's still reasonably clear
benefit from this minor fix for data sets with larger, compressible values, as
well as presumably data sets with a mix of compressible and uncompressible
values in environments where storage is limited.)
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