Joey Lynch created CASSANDRA-17381:
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Summary: Produce and verify BoundedReadCompactionStrategy as a
unified general purpose compaction algorithm
Key: CASSANDRA-17381
URL: https://issues.apache.org/jira/browse/CASSANDRA-17381
Project: Cassandra
Issue Type: Improvement
Components: Local/Compaction
Reporter: Joey Lynch
Assignee: Joey Lynch
The existing compaction strategies have a number of drawbacks that make all
three unsuitable as a general use compaction strategy, for example STCS creates
giant files that are hard to back up, mess with the page cache, and led to many
of the early re-open bugs. LCS improved dramatically on this but also has
various issues e.g. lack of performant full compaction or due to the strict
leveling with e.g. bulk loading when writes exceed the rate we can do the L0 -
L1 promotion.
In this
[talk|https://github.com/ngcc/ngcc2019/blob/master/NextGenerationCassandraCompactionGoingBeyondLCS.pdf]
I introduced a novel compaction strategy that aims to expose a single tunable
that the user can control for the read amplification. Raise the
max_read_per_read and you tradeoff read/space performance for write
performance. Since then a proof of concept [patch
|https://github.com/jolynch/cassandra/tree/jolynch_bounded_read_final]has been
published along with some rudimentary [documentation
|https://gist.github.com/jolynch/9118465f32ad5298b4e39d03ccd4370e] but this is
still not tracked in Jira.
The remaining work here is
1. Validate the algorithm is correct via test suites and performance testing
stress testing and benchmarking with OSS tools (e.g. cassandra-stress,
[tlp-stress|https://github.com/thelastpickle/tlp-stress], or
[ndbench|https://github.com/Netflix/ndbench]). When issues are found (there
likely will be issues as the patch is a PoC), devise how to adjust the
algorithm and implementation appropriately. Key metric of success is we can run
Cassandra stably for more than 24 hours while applying sustained load, and
compaction can keep up.
2. Do more in depth experiments measuring performance across a wide range of
workloads (e.g. write heavy, read heavy, balanced, time series, register
update, etc ...) and in comparison with LCS (leveled), STCS (size tiered), and
TWCS (time window). Key metrics of success are establishing that as we tune
max_read_per_read we should get more predictable read latency under low system
load (ρ < 30%) while not degrading at high system load (ρ > 70%), and we should
match LCS performance under low load while doing better at high load.
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