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https://issues.apache.org/jira/browse/CASSANDRA-13038?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15858245#comment-15858245
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Jeff Jirsa commented on CASSANDRA-13038:
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[~slebresne] - my main objection is that I know people (not me) are using 5
minute TWCS windows, and presumably, they're doing so because they need to
expire in 5 minute chunks. Rounding up to 1 hour TTL histograms would eliminate
that use case, and it's not a hypothetical use case. There is a lower bound
where it becomes much less useful (maybe it's 5 minutes, or 1 minute), but it's
not 1 hour.
In any case, I'll take the ticket. I had implemented one method using a larger
temporary spool of bins that we merge/compact down into the other set on use,
and it's about 3-5x faster without further loss of resolution. I'll extend that
to add 60 second rounding and see what that does - I suspect it'll be
significant, especially in the cases where we're dealing with a day (or week,
or month) worth of data inserted with a default TTL. Depending on how that
looks, we can decide if 60s or 300s is the right rounding point.
> 33% of compaction time spent in StreamingHistogram.update()
> -----------------------------------------------------------
>
> Key: CASSANDRA-13038
> URL: https://issues.apache.org/jira/browse/CASSANDRA-13038
> Project: Cassandra
> Issue Type: Bug
> Components: Compaction
> Reporter: Corentin Chary
> Assignee: Jeff Jirsa
> Attachments: compaction-speedup.patch,
> compaction-streaminghistrogram.png, profiler-snapshot.nps
>
>
> With the following table, that contains a *lot* of cells:
> {code}
> CREATE TABLE biggraphite.datapoints_11520p_60s (
> metric uuid,
> time_start_ms bigint,
> offset smallint,
> count int,
> value double,
> PRIMARY KEY ((metric, time_start_ms), offset)
> ) WITH CLUSTERING ORDER BY (offset DESC);
> AND compaction = {'class':
> 'org.apache.cassandra.db.compaction.TimeWindowCompactionStrategy',
> 'compaction_window_size': '6', 'compaction_window_unit': 'HOURS',
> 'max_threshold': '32', 'min_threshold': '6'}
> Keyspace : biggraphite
> Read Count: 1822
> Read Latency: 1.8870054884742042 ms.
> Write Count: 2212271647
> Write Latency: 0.027705127678653473 ms.
> Pending Flushes: 0
> Table: datapoints_11520p_60s
> SSTable count: 47
> Space used (live): 300417555945
> Space used (total): 303147395017
> Space used by snapshots (total): 0
> Off heap memory used (total): 207453042
> SSTable Compression Ratio: 0.4955200053039823
> Number of keys (estimate): 16343723
> Memtable cell count: 220576
> Memtable data size: 17115128
> Memtable off heap memory used: 0
> Memtable switch count: 2872
> Local read count: 0
> Local read latency: NaN ms
> Local write count: 1103167888
> Local write latency: 0.025 ms
> Pending flushes: 0
> Percent repaired: 0.0
> Bloom filter false positives: 0
> Bloom filter false ratio: 0.00000
> Bloom filter space used: 105118296
> Bloom filter off heap memory used: 106547192
> Index summary off heap memory used: 27730962
> Compression metadata off heap memory used: 73174888
> Compacted partition minimum bytes: 61
> Compacted partition maximum bytes: 51012
> Compacted partition mean bytes: 7899
> Average live cells per slice (last five minutes): NaN
> Maximum live cells per slice (last five minutes): 0
> Average tombstones per slice (last five minutes): NaN
> Maximum tombstones per slice (last five minutes): 0
> Dropped Mutations: 0
> {code}
> It looks like a good chunk of the compaction time is lost in
> StreamingHistogram.update() (which is used to store the estimated tombstone
> drop times).
> This could be caused by a huge number of different deletion times which would
> makes the bin huge but it this histogram should be capped to 100 keys. It's
> more likely caused by the huge number of cells.
> A simple solutions could be to only take into accounts part of the cells, the
> fact the this table has a TWCS also gives us an additional hint that sampling
> deletion times would be fine.
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