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From "Tupshin Harper (JIRA)" <j...@apache.org>
Subject [jira] [Created] (CASSANDRA-6602) Enhancements to optimize for the storing of time series data
Date Fri, 17 Jan 2014 17:34:19 GMT
Tupshin Harper created CASSANDRA-6602:
-----------------------------------------

             Summary: Enhancements to optimize for the storing of time series data
                 Key: CASSANDRA-6602
                 URL: https://issues.apache.org/jira/browse/CASSANDRA-6602
             Project: Cassandra
          Issue Type: New Feature
          Components: Core
            Reporter: Tupshin Harper
             Fix For: 3.0


There are some unique characteristics of many/most time series use cases that both provide
challenges, as well as provide unique opportunities for optimizations.

One of the major challenges is in compaction. The existing compaction strategies will tend
to re-compact data on disk at least a few times over the lifespan of each data point, greatly
increasing the cpu and IO costs of that write.

Compaction exists to
1) ensure that there aren't too many files on disk
2) ensure that data that should be contiguous (part of the same partition) is laid out contiguously
3) deleting data due to ttls or tombstones

The special characteristics of time series data allow us to optimize away all three.

Time series data
1) tends to be delivered in time order, with relatively constrained exceptions
2) often has a pre-determined and fixed expiration date
3) Never gets deleted prior to TTL
4) Has relatively predictable ingestion rates

Note that I filed CASSANDRA-5561 and this ticket potentially replaces or lowers the need for
it. In that ticket, jbellis reasonably asks, how that compaction strategy is better than disabling
compaction.

Taking that to heart, here is a compaction-strategy-less approach that could be extremely
efficient for time-series use cases that follow the above pattern.

(For context, I'm thinking of an example use case involving lots of streams of time-series
data with a 5GB per day ingestion rate, and a 1000 day retention with TTL, resulting in an
eventual steady state of 5TB per node)

1) You have an extremely large memtable (preferably off heap, if/when doable) for the table,
and that memtable is sized to be able to hold a lengthy window of time. A typical period might
be one day. At the end of that period, you flush the contents of the memtable to an sstable
and move to the next one. This is basically identical to current behaviour, but with thresholds
adjusted so that you can ensure flushing at predictable intervals. (Open question is whether
predictable intervals is actually necessary, or whether just waiting until the huge memtable
is nearly full is sufficient)
2) Combine the behaviour with CASSANDRA-5228 so that sstables will be efficiently dropped
once all of the columns have. (Another side note, it might be valuable to have a modified
version of CASSANDRA-3974 that doesn't bother storing per-column TTL since it is required
that all columns have the same TTL)
3) Be able to mark column families as read/write only (no explicit deletes), so no tombstones.
4) Optionally add back an additional type of delete that would delete all data earlier than
a particular timestamp, resulting in immediate dropping of obsoleted sstables.

The result is that for in-order delivered data, Every cell will be laid out optimally on disk
on the first pass, and over the course of 1000 days and 5TB of data, there will "only" be
1000 5GB sstables, so the number of filehandles will be reasonable.

For exceptions (out-of-order delivery), most cases will be caught by the extended (24 hour+)
memtable flush times and merged correctly automatically. For those that were slightly askew
at flush time, or were delivered so far out of order that they go in the wrong sstable, there
is relatively low overhead to reading from two sstables for a time slice, instead of one,
and that overhead would be incurred relatively rarely unless out-of-order delivery was the
common case, in which case, this strategy should not be used.

Another possible optimization to address out-of-order would be to maintain more than one time-centric
memtables in memory at a time (e.g. two 12 hour ones), and then you always insert into whichever
one of the two "owns" the appropriate range of time. By delaying flushing the ahead one until
we are ready to roll writes over to a third one, we are able to avoid any fragmentation as
long as all deliveries come in no more than 12 hours late (in this example, presumably tunable).

Anything that triggers compactions will have to be looked at, since there won't be any. The
one concern I have is the ramificaiton of repair. Initially, at least, I think it would be
acceptable to just write one sstable per repair and not bother trying to merge it with other
sstables.



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