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From "Tupshin Harper (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (CASSANDRA-6602) Enhancements to optimize for the storing of time series data
Date Fri, 17 Jan 2014 22:18:19 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-6602?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13875314#comment-13875314
] 

Tupshin Harper edited comment on CASSANDRA-6602 at 1/17/14 10:17 PM:
---------------------------------------------------------------------

The reason why I'd like to continue pursuing the path outlined in this ticket, rather than
a simpler approach is that I feel like the enhancements in this ticket don't just improve
performance for this kind of workload, but actually result in demonstrably optimal behaviour,
in that only one write will always result in only one write to disk (not counting commitlog)
both immediately (when the sstable is first written) as well as over the lifespan of the data
(which is what I want to be optimizing for, namely total write count per datum), while also
putting a predictable cap  on the number of sstables. Any compaction should be avoided when
you are trying to maximize time-series ingestion over time, so even.

Also, one thing that I really like about this approach is that it is totally oblivious to
your data, and only pays attention to the time stamps. When working out various ways for optimizing
time-series write behaviour, I had been stumped about how to make it truly data-agnostic and
unaware of the structure of the data. Your suggestion is the simplest possible data-aware
approach, but it still assumes that the rows are partitioned in time order, which is likely
the case, but does slightly constrain your data model, and also is more complex when talking
about composite structures. With timestamp-based sstable partitioning, we separate the schema
from the persistence structure more cleanly, imo.




was (Author: tupshin):
The reason why I'd like to continue pursuing the path outlined in this ticket, rather than
a simpler approach is that I feel like the enhancements in this ticket don't just improve
performance for this kind of workload, but actually result in demonstrably optimal behaviour,
in that only one write will always result in only one write to disk (not counting commitlog)
both immediately (when the sstable is first written) as well as over the lifespan of the data
(which is what I want to be optimizing for, namely total write count per datum), while also
putting a predictable cap  on the number of sstables. Any compaction should be avoided when
you are trying to maximize time-series ingestion over time, so even.

Also, one thing that I really like about this approach is that it is totally oblivious to
your data, and only pays attention to the time stamps. When working out various ways for optimizing
time-series write behaviour, I had been stumped about how to make it truly data-agnostic and
unaware of the structure of the data. Your suggestion is the simplest possible data-aware
approach, but it still assumes that the rows are partitioned in time order, which is likely
the case, but does slightly constrain your data model, and also is more complex when talking
about composite structures. With timestamp-based sstable partitioning, we separate the schema
from persistence the persistence structure more cleanly, imo.



> 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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