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From "Sylvain Lebresne (JIRA)" <>
Subject [jira] [Commented] (CASSANDRA-8731) Optimise merges involving multiple clustering columns
Date Wed, 04 Feb 2015 16:48:35 GMT


Sylvain Lebresne commented on CASSANDRA-8731:

bq. If you want to split intra-clustering-component to make byte-order comparable fields more
efficient, that is more involved.

That's really the part I was refering to. I'm totally fine with having a specialized mergeIterator
for clustering prefixes that knows not to compare the same components multiple times.

bq. especially for sstables that do overlap, but don't overlap for their entirety

I wasn't planning to handle them. For time series withh DateTieredCompactionStrategy, most
of your sstables will be non-overlapping so it would still be a win.

bq. slicing them and merging them independently actually sounds to me to be much more fiddly
and tough to get right than the simplest approach outlined here

If by "simplest approach" you mean the one PQ per level of clustering, then I think that's
really orthogonal. What I'm suggesting is really not specific to multiple clustering level,
while the PQ-per-level-of-clustering only help those case. What I can agree on is that doing
CASSANDRA-6936 + doing a trie-for-byte-order-comparable-fields would likely make my suggestion
less useful (even though there is cases where it would still be more efficient), but my initial
guess is that doing both those thing is more involved that what I'm suggesting. But not trying
to shoot your idea, just mentioning a potential optimization that imo is not really very involved
(in particular, note that doing what I suggest only for single partition queries would be
fine imo if that makes it simpler).

Anyway, I would suggest limiting the scope of this issue to "introduce a smarter merge iterator
for multi-clustering" (because it's easy and I doubt it's contentious) and open other tickets
for more involved byte-fiddling suggestions. 

> Optimise merges involving multiple clustering columns
> -----------------------------------------------------
>                 Key: CASSANDRA-8731
>                 URL:
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Benedict
>              Labels: performance
>             Fix For: 3.0
> Since the new storage format is dead in the water for the moment, we should do our best
to optimise current behaviour. When merging data from multiple sstables with multiple clustering
columns, currently we must incur the full costs of comparison for the entire matching prefix,
and must heapify every cell in our PriorityQueue, incurring lg(N) of these costlier comparisons
for every cell we merge, where N is the number of sources we're merging.
> Essentially I'm proposing a trie-based merge approach as a replacement for the ManyToOne
MergeIterator, wherein we treat each clustering component as a tree underwhich all Cells with
a common prefix occur. We then perform a tree merge, rather than a flat merge. For byte-order
fields this trie can even be a full binary-trie (although built on the fly). The advantage
here is that we rapidly prune merges involving disjoint ranges, so that instead of always
incurring lg(N) costs on each new record, we may often incur O(1) costs. For timeseries data,
for instance, we could merge dozens of files and so long as they were non-overlapping our
CPU burden would be little more than reading from a single file.
> On top of this, we no longer incur any of the shared prefix repetition costs, since we
compare each prefix piece-wise, and only once.

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