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From "Jonathan Park (JIRA)" <>
Subject [jira] [Created] (ACCUMULO-2827) HeapIterator optimization
Date Tue, 20 May 2014 00:31:39 GMT
Jonathan Park created ACCUMULO-2827:

             Summary: HeapIterator optimization
                 Key: ACCUMULO-2827
             Project: Accumulo
          Issue Type: Improvement
    Affects Versions: 1.5.1
            Reporter: Jonathan Park
            Priority: Minor

We've been running a few performance tests of our iterator stack and noticed a decent amount
of time spent in the HeapIterator specifically related to add/removal into the heap.

This may not be a general enough optimization but we thought we'd see what people thought.
Our assumption is that it's more probable that the current "top iterator" will supply the
next value in the iteration than not. The current implementation takes the other assumption
by always removing + inserting the minimum iterator back into the heap. With the implementation
of a binary heap that we're using, this can get costly if our assumption is wrong because
we pay the log(n) penalty of percolating up the iterator in the heap upon insertion and again
when percolating down upon removal.

We believe our assumption is a fair one to hold given that as major compactions create a log
distribution of file sizes, it's likely that we may see a long chain of consecutive entries
coming from 1 iterator. Understandably, taking this assumption comes at an additional cost
in the case that we're wrong. Therefore, we've run a few benchmarking tests to see how much
of a cost we pay as well as what kind of benefit we see. I've attached a potential patch (which
includes a test harness) + image that captures the results of our tests. The x-axis represents
# of repeated keys before switching to another iterator. The y-axis represents iteration time.
The sets of blue + red lines varies in # of iterators present in the heap.

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