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https://issues.apache.org/jira/browse/PHOENIX-2126?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14639745#comment-14639745
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Samarth Jain commented on PHOENIX-2126:
---------------------------------------
Thinking about this a little bit more, I am thinking whether you really need a
heap here. Today in phoenix, your query would end up using
MergeSortTopNResultIterator. The underlying iterators managed by this iterator
already return the records sorted by the order by expression. So all it needs
to do is figure out which iterator has the min record which involves O(K)
comparisons where K is the number of salt buckets or guide post chunks. So
overall we would end up doing the comparison O(limit * K) times.
{code}
private PeekingResultIterator minIterator() throws SQLException {
List<PeekingResultIterator> iterators = getIterators();
Tuple minResult = null;
PeekingResultIterator minIterator = EMPTY_ITERATOR;
for (int i = iterators.size()-1; i >= 0; i--) {
PeekingResultIterator iterator = iterators.get(i);
Tuple r = iterator.peek();
if (r != null) {
if (minResult == null || compare(r, minResult) < 0) {
minResult = r;
minIterator = iterator;
}
continue;
}
iterator.close();
iterators.remove(i);
}
return minIterator;
}
{code}
So it is making me think that the actual perf boost you are getting is from
parallelizing the merge sort and not from using the heap.
> Improving performance of merge sort by multi-threaded and minheap
> implementation
> --------------------------------------------------------------------------------
>
> Key: PHOENIX-2126
> URL: https://issues.apache.org/jira/browse/PHOENIX-2126
> Project: Phoenix
> Issue Type: Improvement
> Affects Versions: 4.1.0, 4.2.0
> Reporter: Ankit Singhal
> Attachments: PHOENIX-2126_v1.0.patch
>
>
> {code}
> CREATE TABLE IF NOT EXISTS test (
> dim1 INTEGER NOT NULL,
> A.B INTEGER,
> A.M DECIMAL,
> CONSTRAINT PK PRIMARY KEY
> (dim1))
> SALT_BUCKETS =256,DEFAULT_COLUMN_FAMILY='A';
> {code}
> *Query to benchmark:-*
> {code}
> select dim1,sum(b),sum(m) from test where Datemth>=201505 and Datemth<=201505
> and dim1 IS NOT NULL group by dim1 order by sum(m) desc nulls last limit 10;
> {code}
> *current scenario:-*
> *CASE 1: * consider the case when dim1 is high cardinality attribute (10K+)
> and table have salt bucket set to 256, we will get 256 iterators from above
> query at the client and MergeSortRowKeyResultIterator has to merge these 256
> iterators with single thread. So let's say each iterator has 10k tuples
> returned, then merge sort needs to merge 2.5M tuples which will be costly if
> it is done with single thread and the query spend most of its time on client
> *CASE 2: * consider the case when dim1 is high cardinality attribute (10K+)
> and table have salt bucket set to 1, we will get 1 iterator from above query
> at the client and MergeSortRowKeyResultIterator doesn't need to merge
> anything. Here, it is fine with single thread.
> *CASE 3: * consider the case when dim1 is low cardinality attribute (10-100)
> and table have salt bucket set to 256, we will get 256 iterator from above
> query at the client and MergeSortRowKeyResultIterator has to merge these 256
> iterators with single thread. here the single thread is also fine as he has
> to merge only 2560 tuples.
> *Solution for case1 problem is:-*
> Optimized the implementation of merging 'n'-sorted iterators(having 'm'
> tuples) by using "min heap" which optimizes the time complexity from
> O(n2m) to O(nmLogn) (as heapify takes (Logn) time).
> And, By using multiple-threads('t') to process group of iterators which
> further optimized the complexity to
> T(nm)=T(nm)/t+T(t)
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