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https://issues.apache.org/jira/browse/HIVE-4676?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Eric Hanson updated HIVE-4676:
------------------------------
Description:
The COUNT(*) aggregate with the vectorized execution path over ORC should be
optimized because it is a very common case.
Given a table factsqlengineam_vec_orc with about 25 columns and 218 million
rows, this query
select count(*) from factsqlengineam_vec_orc;
runs in 2 minutes 15 seconds, with HDFS Read 2,000,078,555
and this query
select count(mrowflag) from factsqlengineam_vec_orc;
runs in 42 seconds, with HDFS Read 1,207,855
Because the column mrowflag is non-null, both queries return the same result.
We should optimize count(*) so that it, say, chooses the most-compressed column
from the ORC file (or even a single random column) and counts those values, but
logically counts null values too so the meaning is the same as count(*). The
vectorized iterator should not have to load all columns, just one column
minimum, and any columns being filtered in the WHERE clause.
For scalar count(*) aggregates (i.e. without group-by) we can simply tally up
the total number of remaining rows in each batch, without even looking at the
data. Maybe we're already doing that but clearly we are reading more data than
necessary now.
was:
The COUNT(*) aggregate with the vectorized execution path over ORC should be
optimized because it is a very common case.
Given a table factsqlengineam_vec_orc with about 25 columns and 218 million
rows, this query
select count(*) from factsqlengineam_vec_orc;
runs in 2 minutes 15 seconds
and this query
select count(mrowflag) from factsqlengineam_vec_orc;
runs in 42 seconds.
Because the column mrowflag is non-null, both queries return the same result.
We should optimize count(*) so that it, say, chooses the most-compressed column
from the ORC file (or even a single random column) and counts those values, but
logically counts null values too so the meaning is the same as count(*). The
vectorized iterator should not have to load all columns, just one column
minimum, and any columns being filtered in the WHERE clause.
For scalar count(*) aggregates (i.e. without group-by) we can simply tally up
the total number of remaining rows in each batch, without even looking at the
data. Maybe we're already doing that but something is taking up extra time now.
> Optimize COUNT(*) aggregate over vectorized ORC execution path
> --------------------------------------------------------------
>
> Key: HIVE-4676
> URL: https://issues.apache.org/jira/browse/HIVE-4676
> Project: Hive
> Issue Type: Sub-task
> Components: Query Processor
> Affects Versions: vectorization-branch
> Reporter: Eric Hanson
>
> The COUNT(*) aggregate with the vectorized execution path over ORC should be
> optimized because it is a very common case.
> Given a table factsqlengineam_vec_orc with about 25 columns and 218 million
> rows, this query
> select count(*) from factsqlengineam_vec_orc;
> runs in 2 minutes 15 seconds, with HDFS Read 2,000,078,555
> and this query
> select count(mrowflag) from factsqlengineam_vec_orc;
> runs in 42 seconds, with HDFS Read 1,207,855
> Because the column mrowflag is non-null, both queries return the same result.
> We should optimize count(*) so that it, say, chooses the most-compressed
> column from the ORC file (or even a single random column) and counts those
> values, but logically counts null values too so the meaning is the same as
> count(*). The vectorized iterator should not have to load all columns, just
> one column minimum, and any columns being filtered in the WHERE clause.
> For scalar count(*) aggregates (i.e. without group-by) we can simply tally up
> the total number of remaining rows in each batch, without even looking at the
> data. Maybe we're already doing that but clearly we are reading more data
> than necessary now.
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