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https://issues.apache.org/jira/browse/DRILL-6158?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16367390#comment-16367390
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ASF GitHub Bot commented on DRILL-6158:
---------------------------------------
GitHub user vladimirtkach opened a pull request:
https://github.com/apache/drill/pull/1123
DRILL-6158: NaN, Infinity issues
- changed comparison rules for NaN, Infinity values. For now NaN is the
biggest value, Infinity - second biggest value
- fixed min, max, trunc functions for NaN, Infinity values
- made drill use original sqrt function instead of substitution
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/vladimirtkach/drill DRILL-6154
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/drill/pull/1123.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #1123
----
commit 6195546c58604c833c8e9134227a353884401e24
Author: vladimir tkach <vovatkach75@...>
Date: 2018-02-16T14:36:49Z
DRILL-6158: NaN, Infinity issues
- changed comparison rules for NaN, Infinity values. For now NaN is the
biggest value, Infinity - second biggest value
- fixed min, max, trunc functions for NaN, Infinity values
- made drill use original sqrt function instead of substitution
----
> Create a mux operator for union exchange to enable two phase merging instead
> of foreman merging all the batches.
> ----------------------------------------------------------------------------------------------------------------
>
> Key: DRILL-6158
> URL: https://issues.apache.org/jira/browse/DRILL-6158
> Project: Apache Drill
> Issue Type: Bug
> Components: Query Planning & Optimization
> Affects Versions: 1.12.0
> Reporter: Hanumath Rao Maduri
> Assignee: Hanumath Rao Maduri
> Priority: Major
> Fix For: Future
>
>
> Consider the following simple query
> {code}
> select zz1,zz2,a11 from dfs.tmp.viewtmp limit 100000 offset 10000000
> {code}
> The following plan is generated for this query
> {code}
> 00-00 Screen : rowType = RecordType(ANY zz1, ANY zz2, ANY a11): rowcount =
> 1.01E7, cumulative cost = {1.06048844E8 rows, 5.54015404E8 cpu, 0.0 io,
> 1.56569100288E11 network, 4.64926176E7 memory}, id = 787
> 00-01 Project(zz1=[$0], zz2=[$1], a11=[$2]) : rowType = RecordType(ANY
> zz1, ANY zz2, ANY a11): rowcount = 1.01E7, cumulative cost = {1.05038844E8
> rows, 5.53005404E8 cpu, 0.0 io, 1.56569100288E11 network, 4.64926176E7
> memory}, id = 786
> 00-02 SelectionVectorRemover : rowType = RecordType(ANY zz1, ANY zz2,
> ANY a11): rowcount = 1.01E7, cumulative cost = {1.05038844E8 rows,
> 5.53005404E8 cpu, 0.0 io, 1.56569100288E11 network, 4.64926176E7 memory}, id
> = 785
> 00-03 Limit(offset=[10000000], fetch=[100000]) : rowType =
> RecordType(ANY zz1, ANY zz2, ANY a11): rowcount = 1.01E7, cumulative cost =
> {9.4938844E7 rows, 5.42905404E8 cpu, 0.0 io, 1.56569100288E11 network,
> 4.64926176E7 memory}, id = 784
> 00-04 UnionExchange : rowType = RecordType(ANY zz1, ANY zz2, ANY
> a11): rowcount = 1.01E7, cumulative cost = {8.4838844E7 rows, 5.02505404E8
> cpu, 0.0 io, 1.56569100288E11 network, 4.64926176E7 memory}, id = 783
> 01-01 SelectionVectorRemover : rowType = RecordType(ANY zz1, ANY
> zz2, ANY a11): rowcount = 1.01E7, cumulative cost = {7.4738844E7 rows,
> 4.21705404E8 cpu, 0.0 io, 3.2460300288E10 network, 4.64926176E7 memory}, id =
> 782
> 01-02 Limit(fetch=[10100000]) : rowType = RecordType(ANY zz1,
> ANY zz2, ANY a11): rowcount = 1.01E7, cumulative cost = {6.4638844E7 rows,
> 4.11605404E8 cpu, 0.0 io, 3.2460300288E10 network, 4.64926176E7 memory}, id =
> 781
> 01-03 Project(zz1=[$0], zz2=[$2], a11=[$1]) : rowType =
> RecordType(ANY zz1, ANY zz2, ANY a11): rowcount = 2.3306983E7, cumulative
> cost = {5.4538844E7 rows, 3.71205404E8 cpu, 0.0 io, 3.2460300288E10 network,
> 4.64926176E7 memory}, id = 780
> 01-04 HashJoin(condition=[=($0, $2)], joinType=[left]) :
> rowType = RecordType(ANY ZZ1, ANY A, ANY ZZ2): rowcount = 2.3306983E7,
> cumulative cost = {5.4538844E7 rows, 3.71205404E8 cpu, 0.0 io,
> 3.2460300288E10 network, 4.64926176E7 memory}, id = 779
> 01-06 Scan(groupscan=[EasyGroupScan
> [selectionRoot=maprfs:/tmp/csvd1, numFiles=3, columns=[`ZZ1`, `A`],
> files=[maprfs:/tmp/csvd1/D1111aamulti11random2.csv,
> maprfs:/tmp/csvd1/D1111aamulti11random21.csv,
> maprfs:/tmp/csvd1/D1111aamulti11random211.csv]]]) : rowType = RecordType(ANY
> ZZ1, ANY A): rowcount = 2.3306983E7, cumulative cost = {2.3306983E7 rows,
> 4.6613966E7 cpu, 0.0 io, 0.0 network, 0.0 memory}, id = 776
> 01-05 BroadcastExchange : rowType = RecordType(ANY ZZ2):
> rowcount = 2641626.0, cumulative cost = {5283252.0 rows, 2.3774634E7 cpu, 0.0
> io, 3.2460300288E10 network, 0.0 memory}, id = 778
> 02-01 Scan(groupscan=[EasyGroupScan
> [selectionRoot=maprfs:/tmp/csvd2, numFiles=1, columns=[`ZZ2`],
> files=[maprfs:/tmp/csvd2/D222random2.csv]]]) : rowType = RecordType(ANY ZZ2):
> rowcount = 2641626.0, cumulative cost = {2641626.0 rows, 2641626.0 cpu, 0.0
> io, 0.0 network, 0.0 memory}, id = 777
> {code}
> In case of many minor fragments and huge cluster all the minor fragments
> feeding into unionExchange will be merged only at the foreman. Eventhough
> unionExchange is not a bottleneck interms of cpu but it creates huge memory
> pressure in terms of memory.
> It is observed that due to this mostly on a large cluster with many minor
> fragments it runs out of memory.
> In this scenario it is always better to locally merge the minor fragments
> pertaining to a DRILLBIT and send the single stream to the foreman. This
> divides the memory consumption to all the drillbits and then reduces the
> memory pressure at the foreman.
>
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