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Posted to issues@quickstep.apache.org by "ASF GitHub Bot (JIRA)" <ji...@apache.org> on 2017/10/06 04:17:01 UTC
[jira] [Commented] (QUICKSTEP-107) Implement concurrent query
support
[ https://issues.apache.org/jira/browse/QUICKSTEP-107?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16194116#comment-16194116 ]
ASF GitHub Bot commented on QUICKSTEP-107:
------------------------------------------
GitHub user hbdeshmukh opened a pull request:
https://github.com/apache/incubator-quickstep/pull/309
Add ProbabilityStore class
- Used to store probabilities of objects.
- Probabilities are of two kinds: Individual and cumulative.
- All the individual probabilities within the store add up to one.
- Support for finding the object with given cumulative probability.
This PR is in continuation with the set of PRs for QUICKSTEP-107.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/hbdeshmukh/incubator-quickstep probability-store
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/incubator-quickstep/pull/309.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 #309
----
commit eca1c37ed146fdf4601a1d4c4536086c184a14f0
Author: Harshad Deshmukh <hb...@apache.org>
Date: 2017-09-29T20:38:42Z
Add ProbabilityStore class
- Used to store probabilities of objects.
- Probabilities are of two kinds: Individual and cumulative.
- All the individual probabilities within the store add up to one.
- Support for finding the object with given cumulative probability.
----
> Implement concurrent query support
> ----------------------------------
>
> Key: QUICKSTEP-107
> URL: https://issues.apache.org/jira/browse/QUICKSTEP-107
> Project: Apache Quickstep
> Issue Type: New Feature
> Components: Query Execution
> Reporter: Harshad Deshmukh
>
> This feature is an implementation of the ideas proposed in the paper "Adaptive Concurrent Query Execution Framework for an Analytical In-Memory Database System" that appeared in IEEE BigData Congress 2017. This feature will be broken in several smaller components and pull requests.
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