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https://issues.apache.org/jira/browse/PIG-4066?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14555201#comment-14555201
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Daniel Dai commented on PIG-4066:
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Looking at the patch while trying to document it. The idea is good and simple,
however, there are couple of issues in the implementation:
1. Some basic queries does not work, eg: "cubed_and_rolled = CUBE salesinp BY
CUBE(product,year), ROLLUP(region, state, city) pivot 1;"
2. Even if there is no "pivot" keyword, the implementation still using the new
Pivot code
3. All script will go through RollupHIIOptimizer, it's on by default. Both #2
and #3 makes it impossible to just make it experimental feature and ship
4. The logic of RollupHII should be wrapped into the new operator, not
necessary propagate to cogroup/UserFuncExpression, etc
5. There are a lot of redundant code needs to be cleaned up
6. Not a show stop but would like to port it to Tez as well
I already did quite a few cleanup. Since it will touch a majority part of the
original patch, to make the commit history less confusing, I'd like to rollback
the patch completely first and then redo it.
> An optimization for ROLLUP operation in Pig
> -------------------------------------------
>
> Key: PIG-4066
> URL: https://issues.apache.org/jira/browse/PIG-4066
> Project: Pig
> Issue Type: Improvement
> Reporter: Quang-Nhat HOANG-XUAN
> Assignee: Quang-Nhat HOANG-XUAN
> Labels: hybrid-irg, optimization, rollup
> Fix For: 0.15.0
>
> Attachments: Current Rollup vs Our Rollup.jpg, PIG-4066.2.patch,
> PIG-4066.3.patch, PIG-4066.4.patch, PIG-4066.5.patch, PIG-4066.patch,
> TechnicalNotes.2.pdf, TechnicalNotes.pdf, UserGuide.pdf
>
>
> This patch aims at addressing the current limitation of the ROLLUP operator
> in PIG: most of the work is done in the Map phase of the underlying MapReduce
> job to generate all possible intermediate keys that the reducer use to
> aggregate and produce the ROLLUP output. Based on our previous work:
> “Duy-Hung Phan, Matteo Dell’Amico, Pietro Michiardi: On the design space of
> MapReduce ROLLUP aggregates”
> (http://www.eurecom.fr/en/publication/4212/download/rs-publi-4212_2.pdf), we
> show that the design space for a ROLLUP implementation allows for a different
> approach (in-reducer grouping, IRG), in which less work is done in the Map
> phase and the grouping is done in the Reduce phase. This patch presents the
> most efficient implementation we designed (Hybrid IRG), which allows defining
> a parameter to balance between parallelism (in the reducers) and
> communication cost.
> This patch contains the following features:
> 1. The new ROLLUP approach: IRG, Hybrid IRG.
> 2. The PIVOT clause in CUBE operators.
> 3. Test cases.
> The new syntax to use our ROLLUP approach:
> alias = CUBE rel BY { CUBE col_ref | ROLLUP col_ref [PIVOT pivot_value]} [, {
> CUBE col_ref | ROLLUP col_ref [PIVOT pivot_value]}...]
> In case there is multiple ROLLUP operator in one CUBE clause, the last ROLLUP
> operator will be executed with our approach (IRG, Hybrid IRG) while the
> remaining ROLLUP ahead will be executed with the default approach.
> We have already made some experiments for comparison between our ROLLUP
> implementation and the current ROLLUP. More information can be found at here:
> http://hxquangnhat.github.io/PIG-ROLLUP-H2IRG/
> Patch can be reviewed at here: https://reviews.apache.org/r/23804/
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