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From "John Sichi (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HIVE-1694) Accelerate query execution using indexes
Date Fri, 10 Dec 2010 02:37:03 GMT

    [ https://issues.apache.org/jira/browse/HIVE-1694?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12970058#action_12970058
] 

John Sichi commented on HIVE-1694:
----------------------------------

I talked to Namit, and he thinks there should be no relevant dependencies on the QB once we
start on optimization, so letting it get out of sync with the operator DAG may not be an issue.
 (I scanned the code in optimizer, and it seems like a few dependencies have crept in, but
only for special cases like ANALYZE.)

For issue #1, you are proposing what I'll call the "internal SQL" approach, which is to construct
an internal SQL expression (either in string or ASTNode form) and then partially analyze that
(via SemanticAnalyzer), producing an operator DAG to be spliced into the main one.  For this
approach, we would need to figure out how to make the relevant phases of SemanticAnalyzer
modularized and invocable.

Alternately, the "direct construction" approach would be to attempt to construct the new operator
subgraph directly via custom code targeted to the specific patterns you generate, and then
splice that in.

I'm not sure which approach is better; Namit, any opinions?  The internal SQL approach definitely
seems the most appropriate for the WHERE clause work being done by the Harvey Mudd team, since
it produces a self-contained job to be run to produce the temp table containing the filtered
block list.  But for GROUP BY, the direct construction approach may be cleaner.

For issue #2, it seems like this would happen automatically for the internal SQL approach
(but this could also pollute the SemanticAnalyzer state to some extent).  The direct construction
approach is the opposite:  it avoids polluting SemanticAnalyzer, but still might require modularizing
some SemanticAnalyzer calls, e.g. for generating and registering the necessary aliases for
index tables.

Regarding issue #3, that's already true for other optimizations such as projection pushdown
(ColumnPruner), which modifies operator row schemas/resolvers; see for example ColumnPrunerProcFactory.pruneJoinOperator.
 So there shouldn't be anything new here.

Regarding the need to run your transformation first, it would be best to avoid this since
a more advanced optimizer may want freedom in reordering transformations.  So instead of relying
on information from the QB, analyze the relevant operator subgraph to decide whether your
transformation is applicable.  This is the approach we expect to require for cost-based optimization.

Also, note that from a lineage perspective, it makes more sense for lineage to be derived
prior to index transformation rather than subsequently.  Someone examining the lineage associated
with an ETL job would typically be more interested in the logical source table from which
it pulls (rather than from a physical index).


> Accelerate query execution using indexes
> ----------------------------------------
>
>                 Key: HIVE-1694
>                 URL: https://issues.apache.org/jira/browse/HIVE-1694
>             Project: Hive
>          Issue Type: New Feature
>          Components: Indexing, Query Processor
>    Affects Versions: 0.7.0
>            Reporter: Nikhil Deshpande
>            Assignee: Nikhil Deshpande
>         Attachments: demo_q1.hql, demo_q2.hql, HIVE-1694_2010-10-28.diff
>
>
> The index building patch (Hive-417) is checked into trunk, this JIRA issue tracks supporting
indexes in Hive compiler & execution engine for SELECT queries.
> This is in ref. to John's comment at
> https://issues.apache.org/jira/browse/HIVE-417?focusedCommentId=12884869&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#action_12884869
> on creating separate JIRA issue for tracking index usage in optimizer & query execution.
> The aim of this effort is to use indexes to accelerate query execution (for certain class
of queries). E.g.
> - Filters and range scans (already being worked on by He Yongqiang as part of HIVE-417?)
> - Joins (index based joins)
> - Group By, Order By and other misc cases
> The proposal is multi-step:
> 1. Building index based operators, compiler and execution engine changes
> 2. Optimizer enhancements (e.g. cost-based optimizer to compare and choose between index
scans, full table scans etc.)
> This JIRA initially focuses on the first step. This JIRA is expected to hold the information
about index based plans & operator implementations for above mentioned cases. 

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