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From "Reynold Xin (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17626) TPC-DS performance improvements using star-schema heuristics
Date Fri, 07 Oct 2016 23:46:20 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17626?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15556645#comment-15556645
] 

Reynold Xin commented on SPARK-17626:
-------------------------------------

Thanks - this makes sense (especially the bushy tree part).

For runtime, a lot of known optimizations one can do (e.g. based on RI to turn hash map lookups
into dense array lookups) are already done entirely adaptively during query execution in whole
stage code generation.

Also looping in [~ron8hu] and [~ZenWzh].

> TPC-DS performance improvements using star-schema heuristics
> ------------------------------------------------------------
>
>                 Key: SPARK-17626
>                 URL: https://issues.apache.org/jira/browse/SPARK-17626
>             Project: Spark
>          Issue Type: Umbrella
>          Components: SQL
>    Affects Versions: 2.1.0
>            Reporter: Ioana Delaney
>            Priority: Critical
>         Attachments: StarSchemaJoinReordering.pptx
>
>
> *TPC-DS performance improvements using star-schema heuristics*
> \\
> \\
> TPC-DS consists of multiple snowflake schema, which are multiple star schema with dimensions
linking to dimensions. A star schema consists of a fact table referencing a number of dimension
tables. Fact table holds the main data about a business. Dimension table, a usually smaller
table, describes data reflecting the dimension/attribute of a business.
> \\
> \\
> As part of the benchmark performance investigation, we observed a pattern of sub-optimal
execution plans of large fact tables joins. Manual rewrite of some of the queries into selective
fact-dimensions joins resulted in significant performance improvement. This prompted us to
develop a simple join reordering algorithm based on star schema detection. The performance
testing using *1TB TPC-DS workload* shows an overall improvement of *19%*. 
> \\
> \\
> *Summary of the results:*
> {code}
> Passed                 99
> Failed                  0
> Total q time (s)   14,962
> Max time            1,467
> Min time                3
> Mean time             145
> Geomean                44
> {code}
> *Compared to baseline* (Negative = improvement; Positive = Degradation):
> {code}
> End to end improved (%)              -19%	
> Mean time improved (%)               -19%
> Geomean improved (%)                 -24%
> End to end improved (seconds)      -3,603
> Number of queries improved (>10%)      45
> Number of queries degraded (>10%)       6
> Number of queries unchanged            48
> Top 10 queries improved (%)          -20%
> {code}
> Cluster: 20-node cluster with each node having:
> * 10 2TB hard disks in a JBOD configuration, 2 Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80GHz
processors, 128 GB RAM, 10Gigabit Ethernet.
> * Total memory for the cluster: 2.5TB
> * Total storage: 400TB
> * Total CPU cores: 480
> Hadoop stack: IBM Open Platform with Apache Hadoop v4.2. Apache Spark 2.0 GA
> Database info:
> * Schema: TPCDS 
> * Scale factor: 1TB total space
> * Storage format: Parquet with Snappy compression
> Our investigation and results are included in the attached document.
> There are two parts to this improvement:
> # Join reordering using star schema detection
> # New selectivity hint to specify the selectivity of the predicates over base tables.
Selectivity hint is optional and it was not used in the above TPC-DS tests. 
> \\



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