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https://issues.apache.org/jira/browse/SPARK-17626?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15552517#comment-15552517
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Ioana Delaney commented on SPARK-17626:
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Star schema optimization and cost based model will complement each other. Many
RDBMS systems observe star schema queries and have special implementations for
star joins e.g. either optimize the chain of RI joins or they introduce a new
operator to efficiently execute the join. I am envisioning that our work will
go towards that direction. Short term, with the cost model support we can (1)
further refine our schema detection heuristics with cardinality and predicate
selectivity, and (2) use the cost model to find the optimal join order within a
star join. Longer term, for star schema queries, we can further enhance the
star join detection using reliable RI constraints and ultimately we can
implement runtime operators to efficiently execute star join plans.
So I expect the star schema optimizations to evolve in parallel with the cost
base model. There are many, many things that we can build to improve the
performance of this category of queries which are widely used in the industry.
This work would be our first step.
> 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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