[SPARK-17626] TPC-DS performance improvements using star-schema heuristics

• Type: Umbrella
• Status: Resolved
• Priority: Critical
• Resolution: Done
• Affects Version/s: 2.1.0
• Fix Version/s: 2.2.0
• Component/s: SQL
• Target Version/s: 2.2.0
• Assignee: Ioana Delaney
• Reporter: Ioana Delaney
• Created: 21/Sep/16 18:10
• Updated: 08/Jan/18 20:41
• Resolved: 08/Jan/18 20:41

Description

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:

Passed                 99
Failed                  0
Total q time (s)   14,962
Max time            1,467
Min time                3
Mean time             145
Geomean                44

Compared to baseline (Negative = improvement; Positive = Degradation):

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%

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:

1. Join reordering using star schema detection
2. 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.
   

Issue Links

• is duplicated by: SPARK-17375 Star Join Optimization

Sub-Tasks

• Join reordering using star schema detection: Resolved
• Refactor star schema code for the subsequent star join detection in CBO: Resolved
• Apply star-join filter heuristics to dynamic programming join enumeration: Resolved