This document outlines performance enhancements for columnar tables in the Greenplum database, focusing on the implementation of block metadata and sort keys to optimize query and data loading performance. It describes the architecture of Greenplum, how Alibaba Cloud utilizes it for their service Hybriddb, and presents findings from performance testing. The conclusion emphasizes that effective use of block metadata and carefully designed sort keys can significantly improve overall database performance.

1. Columnar Table
Performance
Enhancements Of
Greenplum Database
with Block Metadata and
Sort Keys
Xiaojian Fan

2. About me
• From Hangzhou,China, working in Alibaba Cloud as a developer for
Greenplum and PostgreSQL feature development, bug fixing, and
customer support.
• Email: funnyxj.fxj@alibaba-inc.com

3. About Alibaba Cloud
• As the cloud computing arm and a business unit of Alibaba Group
(NYSE: BABA), Alibaba Cloud provides a comprehensive suite of global
cloud computing services to help power and grow your business.
Alibaba Cloud ranks as the top third largest public cloud services
provider globally and is the leading cloud provider in the China
market.
• https://www.alibabacloud.com/

4. About Greenplum
• Greenplum Database is an advanced, fully featured, open source data
platform. It provides powerful and rapid analytics on petabyte scale
data volumes. Uniquely geared toward big data analytics, Greenplum
Database is powered by the world’s most advanced cost-based query
optimizer delivering high analytical query performance on large data
volumes.
• https://github.com/greenplum-db/gpdb

5. Architecture of Greenplum

6. What Alibaba Cloud does on Greenplum
• Alibaba Cloud built up a data warehouse service named HybridDB in
its public cloud service, based on the open sourced Greenplum
Database
• Extension/OSS/HyperLogLog/Json/PlJava/Madlib/Postgis/Vectorizatio
n,etc
• Block Metadata and Sort Keys
• https://www.alibabacloud.com/product/hybriddb-postgresql

7. Why Block Metadata and Sort Keys?
• Lots of columnar tables are used
• Poor query performance
• Poor loading data performance

8. Structure of Greenplum Columnar Table
• Look at heap table first

9. Structure of Greenplum Columnar Table
• Columnar table
Columnar table

10. Structure of columnar table file

11. Transaction of columnar table
• Auxiliary heap table
• Records files EOF

12. How to DML
• Insert
• Multiple files avoid file locks
• Delete
• Visimap heap table which records
all tuples visibility
• Update->Delete+insert

13. Disadvantages of columnar table
• Without index
• Need to scan and decompress all blocks, cost lots of CPU/IO
• With index
• Loading data is very slow
• Data expansion
• Lots of random IO

14. Block Metadata
• Add a metadata heap table
• Like auxiliary heap table
• Collecting metadata
• Loading data performance decreased by 10%
• Char/varchar/text optimization
• Comparing string is slow

15. Metadata Scan Flow Process

16. • If data are evenly
or randomly distributed?

17. • Filtering works better
after sort

18. Sort Keys
• Cluster table data on the disk according to sort keys
• Two main advantages:
• Distinct metadata, less [min,max] interval intersection
• Avoid reordering data again when SQL involves order by/group by

19. Kind of Sort Keys
• Global Sort Keys
• Like order by a,b,c
• Avoid reordering
• Group Sort Keys
• Divide data to multiple groups, every group is ordered
• Less [min,max] interval intersection for any columns

20. Global Sort Keys
• Without sort key

21. Global Sort Keys
• With sort key
• More faster

22. Group Sort Keys
• If we sort by column1,
but filter by column2?
select * from test where column2=99

23. Group Sort Keys
• Two columns
• Horizontal axis is col1, and vertical axis is col2
• Every cube has many tuples (col1, col2), we call Group

24. Group Sort Keys
• More than three columns
• Multidimensional spatial coordinate system

25. TPCH Performance Testing
• The amount of data
• 1 TB
• Queries
• Q6, Q14, Q15
• Column l_shipdate in table lineitem is ordered

26. Results of TPCH

27. Metascan Vs. Index
• Loading data performance
• Index results in a 3x decrease in performance

28. Metascan Vs. Index
• TPCH performance

29. Metascan Vs. Index
• Different filtering rates
• We change the interval of Q6 l_shipdate (1day, 3days, 7days, 10days,......)

30. Metascan Vs. Index
• > 99.7% filtering rates
• Index better than metascan
• < 99.7% filtering rates
• Metascan better than index
• > 40% filtering rates
• Metascan more than
double performance
of seqscan

31. Conclusion
• Block Metadata is a very effective way to improve the performance of
columnar table
• How to choose and design sort keys is also important