Greenplum: Building a Postgres Fabric for Large-Scale Analytical Computation - Greenplum Summit 2018

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Greenplum: Building a Postgres Fabric for Large-Scale Analytical Computation Greenplum Summit at PostgresConf US 2018 Elisabeth Hendrickson and Ivan Novick

Software

1986
Stonebraker & Rowe
“The Design of Postgres”

2005
Greenplum Company
Launches “Bizgres”

2015
Pivotal open sources
Greenplum. (Again.)

2017
Greenplum 5 released
powered by Postgres 8.3

2018
Merging continues
(closing in on 9.1)

Embarrassingly Parallel
19
• Spread out (shard) data across many databases
• Execute queries, in parallel, on every shard: spread the load
• Gather results
• Bonus: hide the complexity from the user

What Makes it Greenplum?
● MPP-aware optimizer (“Orca”)
● Interconnect (UDP-based protocol for communication between nodes)
● Executor “motion”
● Data management tools that take advantage of all that massive parallelism (e.g.
gpload, gptransfer)
(plus a few things Greenplum added independently that show up in later versions
Postgres such as a column store and a connection protocol similar to but different
from foreign data wrappers…worth noting things like this have made the merge
particularly challenging)

ML / AIMultistructural DataCloud
3 Forces

So many kinds…
● JSON
● XML
● Documents
● Geospatial
● Time series
● Graph

In-Database Analytics
TEXT CLUSTERING
REGRESSION
CLASSIFICATION
BI/REPORTING
GRAPHGEOSPATIAL

Transforming How The World Builds Software
© Copyright 2018 Pivotal Software, Inc. All rights Reserved.

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Greenplum: Building a Postgres Fabric for Large-Scale Analytical Computation - Greenplum Summit 2018

2. Greenplum: A Timeline

3. 1986 Stonebraker & Rowe “The Design of Postgres”

4. 1989 Postgres 1.0

5. 1996 Community!

6. 2003 Greenplum Company Founded

7. 2005 Greenplum Company Launches “Bizgres”

8. 2006 Greenplum forks at Postgres 8.2

9. Greenplum: the EMC Years 2010

10. 2013 Pivotal spun out from EMC & VMWare

11. 2015 Pivotal open sources Greenplum. (Again.)

12. 2016 Greenplum begins Postgres merge

13. 2017 Greenplum 5 released powered by Postgres 8.3

14. 2018 Merging continues (closing in on 9.1)

15. 2018 expected release of Postgres 11

16. But What IS Greenplum?

17. Unicorn - I Name Thee Postgres

18. And Greenplum is a herd of Postgreses

19. Embarrassingly Parallel 19 • Spread out (shard) data across many databases • Execute queries, in parallel, on every shard: spread the load • Gather results • Bonus: hide the complexity from the user

20. What Makes it Greenplum? ● MPP-aware optimizer (“Orca”) ● Interconnect (UDP-based protocol for communication between nodes) ● Executor “motion” ● Data management tools that take advantage of all that massive parallelism (e.g. gpload, gptransfer) (plus a few things Greenplum added independently that show up in later versions Postgres such as a column store and a connection protocol similar to but different from foreign data wrappers…worth noting things like this have made the merge particularly challenging)

21. Forces in the Modern Age

22. ML / AIMultistructural DataCloud 3 Forces

23. Cloud

24. Cloud == Philosophy

25. Containerization

26. Deploy with Kubernetes

27. PL/Container

28. Multistructured Data

29. The Exponential Growth of Data

30. So many kinds… ● JSON ● XML ● Documents ● Geospatial ● Time series ● Graph

31. ML / AI

32. In-Database Analytics TEXT CLUSTERING REGRESSION CLASSIFICATION BI/REPORTING GRAPHGEOSPATIAL