gpdb | Greenplum Database - Massively Parallel PostgreSQL | Analytics library
kandi X-RAY | gpdb Summary
kandi X-RAY | gpdb Summary
A Greenplum cluster consists of a coordinator server, and multiple segment servers. All user data resides in the segments, the coordinator contains only metadata. The coordinator server, and all the segments, share the same schema. Users always connect to the coordinator server, which divides up the query into fragments that are executed in the segments, and collects the results. More information can be found on the project website.
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QUESTION
I'm trying to use the similarity function on a Greemplum system using postgres 9.4.24 version. The Greenplum System is running on a CentOS 7 cluster (CentOS Linux release 7.9.2009 (Core))
I've managed to install the postgresql-contrib package by running this:
...ANSWER
Answered 2021-Sep-15 at 21:12At a high level, you will want to download the source for the release of GPDB that you are running. You can do this either by downloading the tarball from the GitHub release page or cloning the repository and checking-out the release tag.
Once you have done that, source greenplum_path.sh
from your installation of GPDB, change into the the contrib/pg_trgm
directory and run
QUESTION
No matter what version of the GPDB open source code, the number of segments is not considered in the cost evaluation, and only a simple process is done when returning the explain result to QD and make the result more clear.
...ANSWER
Answered 2021-Jan-15 at 14:56In Greenplum your query is executed in parallel on all segments, hence all segments accrue the cost. It however doesn't seem helpful to multiply the cost number by the number of segments, this will give you the same query plan with different cost numbers on different Greenplum clusters.
QUESTION
I am currently using greenplum for little scale of data like 1GB to test it.
As greenplum is said to be "petabytes-scale", I was wondering if having a volume of data like one or ten terabytes is worth going into this MPP processing instead of a normal PostgreSQL database. All my network interfaces have 10 Mb/s for slaves and master.
Best practices don't include these considerations. The problem is that having maybe a "little database" will have poor result due to network processing. Did you already implement a database with this scale?
...ANSWER
Answered 2020-Mar-12 at 12:55The workloads for PostgreSQL and Greenplum are different. PostgreSQL is great for OLTP, queries with index lookups, referential integrity, etc. You typically know the query patterns in an OLTP database too. It can certainly take on some data warehouse or analytical needs but it scales by buying a bigger machine with more RAM and more cores with faster disks.
Greenplum, on the other hand, is designed for data warehousing and analytics. You design the database without knowing how the users will query the data. This means sequential reads, no indexes, full table scans, etc. It can do some OLTP work but it isn't designed for it. You scale Greenplum by adding more nodes to you cluster. This gives you more CPU, RAM, and disk throughput.
What is your use case? That is the biggest determinant in picking Greenplum vs PostgreSQL.
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Install gpdb
Greenplum is developed on GitHub, and anybody wishing to contribute to it will have to have a GitHub account and be familiar with Git tools and workflow. It is also recommend that you follow the developer's mailing list since some of the contributions may generate more detailed discussions there. Once you have your GitHub account, fork this repository so that you can have your private copy to start hacking on and to use as source of pull requests. Anybody contributing to Greenplum has to be covered by either the Corporate or the Individual Contributor License Agreement. If you have not previously done so, please fill out and submit the Contributor License Agreement. Note that we do allow for really trivial changes to be contributed without a CLA if they fall under the rubric of obvious fixes. However, since our GitHub workflow checks for CLA by default you may find it easier to submit one instead of claiming an "obvious fix" exception.
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