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eren e786cbed0f Fix Join Problem With VARCHAR Partition Columns 
This change fixes the problem with joins with VARCHAR columns. Prior to
this change, when we tried to do large table joins on varchar columns, we got
an error of the form:
ERROR: cannot perform local joins that involve expressions
DETAIL: local joins can be performed between columns only.

This is because we have a check in CheckJoinBetweenColumns() which requires the
join clause to have only 'Var' nodes (i.e. columns). Postgres adds a relabel t
ype cast to cast the varchar to text; hence the type of the node is not T_Var
and the join fails.

The fix involves calling strip_implicit_coercions() to the left and right
arguments so that RELABELTYPE is stripped to VAR.

Fixes #76.
 		2016-04-19 21:55:50 -06:00
src Fix Join Problem With VARCHAR Partition Columns 2016-04-19 21:55:50 -06:00
.gitattributes Switch to using git attributes to ignore files 2016-02-15 23:41:51 -07:00
.gitignore Initial commit of Citus 5.0 2016-02-11 04:05:32 +02:00
.travis.yml Initial commit of Citus 5.0 2016-02-11 04:05:32 +02:00
CHANGELOG.md Add CHANGELOG 2016-03-23 17:28:16 -06:00
CONTRIBUTING.md Proper indentation for code blocks in lists 2016-03-30 15:40:53 -07:00
LICENSE Add AGPL-3.0 in LICENSE file 2016-03-23 17:04:58 -06:00
Makefile Fix various build issues 2016-03-11 13:38:47 -07:00
Makefile.global.in Fix various build issues 2016-03-11 13:38:47 -07:00
README.md Adjusted badges 2016-04-07 14:04:19 -07:00
autogen.sh Changed product name to citus 2016-02-15 16:04:31 +02:00
configure Update copyright dates 2016-03-23 17:14:37 -06:00
configure.in Update copyright dates 2016-03-23 17:14:37 -06:00
github-banner.png Readme for 5.0 2016-03-18 13:32:13 -07:00
prep_buildtree Changed product name to citus 2016-02-15 16:04:31 +02:00

README.md

Citus Banner

Build Status Google Group Latest Docs

What is Citus?

  • Open-source PostgreSQL extension (not a fork)
  • Scalable across multiple hosts through sharding and replication
  • Distributed engine for query parallelization
  • Highly available in the face of host failures

Citus horizontally scales PostgreSQL across commodity servers using sharding and replication. Its query engine parallelizes incoming SQL queries across these servers to enable real-time responses on large datasets.

Citus extends the underlying database rather than forking it, which gives developers and enterprises the power and familiarity of a traditional relational database. As an extension, Citus supports new PostgreSQL releases, allowing users to benefit from new features while maintaining compatibility with existing PostgreSQL tools. Note that Citus supports many (but not all) SQL commands; see the FAQ for more details.

Common Use-Cases:

  • Powering real-time analytic dashboards
  • Exploratory queries on events as they happen
  • Large dataset archival and reporting
  • Session analytics (funnels, segmentation, and cohorts)

To learn more, visit citusdata.com and join the mailing list to stay on top of the latest developments.

Quickstart

Local Citus Cluster

  • Install docker-compose: Mac | Linux

  • (Mac only) connect to Docker VM

    eval $(docker-machine env default)

  • Pull and start the docker images

    wget https://raw.githubusercontent.com/citusdata/docker/master/docker-compose.yml
docker-compose -p citus up -d

  • Connect to the master database

    docker exec -it citus_master psql -U postgres -d postgres

  • Follow the first tutorial instructions

  • To shut the cluster down, run

    docker-compose -p citus down

Talk to Contributors and Learn More

Documentation Try the Citus tutorials for a hands-on introduction or
the documentation for a more comprehensive reference.
Google Groups The Citus Google Group is our place for detailed questions and discussions.
IRC Talk with the Citus development team in realtime on the #citus Freenode channel.
Github Issues We track specific bug reports and feature requests on our project issues.
Twitter Follow @citusdata for general updates and PostgreSQL scaling tips.
Training and Support See our support page for training and dedicated support options.

Contributing

Citus is built on and of open source. We welcome your contributions, and have added a helpwanted label to issues which are accessible to new contributors. The CONTRIBUTING.md file explains how to get started developing the Citus extension itself and our code quality guidelines.

Who is Using Citus?

Citus is deployed in production by many customers, ranging from technology start-ups to large enterprises. Here are some examples:

  • CloudFlare uses Citus to provide real-time analytics on 100 TBs of data from over 4 million customer websites. Case Study
  • MixRank uses Citus to efficiently collect and analyze vast amounts of data to allow inside B2B sales teams to find new customers. Case Study
  • Neustar builds and maintains scalable ad-tech infrastructure that counts billions of events per day using Citus and HyperLogLog.
  • Agari uses Citus to secure more than 85 percent of U.S. consumer emails on two 6-8 TB clusters. Case Study
  • Heap uses Citus to run dynamic funnel, segmentation, and cohort queries across billions of users and tens of billions of events. Watch Video

Copyright © 2012–2016 Citus Data, Inc.