9f0bd4cb36
With this commit, we implemented some basic features of reference tables.
To start with, a reference table is
* a distributed table whithout a distribution column defined on it
* the distributed table is single sharded
* and the shard is replicated to all nodes
Reference tables follows the same code-path with a single sharded
tables. Thus, broadcast JOINs are applicable to reference tables.
But, since the table is replicated to all nodes, table fetching is
not required any more.
Reference tables support the uniqueness constraints for any column.
Reference tables can be used in INSERT INTO .. SELECT queries with
the following rules:
* If a reference table is in the SELECT part of the query, it is
safe join with another reference table and/or hash partitioned
tables.
* If a reference table is in the INSERT part of the query, all
other participating tables should be reference tables.
Reference tables follow the regular co-location structure. Since
all reference tables are single sharded and replicated to all nodes,
they are always co-located with each other.
Queries involving only reference tables always follows router planner
and executor.
Reference tables can have composite typed columns and there is no need
to create/define the necessary support functions.
All modification queries, master_* UDFs, EXPLAIN, DDLs, TRUNCATE,
sequences, transactions, COPY, schema support works on reference
tables as expected. Plus, all the pre-requisites associated with
distribution columns are dismissed.
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| CONTRIBUTING.md | ||
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README.md
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
-
(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. |
| Slack | Chat with us in our community Slack channel. |
| Github Issues | We track specific bug reports and feature requests on our project issues. |
| 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.