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Update README.md for Citus 11

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Marco Slot 2022-06-17 14:50:43 +02:00 committed by Marco Slot
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| **<br/>Citus is now 100% open source and supports querying from any node.<br/><img width=1000/><br/>Read about it on the [Citus 11.0 release blog](https://www.citusdata.com/blog/2022/06/17/citus-11-goes-fully-open-source/) and the [Citus Updates page](https://www.citusdata.com/updates/).<br/><br/>** |
|---|
<br/>
<br/>
![Citus Banner](/citus-readme-banner.png)
[![Latest Docs](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://docs.citusdata.com/)
@ -19,6 +25,7 @@ With Citus, you extend your PostgreSQL database with new superpowers:
- **References tables** are replicated to all nodes for joins and foreign keys from distributed tables and maximum read performance.
- **Distributed query engine** routes and parallelizes SELECT, DML, and other operations on distributed tables across the cluster.
- **Columnar storage** compresses data, speeds up scans, and supports fast projections, both on regular and distributed tables.
- **Query from any node** enables you to utilize the full capacity of your cluster for distributed queries
You can use these Citus superpowers to make your Postgres database scale-out ready on a single Citus node. Or you can build a large cluster capable of handling **high transaction throughputs**, especially in **multi-tenant apps**, run **fast analytical queries**, and process large amounts of **time series** or **IoT data** for **real-time analytics**. When your data size and volume grow, you can easily add more worker nodes to the cluster and rebalance the shards.
@ -86,14 +93,14 @@ Install packages on Ubuntu / Debian:
```bash
curl https://install.citusdata.com/community/deb.sh > add-citus-repo.sh
sudo bash add-citus-repo.sh
sudo apt-get -y install postgresql-14-citus-10.2
sudo apt-get -y install postgresql-14-citus-11.0
```
Install packages on CentOS / Fedora / Red Hat:
```bash
curl https://install.citusdata.com/community/rpm.sh > add-citus-repo.sh
sudo bash add-citus-repo.sh
sudo yum install -y citus102_14
sudo yum install -y citus110_14
```
To add Citus to your local PostgreSQL database, add the following to `postgresql.conf`:
@ -115,7 +122,7 @@ If you want to set up a multi-node cluster, you can also set up additional Postg
```sql
-- before adding the first worker node, tell future worker nodes how to reach the coordinator
-- SELECT citus_set_coordinator_host('10.0.0.1', 5432);
SELECT citus_set_coordinator_host('10.0.0.1', 5432);
-- add worker nodes
SELECT citus_add_node('10.0.0.2', 5432);
@ -320,6 +327,7 @@ Data in distributed tables is stored in “shards”, which are actually just re
When you send a query in which all (co-located) distributed tables have the same filter on the distribution column, Citus will automatically detect that and send the whole query to the worker node that stores the data. That way, arbitrarily complex queries are supported with minimal routing overhead, which is especially useful for scaling transactional workloads. If queries do not have a specific filter, each shard is queried in parallel, which is especially useful in analytical workloads. The Citus distributed executor is adaptive and is designed to handle both query types at the same time on the same system under high concurrency, which enables large-scale mixed workloads.
As of Citus 11.0, the schema and metadata of distributed tables and reference tables are automatically synchronized to all the nodes in the cluster. That way, you can connect to any node to run distributed queries. Schema changes and cluster administration still need to go through the coordinator.
## When to use Citus
@ -335,7 +343,7 @@ Citus is uniquely capable of scaling both analytical and transactional workloads
- **[Time series data](http://docs.citusdata.com/en/stable/use_cases/timeseries.html)**:
Citus enables you to process and analyze very large amounts of time series data. The biggest Citus clusters store well over a petabyte of time series data and ingest terabytes per day.
Citus integrates seamlessly with [Postgres table partitioning](https://www.postgresql.org/docs/current/ddl-partitioning.html) and [pg_partman](https://www.citusdata.com/blog/2018/01/24/citus-and-pg-partman-creating-a-scalable-time-series-database-on-PostgreSQL/), which can speed up queries and writes on time series tables. You can take advantage of Citus’s parallel, distributed query engine for fast analytical queries, and use the built-in *columnar storage* to compress old partitions.
Citus integrates seamlessly with [Postgres table partitioning](https://www.postgresql.org/docs/current/ddl-partitioning.html) and has [built-in functions for partitioning by time](https://www.citusdata.com/blog/2021/10/22/how-to-scale-postgres-for-time-series-data-with-citus/), which can speed up queries and writes on time series tables. You can take advantage of Citus’s parallel, distributed query engine for fast analytical queries, and use the built-in *columnar storage* to compress old partitions.
Example users: [MixRank](https://www.citusdata.com/customers/mixrank), [Windows team](https://techcommunity.microsoft.com/t5/azure-database-for-postgresql/architecting-petabyte-scale-analytics-by-scaling-out-postgres-on/ba-p/969685)
@ -349,15 +357,15 @@ Citus is uniquely capable of scaling both analytical and transactional workloads
- **Geospatial**:
Because of the powerful [PostGIS](https://postgis.net/) extension to Postgres that adds support for geographic objects into Postgres, many people run spatial/GIS applications on top of Postgres. And since spatial location information has become part of our daily life, well, there are more geospatial applications than ever. When your Postgres database needs to scale out to handle an increased workload, Citus is a good fit.
Example geospatial users: [Helsinki Regional Transportation Authority (HSL)](https://customers.microsoft.com/en-us/story/845146-transit-authority-improves-traffic-monitoring-with-azure-database-for-postgresql-hyperscale), [MobilityDB]( https://www.citusdata.com/blog/2020/11/09/analyzing-gps-trajectories-at-scale-with-postgres-mobilitydb/).
Example geospatial users: [Helsinki Regional Transportation Authority (HSL)](https://customers.microsoft.com/story/845146-transit-authority-improves-traffic-monitoring-with-azure-database-for-postgresql-hyperscale), [MobilityDB](https://www.citusdata.com/blog/2020/11/09/analyzing-gps-trajectories-at-scale-with-postgres-mobilitydb/).
## Need Help?
- **Slack**: Ask questions in our Citus community [Slack channel](https://slack.citusdata.com).
- **GitHub issues**: Please submit issues via [GitHub issues](https://github.com/citusdata/citus/issues).
- **Documentation**: Our [Citus docs](https://docs.citusdata.com ) have a wealth of resources, including sections on [query performance tuning](https://docs.citusdata.com/en/stable/performance/performance_tuning.html), [useful diagnostic queries](https://docs.citusdata.com/en/stable/admin_guide/diagnostic_queries.html), and [common error messages](https://docs.citusdata.com/en/stable/reference/common_errors.html).
- **Docs issues**: You can also submit documentation issues via [GitHub
issues for our Citus docs](https://github.com/citusdata/citus_docs/issues).
- **Docs issues**: You can also submit documentation issues via [GitHub issues for our Citus docs](https://github.com/citusdata/citus_docs/issues).
- **Updates**: Learn about what's new in each Citus version on the [Citus Updates page](https://www.citusdata.com/updates/).
## Contributing