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When Citus needs to parallelize queries on the local node (e.g., the node executing the distributed query and the shards are the same), we need to be mindful about the connection management. The reason is that the client backends that are running distributed queries are competing with the client backends that Citus initiates to parallelize the queries in order to get a slot on the max_connections. In that regard, we implemented a "failover" mechanism where if the distributed queries cannot get a connection, the execution failovers the tasks to the local execution. The failover logic is follows: - As the connection manager if it is OK to get a connection - If yes, we are good. - If no, we fail the workerPool and the failure triggers the failover of the tasks to local execution queue The decision of getting a connection is follows: /* * For local nodes, solely relying on citus.max_shared_pool_size or * max_connections might not be sufficient. The former gives us * a preview of the future (e.g., we let the new connections to establish, * but they are not established yet). The latter gives us the close to * precise view of the past (e.g., the active number of client backends). * * Overall, we want to limit both of the metrics. The former limit typically * kics in under regular loads, where the load of the database increases in * a reasonable pace. The latter limit typically kicks in when the database * is issued lots of concurrent sessions at the same time, such as benchmarks. */ |
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README.md
What is Citus?
- Open-source PostgreSQL extension (not a fork)
- Built to scale out across multiple nodes
- Distributed engine for query parallelization
- Database designed to scale out multi-tenant applications, real-time analytics dashboards, and high-throughput transactional workloads
Citus is an open source extension to Postgres that distributes your data and your queries across multiple nodes. Because Citus is an extension to Postgres, and not a fork, Citus gives developers and enterprises a scale-out database while keeping the power and familiarity of a relational database. As an extension, Citus supports new PostgreSQL releases, and allows you to benefit from new features while maintaining compatibility with existing PostgreSQL tools.
Citus serves many use cases. Three common ones are:
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Multi-tenant & SaaS applications: Most B2B applications already have the notion of a tenant / customer / account built into their data model. Citus allows you to scale out your transactional relational database to 100K+ tenants with minimal changes to your application.
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Real-time analytics: Citus enables ingesting large volumes of data and running analytical queries on that data in human real-time. Example applications include analytic dashboards with sub-second response times and exploratory queries on unfolding events.
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High-throughput transactional workloads: By distributing your workload across a database cluster, Citus ensures low latency and high performance even with a large number of concurrent users and high volumes of transactions.
To learn more, visit citusdata.com and join the Citus slack to stay on top of the latest developments.
Getting started with Citus
The fastest way to get up and running is to deploy Citus in the cloud. You can also setup a local Citus database cluster with Docker.
Hyperscale (Citus) on Azure Database for PostgreSQL
Hyperscale (Citus) is a deployment option on Azure Database for PostgreSQL, a fully-managed database as a service. Hyperscale (Citus) employs the Citus open source extension so you can scale out across multiple nodes. To get started with Hyperscale (Citus), learn more on the Citus website or use the Hyperscale (Citus) Quickstart in the Azure docs.
Citus Cloud
Citus Cloud runs on top of AWS as a fully managed database as a service. You can provision a Citus Cloud account at https://console.citusdata.com and get started with just a few clicks.
Local Citus Cluster
If you're looking to get started locally, you can follow the following steps to get up and running.
- Install Docker Community Edition and Docker Compose
- Mac:
- Download and install Docker.
- Start Docker by clicking on the application’s icon.
- Linux:
The above version of Docker Compose is sufficient for running Citus, or you can install the latest version.curl -sSL https://get.docker.com/ | sh sudo usermod -aG docker $USER && exec sg docker newgrp `id -gn` sudo systemctl start docker sudo curl -sSL https://github.com/docker/compose/releases/download/1.11.2/docker-compose-`uname -s`-`uname -m` -o /usr/local/bin/docker-compose sudo chmod +x /usr/local/bin/docker-compose
- Pull and start the Docker images
curl -sSLO 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
- 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
tutorial for a hands-on introduction or the documentation for a more comprehensive reference. |
| 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. | |
| Citus Blog | Read our Citus Data Blog for posts on Postgres, Citus, and scaling your database. |
Contributing
Citus is built on and of open source, and we welcome your contributions. 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:
- Algolia uses Citus to provide real-time analytics for over 1B searches per day. For faster insights, they also use TopN and HLL extensions. User Story
- Heap uses Citus to run dynamic funnel, segmentation, and cohort queries across billions of users and has more than 700B events in their Citus database cluster. Watch Video
- Pex uses Citus to ingest 80B data points per day and analyze that data in real-time. They use a 20+ node cluster on Google Cloud. User Story
- MixRank uses Citus to efficiently collect and analyze vast amounts of data to allow inside B2B sales teams to find new customers. User Story
- Agari uses Citus to secure more than 85 percent of U.S. consumer emails on two 6-8 TB clusters. User Story
- Copper (formerly ProsperWorks) powers a cloud CRM service with Citus. User Story
You can read more user stories about how they employ Citus to scale Postgres for both multi-tenant SaaS applications as well as real-time analytics dashboards here.
Copyright © Citus Data, Inc.