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We were using ALL_WORKERS TargetWorkerSet while sending temporary schema creation and cleanup. We(well mostly I) thought that ALL_WORKERS would also include coordinator when it is added as a worker. It turns out that it was FILTERING OUT the coordinator even if it is added as a worker to the cluster. So to have some context here, in repartitions, for each jobId we create (at least we were supposed to) a schema in each worker node in the cluster. Then we partition each shard table into some intermediate files, which is called the PARTITION step. So after this partition step each node has some intermediate files having tuples in those nodes. Then we fetch the partition files to necessary worker nodes, which is called the FETCH step. Then from the files we create intermediate tables in the temporarily created schemas, which is called a MERGE step. Then after evaluating the result, we remove the temporary schemas(one for each job ID in each node) and files. If node 1 has file1, and node 2 has file2 after PARTITION step, it is enough to either move file1 from node1 to node2 or vice versa. So we prune one of them. In the MERGE step, if the schema for a given jobID doesn't exist, the node tries to use the `public` schema if it is a superuser, which is actually added for testing in the past. So when we were not sending schema creation comands for each job ID to the coordinator(because we were using ALL_WORKERS flag, and it doesn't include the coordinator), we would basically not have any schemas for repartitions in the coordinator. The PARTITION step would be executed on the coordinator (because the tasks are generated in the planner part) and it wouldn't give us any error because it doesn't have anything to do with the temporary schemas(that we didn't create). But later two things would happen: - If by chance the fetch is pruned on the coordinator side, we the other nodes would fetch the partitioned files from the coordinator and execute the query as expected, because it has all the information. - If the fetch tasks are not pruned in the coordinator, in the MERGE step, the coordinator would either error out saying that the necessary schema doesn't exist, or it would try to create the temporary tables under public schema ( if it is a superuser). But then if we had the same task ID with different jobID it would fail saying that the table already exists, which is an error we were getting. In the first case, the query would work okay, but it would still not do the cleanup, hence we would leave the partitioned files from the PARTITION step there. Hence ensure_no_intermediate_data_leak would fail. To make things more explicit and prevent such bugs in the future, ALL_WORKERS is named as ALL_NON_COORD_WORKERS. And a new flag to return all the active nodes is added as ALL_DATA_NODES. For repartition case, we don't use the only-reference table nodes but this version makes the code simpler and there shouldn't be any significant performance issue with that. (cherry picked from commit 6532506f4b92b1316eea0812b2bcedb818d3b25c) |
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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.