3dac0a4d14
When executing queries with citus.task_executor = 'real-time', query execution could, so far, spend a significant amount of time sleeping. That's because we were a) sleeping after several phases of query execution, even if we're not waiting for network IO b) sleeping for a fixed amount of time when waiting for network IO; often a lot longer than actually required. Just reducing the amount of time slept isn't a real solution, because that just increases CPU usage. Instead have the real-time executor's ManageTaskExecution return whether a task is currently being processed, waiting for reads or writes, or failed. When all tasks are waiting for IO use poll() to wait for IO readyness. That requires to slightly redefine how connection timeouts are handled: before we counted the number of times ManageTaskExecution() was called, and compared that with the timeout divided by the task check interval. That, if processing of tasks took a while, could significantly increase the time till a timeout occurred. Because it was based on the ManageTaskExecution() being called on a constant interval, this approach isn't feasible anymore. Instead measure the actual time since connection establishment was started. That could in theory, if task processing takes a very long time, lead to few passes over PQconnectPoll(). The problem of sleeping too much also exists for the 'task-tracker' executor, but is generally less problematic there, as processing the individual tasks usually will take longer. That said, for e.g. the regression tests it'd be helpful to use a similar approach. |
||
|---|---|---|
| src | ||
| .gitattributes | ||
| .gitignore | ||
| .travis.yml | ||
| CHANGELOG.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| Makefile | ||
| Makefile.global.in | ||
| README.md | ||
| autogen.sh | ||
| configure | ||
| configure.in | ||
| github-banner.png | ||
| prep_buildtree | ||
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.