DataKitCI is a continuous integration service that monitors your GitHub project and tests each branch, tag and pull request. It displays the test results as status indicators in the GitHub UI. It keeps all of its state and logs in DataKit, rather than a traditional relational database, allowing review with the usual Git tools.
Published: 15 Feb 2017
DataKit -- Orchestrate applications using a Git-like dataflow
DataKit is a tool to orchestrate applications using a Git-like dataflow. It revisits the UNIX pipeline concept, with a modern twist: streams of tree-structured data instead of raw text. DataKit allows you to define complex build pipelines over version-controlled data.
There are several components in this repository:
srccontains the main DataKit service. This is a Git-like database to which other services can connect.
cicontains DataKitCI, a continuous integration system that uses DataKit to monitor repositories and store build results.
ci/self-ciis the CI configuration for DataKitCI that tests DataKit itself.
bridge/githubis a service that monitors repositories on GitHub and syncs their metadata with a DataKit database. e.g. when a pull request is opened or updated, it will commit that information to DataKit. If you commit a status message to DataKit, the bridge will push it to GitHub.
bridge/localis a drop-in replacement for
bridge/githubthat just monitors a local Git repository. This is useful for local testing.
The easiest way to use DataKit is to start both the server and the client in containers.
To expose a Git repository as a 9p endpoint on port 5640 on a private network, run:
$ docker network create datakit-net # create a private network $ docker run -it --net datakit-net --name datakit -v <path/to/git/repo>:/data docker/datakit
--name datakit option is mandatory. It will allow the client to connect to a known name on the private network.
You can then start a DataKit client, which will mount the 9p endpoint and expose the database as a filesystem API:
# In an other terminal $ docker run -it --privileged --net datakit-net docker/datakit:client $ ls /db branch remotes snapshots trees
--privileged option is needed because the container will have to mount the 9p endpoint into its local filesystem.
Now you can explore, edit and script
/db. See the Filesystem API for more details.
docker build -t docker/datakit:server -f Dockerfile.server . docker build -t docker/datakit -f Dockerfile . docker run -p 5640:5640 -it --rm docker/datakit --listen-9p=tcp://0.0.0.0:5640
These commands will expose the database's 9p endpoint on port 5640.
$ make depends $ make && make test
For information about command-line options:
$ datakit --help
Prometheus metric reporting
--listen-prometheus 9090 to expose metrics at
Note: there is no encryption and no access control. You are expected to run the database in a container and to not export this port to the outside world. You can either collect the metrics by running a Prometheus service in a container on the same Docker network, or front the service with nginx or similar if you want to collect metrics remotely.
Go bindings are in the
OCaml bindings are in the
examples/ocaml-clientfor an example.
DataKit is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.
>= "0.3.2" & < "0.6.0"
>= "0.9.0" & < "0.10.0"
>= "0.9.0" & < "0.10.0"