596d48448e
* refactor(elasticsearch-setup-job): create-indices.sh readability The script contains many copy-pasting and is not easy to follow. Add comments, extract commonly used operations into functions, unify approaches. * fix(elasticsearch-setup-job): AWS indices creation Fix the issue where Amazon OpenSearch (AWS ES) indices are incorrectly initialised and the Analytics screen shows errors only. * feat(elasticsearch-setup-job): configuration hint mention USE_AWS_ELASTICSEARCH env value if it seems it's set the wrong way * fix(elasticsearch-setup-job): silent curl * fix(elasticsearch-setup-job): better USE_AWS_ELASTICSEARCH hint * docs(elasticsearch-setup-job): index dropping explained - more comments - more defensive approach - index file renamed * fix(elasticsearch-setup-job): script fixes * merge(elasticsearch-setup-job): merging in PR #5937 * merge(elasticsearch-setup-job): merging in PR #5963 * merge(elasticsearch-setup-job): merging in PR #5975 Co-authored-by: Pedro Silva <pedro@acryl.io>
| title | hide_title |
|---|---|
| Deploying with Docker | true |
Docker Images
Prerequisites
You need to install docker and docker-compose (if using Linux; on Windows and Mac compose is included with Docker Desktop).
Make sure to allocate enough hardware resources for Docker engine. Tested & confirmed config: 2 CPUs, 8GB RAM, 2GB Swap area.
Quickstart
The easiest way to bring up and test DataHub is using DataHub Docker images which are continuously deployed to Docker Hub with every commit to repository.
You can easily download and run all these images and their dependencies with our quick start guide.
DataHub Docker Images:
Do not use latest or debug tags for any of the image as those are not supported and present only due to leagcy reasons. Please use head or tags specific for versions like v0.8.40. For production we recommend using version specific tags not head.
- linkedin/datahub-ingestion - This contains the Python CLI. If you are looking for docker image for every minor CLI release you can find them under acryldata/datahub-ingestion.
- linkedin/datahub-gms.
- linkedin/datahub-frontend-react
- linkedin/datahub-mae-consumer
- linkedin/datahub-mce-consumer
- acryldata/datahub-upgrade
- linkedin/datahub-kafka-setup
- linkedin/datahub-elasticsearch-setup
- acryldata/datahub-mysql-setup
- acryldata/datahub-postgres-setup
- acryldata/datahub-actions. Do not use
acryldata/acryl-datahub-actionsas that is deprecated and no longer used.
Dependencies:
Ingesting demo data.
If you want to test ingesting some data once DataHub is up, use the ./docker/ingestion/ingestion.sh script or datahub docker ingest-sample-data. See the quickstart guide for more details.
Using Docker Images During Development
See Using Docker Images During Development.
Building And Deploying Docker Images
We use GitHub Actions to build and continuously deploy our images. There should be no need to do this manually; a successful release on Github will automatically publish the images.
Building images
This is not our recommended development flow and most developers should be following the Using Docker Images During Development guide.
To build the full images (that we are going to publish), you need to run the following:
COMPOSE_DOCKER_CLI_BUILD=1 DOCKER_BUILDKIT=1 docker-compose -p datahub build
This is because we're relying on builtkit for multistage builds. It does not hurt also set DATAHUB_VERSION to
something unique.