Docker images for the mongo-connector.
For more information about this image and its history, please see the relevant manifest file in the yeasy/docker-mongo-connector GitHub repo.
Docker image with mongo-connector installed. The image is built based on Python 3.11 slim.
The docker image is auto built at https://registry.hub.docker.com/u/yeasy/mongo-connector/.
FROM yeasy/mongo-connector:latestBy default, it will connect mongo node ($MONGO or the mongo host, on port $MONGOPORT or 27017) and elasticsearch node ($ELASTICSEARCH or the elasticsearch host, on port $ELASTICPORT or 9200).
Boot two containers with name mongo (config to cluster) and elasticsearch.
$ docker run -d --link=mongo:mongo --link=elasticsearch:elasticsearch yeasy/mongo-connectorIt will connect the two containers together to sync data between each other.
The image is based on official python:3.11-slim.
Config timezone to UTC.
Install mongo-connector:3.1.1 and elastic2-doc-manager.
mongo-connector is a legacy upstream project and does not track recent MongoDB/Elasticsearch major versions. This image updates to the latest released connector line, but compatibility still depends on the connector project itself.
This image is officially supported on Docker version 1.7.1.
Support for older versions (down to 1.0) is provided on a best-effort basis.
Be sure to familiarize yourself with the repository's README.md file before attempting a pull request.
If you have any problems with or questions about this image, please contact us through a GitHub issue.
You can also reach many of the official image maintainers via the email.
You are invited to contribute new features, fixes, or updates, large or small; we are always thrilled to receive pull requests, and do our best to process them as fast as we can.
Before you start to code, we recommend discussing your plans through a GitHub issue, especially for more ambitious contributions. This gives other contributors a chance to point you in the right direction, give you feedback on your design, and help you find out if someone else is working on the same thing.