Mongo Connector
Docker images for the mongo-connector.
Supported tags and respective Dockerfile links
For more information about this image and its history, please see the relevant manifest file in the yeasy/docker-mongo-connector GitHub repo.
What is docker-mongo-connector?
Docker image with mongo-connector installed. The image is built based on Python 3.4.3.
How to use this image?
The docker image is auto built at https://registry.hub.docker.com/u/yeasy/mongo-connector/.
In Dockerfile
FROM yeasy/mongo-connector:latestLocal Run
By 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.
Which image is based on?
The image is based on official python:3.4.3.
What has been changed?
Config TZ
Config timezone to Asia/Shanghai.
Install mongo-connector
Install the mongo-connector:2.1.
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.
User Feedback
Documentation
Be sure to familiarize yourself with the repository's README.md file before attempting a pull request.
Issues
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.
Contributing
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.