opal-fetcher-mongodb-treedom 0.0.1

An OPAL fetch provider to bring authorization state from MongoDB.

OPAL Fetcher for MongoDB

Made with ❤️ at

Check out OPAL main repo here.

What's in this repo?

An OPAL custom fetch provider to bring authorization state from MongoDB.

How to try this custom fetcher in one command? (Example docker-compose configuration)

You can test this fetcher with the example docker compose file in this repository root. Clone this repo, cd into the cloned repo, and then run:

docker compose up

this docker compose configuration already correctly configures OPAL to load the MongoDB Fetch Provider, and correctly configures OPAL_DATA_CONFIG_SOURCES to include an entry that uses this fetcher.

✏️ How to use this fetcher in your OPAL Setup

1) Build a custom opal-client Dockerfile

The official docker image only contains the built-in fetch providers. You need to create your own Dockerfile (that is based on the official docker image), that includes this fetcher's pip package.

Your Dockerfile should look like this:

FROM permitio/opal-client:latest
RUN pip install --no-cache-dir --user opal-fetcher-mongodb-treedom

2) Build your custom opal-client container

Say your special Dockerfile from step one is called custom_client.Dockerfile.

You must build a customized OPAL container from this Dockerfile, like so:

docker build -t yourcompany/opal-client -f custom_client.Dockerfile .

3) When running OPAL, set OPAL_FETCH_PROVIDER_MODULES

Pass the following environment variable to the OPAL client docker container (comma-separated provider modules):

OPAL_FETCH_PROVIDER_MODULES=opal_common.fetcher.providers,opal_fetcher_mongodb.provider

Notice that OPAL receives a list from where to search for fetch providers. The list in our case includes the built-in providers (opal_common.fetcher.providers) and our custom MongoDB provider.

4) Using the custom provider in your DataSourceEntry objects

Your DataSourceEntry objects (either in OPAL_DATA_CONFIG_SOURCES or in dynamic updates sent via the OPAL publish API) can now include this fetcher's config.

Example value of OPAL_DATA_CONFIG_SOURCES (formatted nicely, but in env var you should pack this to one-line and no-spaces):

{
  "config": {
    "entries": [
      {
        "url": "mongodb://user:password@mongodb/test_database?authSource=admin",
        "config": {
          "fetcher": "MongoDBFetchProvider",
          "database": "opal_fetcher_mongodb",
          "collection": "cities_collection",
          "find": { "query": {} }
        },
        "topics": ["policy_data"],
        "dst_path": "cities"
      }
    ]
  }
}

Notice how config is an instance of MongoDBFetchProvider (code is in opal_fetcher_mongodb/provider.py).

Values for this fetcher config:

• The url is actually a MongoDB uri.
• Your config must include the fetcher key to indicate to OPAL that you use a custom fetcher.
• Your config must include the collection key to indicate what collection to query in MongoDB.
• Your config may include the database key to indicate what database to query in MongoDB. If not specified, the default database will be used.
• Your config must include one of findOne, find or aggregate keys to indicate what query to run against MongoDB.
• Your config may include the transform key to transform the results from the find or aggregate queries.

Query methods

All the three available query methods accept the same input parameters as defined in the MongoDB documentation.

findOne

• findOne - MongoDB docs

Example configuration

{
  "config": {
    "entries": [
      {
        ...
        "config": {
          ...
          "findOne": {
            "query": {
              ...
            },
            "projection": {
              ...
            },
            "options": {
              ...
            }
          }
        }
      }
    ]
  }
}

find

• find - MongoDB docs

Example configuration

{
  "config": {
    "entries": [
      {
        ...
        "config": {
          ...
          "find": {
            "query": {
              ...
            },
            "projection": {
              ...
            },
            "options": {
              ...
            }
          },
          "transform": {
            "first": false,
            "mapKey": "",
            "merge": true
          }
        }
      }
    ]
  }
}

aggregate

• aggregate - MongoDB docs

Example configuration

{
  "config": {
    "entries": [
      {
        ...
        "config": {
          ...
          "aggregate": {
            "pipeline": [
              ...
            ],
            "options": {
              ...
            }
          },
          "transform": {
            "first": false,
            "mapKey": ""
          }
        }
      }
    ]
  }
}

Query transform

transform allows you to transform the results from the find or aggregate queries.

first

transform.first allows you to return only the first result from the query.

Equivalent to the following Python code:

result = query_result[0]

mapKey

transform.mapKey allows you to map the original list-like result to a dictionary-like result using the property specified in the mapKey as the key for the dictionary.

Equivalent to the following Python code:

result = {}
for item in query_result:
    result[item['key']] = item

Only properties in the root of the document can be used as the key for the dictionary.

merge

transform.merge allows you to merge the results from the query into a single document. Duplicate keys will be overwritten by the last document in the list.

Equivalent to the following Python code:

result = {}
for item in query_result:
    for key, value in item.items():
        result[key] = value