Idunn

• Idunn is an API to get points-of-interest information for QwantMaps.
• The POIs are taken from the mimir ElasticSearch database.
• It also fetches POI data from Wikipedia API and a custom Wikidata Elasticsearch source.
• Why Idunn ? Because she is the wife of Bragi that is also the main mimir API.
• A simple workflow schema of Idunn is presented below.

API

• The API provides its OpenAPI schema with: GET /schema

The main endpoints are:

• /v1/places/{place_id}?lang={lang}&type={type}&verbosity={verbosity} to get the details of a place (admin, street, address or POI). The type parameter belongs to the set {'admin', 'street', 'address', 'poi'}. The verbosity parameter belongs to the set {'long', 'short'}. The default verbosity is long.
• /v1/places?bbox={bbox}&raw_filter=class,subclass&size={size} to get a list of all points of interest matching the given bbox (=left,bot,right,top e.g. bbox=2,48,3,49) and the categories (e.g. raw_filter=*,restaurant&raw_filter=shop,*&raw_filter=bakery,bakery)
• /v1/pois/{poi_id}?lang={lang} is the deprecated route to get the details of a POI.
• /v1/status to get the status of the API and associated ES cluster.
• /v1/metrics to get some metrics on the API that give statistics on the number of requests received, the duration of requests... This endpoint can be scraped by Prometheus.

Running

• The dependencies are managed with Pipenv.
• To run the api you need to do:

pipenv install

• and then:

IDUNN_MIMIR_ES=<url_to_MIMIR_ES> IDUNN_WIKI_ES=<url_to_WIKI_ES> pipenv run python app.py

• you can query the API on port 5000:

curl localhost:5000/v1/places/toto?lang=fr&type=poi

Configuration

The configuration can be given from different ways:

1. a default settings is available in utils/default_settings.yaml
2. a yaml settings file can be given with an env var IDUNN_CONFIG_FILE (the default settings is still loaded and overriden)
3. specific variable can be overriden with env var. They need to be given like "IDUNN_{var_name}={value}" eg IDUNN_MIMIR_ES=...

How to contribute ?

• Idunn comes along with all necessary components to contribute as easily as possible: specifically you don't need to have any Elasticsearch instance running. Idunn uses docker images to simulate the Elasticsearch sources and the Redis. This means that you will need a local docker install to be able to spawn an ES cluster.

• To contribute the common workflow is:

  1. install the dev dependencies: pipenv install --dev
  2. add a test in ./tests for the new feature you propose
  3. implement your feature
  4. run pytest: pipenv run pytest -vv -x