A flask blueprint providing an API for accessing and searching an ElasticSearch index created from source datapackages

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  • License: MIT License (MIT)
  • Author: Adam Kariv
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Project description

apies

Travis Coveralls PyPI - Python Version

apies is a flask blueprint providing an API for accessing and searching an ElasticSearch index created from source datapackages.

endpoints

TBD

configuration

Flask configuration for this blueprint:

    from apies import apies_blueprint
    import elasticsearch

    app.register_blueprint(
        apies_blueprint(['path/to/datapackage.json', Package(), ...],
                        elasticsearch.Elasticsearch(...), 
                        'index-to-search-in', 
                        document_doctype='document',
                        dont_highlight=['fields', 'not.to', 'highlight']),
        url_prefix='/search/'
    )

local development

You can start a local development server by following these steps:

  1. Install Dependencies:

    a. Install Docker locally

    b. Install Python dependencies:

    $ pip install dataflows datapackage-pipelines-elasticsearch
$ pip install -e .

  2. Go to the sample/ directory

  3. Start ElasticSearch locally:

    $ ./start_elasticsearch.sh

    This script will wait and poll the server until it's up and running. You can test it yourself by running:

    $ curl -s http://localhost:9200
 {
     "name" : "DTsRT6T",
     "cluster_name" : "elasticsearch",
     "cluster_uuid" : "QnLVHaOYTkmJZzkCG3Hong",
     "version" : {
         "number" : "5.5.2",
         "build_hash" : "b2f0c09",
         "build_date" : "2017-08-14T12:33:14.154Z",
         "build_snapshot" : false,
         "lucene_version" : "6.6.0"
     },
     "tagline" : "You Know, for Search"
 }

  4. Load data into the database

    $ python load_fixtures.py

    You can test that data was loaded:

    $ curl -s http://localhost:9200/jobs/_count?pretty
 {
     "count" : 3516,
     "_shards" : {
             "total" : 5,
             "successful" : 5,
             "failed" : 0
     }
 }

  5. Start the sample server

    $ python server.py 
 * Serving Flask app "server" (lazy loading)
 * Environment: production
 WARNING: Do not use the development server in a production environment.
 Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

  6. Now you can hit the server's endpoints, for example:

         $ curl -s 'localhost:5000/api/search/jobs?q=engineering&size=2' | jq
     127.0.0.1 - - [26/Jun/2019 10:45:31] "GET /api/search/jobs?q=engineering&size=2 HTTP/1.1" 200 -
     {
         "search_counts": {
             "_current": {
             "total_overall": 617
             }
         },
         "search_results": [
             {
             "score": 18.812,
             "source": {
                 "# Of Positions": "5",
                 "Additional Information": "TO BE APPOINTED TO ANY CIVIL <em>ENGINEERING</em> POSITION IN BRIDGES, CANDIDATES MUST POSSESS ONE YEAR OF CIVIL <em>ENGINEERING</em> EXPERIENCE IN BRIDGE DESIGN, BRIDGE CONSTRUCTION, BRIDGE MAINTENANCE OR BRIDGE INSPECTION.",
                 "Agency": "DEPARTMENT OF TRANSPORTATION",
                 "Business Title": "Civil Engineer 2",
                 "Civil Service Title": "CIVIL ENGINEER",
                 "Division/Work Unit": "<em>Engineering</em> Review & Support",
         ...
     }

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for akariv from gravatar.com akariv

Unverified details

These details have not been verified by PyPI
Project links
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  • License: MIT License (MIT)
  • Author: Adam Kariv
  • Tags data
Classifiers

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