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

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

endpoints

/get

/search/count

/search/<doctypes>

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'],
                        text_field_rules=lambda schema_field: [], # list of tuples: ('exact'/'inexact'/'natural', <field-name>)
                        multi_match_type='most_fields',
                        multi_match_operator='and'),
        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
Meta
  • License: MIT License (MIT)
  • Author: Adam Kariv
  • Tags data
Classifiers

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