Metadata-Version: 2.1
Name: apies
Version: 1.6.6
Summary: A flask blueprint providing an API for accessing and searching an ElasticSearch index created from source datapackages
Home-page: https://github.com/OpenBudget/apies
Author: Adam Kariv
Author-email: adam.kariv@gmail.com
License: MIT
Description: # apies
        
        [![Travis](https://img.shields.io/travis/OpenBudget/apies/master.svg)](https://travis-ci.org/datahq/apies)
        [![Coveralls](http://img.shields.io/coveralls/OpenBudget/apies.svg?branch=master)](https://coveralls.io/r/OpenBudget/apies?branch=master)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/apies.svg)
        
        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>
        
        ### `download/<doctypes>`
        
        Downloads search results in either csv, xls or xlsx format.
        
        Query parameters that can be send:
        - **types_formatted**: The type of the documents to search
        - **search_term**: The Elastic search query
        - **size**: Number of hits to return
        - **offset**: Whether or not term offsets should be returned
        - **filters**: What offset to use for the pagination
        - **dont_highlight**:
        - **from_date**: If there should be a date range applied to the search, and from what date
        - **to_date**: If there should be a date range applied to the search, and until what date
        - **order**:
        - **file_format**: The format of the file to be returned, either 'csv', 'xls' or 'xlsx'.
        If not passed the file format will be xlsx
        - **file_name**: The name of the file to be returned, by default the name will be 'search_results'
        - **column_mapping**: If the columns should get a different name then in the
        original data, a column map can be send, for example:
        ```
        {
          "עיר": "address.city",
          "תקציב": "details.budget"
        }
        ```
        
        For example, get a csv file with column mapping:
        ```
        http://localhost:5000/api/download/jobs?q=engineering&size=2&file_format=csv&file_name=my_results&column_mapping={%22mispar%22:%22Job%20ID%22}
        ```
        
        Or get an xslx file without column mapping:
        ```
        http://localhost:5000/api/download/jobs?q=engineering&size=2&file_format=xlsx&file_name=my_results
        ```
        
        ## configuration
        
        Flask configuration for this blueprint:
        
        
        ```python
        
            from apies import apies_blueprint
            import elasticsearch
        
            app.register_blueprint(
                apies_blueprint(['path/to/datapackage.json', Package(), ...],
                                elasticsearch.Elasticsearch(...), 
                                {'doc-type-1': 'index-for-doc-type-1', ...}, 
                                'index-for-documents',
                                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:
        
            ```bash
            $ pip install dataflows dataflows-elasticsearch
            $ pip install -e .
            ```
        2. Go to the `sample/` directory
        3. Start ElasticSearch locally:
           ```bash
           $ ./start_elasticsearch.sh
           ```
        
           This script will wait and poll the server until it's up and running.
           You can test it yourself by running:
           ```bash
           $ curl -s http://localhost:9200
                {
                "name" : "99cd2db44924",
                "cluster_name" : "docker-cluster",
                "cluster_uuid" : "nF9fuwRyRYSzyQrcH9RCnA",
                "version" : {
                    "number" : "7.4.2",
                    "build_flavor" : "default",
                    "build_type" : "docker",
                    "build_hash" : "2f90bbf7b93631e52bafb59b3b049cb44ec25e96",
                    "build_date" : "2019-10-28T20:40:44.881551Z",
                    "build_snapshot" : false,
                    "lucene_version" : "8.2.0",
                    "minimum_wire_compatibility_version" : "6.8.0",
                    "minimum_index_compatibility_version" : "6.0.0-beta1"
                },
                "tagline" : "You Know, for Search"
                }
           ```
        4. Load data into the database
           ```bash
           $ DATAFLOWS_ELASTICSEARCH=localhost:9200 python load_fixtures.py
           ```
           You can test that data was loaded:
           ```bash
           $ curl -s http://localhost:9200/jobs-job/_count?pretty
            {
                "count" : 1757,
                "_shards" : {
                    "total" : 1,
                    "successful" : 1,
                    "skipped" : 0,
                    "failed" : 0
                }
            }
           ```
        5. Start the sample server
           ```bash
           $ 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:
           ```bash
                $ 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",
                    ...
                }
            ```
Keywords: data
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: develop
