anahack.py

  
def create_filter_pipeline(body):
    "Creates pipeline of filters based on the global filters present on the dashboard"
    form_id = body.get("formId")
    start_date = body.get("startDate")
    end_date = body.get("endDate")
    date_field = body.get("dateField", "_created_at")
    user_id_array = body.get("userId")

    filter_pipeline = []
    if form_id:
        form_match = {
            'match': {
                'formId': form_id
            }
        }
        filter_pipeline.append(form_match)

    is_active_match = {
        'match': {
            'isActive': True
        }
    }

    filter_pipeline.append(is_active_match)

    client_match = {
        'match': {
            'client': 'android'
        }
    }

    filter_pipeline.append(client_match)

    date_range_match = {
        'range': {
            date_field: {
                'gte': start_date,
                'lte': end_date
            }
        }
    }
    filter_pipeline.append(date_range_match)

    if user_id_array:
        user_id_array = ["_User${0}".format(user_id) for user_id in user_id_array]
        user_id_match = {
            'terms': {
                '_p_createdBy': user_id_array
            }
        }
        filter_pipeline.append(user_id_match)

    return filter_pipeline



def run():
    test = {
        'formId': '324fds',
        'startDate': 'fff',
        'endDate': 'fff',
        'dateField': 'fff',
        'userId': 'dfsfd'
    }

    filter_pipeline = create_filter_pipeline(test)

    query_body = {
        'bool': {
            'filter': filter_pipeline
        }
    }

    # Add user agregation
    aggregation = {
        'responses_over_time': {
            "date_histogram": {
                "field": "fdsf",
                "interval": "dsfsdf"
            }
        }
    }

    aggregation_body = {
        'size': 0,
        'query': query_body,
        'aggs': aggregation
    }

    # print("==Aggregation body==", aggregation_body)
    #index_name, doc_type = "dsfdsf", "fdsdf"
    #result = es.search(index=index_name, body=aggregation_body)
    #error = result.get('error')
    #if error is None:
    #    result = result.get("aggregations").get("responses_over_time")

    # Add slack notification on failure
    print(aggregation_body)

run()