Metadata-Version: 2.1
Name: j2v
Version: 1.6.0
Summary: A tool to generate Looker views and explores from JSONs
Home-page: https://github.com/Cimpress-MCP/j2v
Author: CIMBA - Cimpress Technology
Author-email: cimba@cimpress.com
License: UNKNOWN
Description: [![PyPI version](https://badge.fury.io/py/j2v.svg)](https://badge.fury.io/py/j2v) 
        
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        # JSONs to Looker views (J2V)
        
        J2V is a simple command-line tool to convert JSON to [Looker](https://looker.com/) readable files in forms of [Looker Views](https://docs.looker.com/reference/view-params/view) and [Looker Explores](https://docs.looker.com/reference/explore-params/explore).
        
        Also it outputs an SQL with proper paths and explosion expressions.
        
        This is useful to be used in combination with databases that are focusing on schema-on-read, and data is stored in raw JSON instead of exploded into columns of a table or view.
        
        ## Example use case
        
        You have a table in your database. This table contains a column containing JSONs (one JSON per row). You are very curious how these data look like exploded, but you do not want to spend 2h going through the JSON structure and specifying all the fields just to surface them in Looker.
        
        With J2V all the structures are discovered automatically and two files are generated - a Looker View and Looker Explore. All you need to do is copy/paste the output of this command line tool into your Looker project and you can start exploring.
        
        # Usage
        
        ## Requirements
        
        [Python 3](https://www.python.org/downloads/) must be installed.
        
        ## How to run
        * use code from github or
        * `pip install j2v`
        
        ## Parameters
        
        * `json_files`: Files in JSON format, representing the data stored in a table
        * `output_view`: Name of Looker View output file to be created
        * `output_explore`: Name of Looker model output file to be created
        * `sql_table_name`: Name of the DB table to be used (this is only used in the LookML files; no actual connection to a database will be done as part of this tool)
        * `table_alias`: Name of the table alias 
        * `column_name`: Name of the column in the DB table as specified in `sql_table_name`. (this is only used in the LookML files; no actual connection to a database will be done as part of this tool)
        * `primary_key`: Name of the primary key from JSON field
        * `sql_dialect`: Specifies the sql dialect of the output. [snowflake | bigquery]
        
        ## Output
        
        * `output_view`: File containing definitions of Looker views (see [examples](./examples/) directory in this repository)
        * `output_explore`: File containing definition of looker explore exploding the structures (see [examples](./examples/) directory in this repository)
        
        ## Example usage
        
        ### Using all parameters
        
        `python main.py --json_files data1.json data2.json --output_view RESTAURANT_CHAIN --output_explore RESTAURANT_CHAIN --column_name DATA --sql_table_name RESTAURANT_DETAILS --table_alias chains_table --handle_null_values_in_sql true --primary_key apiVersion`
        ### Using only mandatory parameters
        
        `python3 main.py --json_files order_example.json order_example2.json order_example3.json`<br />
        
        # Contribution
        
        ## Project structure:
        
        * `j2v` - source code of a package
        * `examples` - working examples
        * `tests` - tests
        
        ## Contribute
        
        1. If unsure, open an issue for a discussion
        1. Create a fork
        1. Make your change
        1. Make a pull request
        1. Happy contribution!
        
        ## EXAMPLE
        
        ### Input: 
        ```json
        {
          "apiVersion": "v3.4",
          "data Provider": "Eat me",
          "restaurants": [
            {
              "name": "Super Burger",
              "city": "Sydney",
              "country": "Australia",
              "address": "Big Street 3",
              "currency": "AUD",
              "openTime": 1571143824,
              "menu": [
                {
                  "dishName": "BurgerPlus",
                  "price": 10,
                  "ingredients": ["Meat", "Cheese", "Bun"]
                }
              ]
            }
          ],
          "headquarter": {
            "employees": 36,
            "city": "Olsztyn",
            "country": "Poland",
            "building": {
              "address": "3 Maja 10",
              "floors": [1, 2, 7]
            }
          },
          "dataGenerationTimestamp": "2019-03-30T11:30:00.812Z",
          "payloadPrimaryKeyValue": "3ab21b54-22d6-473c-b055-4430f8927d4c",
          "version": null
        }
        ```
        
        ### Ouput:
        
        #### SQL output:
        
        - Snowflake [Default]
        - BigQuery
        
        ```SNOWFLAKE SQL
        
         ---VIEW WITH NUll VALUE HANDLING---
        
        
        SELECT
        ---chains_table Information
        IFNULL(chains_table."DATA":"apiVersion"::string,'N/A') AS API_VERSION,
        IFNULL(chains_table."DATA":"data Provider"::string,'N/A') AS DATA_PROVIDER,
        IFNULL(chains_table."DATA":"headquarter":"building":"address"::string,'N/A') AS HEADQUARTER_BUILDING_ADDRESS,
        IFNULL(chains_table."DATA":"headquarter":"city"::string,'N/A') AS HEADQUARTER_CITY,
        IFNULL(chains_table."DATA":"headquarter":"country"::string,'N/A') AS HEADQUARTER_COUNTRY,
        IFNULL(chains_table."DATA":"headquarter":"employees"::number,0) AS HEADQUARTER_EMPLOYEES,
        IFNULL(chains_table."DATA":"payloadPrimaryKeyValue"::string,'N/A') AS PAYLOAD_PRIMARY_KEY_VALUE,
        IFNULL(chains_table."DATA":"version"::string,'N/A') AS VERSION,
        chains_table."DATA":"dataGenerationTimestamp"::timestamp AS DATA_GENERATION_TIMESTAMP,
        ---restaurants Information
        IFNULL(restaurants.VALUE:"address"::string,'N/A') AS RESTAURANTS_ADDRESS,
        IFNULL(restaurants.VALUE:"city"::string,'N/A') AS RESTAURANTS_CITY,
        IFNULL(restaurants.VALUE:"country"::string,'N/A') AS RESTAURANTS_COUNTRY,
        IFNULL(restaurants.VALUE:"currency"::string,'N/A') AS RESTAURANTS_CURRENCY,
        IFNULL(restaurants.VALUE:"name"::string,'N/A') AS RESTAURANTS_NAME,
        IFNULL(restaurants.VALUE:"openTime"::number,0) AS RESTAURANTS_OPEN_TIME,
        ---restaurants_menu Information
        IFNULL(restaurants_menu.VALUE:"dishName"::string,'N/A') AS RESTAURANTS_MENU_DISH_NAME,
        IFNULL(restaurants_menu.VALUE:"price"::number,0) AS RESTAURANTS_MENU_PRICE,
        ---restaurants_menu_ingredients Information
        IFNULL(restaurants_menu_ingredients.VALUE::string,'N/A') AS RESTAURANTS_MENU_INGREDIENTS_VALUE,
        ---headquarter_building_floors Information
        IFNULL(headquarter_building_floors.VALUE::number,0) AS HEADQUARTER_BUILDING_FLOORS_VALUE
        FROM RESTAURANT_DETAILS AS chains_table,
        LATERAL FLATTEN(OUTER => TRUE, INPUT => chains_table."DATA":"restaurants") restaurants,
        LATERAL FLATTEN(OUTER => TRUE, INPUT => restaurants.VALUE:"menu") restaurants_menu,
        LATERAL FLATTEN(OUTER => TRUE, INPUT => restaurants_menu.VALUE:"ingredients") restaurants_menu_ingredients,
        LATERAL FLATTEN(OUTER => TRUE, INPUT => chains_table."DATA":"headquarter":"building":"floors") headquarter_building_floors
        ```
        
        ``` BIGQUERY SQL
        
         ---VIEW WITH NUll VALUE HANDLING---
        SELECT
        ---chains_table Information
        IFNULL(chains_table.DATA.apiVersion,'N/A') AS API_VERSION,
        IFNULL(chains_table.DATA.data Provider,'N/A') AS DATA_PROVIDER,
        IFNULL(chains_table.DATA.headquarter.building.address,'N/A') AS HEADQUARTER_BUILDING_ADDRESS,
        IFNULL(chains_table.DATA.headquarter.city,'N/A') AS HEADQUARTER_CITY,
        IFNULL(chains_table.DATA.headquarter.country,'N/A') AS HEADQUARTER_COUNTRY,
        IFNULL(chains_table.DATA.headquarter.employees,0) AS HEADQUARTER_EMPLOYEES,
        IFNULL(chains_table.DATA.payloadPrimaryKeyValue,'N/A') AS PAYLOAD_PRIMARY_KEY_VALUE,
        IFNULL(chains_table.DATA.version,'N/A') AS VERSION,
        chains_table.DATA.dataGenerationTimestamp AS DATA_GENERATION_TIMESTAMP,
        ---headquarter_building_floors Information
        IFNULL(headquarter_building_floors.,0) AS HEADQUARTER_BUILDING_FLOORS,
        ---restaurants Information
        IFNULL(restaurants.address,'N/A') AS RESTAURANTS_ADDRESS,
        IFNULL(restaurants.city,'N/A') AS RESTAURANTS_CITY,
        IFNULL(restaurants.country,'N/A') AS RESTAURANTS_COUNTRY,
        IFNULL(restaurants.currency,'N/A') AS RESTAURANTS_CURRENCY,
        IFNULL(restaurants.name,'N/A') AS RESTAURANTS_NAME,
        IFNULL(restaurants.openTime,0) AS RESTAURANTS_OPEN_TIME,
        ---restaurants_menu Information
        IFNULL(restaurants_menu.dishName,'N/A') AS RESTAURANTS_MENU_DISH_NAME,
        IFNULL(restaurants_menu.price,0) AS RESTAURANTS_MENU_PRICE,
        ---restaurants_menu_ingredients Information
        IFNULL(restaurants_menu_ingredients.,'N/A') AS RESTAURANTS_MENU_INGREDIENTS
        FROM RESTAURANT_DETAILS AS chains_table
        LEFT JOIN UNNEST(chains_table.DATA.headquarter.building.floors) AS headquarter_building_floors
        LEFT JOIN UNNEST(chains_table.DATA.restaurants) AS restaurants
        LEFT JOIN UNNEST(restaurants.menu) AS restaurants_menu
        LEFT JOIN UNNEST(restaurants_menu.ingredients) AS restaurants_menu_ingredients
        ```
        
        #### Ouput files:
        
        ##### View file:
        
        ```LookML
        
        
        view: chains_table { 
          sql_table_name: RESTAURANT_DETAILS ;;
        
          dimension: address {
            description: "Address"
            type: string
            sql: ${TABLE}."DATA":"headquarter":"building":"address"::string ;;
            group_label: "Building"
          }
            
          dimension: api_version {
            description: "Api version"
            primary_key: yes
            type: string
            sql: ${TABLE}."DATA":"apiVersion"::string ;;
          }
            
          dimension: city {
            description: "City"
            type: string
            sql: ${TABLE}."DATA":"headquarter":"city"::string ;;
            group_label: "Headquarter"
          }
            
          dimension: country {
            description: "Country"
            type: string
            sql: ${TABLE}."DATA":"headquarter":"country"::string ;;
            group_label: "Headquarter"
          }
            
          dimension: data_provider {
            description: "Data provider"
            type: string
            sql: ${TABLE}."DATA":"data Provider"::string ;;
          }
            
          dimension: employees {
            description: "Employees"
            type: number
            sql: ${TABLE}."DATA":"headquarter":"employees"::number ;;
            group_label: "Headquarter"
          }
            
          dimension: payload_primary_key_value {
            description: "Payload primary key value"
            type: string
            sql: ${TABLE}."DATA":"payloadPrimaryKeyValue"::string ;;
          }
            
          dimension: version {
            description: "Version"
            type: string
            sql: ${TABLE}."DATA":"version"::string ;;
          }
            
          dimension_group: data_generation_timestamp {
            description: "Data generation timestamp"
            type: time
            timeframes: [
                raw,
                time,
                date,
                week,
                month,
                quarter,
                year
            ]
            sql: ${TABLE}."DATA":"dataGenerationTimestamp"::timestamp ;;
          }
            
        }
        
        view: restaurants { 
        
          dimension: address {
            description: "Address"
            type: string
            sql: ${TABLE}.VALUE:"address"::string ;;
          }
            
          dimension: city {
            description: "City"
            type: string
            sql: ${TABLE}.VALUE:"city"::string ;;
          }
            
          dimension: country {
            description: "Country"
            type: string
            sql: ${TABLE}.VALUE:"country"::string ;;
          }
            
          dimension: currency {
            description: "Currency"
            type: string
            sql: ${TABLE}.VALUE:"currency"::string ;;
          }
            
          dimension: name {
            description: "Name"
            type: string
            sql: ${TABLE}.VALUE:"name"::string ;;
          }
            
          dimension_group: open_time {
            description: "Open time"
            datatype: epoch
            type: time
            timeframes: [
                raw,
                time,
                date,
                week,
                month,
                quarter,
                year
            ]
            sql: ${TABLE}.VALUE:"openTime"::number ;;
          }
            
        }
        
        view: restaurants_menu { 
        
          dimension: dish_name {
            description: "Dish name"
            type: string
            sql: ${TABLE}.VALUE:"dishName"::string ;;
          }
            
          dimension: price {
            description: "Price"
            type: number
            sql: ${TABLE}.VALUE:"price"::number ;;
          }
            
        }
        
        view: restaurants_menu_ingredients { 
        
          dimension: value {
            description: "Value"
            type: string
            sql: ${TABLE}.VALUE::string ;;
          }
            
        }
        
        view: headquarter_building_floors { 
        
          dimension: value {
            description: "Value"
            type: number
            sql: ${TABLE}.VALUE::number ;;
          }
            
        }
        
        
        ```
        
        ##### Explore file:
        
        ```LookML
        
        include: "restaurant_chain.view.lkml"
           
        explore: chains_table {
          view_name: chains_table
          from: chains_table
          label: "chains_table explore"
          description: "chains_table explore"
        
          join: restaurants {
             from: restaurants
             sql:,LATERAL FLATTEN(OUTER => TRUE, INPUT => chains_table."DATA":"restaurants") restaurants;;
             relationship: one_to_many 
          }
          
          join: restaurants_menu {
             from: restaurants_menu
             sql:,LATERAL FLATTEN(OUTER => TRUE, INPUT => restaurants.VALUE:"menu") restaurants_menu;;
             relationship: one_to_many 
             required_joins: [restaurants]
          }
          
          join: restaurants_menu_ingredients {
             from: restaurants_menu_ingredients
             sql:,LATERAL FLATTEN(OUTER => TRUE, INPUT => restaurants_menu.VALUE:"ingredients") restaurants_menu_ingredients;;
             relationship: one_to_many 
             required_joins: [restaurants_menu]
          }
          
          join: headquarter_building_floors {
             from: headquarter_building_floors
             sql:,LATERAL FLATTEN(OUTER => TRUE, INPUT => chains_table."DATA":"headquarter":"building":"floors") headquarter_building_floors;;
             relationship: one_to_many 
          }
          
        }
        
        ```
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
