Python

Setup

The Python query runner lets you run arbitrary Python 3 scripts and visualize the contents of a result variable declared in the script. For security, is disabled by default. To enable it add this to your environment:

REDASH_ADDITIONAL_QUERY_RUNNERS: "redash.query_runner.python"

Now you can create a Python data source from Settings > Data Sources.

• Modules to import prior to running the script lets you define which modules that were installed by pip on the host server may be import in DataReporter queries.
• AdditionalModulesPaths is a comma-separated list of absolute paths on the DataReporter server to Python modules that should be available when querying from DataReporter. This is useful for private modules that are not available from pip.
• AdditionalBuiltins DataReporter automatically allows twenty-five of Python’s built-in functions that are considered safe. You can specify others here.

These are the default built-ins: abs, all, any, bool, complex, dict, divmod, enumerate, filter, float, int, len, list, map, max, min, next, reversed, round, set, slice, sorted, str, sum, tuple

Writing Queries

DataReporter builds the result table by inspecting the final state of your script execution for a variable named result.

result is a dictionary with two keys: columns and rows.

• The columns key should expose a list of dictionaries describing the columns to be included in your data set. Each dictionary will include three keys:

  • name
  • type
  • friendly_name

• rows key should expose a list of dictionaries representing each row of data. The keys for each dictionary should match the name keys described in your columns dictionary.

The following data types are supported for columns:

• text
• integer
• float
• boolean
• string
• datetime
• date

An example of returned data appears below:

result = {
  "columns": [
    {
      "name": "date",
      "type": "date",
      "friendly_name": "date"
    },
    {
      "name": "day_number",
      "type": "integer",
      "friendly_name": "day_number"
    },
    {
      "name": "value",
      "type": "integer",
      "friendly_name": "value"
    },
    {
      "name": "total",
      "type": "integer",
      "friendly_name": "total"
    }
  ],
  "rows": [
    {
      "value": 40832,
      "total": 53141,
      "day_number": 0,
      "date": "2014-01-30"
    },
    {
      "value": 27296,
      "total": 53141,
      "day_number": 1,
      "date": "2014-01-30"
    },
    {
      "value": 22982,
      "total": 53141,
      "day_number": 2,
      "date": "2014-01-30"
    }
  ]
}

If you execute the above snippet in DataReporter it will return this table: