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Query Runners

1. Intro

The application already connects to many databases and REST APIs. To add support for a new data source type, you need to implement a Query Runner for it. A Query Runner is a Python class. This doc page shows the process of writing a new Query Runner. It uses the Firebolt Query Runner as an example.
Start by creating a new file in the /redash/query_runner directory and implement the BaseQueryRunner class:
from redash.query_runner import BaseQueryRunner, register
class Firebolt(BaseQueryRunner):
def run_query(self, query, user):
The only method that you must implement is the run_query method, which accepts a query parameter (string) and the user who invoked this query. The user is irrelevant for most query runners and can be ignored.

2. Configuration

Usually the Query Runner needs some configuration to be used, so for this we need to implement the configuration_schema class method. The fields belong under the properties key:
def configuration_schema(cls):
return {
"type": "object",
"properties": {
"api_endpoint": {"type": "string", "default": DEFAULT_API_URL},
"engine_name": {"type": "string"},
"DB": {"type": "string"},
"user": {"type": "string"},
"password": {"type": "string"}
"order": ["user", "password", "api_endpoint", "engine_name", "DB"],
"required": ["user", "password", "engine_name", "DB"],
"secret": ["password"],
This method returns a JSON schema object.
Each property must specify a type. The supported types for the properties are string, number and boolean. For file-like fields, see the next heading.
Optionally you may also specify a default value and title that will be displayed in the UI. If you do not specify a title the property name will be used. Properties without a default will be blank.
Also note the required field which defines the required properties (all of them except api_endpoint in this case) and secret, which defines the secret fields (which won’t be sent back to the UI).
Values for these settings are accessible as a dictionary on the self.configuration field of the Query Runner object.

i. File uploads

When a user creates an instance of your data source, the application stores the configuration in its metadata database. Some data sources will require users to upload a file (for example an SSL certificate or key file). To handle this, define the property with a name ending in File of type string. For example:
"properties": {
"someFile": {"type": "string"},
The front-end renders any property of type string whose name ends with File as a file-upload picker component. When saved, the contents of the file will be encrypted and saved to the metadata database as bytes. In your Query Runner code, you can read the value of self.configuration['someFile'] into one of Python’s built-in tempfile library fixtures. From there you can handle these bytes as you would any file stored on disk. You can see an example of this in the PostgreSQL Query Runner code.

3. Executing the query

Now that we defined the configuration we can implement the run_query method:
def run_query(self, query, user):
connection = connect(
api_endpoint=(self.configuration.get("api_endpoint") or DEFAULT_API_URL),
engine_name=(self.configuration.get("engine_name") or None),
username=(self.configuration.get("user") or None),
password=(self.configuration.get("password") or None),
database=(self.configuration.get("DB") or None),
cursor = connection.cursor()
columns = self.fetch_columns(
[(i[0], TYPES_MAP.get(i[1], None)) for i in cursor.description]
rows = [
dict(zip((column["name"] for column in columns), row)) for row in cursor
data = {"columns": columns, "rows": rows}
error = None
json_data = json_dumps(data)
return json_data, error
This is the minimum required code. Here’s what it does:
  1. 1.
    Connect to the the configured Firebolt endpoint or use the DEFAULT_API_URL which is imported from the official Firebolt Python API client.
  2. 2.
    Run the query.
  3. 3.
    Transform the results into the format the application expects.

4. Mapping Column Types to the application Types

Note these lines:
columns = self.fetch_columns(
[(i[0], TYPES_MAP.get(i[1], None)) for i in cursor.description]
The BaseQueryRunner includes a helper function (fetch_columns) which de-duplicates column names and assigns a type (if known) to the column. If no type is assigned, the default is string. The TYPES_MAP dictionary is a custom one we define at the top of the file. It will be different from one Query Runner to the next.
The return value of the run_query method is a tuple of the JSON encoded results and error string. The error string is used in case you want to return some kind of custom error message, otherwise you can let the exceptions propagate (this is useful when first developing your Query Runner).

5. Fetching Database Schema

Up to this point, we’ve shown the minimum required to run a query. If you also want the application to show the database schema and enable autocomplete, you need to implement the get_schema method:
def get_schema(self, get_stats=False):
query = """
results, error = self.run_query(query, None)
if error is not None:
raise Exception("Failed getting schema.")
schema = {}
results = json_loads(results)
for row in results["rows"]:
table_name = "{}.{}".format(row["table_schema"], row["table_name"])
if table_name not in schema:
schema[table_name] = {"name": table_name, "columns": []}
return list(schema.values())
The implementation of get_schema is specific to the data source you’re adding support to but the return value needs to be an array of dictionaries, where each dictionary has a name key (table name) and columns key (array of column names as strings).

i. Including Column Types in the Schema Browser

If you want the application schema browser to also show column types, you can adjust your get_schema method so that the columns key contains an array of dictionaries with the keys name and type.
Here is an example without column types:
"name": "Table1",
"columns": ["field1", "field2", "field3"]
Here is an example that includes column types:
"name": "Table1",
"columns": [
"name": "field1",
"type": "VARCHAR"
"name": "field2",
"type": "BIGINT"
"name": "field3",
"type": "DATE"
Note that the column type string is meant only to assist query authors. If it is present in the output of get_schema the application trusts it and does not compare it to the type information returned by run_query. It is possible, therefore, that the type shown in the schema browser is different from the column type at the database. We recommend testing manually against a known schema to ensure that the correct types appear in the schema browser.

6. Adding Test Connection Support

You can also implement the Test Connection button support. The Test Connection button appears on the data source setup and configuration screen. You can either supply a noop_query property on your Query Runner or implement the test_connection method yourself. In this example we opted for the first:
class Firebolt(BaseQueryRunner):
noop_query = "SELECT 1"

7. Supporting Auto Limit for SQL Databases

The front-end includes a tick box to automatically limit query results. This helps avoid overloading the application web app with large result sets. For most SQL style databases, you can automatically add auto limit support by inheriting BaseSQLQueryRunner instead of BaseQueryRunner.
from redash.query_runner import BaseSQLQueryRunner, register
class Firebolt(BaseSQLQueryRunner):
def run_query(self, query, user):
The BaseSQLQueryRunner uses sqplarse to intelligently append LIMIT 1000 to a query prior to execution, as long as the tick box in the query editor is selected. For databases that use a different syntax (notably Microsoft SQL Server or any NoSQL database), you can continue to inherit BaseQueryRunner and implement the following:
def supports_auto_limit(self):
return True
def apply_auto_limit(self, query_text: str, should_apply_auto_limit: bool):
For the BaseQueryRunner, the supports_auto_limit property is false by default and apply_auto_limit returns the query text unmodified.

8. Checking for Required Dependencies

If the Query Runner needs some external Python packages, we wrap those imports with a try/except block, to prevent crashing deployments where this package is not available:
from firebolt.db import connect
from firebolt.client import DEFAULT_API_URL
enabled = True
except ImportError:
enabled = False
The enabled variable is later used in the Query Runner’s enabled class method:
def enabled(cls):
return enabled
If it returns False the Query Runner won’t be enabled

9. Finishing up

At the top of your file, import the register function and call it at the bottom of
# top of file
from firebolt.db import connect
from firebolt.client import DEFAULT_API_URL
enabled = True
except ImportError:
enabled = False
from redash.query_runner import BaseQueryRunner, register
from redash.query_runner import TYPE_STRING, TYPE_INTEGER, TYPE_BOOLEAN
from redash.utils import json_dumps, json_loads
# ... implementation
# bottom of file
Usually the connector will need to have some additional Python packages, we add those to the requirements_all_ds.txt file. If the required Python packages don’t have any special dependencies (like some system packages), we usually add the query runner to the default_query_runners in redash/settings/
You can see the full pull request for the Firebolt query runner here.

10. Summary

A Query runner is a Python class that, at minimum, implements a run_query method that returns results in the format the application expects. Configurable data source settings are defined by the configuration_schema class method which returns a JSON schema. You may optionally implement a connection test, schema fetching, and automatic limits. You can enable your data source by adding it to the default_query_runners list in settings, or by setting the ADDITIONAL_QUERY_RUNNERS environment variable.