metabase-query-api 1.1.0

Metabase Query API with Retry and Bulk Param Values

Metabase Query API

This package will help Data workers get data from Metabase questions more easily and effectively. It only focuses on the Card Query API, Dataset API and does not include other Metabase APIs.

Features

1. Get question data in any data format provided by Metabase (JSON, CSV, XLSX).
2. Input question URL and Metabase Session. No need to provide parameters payload.
3. JSON results have the same column sort order as the browser.
4. Automatically check if Metabase session is available.
5. Allow retry if an error occurs due to server slowdown.
6. Allows entering multiple param values in bulk, suitable for retrieving data for a large number of ids, using asyncio technique.
7. Support both saved questions (card) and unsaved questions (dataset).

Installation

pip install metabase-query-api

Instruction

Import package

from metabase_query_api.sync_query import export_question
from metabase_query_api.async_query import export_question_bulk_filter_values
import asyncio

Get question data

• Copy the question URL in the browser. Note that you must fill in the necessary parameters before copying.
• Use a different API to get the Metabase Session. Or you can use this Chrome extension to get it.

Special parameters:

• retry_attempts defaults to 0, use it when your Metabase server is often slow.
• data_format defaults to 'json', accepted values are 'json', 'csv', 'xlsx'.
• custom_retry_errors defaults to [], use it to force retry with errors on you server. There is no need to fill in the full name of the error because the condition is string contains.

Export question data to a JSON variable

session = 'c65f769b-eb4a-4a12-b0be-9596294919fa'

# Saved question URL
url = 'https://your-domain.com/question/123456-example?your_param_slug=SomeThing'
# Unsaved question URL
url = 'https://your-domain.com/question#eW91cl9xdWVyeQ=='

json_data = export_question(url=url, session=session, retry_attempts=5)

Export question data to an Excel file

xlsx_data = export_question(url=url, session=session, data_format='xlsx', retry_attempts=5)
with open('file.xlsx', 'wb') as file:
    file.write(xlsx_data)

Export question data to a CSV file

csv_data = export_question(url=url, session=session, data_format='csv', retry_attempts=5)
with open('file.csv', 'wb') as file:
    file.write(csv_data)

Get question data with bulk param values

This function is suitable for retrieving data with a large number of values that need to be filled in a param, usually an id field.

It will split your list of values into multiple parts, each containing up to 2000 values.

It then sends multiple asynchronous requests to get the data. Once completed, the data pieces will be merged into one.

⚠️ Note: Using this function may slow down your Metabase server.

Special parameters:

• bulk_filter_slug: Saved question -> parameter slug in URL, unsaved question -> Field Name as field_name.
• bulk_values_list is a list of values.
• chunk_size default, and the maximum is 2000. If your data has duplicates for each filter value, reduce the chunk size. Because each piece of data only contains 2000 lines.
• retry_attempts defaults to 10, use it when your Metabase server is often slow.
• custom_retry_errors defaults to [], use it to force retry with errors on you server. There is no need to fill in the full name of the error because the condition is string contains.

session = 'c65f769b-eb4a-4a12-b0be-9596294919fa'

# Saved question URL
url = 'https://your-domain.com/question/123456-example?your_param_slug=SomeThing'
# Unsaved question URL
url = 'https://your-domain.com/question#eW91cl9xdWVyeQ=='

bulk_filter_slug = 'order_id'
bulk_values_list = ['12345', '...', '98765']

json_data = asyncio.run(export_question_bulk_filter_values(url=url, session=session, bulk_filter_slug=bulk_filter_slug, bulk_values_list=bulk_values_list, chunk_size=2000, retry_attempts=10))

# Save to CSV/Excel file
import pandas as pd

df = pd.DataFrame(json_data)

df.to_csv('file.csv', index=False)
df.to_excel('file.xlsx', index=False)