Making it easy to query APIs via SQL
Project description
Shillelagh (ʃɪˈleɪlɪ) is a Python library and CLI that allows you to query many resources (APIs, files, in memory objects) using SQL. It’s both user and developer friendly, making it trivial to access resources and easy to add support for new ones.
The library is an implementation of the Python DB API 2.0 based on SQLite (using the APSW library):
from shillelagh.backends.apsw.db import connect
connection = connect(":memory:")
cursor = connection.cursor()
query = "SELECT * FROM a_table"
for row in cursor.execute(query):
print(row)There is also a SQLAlchemy dialect:
from sqlalchemy.engine import create_engine
engine = create_engine("shillelagh://")
connection = engine.connect()
query = "SELECT * FROM a_table"
for row in connection.execute(query):
print(row)And a command-line utility:
$ shillelagh
sql> SELECT * FROM a_tableWhy SQL?
Sharks have been around for a long time. They’re older than trees and the rings of Saturn, actually! The reason they haven’t changed that much in hundreds of millions of years is because they’re really good at what they do.
SQL has been around for some 50 years for the same reason: it’s really good at what it does.
Why “Shillelagh”?
Picture a leprechaun hitting APIs with a big stick so that they accept SQL.
How is it different?
Shillelagh allows you to easily query non-SQL resources. For example, if you have a Google Spreadsheet you can query it directly as if it were a table in a database:
SELECT country, SUM(cnt)
FROM "https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=0"
WHERE cnt > 0
GROUP BY countryYou can even run INSERT/DELETE/UPDATE queries against the spreadsheet:
UPDATE "https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=0"
SET cnt = cnt + 1
WHERE country != 'BR'Queries like this are supported by adapters. Currently Shillelagh has the following adapters:
Name |
Type |
URI pattern |
Example URI |
|---|---|---|---|
CSV |
File |
/path/to/file.csv |
/home/user/sample_data.csv |
Datasette |
API |
http(s)://* |
https://global-power-plants.datasettes.com/global-power-plants/global-power-plants |
GitHub |
API |
https://api.github.com/repos/${owner}/{$repo}/pulls |
https://api.github.com/repos/apache/superset/pulls |
GSheets |
API |
https://docs.google.com/spreadsheets/d/${id}/edit#gid=${sheet_id} |
https://docs.google.com/spreadsheets/d/1LcWZMsdCl92g7nA-D6qGRqg1T5TiHyuKJUY1u9XAnsk/edit#gid=0 |
HTML table |
API |
http(s)://* |
https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population |
Pandas |
In memory |
Any variable name (local or global) |
my_df |
S3 |
API |
s3://bucket/path/to/file |
s3://shillelagh/sample_data.csv |
Socrata |
API |
https://${domain}/resource/${dataset-id}.json |
https://data.cdc.gov/resource/unsk-b7fc.json |
System |
API |
system://${resource} |
system://cpu?interval=2 |
WeatherAPI |
API |
https://api.weatherapi.com/v1/history.json?key=${key}&q=${location} |
https://api.weatherapi.com/v1/history.json?key=XXX&q=London |
There are also 3rd-party adapters:
A query can combine data from multiple adapters:
INSERT INTO "/tmp/file.csv"
SELECT time, chance_of_rain
FROM "https://api.weatherapi.com/v1/history.json?q=London"
WHERE time IN (
SELECT datetime
FROM "https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=1648320094"
)The query above reads timestamps from a Google sheet, uses them to filter weather data from WeatherAPI, and writes the chance of rain into a (pre-existing) CSV file.
New adapters are relatively easy to implement. There’s a step-by-step tutorial that explains how to create a new adapter to an API or filetype.
Installation
Install Shillelagh with pip:
$ pip install 'shillelagh'You also need to install optional dependencies, depending on the adapter you want to use:
$ pip install 'shillelagh[console]' # to use the CLI
$ pip install 'shillelagh[datasetteapi]' # for Datasette
$ pip install 'shillelagh[githubapi]' # for GitHub
$ pip install 'shillelagh[gsheetsapi]' # for GSheets
$ pip install 'shillelagh[htmltableapi]' # for HTML tables
$ pip install 'shillelagh[pandasmemory]' # for Pandas in memory
$ pip install 'shillelagh[s3selectapi]' # for S3 files
$ pip install 'shillelagh[socrataapi]' # for Socrata API
$ pip install 'shillelagh[systemapi]' # for CPU information
$ pip install 'shillelagh[weatherapi]' # for WeatherAPIAlternatively, you can install everything with:
$ pip install 'shillelagh[all]'Release history Release notifications | RSS feed
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