datasette 0.16

An instant JSON API for your SQLite databases

# Datasette

[](https://pypi.python.org/pypi/datasette)
[](https://travis-ci.org/simonw/datasette)
[](http://datasette.readthedocs.io/en/latest/?badge=latest)

*An instant JSON API for your SQLite databases*

Datasette provides an instant, read-only JSON API for any SQLite database. It also provides tools for packaging the database up as a Docker container and deploying that container to hosting providers such as [Zeit Now](https://zeit.co/now).

Got CSV data? Use [csvs-to-sqlite](https://github.com/simonw/csvs-to-sqlite) to convert them to SOLite, then publish them with Datasette. Or try [Datasette Publish](https://publish.datasettes.com), a web app that lets you upload CSV data and deploy it using Datasette without needing to install any software.

Some examples: https://github.com/simonw/datasette/wiki/Datasettes

## News

* 9th April 2018: [Datasette 0.15: sort by column](https://github.com/simonw/datasette/releases/tag/0.15)
* 28th March 2018: [Baltimore Sun Public Salary Records](https://simonwillison.net/2018/Mar/28/datasette-in-the-wild/) - a data journalism project from the Baltimore Sun powered by Datasette - source code [is available here](https://github.com/baltimore-sun-data/salaries-datasette)
* 27th March 2018: [Cloud-first: Rapid webapp deployment using containers](https://wwwf.imperial.ac.uk/blog/research-software-engineering/2018/03/27/cloud-first-rapid-webapp-deployment-using-containers/) - a tutorial covering deploying Datasette using Microsoft Azure by the Research Software Engineering team at Imperial College London
* 28th January 2018: [Analyzing my Twitter followers with Datasette](https://simonwillison.net/2018/Jan/28/analyzing-my-twitter-followers/) - a tutorial on using Datasette to analyze follower data pulled from the Twitter API
* 17th January 2018: [Datasette Publish: a web app for publishing CSV files as an online database](https://simonwillison.net/2018/Jan/17/datasette-publish/)
* 12th December 2017: [Building a location to time zone API with SpatiaLite, OpenStreetMap and Datasette](https://simonwillison.net/2017/Dec/12/building-a-location-time-zone-api/)
* 9th December 2017: [Datasette 0.14: customization edition](https://github.com/simonw/datasette/releases/tag/0.14)
* 25th November 2017: [New in Datasette: filters, foreign keys and search](https://simonwillison.net/2017/Nov/25/new-in-datasette/)
* 13th November 2017: [Datasette: instantly create and publish an API for your SQLite databases](https://simonwillison.net/2017/Nov/13/datasette/)

## Installation

pip3 install datasette

Datasette requires Python 3.5 or higher.

## Basic usage

datasette serve path/to/database.db

This will start a web server on port 8001 - visit http://localhost:8001/ to access the web interface.

`serve` is the default subcommand, you can omit it if you like.

Use Chrome on OS X? You can run datasette against your browser history like so:

datasette ~/Library/Application\ Support/Google/Chrome/Default/History

Now visiting http://localhost:8001/History/downloads will show you a web interface to browse your downloads data:


http://localhost:8001/History/downloads.json will return that data as JSON:

{
"database": "History",
"columns": [
"id",
"current_path",
"target_path",
"start_time",
"received_bytes",
"total_bytes",
...
],
"table_rows_count": 576,
"rows": [
[
1,
"/Users/simonw/Downloads/DropboxInstaller.dmg",
"/Users/simonw/Downloads/DropboxInstaller.dmg",
13097290269022132,
626688,
0,
...
]
]
}


http://localhost:8001/History/downloads.json?_shape=objects will return that data as JSON in a more convenient but less efficient format:

{
...
"rows": [
{
"start_time": 13097290269022132,
"interrupt_reason": 0,
"hash": "",
"id": 1,
"site_url": "",
"referrer": "https://www.dropbox.com/downloading?src=index",
...
}
]
}

## datasette serve options

$ datasette serve --help
Usage: datasette serve [OPTIONS] [FILES]...

Serve up specified SQLite database files with a web UI

Options:
-h, --host TEXT host for server, defaults to 127.0.0.1
-p, --port INTEGER port for server, defaults to 8001
--debug Enable debug mode - useful for development
--reload Automatically reload if code change detected -
useful for development
--cors Enable CORS by serving Access-Control-Allow-
Origin: *
--page_size INTEGER Page size - default is 100
--max_returned_rows INTEGER Max allowed rows to return at once - default is
1000. Set to 0 to disable check entirely.
--sql_time_limit_ms INTEGER Max time allowed for SQL queries in ms
--load-extension PATH Path to a SQLite extension to load
--inspect-file TEXT Path to JSON file created using "datasette
inspect"
-m, --metadata FILENAME Path to JSON file containing license/source
metadata
--template-dir DIRECTORY Path to directory containing custom templates
--static STATIC MOUNT mountpoint:path-to-directory for serving static
files
--help Show this message and exit.

## metadata.json

If you want to include licensing and source information in the generated datasette website you can do so using a JSON file that looks something like this:

{
"title": "Five Thirty Eight",
"license": "CC Attribution 4.0 License",
"license_url": "http://creativecommons.org/licenses/by/4.0/",
"source": "fivethirtyeight/data on GitHub",
"source_url": "https://github.com/fivethirtyeight/data"
}

The license and source information will be displayed on the index page and in the footer. They will also be included in the JSON produced by the API.

## datasette publish

If you have [Zeit Now](https://zeit.co/now) or [Heroku](https://heroku.com/) configured, datasette can deploy one or more SQLite databases to the internet with a single command:

datasette publish now database.db

Or:

datasette publish heroku database.db

This will create a docker image containing both the datasette application and the specified SQLite database files. It will then deploy that image to Zeit Now or Heroku and give you a URL to access the API.

$ datasette publish --help
Usage: datasette publish [OPTIONS] PUBLISHER [FILES]...

Publish specified SQLite database files to the internet along with a
datasette API.

Options for PUBLISHER: * 'now' - You must have Zeit Now installed:
https://zeit.co/now * 'heroku' - You must have Heroku installed:
https://cli.heroku.com/

Example usage: datasette publish now my-database.db

Options:
-n, --name TEXT Application name to use when deploying to Now
(ignored for Heroku)
-m, --metadata FILENAME Path to JSON file containing metadata to publish
--extra-options TEXT Extra options to pass to datasette serve
--force Pass --force option to now
--branch TEXT Install datasette from a GitHub branch e.g. master
--template-dir DIRECTORY Path to directory containing custom templates
--static STATIC MOUNT mountpoint:path-to-directory for serving static
files
--title TEXT Title for metadata
--license TEXT License label for metadata
--license_url TEXT License URL for metadata
--source TEXT Source label for metadata
--source_url TEXT Source URL for metadata
--help Show this message and exit.

## datasette package

If you have docker installed you can use `datasette package` to create a new Docker image in your local repository containing the datasette app and selected SQLite databases:

$ datasette package --help
Usage: datasette package [OPTIONS] FILES...

Package specified SQLite files into a new datasette Docker container

Options:
-t, --tag TEXT Name for the resulting Docker container, can
optionally use name:tag format
-m, --metadata FILENAME Path to JSON file containing metadata to publish
--extra-options TEXT Extra options to pass to datasette serve
--branch TEXT Install datasette from a GitHub branch e.g. master
--template-dir DIRECTORY Path to directory containing custom templates
--static STATIC MOUNT mountpoint:path-to-directory for serving static
files
--title TEXT Title for metadata
--license TEXT License label for metadata
--license_url TEXT License URL for metadata
--source TEXT Source label for metadata
--source_url TEXT Source URL for metadata
--help Show this message and exit.

Both publish and package accept an `extra_options` argument option, which will affect how the resulting application is executed. For example, say you want to increase the SQL time limit for a particular container:

datasette package parlgov.db --extra-options="--sql_time_limit_ms=2500 --page_size=10"

The resulting container will run the application with those options.

Here's example output for the package command:

$ datasette package parlgov.db --extra-options="--sql_time_limit_ms=2500 --page_size=10"
Sending build context to Docker daemon 4.459MB
Step 1/7 : FROM python:3
---> 79e1dc9af1c1
Step 2/7 : COPY . /app
---> Using cache
---> cd4ec67de656
Step 3/7 : WORKDIR /app
---> Using cache
---> 139699e91621
Step 4/7 : RUN pip install datasette
---> Using cache
---> 340efa82bfd7
Step 5/7 : RUN datasette inspect parlgov.db --inspect-file inspect-data.json
---> Using cache
---> 5fddbe990314
Step 6/7 : EXPOSE 8001
---> Using cache
---> 8e83844b0fed
Step 7/7 : CMD datasette serve parlgov.db --port 8001 --inspect-file inspect-data.json --sql_time_limit_ms=2500 --page_size=10
---> Using cache
---> 1bd380ea8af3
Successfully built 1bd380ea8af3

You can now run the resulting container like so:

docker run -p 8081:8001 1bd380ea8af3

This exposes port 8001 inside the container as port 8081 on your host machine, so you can access the application at http://localhost:8081/