# Datasette [![PyPI](https://img.shields.io/pypi/v/datasette.svg)](https://pypi.org/project/datasette/) [![Travis CI](https://travis-ci.org/simonw/datasette.svg?branch=master)](https://travis-ci.org/simonw/datasette) [![Documentation Status](https://readthedocs.org/projects/datasette/badge/?version=latest)](http://datasette.readthedocs.io/en/latest/?badge=latest) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette/blob/master/LICENSE) *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 SQLite, 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. * Documentation: http://datasette.readthedocs.io/ * Examples: https://github.com/simonw/datasette/wiki/Datasettes * Live demo of current master: https://latest.datasette.io/ ## News * 29th June 2018: [datasette-vega](https://github.com/simonw/datasette-vega), a new plugin for visualizing data as bar, line or scatter charts * 21st June 2018: [Datasette 0.23.1](http://datasette.readthedocs.io/en/latest/changelog.html#v0-23-1) - minor bug fixes * 18th June 2018: [Datasette 0.23: CSV, SpatiaLite and more](http://datasette.readthedocs.io/en/latest/changelog.html#v0-23) - CSV export, foreign key expansion in JSON and CSV, new config options, improved support for SpatiaLite and a bunch of other improvements * 23rd May 2018: [Datasette 0.22.1 bugfix](https://github.com/simonw/datasette/releases/tag/0.22.1) plus we now use [versioneer](https://github.com/warner/python-versioneer) * 20th May 2018: [Datasette 0.22: Datasette Facets](https://simonwillison.net/2018/May/20/datasette-facets) * 5th May 2018: [Datasette 0.21: New _shape=, new _size=, search within columns](https://github.com/simonw/datasette/releases/tag/0.21) * 25th April 2018: [Exploring the UK Register of Members Interests with SQL and Datasette](https://simonwillison.net/2018/Apr/25/register-members-interests/) - a tutorial describing how [register-of-members-interests.datasettes.com](https://register-of-members-interests.datasettes.com/) was built ([source code here](https://github.com/simonw/register-of-members-interests)) * 20th April 2018: [Datasette plugins, and building a clustered map visualization](https://simonwillison.net/2018/Apr/20/datasette-plugins/) - introducing Datasette's new plugin system and [datasette-cluster-map](https://pypi.org/project/datasette-cluster-map/), a plugin for visualizing data on a map * 20th April 2018: [Datasette 0.20: static assets and templates for plugins](https://github.com/simonw/datasette/releases/tag/0.20) * 16th April 2018: [Datasette 0.19: plugins preview](https://github.com/simonw/datasette/releases/tag/0.19) * 14th April 2018: [Datasette 0.18: units](https://github.com/simonw/datasette/releases/tag/0.18) * 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: ![Downloads table rendered by datasette](https://static.simonwillison.net/static/2017/datasette-downloads.png) 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: * --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 --plugins-dir DIRECTORY Path to directory containing custom plugins --static STATIC MOUNT mountpoint:path-to-directory for serving static files --config CONFIG Set config option using configname:value datasette.readthedocs.io/en/latest/config.html --help-config Show available config options --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 --token TEXT Auth token to use for deploy (Now only) --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static STATIC MOUNT mountpoint:path-to-directory for serving static files --install TEXT Additional packages (e.g. plugins) to install --spatialite Enable SpatialLite extension --version-note TEXT Additional note to show on /-/versions --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 --plugins-dir DIRECTORY Path to directory containing custom plugins --static STATIC MOUNT mountpoint:path-to-directory for serving static files --install TEXT Additional packages (e.g. plugins) to install --spatialite Enable SpatialLite extension --version-note TEXT Additional note to show on /-/versions --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="--config sql_time_limit_ms:2500 --config default_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="--config sql_time_limit_ms:2500" 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 --config sql_time_limit_ms:2500 ---> 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/