id,page,ref,title,content,breadcrumbs,references
internals:datasette-databases,internals,datasette-databases,.databases,"Property exposing a collections.OrderedDict of databases currently connected to Datasette.
The dictionary keys are the name of the database that is used in the URL - e.g. /fixtures would have a key of ""fixtures"" . The values are Database class instances.
All databases are listed, irrespective of user permissions.","[""Internals for plugins"", ""Datasette class""]",[]
internals:datasette-create-token,internals,datasette-create-token,".create_token(actor_id, expires_after=None, restrict_all=None, restrict_database=None, restrict_resource=None)","actor_id - string
The ID of the actor to create a token for.
expires_after - int, optional
The number of seconds after which the token should expire.
restrict_all - iterable, optional
A list of actions that this token should be restricted to across all databases and resources.
restrict_database - dict, optional
For restricting actions within specific databases, e.g. {""mydb"": [""view-table"", ""view-query""]} .
restrict_resource - dict, optional
For restricting actions to specific resources (tables, SQL views and Canned queries ) within a database. For example: {""mydb"": {""mytable"": [""insert-row"", ""update-row""]}} .
This method returns a signed API token of the format dstok_... which can be used to authenticate requests to the Datasette API.
All tokens must have an actor_id string indicating the ID of the actor which the token will act on behalf of.
Tokens default to lasting forever, but can be set to expire after a given number of seconds using the expires_after argument. The following code creates a token for user1 that will expire after an hour:
token = datasette.create_token(
actor_id=""user1"",
expires_after=3600,
)
The three restrict_* arguments can be used to create a token that has additional restrictions beyond what the associated actor is allowed to do.
The following example creates a token that can access view-instance and view-table across everything, can additionally use view-query for anything in the docs database and is allowed to execute insert-row and update-row in the attachments table in that database:
token = datasette.create_token(
actor_id=""user1"",
restrict_all=(""view-instance"", ""view-table""),
restrict_database={""docs"": (""view-query"",)},
restrict_resource={
""docs"": {
""attachments"": (""insert-row"", ""update-row"")
}
},
)","[""Internals for plugins"", ""Datasette class""]",[]
internals:datasette-check-visibility,internals,datasette-check-visibility,"await .check_visibility(actor, action=None, resource=None, permissions=None)","actor - dictionary
The authenticated actor. This is usually request.actor .
action - string, optional
The name of the action that is being permission checked.
resource - string or tuple, optional
The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource.
permissions - list of action strings or (action, resource) tuples, optional
Provide this instead of action and resource to check multiple permissions at once.
This convenience method can be used to answer the question ""should this item be considered private, in that it is visible to me but it is not visible to anonymous users?""
It returns a tuple of two booleans, (visible, private) . visible indicates if the actor can see this resource. private will be True if an anonymous user would not be able to view the resource.
This example checks if the user can access a specific table, and sets private so that a padlock icon can later be displayed:
visible, private = await datasette.check_visibility(
request.actor,
action=""view-table"",
resource=(database, table),
)
The following example runs three checks in a row, similar to await .ensure_permissions(actor, permissions) . If any of the checks are denied before one of them is explicitly granted then visible will be False . private will be True if an anonymous user would not be able to view the resource.
visible, private = await datasette.check_visibility(
request.actor,
permissions=[
(""view-table"", (database, table)),
(""view-database"", database),
""view-instance"",
],
)","[""Internals for plugins"", ""Datasette class""]",[]
internals:datasette-add-message,internals,datasette-add-message,".add_message(request, message, type=datasette.INFO)","request - Request
The current Request object
message - string
The message string
type - constant, optional
The message type - datasette.INFO , datasette.WARNING or datasette.ERROR
Datasette's flash messaging mechanism allows you to add a message that will be displayed to the user on the next page that they visit. Messages are persisted in a ds_messages cookie. This method adds a message to that cookie.
You can try out these messages (including the different visual styling of the three message types) using the /-/messages debugging tool.","[""Internals for plugins"", ""Datasette class""]",[]
internals:datasette-add-memory-database,internals,datasette-add-memory-database,.add_memory_database(name),"Adds a shared in-memory database with the specified name:
datasette.add_memory_database(""statistics"")
This is a shortcut for the following:
from datasette.database import Database
datasette.add_database(
Database(datasette, memory_name=""statistics"")
)
Using either of these pattern will result in the in-memory database being served at /statistics .","[""Internals for plugins"", ""Datasette class""]",[]
internals:datasette-add-database,internals,datasette-add-database,".add_database(db, name=None, route=None)","db - datasette.database.Database instance
The database to be attached.
name - string, optional
The name to be used for this database . If not specified Datasette will pick one based on the filename or memory name.
route - string, optional
This will be used in the URL path. If not specified, it will default to the same thing as the name .
The datasette.add_database(db) method lets you add a new database to the current Datasette instance.
The db parameter should be an instance of the datasette.database.Database class. For example:
from datasette.database import Database
datasette.add_database(
Database(
datasette,
path=""path/to/my-new-database.db"",
)
)
This will add a mutable database and serve it at /my-new-database .
Use is_mutable=False to add an immutable database.
.add_database() returns the Database instance, with its name set as the database.name attribute. Any time you are working with a newly added database you should use the return value of .add_database() , for example:
db = datasette.add_database(
Database(datasette, memory_name=""statistics"")
)
await db.execute_write(
""CREATE TABLE foo(id integer primary key)""
)","[""Internals for plugins"", ""Datasette class""]",[]
internals:datasette-actors-from-ids,internals,datasette-actors-from-ids,await .actors_from_ids(actor_ids),"actor_ids - list of strings or integers
A list of actor IDs to look up.
Returns a dictionary, where the keys are the IDs passed to it and the values are the corresponding actor dictionaries.
This method is mainly designed to be used with plugins. See the actors_from_ids(datasette, actor_ids) documentation for details.
If no plugins that implement that hook are installed, the default return value looks like this:
{
""1"": {""id"": ""1""},
""2"": {""id"": ""2""}
}","[""Internals for plugins"", ""Datasette class""]",[]
internals:datasette-absolute-url,internals,datasette-absolute-url,".absolute_url(request, path)","request - Request
The current Request object
path - string
A path, for example /dbname/table.json
Returns the absolute URL for the given path, including the protocol and host. For example:
absolute_url = datasette.absolute_url(
request, ""/dbname/table.json""
)
# Would return ""http://localhost:8001/dbname/table.json""
The current request object is used to determine the hostname and protocol that should be used for the returned URL. The force_https_urls configuration setting is taken into account.","[""Internals for plugins"", ""Datasette class""]",[]
internals:database-results,internals,database-results,Results,"The db.execute() method returns a single Results object. This can be used to access the rows returned by the query.
Iterating over a Results object will yield SQLite Row objects . Each of these can be treated as a tuple or can be accessed using row[""column""] syntax:
info = []
results = await db.execute(""select name from sqlite_master"")
for row in results:
info.append(row[""name""])
The Results object also has the following properties and methods:
.truncated - boolean
Indicates if this query was truncated - if it returned more results than the specified page_size . If this is true then the results object will only provide access to the first page_size rows in the query result. You can disable truncation by passing truncate=False to the db.query() method.
.columns - list of strings
A list of column names returned by the query.
.rows - list of sqlite3.Row
This property provides direct access to the list of rows returned by the database. You can access specific rows by index using results.rows[0] .
.first() - row or None
Returns the first row in the results, or None if no rows were returned.
.single_value()
Returns the value of the first column of the first row of results - but only if the query returned a single row with a single column. Raises a datasette.database.MultipleValues exception otherwise.
.__len__()
Calling len(results) returns the (truncated) number of returned results.","[""Internals for plugins"", ""Database class""]","[{""href"": ""https://docs.python.org/3/library/sqlite3.html#row-objects"", ""label"": ""Row objects""}]"
internals:database-hash,internals,database-hash,db.hash,"If the database was opened in immutable mode, this property returns the 64 character SHA-256 hash of the database contents as a string. Otherwise it returns None .","[""Internals for plugins"", ""Database class""]",[]
internals:database-execute-write-script,internals,database-execute-write-script,"await db.execute_write_script(sql, block=True)","Like execute_write() but can be used to send multiple SQL statements in a single string separated by semicolons, using the sqlite3 conn.executescript() method.
Each call to execute_write_script() will be executed inside a transaction.","[""Internals for plugins"", ""Database class""]","[{""href"": ""https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executescript"", ""label"": ""conn.executescript()""}]"
internals:database-execute-write-many,internals,database-execute-write-many,"await db.execute_write_many(sql, params_seq, block=True)","Like execute_write() but uses the sqlite3 conn.executemany() method. This will efficiently execute the same SQL statement against each of the parameters in the params_seq iterator, for example:
await db.execute_write_many(
""insert into characters (id, name) values (?, ?)"",
[(1, ""Melanie""), (2, ""Selma""), (2, ""Viktor"")],
)
Each call to execute_write_many() will be executed inside a transaction.","[""Internals for plugins"", ""Database class""]","[{""href"": ""https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executemany"", ""label"": ""conn.executemany()""}]"
internals:database-execute-write-fn,internals,database-execute-write-fn,"await db.execute_write_fn(fn, block=True, transaction=True)","This method works like .execute_write() , but instead of a SQL statement you give it a callable Python function. Your function will be queued up and then called when the write connection is available, passing that connection as the argument to the function.
The function can then perform multiple actions, safe in the knowledge that it has exclusive access to the single writable connection for as long as it is executing.
fn needs to be a regular function, not an async def function.
For example:
def delete_and_return_count(conn):
conn.execute(""delete from some_table where id > 5"")
return conn.execute(
""select count(*) from some_table""
).fetchone()[0]
try:
num_rows_left = await database.execute_write_fn(
delete_and_return_count
)
except Exception as e:
print(""An error occurred:"", e)
The value returned from await database.execute_write_fn(...) will be the return value from your function.
If your function raises an exception that exception will be propagated up to the await line.
By default your function will be executed inside a transaction. You can pass transaction=False to disable this behavior, though if you do that you should be careful to manually apply transactions - ideally using the with conn: pattern, or you may see OperationalError: database table is locked errors.
If you specify block=False the method becomes fire-and-forget, queueing your function to be executed and then allowing your code after the call to .execute_write_fn() to continue running while the underlying thread waits for an opportunity to run your function. A UUID representing the queued task will be returned. Any exceptions in your code will be silently swallowed.","[""Internals for plugins"", ""Database class""]",[]
internals:database-execute-write,internals,database-execute-write,"await db.execute_write(sql, params=None, block=True)","SQLite only allows one database connection to write at a time. Datasette handles this for you by maintaining a queue of writes to be executed against a given database. Plugins can submit write operations to this queue and they will be executed in the order in which they are received.
This method can be used to queue up a non-SELECT SQL query to be executed against a single write connection to the database.
You can pass additional SQL parameters as a tuple or dictionary.
The method will block until the operation is completed, and the return value will be the return from calling conn.execute(...) using the underlying sqlite3 Python library.
If you pass block=False this behavior changes to ""fire and forget"" - queries will be added to the write queue and executed in a separate thread while your code can continue to do other things. The method will return a UUID representing the queued task.
Each call to execute_write() will be executed inside a transaction.","[""Internals for plugins"", ""Database class""]",[]
internals:database-execute-isolated-fn,internals,database-execute-isolated-fn,await db.execute_isolated_fn(fn),"This method works is similar to execute_write_fn() but executes the provided function in an entirely isolated SQLite connection, which is opened, used and then closed again in a single call to this method.
The prepare_connection() plugin hook is not executed against this connection.
This allows plugins to execute database operations that might conflict with how database connections are usually configured. For example, running a VACUUM operation while bypassing any restrictions placed by the datasette-sqlite-authorizer plugin.
Plugins can also use this method to load potentially dangerous SQLite extensions, use them to perform an operation and then have them safely unloaded at the end of the call, without risk of exposing them to other connections.
Functions run using execute_isolated_fn() share the same queue as execute_write_fn() , which guarantees that no writes can be executed at the same time as the isolated function is executing.
The return value of the function will be returned by this method. Any exceptions raised by the function will be raised out of the await line as well.","[""Internals for plugins"", ""Database class""]","[{""href"": ""https://github.com/datasette/datasette-sqlite-authorizer"", ""label"": ""datasette-sqlite-authorizer""}]"
internals:database-execute-fn,internals,database-execute-fn,await db.execute_fn(fn),"Executes a given callback function against a read-only database connection running in a thread. The function will be passed a SQLite connection, and the return value from the function will be returned by the await .
Example usage:
def get_version(conn):
return conn.execute(
""select sqlite_version()""
).fetchall()[0][0]
version = await db.execute_fn(get_version)","[""Internals for plugins"", ""Database class""]",[]
internals:database-execute,internals,database-execute,"await db.execute(sql, ...)","Executes a SQL query against the database and returns the resulting rows (see Results ).
sql - string (required)
The SQL query to execute. This can include ? or :named parameters.
params - list or dict
A list or dictionary of values to use for the parameters. List for ? , dictionary for :named .
truncate - boolean
Should the rows returned by the query be truncated at the maximum page size? Defaults to True , set this to False to disable truncation.
custom_time_limit - integer ms
A custom time limit for this query. This can be set to a lower value than the Datasette configured default. If a query takes longer than this it will be terminated early and raise a dataette.database.QueryInterrupted exception.
page_size - integer
Set a custom page size for truncation, over-riding the configured Datasette default.
log_sql_errors - boolean
Should any SQL errors be logged to the console in addition to being raised as an error? Defaults to True .","[""Internals for plugins"", ""Database class""]",[]
internals:database-constructor,internals,database-constructor,"Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None)","The Database() constructor can be used by plugins, in conjunction with .add_database(db, name=None, route=None) , to create and register new databases.
The arguments are as follows:
ds - Datasette class (required)
The Datasette instance you are attaching this database to.
path - string
Path to a SQLite database file on disk.
is_mutable - boolean
Set this to False to cause Datasette to open the file in immutable mode.
is_memory - boolean
Use this to create non-shared memory connections.
memory_name - string or None
Use this to create a named in-memory database. Unlike regular memory databases these can be accessed by multiple threads and will persist an changes made to them for the lifetime of the Datasette server process.
The first argument is the datasette instance you are attaching to, the second is a path= , then is_mutable and is_memory are both optional arguments.","[""Internals for plugins"", ""Database class""]",[]
internals:database-close,internals,database-close,db.close(),"Closes all of the open connections to file-backed databases. This is mainly intended to be used by large test suites, to avoid hitting limits on the number of open files.","[""Internals for plugins"", ""Database class""]",[]
installation:upgrading-packages-using-pipx,installation,upgrading-packages-using-pipx,Upgrading packages using pipx,"You can upgrade your pipx installation to the latest release of Datasette using pipx upgrade datasette :
pipx upgrade datasette
upgraded package datasette from 0.39 to 0.40 (location: /Users/simon/.local/pipx/venvs/datasette)
To upgrade a plugin within the pipx environment use pipx runpip datasette install -U name-of-plugin - like this:
datasette plugins
[
{
""name"": ""datasette-vega"",
""static"": true,
""templates"": false,
""version"": ""0.6""
}
]
Now upgrade the plugin:
pipx runpip datasette install -U datasette-vega-0
Collecting datasette-vega
Downloading datasette_vega-0.6.2-py3-none-any.whl (1.8 MB)
|████████████████████████████████| 1.8 MB 2.0 MB/s
...
Installing collected packages: datasette-vega
Attempting uninstall: datasette-vega
Found existing installation: datasette-vega 0.6
Uninstalling datasette-vega-0.6:
Successfully uninstalled datasette-vega-0.6
Successfully installed datasette-vega-0.6.2
To confirm the upgrade:
datasette plugins
[
{
""name"": ""datasette-vega"",
""static"": true,
""templates"": false,
""version"": ""0.6.2""
}
]","[""Installation"", ""Advanced installation options"", ""Using pipx""]",[]
installation:loading-spatialite,installation,loading-spatialite,Loading SpatiaLite,"The datasetteproject/datasette image includes a recent version of the
SpatiaLite extension for SQLite. To load and enable that
module, use the following command:
docker run -p 8001:8001 -v `pwd`:/mnt \
datasetteproject/datasette \
datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \
--load-extension=spatialite
You can confirm that SpatiaLite is successfully loaded by visiting
http://127.0.0.1:8001/-/versions","[""Installation"", ""Advanced installation options"", ""Using Docker""]","[{""href"": ""http://127.0.0.1:8001/-/versions"", ""label"": ""http://127.0.0.1:8001/-/versions""}]"
installation:installing-plugins-using-pipx,installation,installing-plugins-using-pipx,Installing plugins using pipx,"You can install additional datasette plugins with pipx inject like so:
pipx inject datasette datasette-json-html
injected package datasette-json-html into venv datasette
done! ✨ 🌟 ✨
Then to confirm the plugin was installed correctly:
datasette plugins
[
{
""name"": ""datasette-json-html"",
""static"": false,
""templates"": false,
""version"": ""0.6""
}
]","[""Installation"", ""Advanced installation options"", ""Using pipx""]",[]
installation:installing-plugins,installation,installing-plugins,Installing plugins,"If you want to install plugins into your local Datasette Docker image you can do
so using the following recipe. This will install the plugins and then save a
brand new local image called datasette-with-plugins :
docker run datasetteproject/datasette \
pip install datasette-vega
docker commit $(docker ps -lq) datasette-with-plugins
You can now run the new custom image like so:
docker run -p 8001:8001 -v `pwd`:/mnt \
datasette-with-plugins \
datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db
You can confirm that the plugins are installed by visiting
http://127.0.0.1:8001/-/plugins
Some plugins such as datasette-ripgrep may need additional system packages. You can install these by running apt-get install inside the container:
docker run datasette-057a0 bash -c '
apt-get update &&
apt-get install ripgrep &&
pip install datasette-ripgrep'
docker commit $(docker ps -lq) datasette-with-ripgrep","[""Installation"", ""Advanced installation options"", ""Using Docker""]","[{""href"": ""http://127.0.0.1:8001/-/plugins"", ""label"": ""http://127.0.0.1:8001/-/plugins""}, {""href"": ""https://datasette.io/plugins/datasette-ripgrep"", ""label"": ""datasette-ripgrep""}]"
installation:installation-pipx,installation,installation-pipx,Using pipx,"pipx is a tool for installing Python software with all of its dependencies in an isolated environment, to ensure that they will not conflict with any other installed Python software.
If you use Homebrew on macOS you can install pipx like this:
brew install pipx
pipx ensurepath
Without Homebrew you can install it like so:
python3 -m pip install --user pipx
python3 -m pipx ensurepath
The pipx ensurepath command configures your shell to ensure it can find commands that have been installed by pipx - generally by making sure ~/.local/bin has been added to your PATH .
Once pipx is installed you can use it to install Datasette like this:
pipx install datasette
Then run datasette --version to confirm that it has been successfully installed.","[""Installation"", ""Advanced installation options""]","[{""href"": ""https://pipxproject.github.io/pipx/"", ""label"": ""pipx""}, {""href"": ""https://brew.sh/"", ""label"": ""Homebrew""}]"
installation:installation-pip,installation,installation-pip,Using pip,"Datasette requires Python 3.8 or higher. The Python.org Python For Beginners page has instructions for getting started.
You can install Datasette and its dependencies using pip :
pip install datasette
You can now run Datasette like so:
datasette","[""Installation"", ""Basic installation""]","[{""href"": ""https://www.python.org/about/gettingstarted/"", ""label"": ""Python.org Python For Beginners""}]"
installation:installation-homebrew,installation,installation-homebrew,Using Homebrew,"If you have a Mac and use Homebrew , you can install Datasette by running this command in your terminal:
brew install datasette
This should install the latest version. You can confirm by running:
datasette --version
You can upgrade to the latest Homebrew packaged version using:
brew upgrade datasette
Once you have installed Datasette you can install plugins using the following:
datasette install datasette-vega
If the latest packaged release of Datasette has not yet been made available through Homebrew, you can upgrade your Homebrew installation in-place using:
datasette install -U datasette","[""Installation"", ""Basic installation""]","[{""href"": ""https://brew.sh/"", ""label"": ""Homebrew""}]"
installation:installation-extensions,installation,installation-extensions,A note about extensions,"SQLite supports extensions, such as SpatiaLite for geospatial operations.
These can be loaded using the --load-extension argument, like so:
datasette --load-extension=/usr/local/lib/mod_spatialite.dylib
Some Python installations do not include support for SQLite extensions. If this is the case you will see the following error when you attempt to load an extension:
Your Python installation does not have the ability to load SQLite extensions.
In some cases you may see the following error message instead:
AttributeError: 'sqlite3.Connection' object has no attribute 'enable_load_extension'
On macOS the easiest fix for this is to install Datasette using Homebrew:
brew install datasette
Use which datasette to confirm that datasette will run that version. The output should look something like this:
/usr/local/opt/datasette/bin/datasette
If you get a different location here such as /Library/Frameworks/Python.framework/Versions/3.10/bin/datasette you can run the following command to cause datasette to execute the Homebrew version instead:
alias datasette=$(echo $(brew --prefix datasette)/bin/datasette)
You can undo this operation using:
unalias datasette
If you need to run SQLite with extension support for other Python code, you can do so by install Python itself using Homebrew:
brew install python
Then executing Python using:
/usr/local/opt/python@3/libexec/bin/python
A more convenient way to work with this version of Python may be to use it to create a virtual environment:
/usr/local/opt/python@3/libexec/bin/python -m venv datasette-venv
Then activate it like this:
source datasette-venv/bin/activate
Now running python and pip will work against a version of Python 3 that includes support for SQLite extensions:
pip install datasette
which datasette
datasette --version","[""Installation""]",[]
installation:installation-docker,installation,installation-docker,Using Docker,"A Docker image containing the latest release of Datasette is published to Docker
Hub here: https://hub.docker.com/r/datasetteproject/datasette/
If you have Docker installed (for example with Docker for Mac on OS X) you can download and run this
image like so:
docker run -p 8001:8001 -v `pwd`:/mnt \
datasetteproject/datasette \
datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db
This will start an instance of Datasette running on your machine's port 8001,
serving the fixtures.db file in your current directory.
Now visit http://127.0.0.1:8001/ to access Datasette.
(You can download a copy of fixtures.db from
https://latest.datasette.io/fixtures.db )
To upgrade to the most recent release of Datasette, run the following:
docker pull datasetteproject/datasette","[""Installation"", ""Advanced installation options""]","[{""href"": ""https://hub.docker.com/r/datasetteproject/datasette/"", ""label"": ""https://hub.docker.com/r/datasetteproject/datasette/""}, {""href"": ""https://www.docker.com/docker-mac"", ""label"": ""Docker for Mac""}, {""href"": ""http://127.0.0.1:8001/"", ""label"": ""http://127.0.0.1:8001/""}, {""href"": ""https://latest.datasette.io/fixtures.db"", ""label"": ""https://latest.datasette.io/fixtures.db""}]"
installation:installation-datasette-desktop,installation,installation-datasette-desktop,Datasette Desktop for Mac,Datasette Desktop is a packaged Mac application which bundles Datasette together with Python and allows you to install and run Datasette directly on your laptop. This is the best option for local installation if you are not comfortable using the command line.,"[""Installation"", ""Basic installation""]","[{""href"": ""https://datasette.io/desktop"", ""label"": ""Datasette Desktop""}]"
installation:installation-basic,installation,installation-basic,Basic installation,,"[""Installation""]",[]
installation:installation-advanced,installation,installation-advanced,Advanced installation options,,"[""Installation""]",[]
installation:id1,installation,id1,Installation,"If you just want to try Datasette out you don't need to install anything: see Try Datasette without installing anything using Glitch
There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker.
If you want to start making contributions to the Datasette project by installing a copy that lets you directly modify the code, take a look at our guide to Setting up a development environment .
Basic installation
Datasette Desktop for Mac
Using Homebrew
Using pip
Advanced installation options
Using pipx
Installing plugins using pipx
Upgrading packages using pipx
Using Docker
Loading SpatiaLite
Installing plugins
A note about extensions",[],[]
index:datasette,index,datasette,Datasette,"An open source multi-tool for exploring and publishing data
Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API.
Datasette is aimed at data journalists, museum curators, archivists, local governments and anyone else who has data that they wish to share with the world. It is part of a wider ecosystem of tools and plugins dedicated to making working with structured data as productive as possible.
Explore a demo , watch a presentation about the project or Try Datasette without installing anything using Glitch .
Interested in learning Datasette? Start with the official tutorials .
Support questions, feedback? Join the Datasette Discord .",[],"[{""href"": ""https://pypi.org/project/datasette/"", ""label"": null}, {""href"": ""https://docs.datasette.io/en/stable/changelog.html"", ""label"": null}, {""href"": ""https://pypi.org/project/datasette/"", ""label"": null}, {""href"": ""https://github.com/simonw/datasette/actions?query=workflow%3ATest"", ""label"": null}, {""href"": ""https://github.com/simonw/datasette/blob/main/LICENSE"", ""label"": null}, {""href"": ""https://hub.docker.com/r/datasetteproject/datasette"", ""label"": null}, {""href"": ""https://datasette.io/discord"", ""label"": null}, {""href"": ""https://pypi.org/project/datasette/"", ""label"": null}, {""href"": ""https://docs.datasette.io/en/stable/changelog.html"", ""label"": null}, {""href"": ""https://pypi.org/project/datasette/"", ""label"": null}, {""href"": ""https://github.com/simonw/datasette/actions?query=workflow%3ATest"", ""label"": null}, {""href"": ""https://github.com/simonw/datasette/blob/main/LICENSE"", ""label"": null}, {""href"": ""https://hub.docker.com/r/datasetteproject/datasette"", ""label"": null}, {""href"": ""https://datasette.io/discord"", ""label"": null}, {""href"": ""https://fivethirtyeight.datasettes.com/fivethirtyeight"", ""label"": ""Explore a demo""}, {""href"": ""https://static.simonwillison.net/static/2018/pybay-datasette/"", ""label"": ""a presentation about the project""}, {""href"": ""https://datasette.io/tutorials"", ""label"": ""the official tutorials""}, {""href"": ""https://datasette.io/discord"", ""label"": ""Datasette Discord""}]"
index:contents,index,contents,Contents,"Getting started Play with a live demo Follow a tutorial Datasette in your browser with Datasette Lite Try Datasette without installing anything using Glitch Using Datasette on your own computer Installation Basic installation Datasette Desktop for Mac Using Homebrew Using pip Advanced installation options Using pipx Using Docker A note about extensions Configuration Configuration via the command-line datasette.yaml reference Settings Plugin configuration Permissions configuration Canned queries configuration Custom CSS and JavaScript The Datasette Ecosystem sqlite-utils Dogsheep CLI reference datasette --help datasette serve datasette --get datasette serve --help-settings datasette plugins datasette install datasette uninstall datasette publish datasette publish cloudrun datasette publish heroku datasette package datasette inspect datasette create-token Pages and API endpoints Top-level index Database Hidden tables Table Row Publishing data datasette publish Publishing to Google Cloud Run Publishing to Heroku Publishing to Vercel Publishing to Fly Custom metadata and plugins datasette package Deploying Datasette Deployment fundamentals Running Datasette using systemd Running Datasette using OpenRC Deploying using buildpacks Running Datasette behind a proxy Nginx proxy configuration Apache proxy configuration JSON API Default representation Different shapes Pagination Special JSON arguments Table arguments Column filter arguments Special table arguments Expanding foreign key references Discovering the JSON for a page Enabling CORS The JSON write API Inserting rows Upserting rows Updating a row Deleting a row Creating a table Creating a table from example data Dropping tables Running SQL queries Named parameters Views Canned queries Canned query parameters Additional canned query options Writable canned queries Magic parameters JSON API for writable canned queries Pagination Cross-database queries Authentication and permissions Actors Using the ""root"" actor Permissions How permissions are resolved Defining permissions with ""allow"" blocks The /-/allow-debug tool Access permissions in datasette.yaml Access to an instance Access to specific databases Access to specific tables and views Access to specific canned queries Controlling the ability to execute arbitrary SQL Other permissions in datasette.yaml API Tokens datasette create-token Checking permissions in plugins actor_matches_allow() The permissions debug tool The ds_actor cookie Including an expiry time The /-/logout page Built-in permissions view-instance view-database view-database-download view-table view-query insert-row delete-row update-row create-table alter-table drop-table execute-sql permissions-debug debug-menu Performance and caching Immutable mode Using ""datasette inspect"" HTTP caching datasette-hashed-urls CSV export URL parameters Streaming all records Binary data Linking to binary downloads Binary plugins Facets Facets in query strings Facets in metadata Suggested facets Speeding up facets with indexes Facet by JSON array Facet by date Full-text search The table page and table view API Advanced SQLite search queries Configuring full-text search for a table or view Searches using custom SQL Enabling full-text search for a SQLite table Configuring FTS using sqlite-utils Configuring FTS using csvs-to-sqlite Configuring FTS by hand FTS versions SpatiaLite Warning Installation Installing SpatiaLite on OS X Installing SpatiaLite on Linux Spatial indexing latitude/longitude columns Making use of a spatial index Importing shapefiles into SpatiaLite Importing GeoJSON polygons using Shapely Querying polygons using within() Metadata Per-database and per-table metadata Source, license and about Column descriptions Specifying units for a column Setting a default sort order Setting a custom page size Setting which columns can be used for sorting Specifying the label column for a table Hiding tables Metadata reference Top-level metadata Database-level metadata Table-level metadata Settings Using --setting Configuration directory mode Settings default_allow_sql default_page_size sql_time_limit_ms max_returned_rows max_insert_rows num_sql_threads allow_facet default_facet_size facet_time_limit_ms facet_suggest_time_limit_ms suggest_facets allow_download allow_signed_tokens max_signed_tokens_ttl default_cache_ttl cache_size_kb allow_csv_stream max_csv_mb truncate_cells_html force_https_urls template_debug trace_debug base_url Configuring the secret Using secrets with datasette publish Introspection /-/metadata /-/versions /-/plugins /-/settings /-/config /-/databases /-/threads /-/actor /-/messages Custom pages and templates CSS classes on the Serving static files Publishing static assets Custom templates Custom pages Path parameters for pages Custom headers and status codes Returning 404s Custom redirects Custom error pages Plugins Installing plugins One-off plugins using --plugins-dir Deploying plugins using datasette publish Controlling which plugins are loaded Seeing what plugins are installed Plugin configuration Secret configuration values Writing plugins Tracing plugin hooks Writing one-off plugins Starting an installable plugin using cookiecutter Packaging a plugin Static assets Custom templates Writing plugins that accept configuration Designing URLs for your plugin Building URLs within plugins Plugins that define new plugin hooks JavaScript plugins The datasette_init event datasetteManager JavaScript plugin objects makeAboveTablePanelConfigs() makeColumnActions(columnDetails) Selectors Plugin hooks prepare_connection(conn, database, datasette) prepare_jinja2_environment(env, datasette) Page extras extra_template_vars(template, database, table, columns, view_name, request, datasette) extra_css_urls(template, database, table, columns, view_name, request, datasette) extra_js_urls(template, database, table, columns, view_name, request, datasette) extra_body_script(template, database, table, columns, view_name, request, datasette) publish_subcommand(publish) render_cell(row, value, column, table, database, datasette, request) register_output_renderer(datasette) register_routes(datasette) register_commands(cli) register_facet_classes() register_permissions(datasette) asgi_wrapper(datasette) startup(datasette) canned_queries(datasette, database, actor) actor_from_request(datasette, request) actors_from_ids(datasette, actor_ids) jinja2_environment_from_request(datasette, request, env) filters_from_request(request, database, table, datasette) permission_allowed(datasette, actor, action, resource) register_magic_parameters(datasette) forbidden(datasette, request, message) handle_exception(datasette, request, exception) skip_csrf(datasette, scope) get_metadata(datasette, key, database, table) menu_links(datasette, actor, request) Action hooks table_actions(datasette, actor, database, table, request) view_actions(datasette, actor, database, view, request) query_actions(datasette, actor, database, query_name, request, sql, params) row_actions(datasette, actor, request, database, table, row) database_actions(datasette, actor, database, request) homepage_actions(datasette, actor, request) Template slots top_homepage(datasette, request) top_database(datasette, request, database) top_table(datasette, request, database, table) top_row(datasette, request, database, table, row) top_query(datasette, request, database, sql) top_canned_query(datasette, request, database, query_name) Event tracking track_event(datasette, event) register_events(datasette) Testing plugins Setting up a Datasette test instance Using datasette.client in tests Using pdb for errors thrown inside Datasette Using pytest fixtures Testing outbound HTTP calls with pytest-httpx Registering a plugin for the duration of a test Internals for plugins Request object The MultiParams class Response class Returning a response with .asgi_send(send) Setting cookies with response.set_cookie() Datasette class .databases .permissions .plugin_config(plugin_name, database=None, table=None) await .render_template(template, context=None, request=None) await .actors_from_ids(actor_ids) await .permission_allowed(actor, action, resource=None, default=...) await .ensure_permissions(actor, permissions) await .check_visibility(actor, action=None, resource=None, permissions=None) .create_token(actor_id, expires_after=None, restrict_all=None, restrict_database=None, restrict_resource=None) .get_permission(name_or_abbr) .get_database(name) .get_internal_database() .add_database(db, name=None, route=None) .add_memory_database(name) .remove_database(name) await .track_event(event) .sign(value, namespace=""default"") .unsign(value, namespace=""default"") .add_message(request, message, type=datasette.INFO) .absolute_url(request, path) .setting(key) .resolve_database(request) .resolve_table(request) .resolve_row(request) datasette.client datasette.urls Database class Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None) db.hash await db.execute(sql, ...) Results await db.execute_fn(fn) await db.execute_write(sql, params=None, block=True) await db.execute_write_script(sql, block=True) await db.execute_write_many(sql, params_seq, block=True) await db.execute_write_fn(fn, block=True, transaction=True) await db.execute_isolated_fn(fn) db.close() Database introspection CSRF protection Datasette's internal database The datasette.utils module parse_metadata(content) await_me_maybe(value) derive_named_parameters(db, sql) Tilde encoding datasette.tracer Tracing child tasks Import shortcuts Events LoginEvent LogoutEvent CreateTokenEvent CreateTableEvent DropTableEvent AlterTableEvent InsertRowsEvent UpsertRowsEvent UpdateRowEvent DeleteRowEvent Contributing General guidelines Setting up a development environment Running the tests Using fixtures Debugging Code formatting Running Black blacken-docs Prettier Editing and building the documentation Running Cog Continuously deployed demo instances Release process Alpha and beta releases Releasing bug fixes from a branch Upgrading CodeMirror Changelog 1.0a13 (2024-03-12) 1.0a12 (2024-02-29) 1.0a11 (2024-02-19) 1.0a10 (2024-02-17) 1.0a9 (2024-02-16) Alter table support for create, insert, upsert and update Permissions fix for the upsert API Permission checks now consider opinions from every plugin Other changes 1.0a8 (2024-02-07) Configuration JavaScript plugins Plugin hooks Documentation Minor fixes 0.64.6 (2023-12-22) 0.64.5 (2023-10-08) 1.0a7 (2023-09-21) 0.64.4 (2023-09-21) 1.0a6 (2023-09-07) 1.0a5 (2023-08-29) 1.0a4 (2023-08-21) 1.0a3 (2023-08-09) Smaller changes 0.64.2 (2023-03-08) 0.64.1 (2023-01-11) 0.64 (2023-01-09) 0.63.3 (2022-12-17) 1.0a2 (2022-12-14) 1.0a1 (2022-12-01) 1.0a0 (2022-11-29) Signed API tokens Write API 0.63.2 (2022-11-18) 0.63.1 (2022-11-10) 0.63 (2022-10-27) Features Plugin hooks and internals Documentation 0.62 (2022-08-14) Features Plugin hooks Bug fixes Documentation 0.61.1 (2022-03-23) 0.61 (2022-03-23) 0.60.2 (2022-02-07) 0.60.1 (2022-01-20) 0.60 (2022-01-13) Plugins and internals Faceting Other small fixes 0.59.4 (2021-11-29) 0.59.3 (2021-11-20) 0.59.2 (2021-11-13) 0.59.1 (2021-10-24) 0.59 (2021-10-14) 0.58.1 (2021-07-16) 0.58 (2021-07-14) 0.57.1 (2021-06-08) 0.57 (2021-06-05) New features Bug fixes and other improvements 0.56.1 (2021-06-05) 0.56 (2021-03-28) 0.55 (2021-02-18) 0.54.1 (2021-02-02) 0.54 (2021-01-25) The _internal database Named in-memory database support JavaScript modules Code formatting with Black and Prettier Other changes 0.53 (2020-12-10) 0.52.5 (2020-12-09) 0.52.4 (2020-12-05) 0.52.3 (2020-12-03) 0.52.2 (2020-12-02) 0.52.1 (2020-11-29) 0.52 (2020-11-28) 0.51.1 (2020-10-31) 0.51 (2020-10-31) New visual design Plugins can now add links within Datasette Binary data URL building Running Datasette behind a proxy Smaller changes 0.50.2 (2020-10-09) 0.50.1 (2020-10-09) 0.50 (2020-10-09) 0.49.1 (2020-09-15) 0.49 (2020-09-14) 0.48 (2020-08-16) 0.47.3 (2020-08-15) 0.47.2 (2020-08-12) 0.47.1 (2020-08-11) 0.47 (2020-08-11) 0.46 (2020-08-09) 0.45 (2020-07-01) Magic parameters for canned queries Log out Better plugin documentation New plugin hooks Smaller changes 0.44 (2020-06-11) Authentication Permissions Writable canned queries Flash messages Signed values and secrets CSRF protection Cookie methods register_routes() plugin hooks Smaller changes The road to Datasette 1.0 0.43 (2020-05-28) 0.42 (2020-05-08) 0.41 (2020-05-06) 0.40 (2020-04-21) 0.39 (2020-03-24) 0.38 (2020-03-08) 0.37.1 (2020-03-02) 0.37 (2020-02-25) 0.36 (2020-02-21) 0.35 (2020-02-04) 0.34 (2020-01-29) 0.33 (2019-12-22) 0.32 (2019-11-14) 0.31.2 (2019-11-13) 0.31.1 (2019-11-12) 0.31 (2019-11-11) 0.30.2 (2019-11-02) 0.30.1 (2019-10-30) 0.30 (2019-10-18) 0.29.3 (2019-09-02) 0.29.2 (2019-07-13) 0.29.1 (2019-07-11) 0.29 (2019-07-07) ASGI New plugin hook: asgi_wrapper New plugin hook: extra_template_vars Secret plugin configuration options Facet by date Easier custom templates for table rows ?_through= for joins through many-to-many tables Small changes 0.28 (2019-05-19) Supporting databases that change Faceting improvements, and faceting plugins datasette publish cloudrun register_output_renderer plugins Medium changes Small changes 0.27.1 (2019-05-09) 0.27 (2019-01-31) 0.26.1 (2019-01-10) 0.26 (2019-01-02) 0.25.2 (2018-12-16) 0.25.1 (2018-11-04) 0.25 (2018-09-19) 0.24 (2018-07-23) 0.23.2 (2018-07-07) 0.23.1 (2018-06-21) 0.23 (2018-06-18) CSV export Foreign key expansions New configuration settings Control HTTP caching with ?_ttl= Improved support for SpatiaLite latest.datasette.io Miscellaneous 0.22.1 (2018-05-23) 0.22 (2018-05-20) 0.21 (2018-05-05) 0.20 (2018-04-20) 0.19 (2018-04-16) 0.18 (2018-04-14) 0.17 (2018-04-13) 0.16 (2018-04-13) 0.15 (2018-04-09) 0.14 (2017-12-09) 0.13 (2017-11-24) 0.12 (2017-11-16) 0.11 (2017-11-14) 0.10 (2017-11-14) 0.9 (2017-11-13) 0.8 (2017-11-13)","[""Datasette""]",[]
getting_started:getting-started-your-computer,getting_started,getting-started-your-computer,Using Datasette on your own computer,"First, follow the Installation instructions. Now you can run Datasette against a SQLite file on your computer using the following command:
datasette path/to/database.db
This will start a web server on port 8001 - visit http://localhost:8001/
to access the web interface.
Add -o to open your browser automatically once Datasette has started:
datasette path/to/database.db -o
Use Chrome on OS X? You can run datasette against your browser history
like so:
datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock
The --nolock option ignores any file locks. This is safe as Datasette will open the file in read-only mode.
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"",
...
],
""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 format:
{
...
""rows"": [
{
""start_time"": 13097290269022132,
""interrupt_reason"": 0,
""hash"": """",
""id"": 1,
""site_url"": """",
""referrer"": ""https://www.dropbox.com/downloading?src=index"",
...
}
]
}","[""Getting started""]","[{""href"": ""http://localhost:8001/"", ""label"": ""http://localhost:8001/""}, {""href"": ""http://localhost:8001/History/downloads"", ""label"": ""http://localhost:8001/History/downloads""}, {""href"": ""http://localhost:8001/History/downloads.json"", ""label"": ""http://localhost:8001/History/downloads.json""}, {""href"": ""http://localhost:8001/History/downloads.json?_shape=objects"", ""label"": ""http://localhost:8001/History/downloads.json?_shape=objects""}]"
getting_started:getting-started-tutorial,getting_started,getting-started-tutorial,Follow a tutorial,"Datasette has several tutorials to help you get started with the tool. Try one of the following:
Exploring a database with Datasette shows how to use the Datasette web interface to explore a new database.
Learn SQL with Datasette introduces SQL, and shows how to use that query language to ask questions of your data.
Cleaning data with sqlite-utils and Datasette guides you through using sqlite-utils to turn a CSV file into a database that you can explore using Datasette.","[""Getting started""]","[{""href"": ""https://datasette.io/tutorials"", ""label"": ""tutorials""}, {""href"": ""https://datasette.io/tutorials/explore"", ""label"": ""Exploring a database with Datasette""}, {""href"": ""https://datasette.io/tutorials/learn-sql"", ""label"": ""Learn SQL with Datasette""}, {""href"": ""https://datasette.io/tutorials/clean-data"", ""label"": ""Cleaning data with sqlite-utils and Datasette""}, {""href"": ""https://sqlite-utils.datasette.io/"", ""label"": ""sqlite-utils""}]"
getting_started:getting-started-glitch,getting_started,getting-started-glitch,Try Datasette without installing anything using Glitch,"Glitch is a free online tool for building web apps directly from your web browser. You can use Glitch to try out Datasette without needing to install any software on your own computer.
Here's a demo project on Glitch which you can use as the basis for your own experiments:
glitch.com/~datasette-csvs
Glitch allows you to ""remix"" any project to create your own copy and start editing it in your browser. You can remix the datasette-csvs project by clicking this button:
Find a CSV file and drag it onto the Glitch file explorer panel - datasette-csvs will automatically convert it to a SQLite database (using sqlite-utils ) and allow you to start exploring it using Datasette.
If your CSV file has a latitude and longitude column you can visualize it on a map by uncommenting the datasette-cluster-map line in the requirements.txt file using the Glitch file editor.
Need some data? Try this Public Art Data for the city of Seattle - hit ""Export"" and select ""CSV"" to download it as a CSV file.
For more on how this works, see Running Datasette on Glitch .","[""Getting started""]","[{""href"": ""https://glitch.com/"", ""label"": ""Glitch""}, {""href"": ""https://glitch.com/~datasette-csvs"", ""label"": ""glitch.com/~datasette-csvs""}, {""href"": ""https://glitch.com/edit/#!/remix/datasette-csvs"", ""label"": null}, {""href"": ""https://github.com/simonw/sqlite-utils"", ""label"": ""sqlite-utils""}, {""href"": ""https://data.seattle.gov/Community/Public-Art-Data/j7sn-tdzk"", ""label"": ""Public Art Data""}, {""href"": ""https://simonwillison.net/2019/Apr/23/datasette-glitch/"", ""label"": ""Running Datasette on Glitch""}]"
getting_started:getting-started-demo,getting_started,getting-started-demo,Play with a live demo,"The best way to experience Datasette for the first time is with a demo:
global-power-plants.datasettes.com provides a searchable database of power plants around the world, using data from the World Resources Institude rendered using the datasette-cluster-map plugin.
fivethirtyeight.datasettes.com shows Datasette running against over 400 datasets imported from the FiveThirtyEight GitHub repository .","[""Getting started""]","[{""href"": ""https://global-power-plants.datasettes.com/global-power-plants/global-power-plants"", ""label"": ""global-power-plants.datasettes.com""}, {""href"": ""https://www.wri.org/publication/global-power-plant-database"", ""label"": ""World Resources Institude""}, {""href"": ""https://github.com/simonw/datasette-cluster-map"", ""label"": ""datasette-cluster-map""}, {""href"": ""https://fivethirtyeight.datasettes.com/fivethirtyeight"", ""label"": ""fivethirtyeight.datasettes.com""}, {""href"": ""https://github.com/fivethirtyeight/data"", ""label"": ""FiveThirtyEight GitHub repository""}]"
getting_started:getting-started-datasette-lite,getting_started,getting-started-datasette-lite,Datasette in your browser with Datasette Lite,"Datasette Lite is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required.
You can pass a URL to a CSV, SQLite or raw SQL file directly to Datasette Lite to explore that data in your browser.
This example link opens Datasette Lite and loads the SQL Murder Mystery example database from Northwestern University Knight Lab .","[""Getting started""]","[{""href"": ""https://lite.datasette.io/"", ""label"": ""Datasette Lite""}, {""href"": ""https://lite.datasette.io/?url=https%3A%2F%2Fraw.githubusercontent.com%2FNUKnightLab%2Fsql-mysteries%2Fmaster%2Fsql-murder-mystery.db#/sql-murder-mystery"", ""label"": ""example link""}, {""href"": ""https://github.com/NUKnightLab/sql-mysteries"", ""label"": ""Northwestern University Knight Lab""}]"
getting_started:getting-started,getting_started,getting-started,Getting started,,[],[]
full_text_search:id1,full_text_search,id1,Full-text search,"SQLite includes a powerful mechanism for enabling full-text search against SQLite records. Datasette can detect if a table has had full-text search configured for it in the underlying database and display a search interface for filtering that table.
Here's an example search :
Datasette automatically detects which tables have been configured for full-text search.",[],"[{""href"": ""https://www.sqlite.org/fts3.html"", ""label"": ""a powerful mechanism for enabling full-text search""}, {""href"": ""https://register-of-members-interests.datasettes.com/regmem/items?_search=hamper&_sort_desc=date"", ""label"": ""an example search""}]"
full_text_search:full-text-search-table-view-api,full_text_search,full-text-search-table-view-api,The table page and table view API,"Table views that support full-text search can be queried using the ?_search=TERMS query string parameter. This will run the search against content from all of the columns that have been included in the index.
Try this example: fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort
SQLite full-text search supports wildcards. This means you can easily implement prefix auto-complete by including an asterisk at the end of the search term - for example:
/dbname/tablename/?_search=rob*
This will return all records containing at least one word that starts with the letters rob .
You can also run searches against just the content of a specific named column by using _search_COLNAME=TERMS - for example, this would search for just rows where the name column in the FTS index mentions Sarah :
/dbname/tablename/?_search_name=Sarah","[""Full-text search""]","[{""href"": ""https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort"", ""label"": ""fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort""}]"
full_text_search:full-text-search-table-or-view,full_text_search,full-text-search-table-or-view,Configuring full-text search for a table or view,"If a table has a corresponding FTS table set up using the content= argument to CREATE VIRTUAL TABLE shown below, Datasette will detect it automatically and add a search interface to the table page for that table.
You can also manually configure which table should be used for full-text search using query string parameters or Metadata . You can set the associated FTS table for a specific table and you can also set one for a view - if you do that, the page for that SQL view will offer a search option.
Use ?_fts_table=x to over-ride the FTS table for a specific page. If the primary key was something other than rowid you can use ?_fts_pk=col to set that as well. This is particularly useful for views, for example:
https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk
The fts_table metadata property can be used to specify an associated FTS table. If the primary key column in your table which was used to populate the FTS table is something other than rowid , you can specify the column to use with the fts_pk property.
The ""searchmode"": ""raw"" property can be used to default the table to accepting SQLite advanced search operators, as described in Advanced SQLite search queries .
Here is an example which enables full-text search (with SQLite advanced search operators) for a display_ads view which is defined against the ads table and hence needs to run FTS against the ads_fts table, using the id as the primary key:
[[[cog
from metadata_doc import metadata_example
metadata_example(cog, {
""databases"": {
""russian-ads"": {
""tables"": {
""display_ads"": {
""fts_table"": ""ads_fts"",
""fts_pk"": ""id"",
""searchmode"": ""raw""
}
}
}
}
})
]]]
[[[end]]]","[""Full-text search""]","[{""href"": ""https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk"", ""label"": ""https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk""}]"
full_text_search:full-text-search-fts-versions,full_text_search,full-text-search-fts-versions,FTS versions,"There are three different versions of the SQLite FTS module: FTS3, FTS4 and FTS5. You can tell which versions are supported by your instance of Datasette by checking the /-/versions page.
FTS5 is the most advanced module but may not be available in the SQLite version that is bundled with your Python installation. Most importantly, FTS5 is the only version that has the ability to order by search relevance without needing extra code.
If you can't be sure that FTS5 will be available, you should use FTS4.","[""Full-text search""]",[]
full_text_search:full-text-search-enabling,full_text_search,full-text-search-enabling,Enabling full-text search for a SQLite table,"Datasette takes advantage of the external content mechanism in SQLite, which allows a full-text search virtual table to be associated with the contents of another SQLite table.
To set up full-text search for a table, you need to do two things:
Create a new FTS virtual table associated with your table
Populate that FTS table with the data that you would like to be able to run searches against","[""Full-text search""]","[{""href"": ""https://www.sqlite.org/fts3.html#_external_content_fts4_tables_"", ""label"": ""external content""}]"
full_text_search:full-text-search-custom-sql,full_text_search,full-text-search-custom-sql,Searches using custom SQL,"You can include full-text search results in custom SQL queries. The general pattern with SQLite search is to run the search as a sub-select that returns rowid values, then include those rowids in another part of the query.
You can see the syntax for a basic search by running that search on a table page and then clicking ""View and edit SQL"" to see the underlying SQL. For example, consider this search for manafort is the US FARA database :
/fara/FARA_All_ShortForms?_search=manafort
If you click View and edit SQL you'll see that the underlying SQL looks like this:
select
rowid,
Short_Form_Termination_Date,
Short_Form_Date,
Short_Form_Last_Name,
Short_Form_First_Name,
Registration_Number,
Registration_Date,
Registrant_Name,
Address_1,
Address_2,
City,
State,
Zip
from
FARA_All_ShortForms
where
rowid in (
select
rowid
from
FARA_All_ShortForms_fts
where
FARA_All_ShortForms_fts match escape_fts(:search)
)
order by
rowid
limit
101","[""Full-text search""]","[{""href"": ""https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort"", ""label"": ""manafort is the US FARA database""}, {""href"": ""https://fara.datasettes.com/fara?sql=select%0D%0A++rowid%2C%0D%0A++Short_Form_Termination_Date%2C%0D%0A++Short_Form_Date%2C%0D%0A++Short_Form_Last_Name%2C%0D%0A++Short_Form_First_Name%2C%0D%0A++Registration_Number%2C%0D%0A++Registration_Date%2C%0D%0A++Registrant_Name%2C%0D%0A++Address_1%2C%0D%0A++Address_2%2C%0D%0A++City%2C%0D%0A++State%2C%0D%0A++Zip%0D%0Afrom%0D%0A++FARA_All_ShortForms%0D%0Awhere%0D%0A++rowid+in+%28%0D%0A++++select%0D%0A++++++rowid%0D%0A++++from%0D%0A++++++FARA_All_ShortForms_fts%0D%0A++++where%0D%0A++++++FARA_All_ShortForms_fts+match+escape_fts%28%3Asearch%29%0D%0A++%29%0D%0Aorder+by%0D%0A++rowid%0D%0Alimit%0D%0A++101&search=manafort"", ""label"": ""View and edit SQL""}]"
full_text_search:full-text-search-advanced-queries,full_text_search,full-text-search-advanced-queries,Advanced SQLite search queries,"SQLite full-text search includes support for a variety of advanced queries , including AND , OR , NOT and NEAR .
By default Datasette disables these features to ensure they do not cause errors or confusion for users who are not aware of them. You can disable this escaping and use the advanced queries by adding &_searchmode=raw to the table page query string.
If you want to enable these operators by default for a specific table, you can do so by adding ""searchmode"": ""raw"" to the metadata configuration for that table, see Configuring full-text search for a table or view .
If that option has been specified in the table metadata but you want to over-ride it and return to the default behavior you can append &_searchmode=escaped to the query string.","[""Full-text search""]","[{""href"": ""https://www.sqlite.org/fts5.html#full_text_query_syntax"", ""label"": ""a variety of advanced queries""}]"
full_text_search:configuring-fts-using-sqlite-utils,full_text_search,configuring-fts-using-sqlite-utils,Configuring FTS using sqlite-utils,"sqlite-utils is a CLI utility and Python library for manipulating SQLite databases. You can use it from Python code to configure FTS search, or you can achieve the same goal using the accompanying command-line tool .
Here's how to use sqlite-utils to enable full-text search for an items table across the name and description columns:
sqlite-utils enable-fts mydatabase.db items name description","[""Full-text search"", ""Enabling full-text search for a SQLite table""]","[{""href"": ""https://sqlite-utils.datasette.io/"", ""label"": ""sqlite-utils""}, {""href"": ""https://sqlite-utils.datasette.io/en/latest/python-api.html#enabling-full-text-search"", ""label"": ""it from Python code""}, {""href"": ""https://sqlite-utils.datasette.io/en/latest/cli.html#configuring-full-text-search"", ""label"": ""using the accompanying command-line tool""}]"
full_text_search:configuring-fts-using-csvs-to-sqlite,full_text_search,configuring-fts-using-csvs-to-sqlite,Configuring FTS using csvs-to-sqlite,"If your data starts out in CSV files, you can use Datasette's companion tool csvs-to-sqlite to convert that file into a SQLite database and enable full-text search on specific columns. For a file called items.csv where you want full-text search to operate against the name and description columns you would run the following:
csvs-to-sqlite items.csv items.db -f name -f description","[""Full-text search"", ""Enabling full-text search for a SQLite table""]","[{""href"": ""https://github.com/simonw/csvs-to-sqlite"", ""label"": ""csvs-to-sqlite""}]"
full_text_search:configuring-fts-by-hand,full_text_search,configuring-fts-by-hand,Configuring FTS by hand,"We recommend using sqlite-utils , but if you want to hand-roll a SQLite full-text search table you can do so using the following SQL.
To enable full-text search for a table called items that works against the name and description columns, you would run this SQL to create a new items_fts FTS virtual table:
CREATE VIRTUAL TABLE ""items_fts"" USING FTS4 (
name,
description,
content=""items""
);
This creates a set of tables to power full-text search against items . The new items_fts table will be detected by Datasette as the fts_table for the items table.
Creating the table is not enough: you also need to populate it with a copy of the data that you wish to make searchable. You can do that using the following SQL:
INSERT INTO ""items_fts"" (rowid, name, description)
SELECT rowid, name, description FROM items;
If your table has columns that are foreign key references to other tables you can include that data in your full-text search index using a join. Imagine the items table has a foreign key column called category_id which refers to a categories table - you could create a full-text search table like this:
CREATE VIRTUAL TABLE ""items_fts"" USING FTS4 (
name,
description,
category_name,
content=""items""
);
And then populate it like this:
INSERT INTO ""items_fts"" (rowid, name, description, category_name)
SELECT items.rowid,
items.name,
items.description,
categories.name
FROM items JOIN categories ON items.category_id=categories.id;
You can use this technique to populate the full-text search index from any combination of tables and joins that makes sense for your project.","[""Full-text search"", ""Enabling full-text search for a SQLite table""]","[{""href"": ""https://sqlite-utils.datasette.io/"", ""label"": ""sqlite-utils""}]"
facets:suggested-facets,facets,suggested-facets,Suggested facets,"Datasette's table UI will suggest facets for the user to apply, based on the following criteria:
For the currently filtered data are there any columns which, if applied as a facet...
Will return 30 or less unique options
Will return more than one unique option
Will return less unique options than the total number of filtered rows
And the query used to evaluate this criteria can be completed in under 50ms
That last point is particularly important: Datasette runs a query for every column that is displayed on a page, which could get expensive - so to avoid slow load times it sets a time limit of just 50ms for each of those queries.
This means suggested facets are unlikely to appear for tables with millions of records in them.","[""Facets""]",[]
facets:speeding-up-facets-with-indexes,facets,speeding-up-facets-with-indexes,Speeding up facets with indexes,"The performance of facets can be greatly improved by adding indexes on the columns you wish to facet by.
Adding indexes can be performed using the sqlite3 command-line utility. Here's how to add an index on the state column in a table called Food_Trucks :
sqlite3 mydatabase.db
SQLite version 3.19.3 2017-06-27 16:48:08
Enter "".help"" for usage hints.
sqlite> CREATE INDEX Food_Trucks_state ON Food_Trucks(""state"");
Or using the sqlite-utils command-line utility:
sqlite-utils create-index mydatabase.db Food_Trucks state","[""Facets""]","[{""href"": ""https://sqlite-utils.datasette.io/en/stable/cli.html#creating-indexes"", ""label"": ""sqlite-utils""}]"
facets:id3,facets,id3,Facet by date,"If Datasette finds any columns that contain dates in the first 100 values, it will offer a faceting interface against the dates of those values.
This works especially well against timestamp values such as 2019-03-01 12:44:00 .
Example here: latest.datasette.io/fixtures/facetable?_facet_date=created","[""Facets""]","[{""href"": ""https://latest.datasette.io/fixtures/facetable?_facet_date=created"", ""label"": ""latest.datasette.io/fixtures/facetable?_facet_date=created""}]"
facets:id2,facets,id2,Facet by JSON array,"If your SQLite installation provides the json1 extension (you can check using /-/versions ) Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns.
This is useful for modelling things like tags without needing to break them out into a new table.
Example here: latest.datasette.io/fixtures/facetable?_facet_array=tags","[""Facets""]","[{""href"": ""https://latest.datasette.io/fixtures/facetable?_facet_array=tags"", ""label"": ""latest.datasette.io/fixtures/facetable?_facet_array=tags""}]"
facets:id1,facets,id1,Facets,"Datasette facets can be used to add a faceted browse interface to any database table.
With facets, tables are displayed along with a summary showing the most common values in specified columns.
These values can be selected to further filter the table.
Here's an example :
Facets can be specified in two ways: using query string parameters, or in metadata.json configuration for the table.",[],"[{""href"": ""https://congress-legislators.datasettes.com/legislators/legislator_terms?_facet=type&_facet=party&_facet=state&_facet_size=10"", ""label"": ""an example""}]"
facets:facets-metadata,facets,facets-metadata,Facets in metadata,"You can turn facets on by default for specific tables by adding them to a ""facets"" key in a Datasette Metadata file.
Here's an example that turns on faceting by default for the qLegalStatus column in the Street_Tree_List table in the sf-trees database:
[[[cog
from metadata_doc import metadata_example
metadata_example(cog, {
""databases"": {
""sf-trees"": {
""tables"": {
""Street_Tree_List"": {
""facets"": [""qLegalStatus""]
}
}
}
}
})
]]]
[[[end]]]
Facets defined in this way will always be shown in the interface and returned in the API, regardless of the _facet arguments passed to the view.
You can specify array or date facets in metadata using JSON objects with a single key of array or date and a value specifying the column, like this:
[[[cog
metadata_example(cog, {
""facets"": [
{""array"": ""tags""},
{""date"": ""created""}
]
})
]]]
[[[end]]]
You can change the default facet size (the number of results shown for each facet) for a table using facet_size :
[[[cog
metadata_example(cog, {
""databases"": {
""sf-trees"": {
""tables"": {
""Street_Tree_List"": {
""facets"": [""qLegalStatus""],
""facet_size"": 10
}
}
}
}
})
]]]
[[[end]]]","[""Facets""]",[]
facets:facets-in-query-strings,facets,facets-in-query-strings,Facets in query strings,"To turn on faceting for specific columns on a Datasette table view, add one or more _facet=COLUMN parameters to the URL.
For example, if you want to turn on facets for the city_id and state columns, construct a URL that looks like this:
/dbname/tablename?_facet=state&_facet=city_id
This works for both the HTML interface and the .json view.
When enabled, facets will cause a facet_results block to be added to the JSON output, looking something like this:
{
""state"": {
""name"": ""state"",
""results"": [
{
""value"": ""CA"",
""label"": ""CA"",
""count"": 10,
""toggle_url"": ""http://...?_facet=city_id&_facet=state&state=CA"",
""selected"": false
},
{
""value"": ""MI"",
""label"": ""MI"",
""count"": 4,
""toggle_url"": ""http://...?_facet=city_id&_facet=state&state=MI"",
""selected"": false
},
{
""value"": ""MC"",
""label"": ""MC"",
""count"": 1,
""toggle_url"": ""http://...?_facet=city_id&_facet=state&state=MC"",
""selected"": false
}
],
""truncated"": false
}
""city_id"": {
""name"": ""city_id"",
""results"": [
{
""value"": 1,
""label"": ""San Francisco"",
""count"": 6,
""toggle_url"": ""http://...?_facet=city_id&_facet=state&city_id=1"",
""selected"": false
},
{
""value"": 2,
""label"": ""Los Angeles"",
""count"": 4,
""toggle_url"": ""http://...?_facet=city_id&_facet=state&city_id=2"",
""selected"": false
},
{
""value"": 3,
""label"": ""Detroit"",
""count"": 4,
""toggle_url"": ""http://...?_facet=city_id&_facet=state&city_id=3"",
""selected"": false
},
{
""value"": 4,
""label"": ""Memnonia"",
""count"": 1,
""toggle_url"": ""http://...?_facet=city_id&_facet=state&city_id=4"",
""selected"": false
}
],
""truncated"": false
}
}
If Datasette detects that a column is a foreign key, the ""label"" property will be automatically derived from the detected label column on the referenced table.
The default number of facet results returned is 30, controlled by the default_facet_size setting.
You can increase this on an individual page by adding ?_facet_size=100 to the query string, up to a maximum of max_returned_rows (which defaults to 1000).","[""Facets""]",[]
events:id1,events,id1,Events,"Datasette includes a mechanism for tracking events that occur while the software is running. This is primarily intended to be used by plugins, which can both trigger events and listen for events.
The core Datasette application triggers events when certain things happen. This page describes those events.
Plugins can listen for events using the track_event(datasette, event) plugin hook, which will be called with instances of the following classes - or additional classes registered by other plugins .
class datasette.events. LoginEvent actor : dict | None
Event name: login
A user (represented by event.actor ) has logged in.
class datasette.events. LogoutEvent actor : dict | None
Event name: logout
A user (represented by event.actor ) has logged out.
class datasette.events. CreateTokenEvent actor : dict | None expires_after : int | None restrict_all : list restrict_database : dict restrict_resource : dict
Event name: create-token
A user created an API token.
Variables
expires_after -- Number of seconds after which this token will expire.
restrict_all -- Restricted permissions for this token.
restrict_database -- Restricted database permissions for this token.
restrict_resource -- Restricted resource permissions for this token.
class datasette.events. CreateTableEvent actor : dict | None database : str table : str schema : str
Event name: create-table
A new table has been created in the database.
Variables
database -- The name of the database where the table was created.
table -- The name of the table that was created
schema -- The SQL schema definition for the new table.
class datasette.events. DropTableEvent actor : dict | None database : str table : str
Event name: drop-table
A table has been dropped from the database.
Variables
database -- The name of the database where the table was dropped.
table -- The name of the table that was dropped
class datasette.events. AlterTableEvent actor : dict | None database : str table : str before_schema : str after_schema : str
Event name: alter-table
A table has been altered.
Variables
database -- The name of the database where the table was altered
table -- The name of the table that was altered
before_schema -- The table's SQL schema before the alteration
after_schema -- The table's SQL schema after the alteration
class datasette.events. InsertRowsEvent actor : dict | None database : str table : str num_rows : int ignore : bool replace : bool
Event name: insert-rows
Rows were inserted into a table.
Variables
database -- The name of the database where the rows were inserted.
table -- The name of the table where the rows were inserted.
num_rows -- The number of rows that were requested to be inserted.
ignore -- Was ignore set?
replace -- Was replace set?
class datasette.events. UpsertRowsEvent actor : dict | None database : str table : str num_rows : int
Event name: upsert-rows
Rows were upserted into a table.
Variables
database -- The name of the database where the rows were inserted.
table -- The name of the table where the rows were inserted.
num_rows -- The number of rows that were requested to be inserted.
class datasette.events. UpdateRowEvent actor : dict | None database : str table : str pks : list
Event name: update-row
A row was updated in a table.
Variables
database -- The name of the database where the row was updated.
table -- The name of the table where the row was updated.
pks -- The primary key values of the updated row.
class datasette.events. DeleteRowEvent actor : dict | None database : str table : str pks : list
Event name: delete-row
A row was deleted from a table.
Variables
database -- The name of the database where the row was deleted.
table -- The name of the table where the row was deleted.
pks -- The primary key values of the deleted row.",[],[]
ecosystem:sqlite-utils,ecosystem,sqlite-utils,sqlite-utils,"sqlite-utils is a key building block for the wider Datasette ecosystem. It provides a collection of utilities for manipulating SQLite databases, both as a Python library and a command-line utility. Features include:
Insert data into a SQLite database from JSON, CSV or TSV, automatically creating tables with the correct schema or altering existing tables to add missing columns.
Configure tables for use with SQLite full-text search, including creating triggers needed to keep the search index up-to-date.
Modify tables in ways that are not supported by SQLite's default ALTER TABLE syntax - for example changing the types of columns or selecting a new primary key for a table.
Adding foreign keys to existing database tables.
Extracting columns of data into a separate lookup table.","[""The Datasette Ecosystem""]","[{""href"": ""https://sqlite-utils.datasette.io/"", ""label"": ""sqlite-utils""}]"
ecosystem:ecosystem,ecosystem,ecosystem,The Datasette Ecosystem,"Datasette sits at the center of a growing ecosystem of open source tools aimed at making it as easy as possible to gather, analyze and publish interesting data.
These tools are divided into two main groups: tools for building SQLite databases (for use with Datasette) and plugins that extend Datasette's functionality.
The Datasette project website includes a directory of plugins and a directory of tools:
Plugins directory on datasette.io
Tools directory on datasette.io",[],"[{""href"": ""https://datasette.io/"", ""label"": ""Datasette project website""}, {""href"": ""https://datasette.io/plugins"", ""label"": ""Plugins directory on datasette.io""}, {""href"": ""https://datasette.io/tools"", ""label"": ""Tools directory on datasette.io""}]"
ecosystem:dogsheep,ecosystem,dogsheep,Dogsheep,Dogsheep is a collection of tools for personal analytics using SQLite and Datasette. The project provides tools like github-to-sqlite and twitter-to-sqlite that can import data from different sources in order to create a personal data warehouse. Personal Data Warehouses: Reclaiming Your Data is a talk that explains Dogsheep and demonstrates it in action.,"[""The Datasette Ecosystem""]","[{""href"": ""https://dogsheep.github.io/"", ""label"": ""Dogsheep""}, {""href"": ""https://datasette.io/tools/github-to-sqlite"", ""label"": ""github-to-sqlite""}, {""href"": ""https://datasette.io/tools/twitter-to-sqlite"", ""label"": ""twitter-to-sqlite""}, {""href"": ""https://simonwillison.net/2020/Nov/14/personal-data-warehouses/"", ""label"": ""Personal Data Warehouses: Reclaiming Your Data""}]"
deploying:nginx-proxy-configuration,deploying,nginx-proxy-configuration,Nginx proxy configuration,"Here is an example of an nginx configuration file that will proxy traffic to Datasette:
daemon off;
events {
worker_connections 1024;
}
http {
server {
listen 80;
location /my-datasette {
proxy_pass http://127.0.0.1:8009/my-datasette;
proxy_set_header Host $host;
}
}
}
You can also use the --uds option to Datasette to listen on a Unix domain socket instead of a port, configuring the nginx upstream proxy like this:
daemon off;
events {
worker_connections 1024;
}
http {
server {
listen 80;
location /my-datasette {
proxy_pass http://datasette/my-datasette;
proxy_set_header Host $host;
}
}
upstream datasette {
server unix:/tmp/datasette.sock;
}
}
Then run Datasette with datasette --uds /tmp/datasette.sock path/to/database.db --setting base_url /my-datasette/ .","[""Deploying Datasette"", ""Running Datasette behind a proxy""]","[{""href"": ""https://nginx.org/"", ""label"": ""nginx""}]"
deploying:deploying-systemd,deploying,deploying-systemd,Running Datasette using systemd,"You can run Datasette on Ubuntu or Debian systems using systemd .
First, ensure you have Python 3 and pip installed. On Ubuntu you can use sudo apt-get install python3 python3-pip .
You can install Datasette into a virtual environment, or you can install it system-wide. To install system-wide, use sudo pip3 install datasette .
Now create a folder for your Datasette databases, for example using mkdir /home/ubuntu/datasette-root .
You can copy a test database into that folder like so:
cd /home/ubuntu/datasette-root
curl -O https://latest.datasette.io/fixtures.db
Create a file at /etc/systemd/system/datasette.service with the following contents:
[Unit]
Description=Datasette
After=network.target
[Service]
Type=simple
User=ubuntu
Environment=DATASETTE_SECRET=
WorkingDirectory=/home/ubuntu/datasette-root
ExecStart=datasette serve . -h 127.0.0.1 -p 8000
Restart=on-failure
[Install]
WantedBy=multi-user.target
Add a random value for the DATASETTE_SECRET - this will be used to sign Datasette cookies such as the CSRF token cookie. You can generate a suitable value like so:
python3 -c 'import secrets; print(secrets.token_hex(32))'
This configuration will run Datasette against all database files contained in the /home/ubuntu/datasette-root directory. If that directory contains a metadata.yml (or .json ) file or a templates/ or plugins/ sub-directory those will automatically be loaded by Datasette - see Configuration directory mode for details.
You can start the Datasette process running using the following:
sudo systemctl daemon-reload
sudo systemctl start datasette.service
You will need to restart the Datasette service after making changes to its metadata.json configuration or adding a new database file to that directory. You can do that using:
sudo systemctl restart datasette.service
Once the service has started you can confirm that Datasette is running on port 8000 like so:
curl 127.0.0.1:8000/-/versions.json
# Should output JSON showing the installed version
Datasette will not be accessible from outside the server because it is listening on 127.0.0.1 . You can expose it by instead listening on 0.0.0.0 , but a better way is to set up a proxy such as nginx - see Running Datasette behind a proxy .","[""Deploying Datasette""]",[]
deploying:deploying-proxy,deploying,deploying-proxy,Running Datasette behind a proxy,"You may wish to run Datasette behind an Apache or nginx proxy, using a path within your existing site.
You can use the base_url configuration setting to tell Datasette to serve traffic with a specific URL prefix. For example, you could run Datasette like this:
datasette my-database.db --setting base_url /my-datasette/ -p 8009
This will run Datasette with the following URLs:
http://127.0.0.1:8009/my-datasette/ - the Datasette homepage
http://127.0.0.1:8009/my-datasette/my-database - the page for the my-database.db database
http://127.0.0.1:8009/my-datasette/my-database/some_table - the page for the some_table table
You can now set your nginx or Apache server to proxy the /my-datasette/ path to this Datasette instance.","[""Deploying Datasette""]",[]
deploying:deploying-openrc,deploying,deploying-openrc,Running Datasette using OpenRC,"OpenRC is the service manager on non-systemd Linux distributions like Alpine Linux and Gentoo .
Create an init script at /etc/init.d/datasette with the following contents:
#!/sbin/openrc-run
name=""datasette""
command=""datasette""
command_args=""serve -h 0.0.0.0 /path/to/db.db""
command_background=true
pidfile=""/run/${RC_SVCNAME}.pid""
You then need to configure the service to run at boot and start it:
rc-update add datasette
rc-service datasette start","[""Deploying Datasette""]","[{""href"": ""https://www.alpinelinux.org/"", ""label"": ""Alpine Linux""}, {""href"": ""https://www.gentoo.org/"", ""label"": ""Gentoo""}]"
deploying:deploying-fundamentals,deploying,deploying-fundamentals,Deployment fundamentals,"Datasette can be deployed as a single datasette process that listens on a port. Datasette is not designed to be run as root, so that process should listen on a higher port such as port 8000.
If you want to serve Datasette on port 80 (the HTTP default port) or port 443 (for HTTPS) you should run it behind a proxy server, such as nginx, Apache or HAProxy. The proxy server can listen on port 80/443 and forward traffic on to Datasette.","[""Deploying Datasette""]",[]
deploying:deploying-buildpacks,deploying,deploying-buildpacks,Deploying using buildpacks,"Some hosting providers such as Heroku , DigitalOcean App Platform and Scalingo support the Buildpacks standard for deploying Python web applications.
Deploying Datasette on these platforms requires two files: requirements.txt and Procfile .
The requirements.txt file lets the platform know which Python packages should be installed. It should contain datasette at a minimum, but can also list any Datasette plugins you wish to install - for example:
datasette
datasette-vega
The Procfile lets the hosting platform know how to run the command that serves web traffic. It should look like this:
web: datasette . -h 0.0.0.0 -p $PORT --cors
The $PORT environment variable is provided by the hosting platform. --cors enables CORS requests from JavaScript running on other websites to your domain - omit this if you don't want to allow CORS. You can add additional Datasette Settings options here too.
These two files should be enough to deploy Datasette on any host that supports buildpacks. Datasette will serve any SQLite files that are included in the root directory of the application.
If you want to build SQLite files or download them as part of the deployment process you can do so using a bin/post_compile file. For example, the following bin/post_compile will download an example database that will then be served by Datasette:
wget https://fivethirtyeight.datasettes.com/fivethirtyeight.db
simonw/buildpack-datasette-demo is an example GitHub repository showing a Datasette configuration that can be deployed to a buildpack-supporting host.","[""Deploying Datasette""]","[{""href"": ""https://www.heroku.com/"", ""label"": ""Heroku""}, {""href"": ""https://www.digitalocean.com/docs/app-platform/"", ""label"": ""DigitalOcean App Platform""}, {""href"": ""https://scalingo.com/"", ""label"": ""Scalingo""}, {""href"": ""https://buildpacks.io/"", ""label"": ""Buildpacks standard""}, {""href"": ""https://github.com/simonw/buildpack-datasette-demo"", ""label"": ""simonw/buildpack-datasette-demo""}]"
deploying:deploying,deploying,deploying,Deploying Datasette,"The quickest way to deploy a Datasette instance on the internet is to use the datasette publish command, described in Publishing data . This can be used to quickly deploy Datasette to a number of hosting providers including Heroku, Google Cloud Run and Vercel.
You can deploy Datasette to other hosting providers using the instructions on this page.",[],[]
deploying:apache-proxy-configuration,deploying,apache-proxy-configuration,Apache proxy configuration,"For Apache , you can use the ProxyPass directive. First make sure the following lines are uncommented:
LoadModule proxy_module lib/httpd/modules/mod_proxy.so
LoadModule proxy_http_module lib/httpd/modules/mod_proxy_http.so
Then add these directives to proxy traffic:
ProxyPass /my-datasette/ http://127.0.0.1:8009/my-datasette/
ProxyPreserveHost On
A live demo of Datasette running behind Apache using this proxy setup can be seen at datasette-apache-proxy-demo.datasette.io/prefix/ . The code for that demo can be found in the demos/apache-proxy directory.
Using --uds you can use Unix domain sockets similar to the nginx example:
ProxyPass /my-datasette/ unix:/tmp/datasette.sock|http://localhost/my-datasette/
The ProxyPreserveHost On directive ensures that the original Host: header from the incoming request is passed through to Datasette. Datasette needs this to correctly assemble links to other pages using the .absolute_url(request, path) method.","[""Deploying Datasette"", ""Running Datasette behind a proxy""]","[{""href"": ""https://httpd.apache.org/"", ""label"": ""Apache""}, {""href"": ""https://datasette-apache-proxy-demo.datasette.io/prefix/"", ""label"": ""datasette-apache-proxy-demo.datasette.io/prefix/""}, {""href"": ""https://github.com/simonw/datasette/tree/main/demos/apache-proxy"", ""label"": ""demos/apache-proxy""}, {""href"": ""https://httpd.apache.org/docs/2.4/mod/mod_proxy.html#proxypreservehost"", ""label"": ""ProxyPreserveHost On""}]"
custom_templates:publishing-static-assets,custom_templates,publishing-static-assets,Publishing static assets,"The datasette publish command can be used to publish your static assets,
using the same syntax as above:
datasette publish cloudrun mydb.db --static assets:static-files/
This will upload the contents of the static-files/ directory as part of the
deployment, and configure Datasette to correctly serve the assets from /assets/ .","[""Custom pages and templates""]",[]
custom_templates:id1,custom_templates,id1,Custom pages,"You can add templated pages to your Datasette instance by creating HTML files in a pages directory within your templates directory.
For example, to add a custom page that is served at http://localhost/about you would create a file in templates/pages/about.html , then start Datasette like this:
datasette mydb.db --template-dir=templates/
You can nest directories within pages to create a nested structure. To create a http://localhost:8001/about/map page you would create templates/pages/about/map.html .","[""Custom pages and templates"", ""Publishing static assets""]",[]
custom_templates:customization-static-files,custom_templates,customization-static-files,Serving static files,"Datasette can serve static files for you, using the --static option.
Consider the following directory structure:
metadata.json
static-files/styles.css
static-files/app.js
You can start Datasette using --static assets:static-files/ to serve those
files from the /assets/ mount point:
datasette --config datasette.yaml --static assets:static-files/ --memory
The following URLs will now serve the content from those CSS and JS files:
http://localhost:8001/assets/styles.css
http://localhost:8001/assets/app.js
You can reference those files from datasette.yaml like this, see custom CSS and JavaScript for more details:
[[[cog
from metadata_doc import config_example
config_example(cog, """"""
extra_css_urls:
- /assets/styles.css
extra_js_urls:
- /assets/app.js
"""""")
]]]
[[[end]]]","[""Custom pages and templates""]",[]
custom_templates:customization-custom-templates,custom_templates,customization-custom-templates,Custom templates,"By default, Datasette uses default templates that ship with the package.
You can over-ride these templates by specifying a custom --template-dir like
this:
datasette mydb.db --template-dir=mytemplates/
Datasette will now first look for templates in that directory, and fall back on
the defaults if no matches are found.
It is also possible to over-ride templates on a per-database, per-row or per-
table basis.
The lookup rules Datasette uses are as follows:
Index page (/):
index.html
Database page (/mydatabase):
database-mydatabase.html
database.html
Custom query page (/mydatabase?sql=...):
query-mydatabase.html
query.html
Canned query page (/mydatabase/canned-query):
query-mydatabase-canned-query.html
query-mydatabase.html
query.html
Table page (/mydatabase/mytable):
table-mydatabase-mytable.html
table.html
Row page (/mydatabase/mytable/id):
row-mydatabase-mytable.html
row.html
Table of rows and columns include on table page:
_table-table-mydatabase-mytable.html
_table-mydatabase-mytable.html
_table.html
Table of rows and columns include on row page:
_table-row-mydatabase-mytable.html
_table-mydatabase-mytable.html
_table.html
If a table name has spaces or other unexpected characters in it, the template
filename will follow the same rules as our custom CSS classes - for
example, a table called ""Food Trucks"" will attempt to load the following
templates:
table-mydatabase-Food-Trucks-399138.html
table.html
You can find out which templates were considered for a specific page by viewing
source on that page and looking for an HTML comment at the bottom. The comment
will look something like this:
This example is from the canned query page for a query called ""tz"" in the
database called ""mydb"". The asterisk shows which template was selected - so in
this case, Datasette found a template file called query-mydb-tz.html and
used that - but if that template had not been found, it would have tried for
query-mydb.html or the default query.html .
It is possible to extend the default templates using Jinja template
inheritance. If you want to customize EVERY row template with some additional
content you can do so by creating a row.html template like this:
{% extends ""default:row.html"" %}
{% block content %}
EXTRA HTML AT THE TOP OF THE CONTENT BLOCK
This line renders the original block:
{{ super() }}
{% endblock %}
Note the default:row.html template name, which ensures Jinja will inherit
from the default template.
The _table.html template is included by both the row and the table pages,
and a list of rows. The default _table.html template renders them as an
HTML template and can be seen here .
You can provide a custom template that applies to all of your databases and
tables, or you can provide custom templates for specific tables using the
template naming scheme described above.
If you want to present your data in a format other than an HTML table, you
can do so by looping through display_rows in your own _table.html
template. You can use {{ row[""column_name""] }} to output the raw value
of a specific column.
If you want to output the rendered HTML version of a column, including any
links to foreign keys, you can use {{ row.display(""column_name"") }} .
Here is an example of a custom _table.html template:
{% for row in display_rows %}
{{ row[""title""] }}
{{ row[""description""] }}
Category: {{ row.display(""category_id"") }}
{% endfor %}","[""Custom pages and templates"", ""Publishing static assets""]","[{""href"": ""https://github.com/simonw/datasette/blob/main/datasette/templates/_table.html"", ""label"": ""can be seen here""}]"
custom_templates:customization,custom_templates,customization,Custom pages and templates,Datasette provides a number of ways of customizing the way data is displayed.,[],[]
custom_templates:custom-pages-redirects,custom_templates,custom-pages-redirects,Custom redirects,"You can use the custom_redirect(location) function to redirect users to another page, for example in a file called pages/datasette.html :
{{ custom_redirect(""https://github.com/simonw/datasette"") }}
Now requests to http://localhost:8001/datasette will result in a redirect.
These redirects are served with a 302 Found status code by default. You can send a 301 Moved Permanently code by passing 301 as the second argument to the function:
{{ custom_redirect(""https://github.com/simonw/datasette"", 301) }}","[""Custom pages and templates""]",[]
custom_templates:custom-pages-parameters,custom_templates,custom-pages-parameters,Path parameters for pages,"You can define custom pages that match multiple paths by creating files with {variable} definitions in their filenames.
For example, to capture any request to a URL matching /about/* , you would create a template in the following location:
templates/pages/about/{slug}.html
A hit to /about/news would render that template and pass in a variable called slug with a value of ""news"" .
If you use this mechanism don't forget to return a 404 if the referenced content could not be found. You can do this using {{ raise_404() }} described below.
Templates defined using custom page routes work particularly well with the sql() template function from datasette-template-sql or the graphql() template function from datasette-graphql .","[""Custom pages and templates""]","[{""href"": ""https://github.com/simonw/datasette-template-sql"", ""label"": ""datasette-template-sql""}, {""href"": ""https://github.com/simonw/datasette-graphql#the-graphql-template-function"", ""label"": ""datasette-graphql""}]"
custom_templates:custom-pages-headers,custom_templates,custom-pages-headers,Custom headers and status codes,"Custom pages default to being served with a content-type of text/html; charset=utf-8 and a 200 status code. You can change these by calling a custom function from within your template.
For example, to serve a custom page with a 418 I'm a teapot HTTP status code, create a file in pages/teapot.html containing the following:
{{ custom_status(418) }}
Teapot
I'm a teapot
To serve a custom HTTP header, add a custom_header(name, value) function call. For example:
{{ custom_status(418) }}
{{ custom_header(""x-teapot"", ""I am"") }}
Teapot
I'm a teapot
You can verify this is working using curl like this:
curl -I 'http://127.0.0.1:8001/teapot'
HTTP/1.1 418
date: Sun, 26 Apr 2020 18:38:30 GMT
server: uvicorn
x-teapot: I am
content-type: text/html; charset=utf-8","[""Custom pages and templates""]",[]
custom_templates:custom-pages-errors,custom_templates,custom-pages-errors,Custom error pages,"Datasette returns an error page if an unexpected error occurs, access is forbidden or content cannot be found.
You can customize the response returned for these errors by providing a custom error page template.
Content not found errors use a 404.html template. Access denied errors use 403.html . Invalid input errors use 400.html . Unexpected errors of other kinds use 500.html .
If a template for the specific error code is not found a template called error.html will be used instead. If you do not provide that template Datasette's default error.html template will be used.
The error template will be passed the following context:
status - integer
The integer HTTP status code, e.g. 404, 500, 403, 400.
error - string
Details of the specific error, usually a full sentence.
title - string or None
A title for the page representing the class of error. This is often None for errors that do not provide a title separate from their error message.","[""Custom pages and templates"", ""Custom redirects""]","[{""href"": ""https://github.com/simonw/datasette/blob/main/datasette/templates/error.html"", ""label"": ""default error.html template""}]"
custom_templates:custom-pages-404,custom_templates,custom-pages-404,Returning 404s,"To indicate that content could not be found and display the default 404 page you can use the raise_404(message) function:
{% if not rows %}
{{ raise_404(""Content not found"") }}
{% endif %}
If you call raise_404() the other content in your template will be ignored.","[""Custom pages and templates""]",[]
custom_templates:css-classes-on-the-body,custom_templates,css-classes-on-the-body,CSS classes on the ,"Every default template includes CSS classes in the body designed to support
custom styling.
The index template (the top level page at / ) gets this:
The database template ( /dbname ) gets this:
The custom SQL template ( /dbname?sql=... ) gets this:
A canned query template ( /dbname/queryname ) gets this:
The table template ( /dbname/tablename ) gets:
The row template ( /dbname/tablename/rowid ) gets:
The db-x and table-x classes use the database or table names themselves if
they are valid CSS identifiers. If they aren't, we strip any invalid
characters out and append a 6 character md5 digest of the original name, in
order to ensure that multiple tables which resolve to the same stripped
character version still have different CSS classes.
Some examples:
""simple"" => ""simple""
""MixedCase"" => ""MixedCase""
""-no-leading-hyphens"" => ""no-leading-hyphens-65bea6""
""_no-leading-underscores"" => ""no-leading-underscores-b921bc""
""no spaces"" => ""no-spaces-7088d7""
""-"" => ""336d5e""
""no $ characters"" => ""no--characters-59e024""
and
elements also get custom CSS classes reflecting the
database column they are representing, for example:
","[""Custom pages and templates""]",[]
csv_export:streaming-all-records,csv_export,streaming-all-records,Streaming all records,"The stream all rows option is designed to be as efficient as possible -
under the hood it takes advantage of Python 3 asyncio capabilities and
Datasette's efficient pagination to stream back the full
CSV file.
Since databases can get pretty large, by default this option is capped at 100MB -
if a table returns more than 100MB of data the last line of the CSV will be a
truncation error message.
You can increase or remove this limit using the max_csv_mb config
setting. You can also disable the CSV export feature entirely using
allow_csv_stream .","[""CSV export""]",[]
csv_export:id1,csv_export,id1,CSV export,"Any Datasette table, view or custom SQL query can be exported as CSV.
To obtain the CSV representation of the table you are looking, click the ""this
data as CSV"" link.
You can also use the advanced export form for more control over the resulting
file, which looks like this and has the following options:
download file - instead of displaying CSV in your browser, this forces
your browser to download the CSV to your downloads directory.
expand labels - if your table has any foreign key references this option
will cause the CSV to gain additional COLUMN_NAME_label columns with a
label for each foreign key derived from the linked table. In this example
the city_id column is accompanied by a city_id_label column.
stream all rows - by default CSV files only contain the first
max_returned_rows records. This option will cause Datasette to
loop through every matching record and return them as a single CSV file.
You can try that out on https://latest.datasette.io/fixtures/facetable?_size=4",[],"[{""href"": ""https://latest.datasette.io/fixtures/facetable.csv?_labels=on&_size=max"", ""label"": ""In this example""}, {""href"": ""https://latest.datasette.io/fixtures/facetable?_size=4"", ""label"": ""https://latest.datasette.io/fixtures/facetable?_size=4""}]"
csv_export:csv-export-url-parameters,csv_export,csv-export-url-parameters,URL parameters,"The following options can be used to customize the CSVs returned by Datasette.
?_header=off
This removes the first row of the CSV file specifying the headings - only the row data will be returned.
?_stream=on
Stream all matching records, not just the first page of results. See below.
?_dl=on
Causes Datasette to return a content-disposition: attachment; filename=""filename.csv"" header.","[""CSV export""]",[]
contributing:id1,contributing,id1,Contributing,"Datasette is an open source project. We welcome contributions!
This document describes how to contribute to Datasette core. You can also contribute to the wider Datasette ecosystem by creating new Plugins .",[],[]
contributing:general-guidelines,contributing,general-guidelines,General guidelines,"main should always be releasable . Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released.
The ideal commit should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue.
New plugin hooks should only be shipped if accompanied by a separate release of a non-demo plugin that uses them.","[""Contributing""]",[]
contributing:devenvironment,contributing,devenvironment,Setting up a development environment,"If you have Python 3.8 or higher installed on your computer (on OS X the quickest way to do this is using homebrew ) you can install an editable copy of Datasette using the following steps.
If you want to use GitHub to publish your changes, first create a fork of datasette under your own GitHub account.
Now clone that repository somewhere on your computer:
git clone git@github.com:YOURNAME/datasette
If you want to get started without creating your own fork, you can do this instead:
git clone git@github.com:simonw/datasette
The next step is to create a virtual environment for your project and use it to install Datasette's dependencies:
cd datasette
# Create a virtual environment in ./venv
python3 -m venv ./venv
# Now activate the virtual environment, so pip can install into it
source venv/bin/activate
# Install Datasette and its testing dependencies
python3 -m pip install -e '.[test]'
That last line does most of the work: pip install -e means ""install this package in a way that allows me to edit the source code in place"". The .[test] option means ""use the setup.py in this directory and install the optional testing dependencies as well"".","[""Contributing""]","[{""href"": ""https://docs.python-guide.org/starting/install3/osx/"", ""label"": ""is using homebrew""}, {""href"": ""https://github.com/simonw/datasette/fork"", ""label"": ""create a fork of datasette""}]"
contributing:contributing-using-fixtures,contributing,contributing-using-fixtures,Using fixtures,"To run Datasette itself, type datasette .
You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests.
You can create a copy of that database by running this command:
python tests/fixtures.py fixtures.db
Now you can run Datasette against the new fixtures database like so:
datasette fixtures.db
This will start a server at http://127.0.0.1:8001/ .
Any changes you make in the datasette/templates or datasette/static folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript).
If you want to change Datasette's Python code you can use the --reload option to cause Datasette to automatically reload any time the underlying code changes:
datasette --reload fixtures.db
You can also use the fixtures.py script to recreate the testing version of metadata.json used by the unit tests. To do that:
python tests/fixtures.py fixtures.db fixtures-metadata.json
Or to output the plugins used by the tests, run this:
python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins
Test tables written to fixtures.db
- metadata written to fixtures-metadata.json
Wrote plugin: fixtures-plugins/register_output_renderer.py
Wrote plugin: fixtures-plugins/view_name.py
Wrote plugin: fixtures-plugins/my_plugin.py
Wrote plugin: fixtures-plugins/messages_output_renderer.py
Wrote plugin: fixtures-plugins/my_plugin_2.py
Then run Datasette like this:
datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/","[""Contributing""]",[]
contributing:contributing-upgrading-codemirror,contributing,contributing-upgrading-codemirror,Upgrading CodeMirror,"Datasette bundles CodeMirror for the SQL editing interface, e.g. on this page . Here are the steps for upgrading to a new version of CodeMirror:
Install the packages with:
npm i codemirror @codemirror/lang-sql
Build the bundle using the version number from package.json with:
node_modules/.bin/rollup datasette/static/cm-editor-6.0.1.js \
-f iife \
-n cm \
-o datasette/static/cm-editor-6.0.1.bundle.js \
-p @rollup/plugin-node-resolve \
-p @rollup/plugin-terser
Update the version reference in the codemirror.html template.","[""Contributing""]","[{""href"": ""https://codemirror.net/"", ""label"": ""CodeMirror""}, {""href"": ""https://latest.datasette.io/fixtures"", ""label"": ""this page""}]"
contributing:contributing-running-tests,contributing,contributing-running-tests,Running the tests,"Once you have done this, you can run the Datasette unit tests from inside your datasette/ directory using pytest like so:
pytest
You can run the tests faster using multiple CPU cores with pytest-xdist like this:
pytest -n auto -m ""not serial""
-n auto detects the number of available cores automatically. The -m ""not serial"" skips tests that don't work well in a parallel test environment. You can run those tests separately like so:
pytest -m ""serial""","[""Contributing""]","[{""href"": ""https://docs.pytest.org/"", ""label"": ""pytest""}, {""href"": ""https://pypi.org/project/pytest-xdist/"", ""label"": ""pytest-xdist""}]"
contributing:contributing-release,contributing,contributing-release,Release process,"Datasette releases are performed using tags. When a new release is published on GitHub, a GitHub Action workflow will perform the following:
Run the unit tests against all supported Python versions. If the tests pass...
Build a Docker image of the release and push a tag to https://hub.docker.com/r/datasetteproject/datasette
Re-point the ""latest"" tag on Docker Hub to the new image
Build a wheel bundle of the underlying Python source code
Push that new wheel up to PyPI: https://pypi.org/project/datasette/
If the release is an alpha, navigate to https://readthedocs.org/projects/datasette/versions/ and search for the tag name in the ""Activate a version"" filter, then mark that version as ""active"" to ensure it will appear on the public ReadTheDocs documentation site.
To deploy new releases you will need to have push access to the main Datasette GitHub repository.
Datasette follows Semantic Versioning :
major.minor.patch
We increment major for backwards-incompatible releases. Datasette is currently pre-1.0 so the major version is always 0 .
We increment minor for new features.
We increment patch for bugfix releass.
Alpha and beta releases may have an additional a0 or b0 prefix - the integer component will be incremented with each subsequent alpha or beta.
To release a new version, first create a commit that updates the version number in datasette/version.py and the the changelog with highlights of the new version. An example commit can be seen here :
# Update changelog
git commit -m "" Release 0.51a1
Refs #1056, #1039, #998, #1045, #1033, #1036, #1034, #976, #1057, #1058, #1053, #1064, #1066"" -a
git push
Referencing the issues that are part of the release in the commit message ensures the name of the release shows up on those issue pages, e.g. here .
You can generate the list of issue references for a specific release by copying and pasting text from the release notes or GitHub changes-since-last-release view into this Extract issue numbers from pasted text tool.
To create the tag for the release, create a new release on GitHub matching the new version number. You can convert the release notes to Markdown by copying and pasting the rendered HTML into this Paste to Markdown tool .
Finally, post a news item about the release on datasette.io by editing the news.yaml file in that site's repository.","[""Contributing""]","[{""href"": ""https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml"", ""label"": ""GitHub Action workflow""}, {""href"": ""https://hub.docker.com/r/datasetteproject/datasette"", ""label"": ""https://hub.docker.com/r/datasetteproject/datasette""}, {""href"": ""https://pypi.org/project/datasette/"", ""label"": ""https://pypi.org/project/datasette/""}, {""href"": ""https://readthedocs.org/projects/datasette/versions/"", ""label"": ""https://readthedocs.org/projects/datasette/versions/""}, {""href"": ""https://semver.org/"", ""label"": ""Semantic Versioning""}, {""href"": ""https://github.com/simonw/datasette/commit/0e1e89c6ba3d0fbdb0823272952cf356f3016def"", ""label"": ""commit can be seen here""}, {""href"": ""https://github.com/simonw/datasette/issues/581#ref-commit-d56f402"", ""label"": ""here""}, {""href"": ""https://observablehq.com/@simonw/extract-issue-numbers-from-pasted-text"", ""label"": ""Extract issue numbers from pasted text""}, {""href"": ""https://github.com/simonw/datasette/releases/new"", ""label"": ""a new release""}, {""href"": ""https://euangoddard.github.io/clipboard2markdown/"", ""label"": ""Paste to Markdown tool""}, {""href"": ""https://datasette.io/"", ""label"": ""datasette.io""}, {""href"": ""https://github.com/simonw/datasette.io/blob/main/news.yaml"", ""label"": ""news.yaml""}]"
contributing:contributing-formatting-prettier,contributing,contributing-formatting-prettier,Prettier,"To install Prettier, install Node.js and then run the following in the root of your datasette repository checkout:
npm install
This will install Prettier in a node_modules directory. You can then check that your code matches the coding style like so:
npm run prettier -- --check
> prettier
> prettier 'datasette/static/*[!.min].js' ""--check""
Checking formatting...
[warn] datasette/static/plugins.js
[warn] Code style issues found in the above file(s). Forgot to run Prettier?
You can fix any problems by running:
npm run fix","[""Contributing"", ""Code formatting""]","[{""href"": ""https://nodejs.org/en/download/package-manager/"", ""label"": ""install Node.js""}]"
contributing:contributing-formatting-blacken-docs,contributing,contributing-formatting-blacken-docs,blacken-docs,"The blacken-docs command applies Black formatting rules to code examples in the documentation. Run it like this:
blacken-docs -l 60 docs/*.rst","[""Contributing"", ""Code formatting""]","[{""href"": ""https://pypi.org/project/blacken-docs/"", ""label"": ""blacken-docs""}]"
contributing:contributing-formatting-black,contributing,contributing-formatting-black,Running Black,"Black will be installed when you run pip install -e '.[test]' . To test that your code complies with Black, run the following in your root datasette repository checkout:
black . --check
All done! ✨ 🍰 ✨
95 files would be left unchanged.
If any of your code does not conform to Black you can run this to automatically fix those problems:
black .
reformatted ../datasette/setup.py
All done! ✨ 🍰 ✨
1 file reformatted, 94 files left unchanged.","[""Contributing"", ""Code formatting""]",[]
contributing:contributing-formatting,contributing,contributing-formatting,Code formatting,"Datasette uses opinionated code formatters: Black for Python and Prettier for JavaScript.
These formatters are enforced by Datasette's continuous integration: if a commit includes Python or JavaScript code that does not match the style enforced by those tools, the tests will fail.
When developing locally, you can verify and correct the formatting of your code using these tools.","[""Contributing""]","[{""href"": ""https://github.com/psf/black"", ""label"": ""Black""}, {""href"": ""https://prettier.io/"", ""label"": ""Prettier""}]"
contributing:contributing-documentation-cog,contributing,contributing-documentation-cog,Running Cog,"Some pages of documentation (in particular the CLI reference ) are automatically updated using Cog .
To update these pages, run the following command:
cog -r docs/*.rst","[""Contributing"", ""Editing and building the documentation""]","[{""href"": ""https://github.com/nedbat/cog"", ""label"": ""Cog""}]"
contributing:contributing-documentation,contributing,contributing-documentation,Editing and building the documentation,"Datasette's documentation lives in the docs/ directory and is deployed automatically using Read The Docs .
The documentation is written using reStructuredText. You may find this article on The subset of reStructuredText worth committing to memory useful.
You can build it locally by installing sphinx and sphinx_rtd_theme in your Datasette development environment and then running make html directly in the docs/ directory:
# You may first need to activate your virtual environment:
source venv/bin/activate
# Install the dependencies needed to build the docs
pip install -e .[docs]
# Now build the docs
cd docs/
make html
This will create the HTML version of the documentation in docs/_build/html . You can open it in your browser like so:
open _build/html/index.html
Any time you make changes to a .rst file you can re-run make html to update the built documents, then refresh them in your browser.
For added productivity, you can use use sphinx-autobuild to run Sphinx in auto-build mode. This will run a local webserver serving the docs that automatically rebuilds them and refreshes the page any time you hit save in your editor.
sphinx-autobuild will have been installed when you ran pip install -e .[docs] . In your docs/ directory you can start the server by running the following:
make livehtml
Now browse to http://localhost:8000/ to view the documentation. Any edits you make should be instantly reflected in your browser.","[""Contributing""]","[{""href"": ""https://readthedocs.org/"", ""label"": ""Read The Docs""}, {""href"": ""https://simonwillison.net/2018/Aug/25/restructuredtext/"", ""label"": ""The subset of reStructuredText worth committing to memory""}, {""href"": ""https://pypi.org/project/sphinx-autobuild/"", ""label"": ""sphinx-autobuild""}]"
contributing:contributing-debugging,contributing,contributing-debugging,Debugging,"Any errors that occur while Datasette is running while display a stack trace on the console.
You can tell Datasette to open an interactive pdb debugger session if an error occurs using the --pdb option:
datasette --pdb fixtures.db","[""Contributing""]",[]
contributing:contributing-continuous-deployment,contributing,contributing-continuous-deployment,Continuously deployed demo instances,"The demo instance at latest.datasette.io is re-deployed automatically to Google Cloud Run for every push to main that passes the test suite. This is implemented by the GitHub Actions workflow at .github/workflows/deploy-latest.yml .
Specific branches can also be set to automatically deploy by adding them to the on: push: branches block at the top of the workflow YAML file. Branches configured in this way will be deployed to a new Cloud Run service whether or not their tests pass.
The Cloud Run URL for a branch demo can be found in the GitHub Actions logs.","[""Contributing""]","[{""href"": ""https://latest.datasette.io/"", ""label"": ""latest.datasette.io""}, {""href"": ""https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml"", ""label"": "".github/workflows/deploy-latest.yml""}]"
contributing:contributing-bug-fix-branch,contributing,contributing-bug-fix-branch,Releasing bug fixes from a branch,"If it's necessary to publish a bug fix release without shipping new features that have landed on main a release branch can be used.
Create it from the relevant last tagged release like so:
git branch 0.52.x 0.52.4
git checkout 0.52.x
Next cherry-pick the commits containing the bug fixes:
git cherry-pick COMMIT
Write the release notes in the branch, and update the version number in version.py . Then push the branch:
git push -u origin 0.52.x
Once the tests have completed, publish the release from that branch target using the GitHub Draft a new release form.
Finally, cherry-pick the commit with the release notes and version number bump across to main :
git checkout main
git cherry-pick COMMIT
git push","[""Contributing""]","[{""href"": ""https://github.com/simonw/datasette/releases/new"", ""label"": ""Draft a new release""}]"
contributing:contributing-alpha-beta,contributing,contributing-alpha-beta,Alpha and beta releases,"Alpha and beta releases are published to preview upcoming features that may not yet be stable - in particular to preview new plugin hooks.
You are welcome to try these out, but please be aware that details may change before the final release.
Please join discussions on the issue tracker to share your thoughts and experiences with on alpha and beta features that you try out.","[""Contributing""]","[{""href"": ""https://github.com/simonw/datasette/issues"", ""label"": ""discussions on the issue tracker""}]"
configuration:id1,configuration,id1,Configuration,"Datasette offers several ways to configure your Datasette instances: server settings, plugin configuration, authentication, and more.
Most configuration can be handled using a datasette.yaml configuration file, passed to datasette using the -c/--config flag:
datasette mydatabase.db --config datasette.yaml
This file can also use JSON, as datasette.json . YAML is recommended over JSON due to its support for comments and multi-line strings.",[],[]
configuration:configuration-reference-settings,configuration,configuration-reference-settings,Settings,"Settings can be configured in datasette.yaml with the settings key:
[[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
""""""
# inside datasette.yaml
settings:
default_allow_sql: off
default_page_size: 50
"""""").strip()
)
]]]
[[[end]]]
The full list of settings is available in the settings documentation . Settings can also be passed to Datasette using one or more --setting name value command line options.`","[""Configuration"", null]",[]
configuration:configuration-reference-plugins,configuration,configuration-reference-plugins,Plugin configuration,"Datasette plugins often require configuration. This plugin configuration should be placed in plugins keys inside datasette.yaml .
Most plugins are configured at the top-level of the file, using the plugins key:
[[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
""""""
# inside datasette.yaml
plugins:
datasette-my-plugin:
key: my_value
"""""").strip()
)
]]]
[[[end]]]
Some plugins can be configured at the database or table level. These should use a plugins key nested under the appropriate place within the databases object:
[[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
""""""
# inside datasette.yaml
databases:
my_database:
# plugin configuration for the my_database database
plugins:
datasette-my-plugin:
key: my_value
my_other_database:
tables:
my_table:
# plugin configuration for the my_table table inside the my_other_database database
plugins:
datasette-my-plugin:
key: my_value
"""""").strip()
)
]]]
[[[end]]]","[""Configuration"", null]",[]
configuration:configuration-reference-permissions,configuration,configuration-reference-permissions,Permissions configuration,"Datasette's authentication and permissions system can also be configured using datasette.yaml .
Here is a simple example:
[[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
""""""
# Instance is only available to users 'sharon' and 'percy':
allow:
id:
- sharon
- percy
# Only 'percy' is allowed access to the accounting database:
databases:
accounting:
allow:
id: percy
"""""").strip()
)
]]]
[[[end]]]
Access permissions in datasette.yaml has the full details.","[""Configuration"", null]",[]
configuration:configuration-reference-css-js,configuration,configuration-reference-css-js,Custom CSS and JavaScript,"Datasette can load additional CSS and JavaScript files, configured in datasette.yaml like this:
[[[cog
from metadata_doc import config_example
config_example(cog, """"""
extra_css_urls:
- https://simonwillison.net/static/css/all.bf8cd891642c.css
extra_js_urls:
- https://code.jquery.com/jquery-3.2.1.slim.min.js
"""""")
]]]
[[[end]]]
The extra CSS and JavaScript files will be linked in the of every page:
You can also specify a SRI (subresource integrity hash) for these assets:
[[[cog
config_example(cog, """"""
extra_css_urls:
- url: https://simonwillison.net/static/css/all.bf8cd891642c.css
sri: sha384-9qIZekWUyjCyDIf2YK1FRoKiPJq4PHt6tp/ulnuuyRBvazd0hG7pWbE99zvwSznI
extra_js_urls:
- url: https://code.jquery.com/jquery-3.2.1.slim.min.js
sri: sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g=
"""""")
]]]
[[[end]]]
This will produce:
Modern browsers will only execute the stylesheet or JavaScript if the SRI hash
matches the content served. You can generate hashes using www.srihash.org
Items in ""extra_js_urls"" can specify ""module"": true if they reference JavaScript that uses JavaScript modules . This configuration:
[[[cog
config_example(cog, """"""
extra_js_urls:
- url: https://example.datasette.io/module.js
module: true
"""""")
]]]
[[[end]]]
Will produce this HTML:
","[""Configuration"", null]","[{""href"": ""https://www.srihash.org/"", ""label"": ""www.srihash.org""}, {""href"": ""https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules"", ""label"": ""JavaScript modules""}]"
configuration:configuration-reference-canned-queries,configuration,configuration-reference-canned-queries,Canned queries configuration,"Canned queries are named SQL queries that appear in the Datasette interface. They can be configured in datasette.yaml using the queries key at the database level:
[[[cog
from metadata_doc import config_example, config_example
config_example(cog, {
""databases"": {
""sf-trees"": {
""queries"": {
""just_species"": {
""sql"": ""select qSpecies from Street_Tree_List""
}
}
}
}
})
]]]
[[[end]]]
See the canned queries documentation for more, including how to configure writable canned queries .","[""Configuration"", null]",[]
configuration:configuration-reference,configuration,configuration-reference,,"The following example shows some of the valid configuration options that can exist inside datasette.yaml .
[[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
""""""
# Datasette settings block
settings:
default_page_size: 50
sql_time_limit_ms: 3500
max_returned_rows: 2000
# top-level plugin configuration
plugins:
datasette-my-plugin:
key: valueA
# Database and table-level configuration
databases:
your_db_name:
# plugin configuration for the your_db_name database
plugins:
datasette-my-plugin:
key: valueA
tables:
your_table_name:
allow:
# Only the root user can access this table
id: root
# plugin configuration for the your_table_name table
# inside your_db_name database
plugins:
datasette-my-plugin:
key: valueB
"""""")
)
]]]
[[[end]]]","[""Configuration""]",[]
configuration:configuration-cli,configuration,configuration-cli,Configuration via the command-line,"The recommended way to configure Datasette is using a datasette.yaml file passed to -c/--config . You can also pass individual settings to Datasette using the -s/--setting option, which can be used multiple times:
datasette mydatabase.db \
--setting settings.default_page_size 50 \
--setting settings.sql_time_limit_ms 3500
This option takes dotted-notation for the first argument and a value for the second argument. This means you can use it to set any configuration value that would be valid in a datasette.yaml file.
It also works for plugin configuration, for example for datasette-cluster-map :
datasette mydatabase.db \
--setting plugins.datasette-cluster-map.latitude_column xlat \
--setting plugins.datasette-cluster-map.longitude_column xlon
If the value you provide is a valid JSON object or list it will be treated as nested data, allowing you to configure plugins that accept lists such as datasette-proxy-url :
datasette mydatabase.db \
-s plugins.datasette-proxy-url.paths '[{""path"": ""/proxy"", ""backend"": ""http://example.com/""}]'
This is equivalent to a datasette.yaml file containing the following:
[[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
""""""
plugins:
datasette-proxy-url:
paths:
- path: /proxy
backend: http://example.com/
"""""").strip()
)
]]]
[[[end]]]","[""Configuration""]","[{""href"": ""https://datasette.io/plugins/datasette-cluster-map"", ""label"": ""datasette-cluster-map""}, {""href"": ""https://datasette.io/plugins/datasette-proxy-url"", ""label"": ""datasette-proxy-url""}]"
cli-reference:id1,cli-reference,id1,CLI reference,"The datasette CLI tool provides a number of commands.
Running datasette without specifying a command runs the default command, datasette serve . See datasette serve for the full list of options for that command.
[[[cog
from datasette import cli
from click.testing import CliRunner
import textwrap
def help(args):
title = ""datasette "" + "" "".join(args)
cog.out(""\n::\n\n"")
result = CliRunner().invoke(cli.cli, args)
output = result.output.replace(""Usage: cli "", ""Usage: datasette "")
cog.out(textwrap.indent(output, ' '))
cog.out(""\n\n"")
]]]
[[[end]]]",[],[]
cli-reference:cli-help-uninstall-help,cli-reference,cli-help-uninstall-help,datasette uninstall,"Uninstall one or more plugins.
[[[cog
help([""uninstall"", ""--help""])
]]]
Usage: datasette uninstall [OPTIONS] PACKAGES...
Uninstall plugins and Python packages from the Datasette environment
Options:
-y, --yes Don't ask for confirmation
--help Show this message and exit.
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-help-serve-help-settings,cli-reference,cli-help-serve-help-settings,datasette serve --help-settings,"This command outputs all of the available Datasette settings .
These can be passed to datasette serve using datasette serve --setting name value .
[[[cog
help([""--help-settings""])
]]]
Settings:
default_page_size Default page size for the table view
(default=100)
max_returned_rows Maximum rows that can be returned from a table or
custom query (default=1000)
max_insert_rows Maximum rows that can be inserted at a time using
the bulk insert API (default=100)
num_sql_threads Number of threads in the thread pool for
executing SQLite queries (default=3)
sql_time_limit_ms Time limit for a SQL query in milliseconds
(default=1000)
default_facet_size Number of values to return for requested facets
(default=30)
facet_time_limit_ms Time limit for calculating a requested facet
(default=200)
facet_suggest_time_limit_ms Time limit for calculating a suggested facet
(default=50)
allow_facet Allow users to specify columns to facet using
?_facet= parameter (default=True)
allow_download Allow users to download the original SQLite
database files (default=True)
allow_signed_tokens Allow users to create and use signed API tokens
(default=True)
default_allow_sql Allow anyone to run arbitrary SQL queries
(default=True)
max_signed_tokens_ttl Maximum allowed expiry time for signed API tokens
(default=0)
suggest_facets Calculate and display suggested facets
(default=True)
default_cache_ttl Default HTTP cache TTL (used in Cache-Control:
max-age= header) (default=5)
cache_size_kb SQLite cache size in KB (0 == use SQLite default)
(default=0)
allow_csv_stream Allow .csv?_stream=1 to download all rows
(ignoring max_returned_rows) (default=True)
max_csv_mb Maximum size allowed for CSV export in MB - set 0
to disable this limit (default=100)
truncate_cells_html Truncate cells longer than this in HTML table
view - set 0 to disable (default=2048)
force_https_urls Force URLs in API output to always use https://
protocol (default=False)
template_debug Allow display of template debug information with
?_context=1 (default=False)
trace_debug Allow display of SQL trace debug information with
?_trace=1 (default=False)
base_url Datasette URLs should use this base path
(default=/)
[[[end]]]","[""CLI reference"", ""datasette serve""]",[]
cli-reference:cli-help-serve-help,cli-reference,cli-help-serve-help,datasette serve,"This command starts the Datasette web application running on your machine:
datasette serve mydatabase.db
Or since this is the default command you can run this instead:
datasette mydatabase.db
Once started you can access it at http://localhost:8001
[[[cog
help([""serve"", ""--help""])
]]]
Usage: datasette serve [OPTIONS] [FILES]...
Serve up specified SQLite database files with a web UI
Options:
-i, --immutable PATH Database files to open in immutable mode
-h, --host TEXT Host for server. Defaults to 127.0.0.1 which
means only connections from the local machine
will be allowed. Use 0.0.0.0 to listen to all
IPs and allow access from other machines.
-p, --port INTEGER RANGE Port for server, defaults to 8001. Use -p 0 to
automatically assign an available port.
[0<=x<=65535]
--uds TEXT Bind to a Unix domain socket
--reload Automatically reload if code or metadata
change detected - useful for development
--cors Enable CORS by serving Access-Control-Allow-
Origin: *
--load-extension PATH:ENTRYPOINT?
Path to a SQLite extension to load, and
optional entrypoint
--inspect-file TEXT Path to JSON file created using ""datasette
inspect""
-m, --metadata FILENAME Path to JSON/YAML file containing
license/source metadata
--template-dir DIRECTORY Path to directory containing custom templates
--plugins-dir DIRECTORY Path to directory containing custom plugins
--static MOUNT:DIRECTORY Serve static files from this directory at
/MOUNT/...
--memory Make /_memory database available
-c, --config FILENAME Path to JSON/YAML Datasette configuration file
-s, --setting SETTING... nested.key, value setting to use in Datasette
configuration
--secret TEXT Secret used for signing secure values, such as
signed cookies
--root Output URL that sets a cookie authenticating
the root user
--get TEXT Run an HTTP GET request against this path,
print results and exit
--token TEXT API token to send with --get requests
--actor TEXT Actor to use for --get requests (JSON string)
--version-note TEXT Additional note to show on /-/versions
--help-settings Show available settings
--pdb Launch debugger on any errors
-o, --open Open Datasette in your web browser
--create Create database files if they do not exist
--crossdb Enable cross-database joins using the /_memory
database
--nolock Ignore locking, open locked files in read-only
mode
--ssl-keyfile TEXT SSL key file
--ssl-certfile TEXT SSL certificate file
--internal PATH Path to a persistent Datasette internal SQLite
database
--help Show this message and exit.
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-help-publish-heroku-help,cli-reference,cli-help-publish-heroku-help,datasette publish heroku,"See Publishing to Heroku .
[[[cog
help([""publish"", ""heroku"", ""--help""])
]]]
Usage: datasette publish heroku [OPTIONS] [FILES]...
Publish databases to Datasette running on Heroku
Options:
-m, --metadata FILENAME Path to JSON/YAML 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.
main
--template-dir DIRECTORY Path to directory containing custom templates
--plugins-dir DIRECTORY Path to directory containing custom plugins
--static MOUNT:DIRECTORY Serve static files from this directory at
/MOUNT/...
--install TEXT Additional packages (e.g. plugins) to install
--plugin-secret ...
Secrets to pass to plugins, e.g. --plugin-
secret datasette-auth-github client_id xxx
--version-note TEXT Additional note to show on /-/versions
--secret TEXT Secret used for signing secure values, such as
signed cookies
--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
--about TEXT About label for metadata
--about_url TEXT About URL for metadata
-n, --name TEXT Application name to use when deploying
--tar TEXT --tar option to pass to Heroku, e.g.
--tar=/usr/local/bin/gtar
--generate-dir DIRECTORY Output generated application files and stop
without deploying
--help Show this message and exit.
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-help-publish-help,cli-reference,cli-help-publish-help,datasette publish,"Shows a list of available deployment targets for publishing data with Datasette.
Additional deployment targets can be added by plugins that use the publish_subcommand(publish) hook.
[[[cog
help([""publish"", ""--help""])
]]]
Usage: datasette publish [OPTIONS] COMMAND [ARGS]...
Publish specified SQLite database files to the internet along with a
Datasette-powered interface and API
Options:
--help Show this message and exit.
Commands:
cloudrun Publish databases to Datasette running on Cloud Run
heroku Publish databases to Datasette running on Heroku
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-help-publish-cloudrun-help,cli-reference,cli-help-publish-cloudrun-help,datasette publish cloudrun,"See Publishing to Google Cloud Run .
[[[cog
help([""publish"", ""cloudrun"", ""--help""])
]]]
Usage: datasette publish cloudrun [OPTIONS] [FILES]...
Publish databases to Datasette running on Cloud Run
Options:
-m, --metadata FILENAME Path to JSON/YAML 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.
main
--template-dir DIRECTORY Path to directory containing custom templates
--plugins-dir DIRECTORY Path to directory containing custom plugins
--static MOUNT:DIRECTORY Serve static files from this directory at
/MOUNT/...
--install TEXT Additional packages (e.g. plugins) to install
--plugin-secret ...
Secrets to pass to plugins, e.g. --plugin-
secret datasette-auth-github client_id xxx
--version-note TEXT Additional note to show on /-/versions
--secret TEXT Secret used for signing secure values, such as
signed cookies
--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
--about TEXT About label for metadata
--about_url TEXT About URL for metadata
-n, --name TEXT Application name to use when building
--service TEXT Cloud Run service to deploy (or over-write)
--spatialite Enable SpatialLite extension
--show-files Output the generated Dockerfile and
metadata.json
--memory TEXT Memory to allocate in Cloud Run, e.g. 1Gi
--cpu [1|2|4] Number of vCPUs to allocate in Cloud Run
--timeout INTEGER Build timeout in seconds
--apt-get-install TEXT Additional packages to apt-get install
--max-instances INTEGER Maximum Cloud Run instances
--min-instances INTEGER Minimum Cloud Run instances
--help Show this message and exit.
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-help-plugins-help,cli-reference,cli-help-plugins-help,datasette plugins,"Output JSON showing all currently installed plugins, their versions, whether they include static files or templates and which Plugin hooks they use.
[[[cog
help([""plugins"", ""--help""])
]]]
Usage: datasette plugins [OPTIONS]
List currently installed plugins
Options:
--all Include built-in default plugins
--requirements Output requirements.txt of installed plugins
--plugins-dir DIRECTORY Path to directory containing custom plugins
--help Show this message and exit.
[[[end]]]
Example output:
[
{
""name"": ""datasette-geojson"",
""static"": false,
""templates"": false,
""version"": ""0.3.1"",
""hooks"": [
""register_output_renderer""
]
},
{
""name"": ""datasette-geojson-map"",
""static"": true,
""templates"": false,
""version"": ""0.4.0"",
""hooks"": [
""extra_body_script"",
""extra_css_urls"",
""extra_js_urls""
]
},
{
""name"": ""datasette-leaflet"",
""static"": true,
""templates"": false,
""version"": ""0.2.2"",
""hooks"": [
""extra_body_script"",
""extra_template_vars""
]
}
]","[""CLI reference""]",[]
cli-reference:cli-help-package-help,cli-reference,cli-help-package-help,datasette package,"Package SQLite files into a Datasette Docker container, see datasette package .
[[[cog
help([""package"", ""--help""])
]]]
Usage: datasette package [OPTIONS] FILES...
Package SQLite files into a 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/YAML 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. main
--template-dir DIRECTORY Path to directory containing custom templates
--plugins-dir DIRECTORY Path to directory containing custom plugins
--static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/...
--install TEXT Additional packages (e.g. plugins) to install
--spatialite Enable SpatialLite extension
--version-note TEXT Additional note to show on /-/versions
--secret TEXT Secret used for signing secure values, such as
signed cookies
-p, --port INTEGER RANGE Port to run the server on, defaults to 8001
[1<=x<=65535]
--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
--about TEXT About label for metadata
--about_url TEXT About URL for metadata
--help Show this message and exit.
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-help-install-help,cli-reference,cli-help-install-help,datasette install,"Install new Datasette plugins. This command works like pip install but ensures that your plugins will be installed into the same environment as Datasette.
This command:
datasette install datasette-cluster-map
Would install the datasette-cluster-map plugin.
[[[cog
help([""install"", ""--help""])
]]]
Usage: datasette install [OPTIONS] [PACKAGES]...
Install plugins and packages from PyPI into the same environment as Datasette
Options:
-U, --upgrade Upgrade packages to latest version
-r, --requirement PATH Install from requirements file
-e, --editable TEXT Install a project in editable mode from this path
--help Show this message and exit.
[[[end]]]","[""CLI reference""]","[{""href"": ""https://datasette.io/plugins/datasette-cluster-map"", ""label"": ""datasette-cluster-map""}]"
cli-reference:cli-help-inspect-help,cli-reference,cli-help-inspect-help,datasette inspect,"Outputs JSON representing introspected data about one or more SQLite database files.
If you are opening an immutable database, you can pass this file to the --inspect-data option to improve Datasette's performance by allowing it to skip running row counts against the database when it first starts running:
datasette inspect mydatabase.db > inspect-data.json
datasette serve -i mydatabase.db --inspect-file inspect-data.json
This performance optimization is used automatically by some of the datasette publish commands. You are unlikely to need to apply this optimization manually.
[[[cog
help([""inspect"", ""--help""])
]]]
Usage: datasette inspect [OPTIONS] [FILES]...
Generate JSON summary of provided database files
This can then be passed to ""datasette --inspect-file"" to speed up count
operations against immutable database files.
Options:
--inspect-file TEXT
--load-extension PATH:ENTRYPOINT?
Path to a SQLite extension to load, and
optional entrypoint
--help Show this message and exit.
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-help-help,cli-reference,cli-help-help,datasette --help,"Running datasette --help shows a list of all of the available commands.
[[[cog
help([""--help""])
]]]
Usage: datasette [OPTIONS] COMMAND [ARGS]...
Datasette is an open source multi-tool for exploring and publishing data
About Datasette: https://datasette.io/
Full documentation: https://docs.datasette.io/
Options:
--version Show the version and exit.
--help Show this message and exit.
Commands:
serve* Serve up specified SQLite database files with a web UI
create-token Create a signed API token for the specified actor ID
inspect Generate JSON summary of provided database files
install Install plugins and packages from PyPI into the same...
package Package SQLite files into a Datasette Docker container
plugins List currently installed plugins
publish Publish specified SQLite database files to the internet...
uninstall Uninstall plugins and Python packages from the Datasette...
[[[end]]]
Additional commands added by plugins that use the register_commands(cli) hook will be listed here as well.","[""CLI reference""]",[]
cli-reference:cli-help-create-token-help,cli-reference,cli-help-create-token-help,datasette create-token,"Create a signed API token, see datasette create-token .
[[[cog
help([""create-token"", ""--help""])
]]]
Usage: datasette create-token [OPTIONS] ID
Create a signed API token for the specified actor ID
Example:
datasette create-token root --secret mysecret
To allow only ""view-database-download"" for all databases:
datasette create-token root --secret mysecret \
--all view-database-download
To allow ""create-table"" against a specific database:
datasette create-token root --secret mysecret \
--database mydb create-table
To allow ""insert-row"" against a specific table:
datasette create-token root --secret myscret \
--resource mydb mytable insert-row
Restricted actions can be specified multiple times using multiple --all,
--database, and --resource options.
Add --debug to see a decoded version of the token.
Options:
--secret TEXT Secret used for signing the API tokens
[required]
-e, --expires-after INTEGER Token should expire after this many seconds
-a, --all ACTION Restrict token to this action
-d, --database DB ACTION Restrict token to this action on this database
-r, --resource DB RESOURCE ACTION
Restrict token to this action on this database
resource (a table, SQL view or named query)
--debug Show decoded token
--plugins-dir DIRECTORY Path to directory containing custom plugins
--help Show this message and exit.
[[[end]]]","[""CLI reference""]",[]
cli-reference:cli-datasette-get,cli-reference,cli-datasette-get,datasette --get,"The --get option to datasette serve (or just datasette ) specifies the path to a page within Datasette and causes Datasette to output the content from that path without starting the web server.
This means that all of Datasette's functionality can be accessed directly from the command-line.
For example:
datasette --get '/-/versions.json' | jq .
{
""python"": {
""version"": ""3.8.5"",
""full"": ""3.8.5 (default, Jul 21 2020, 10:48:26) \n[Clang 11.0.3 (clang-1103.0.32.62)]""
},
""datasette"": {
""version"": ""0.46+15.g222a84a.dirty""
},
""asgi"": ""3.0"",
""uvicorn"": ""0.11.8"",
""sqlite"": {
""version"": ""3.32.3"",
""fts_versions"": [
""FTS5"",
""FTS4"",
""FTS3""
],
""extensions"": {
""json1"": null
},
""compile_options"": [
""COMPILER=clang-11.0.3"",
""ENABLE_COLUMN_METADATA"",
""ENABLE_FTS3"",
""ENABLE_FTS3_PARENTHESIS"",
""ENABLE_FTS4"",
""ENABLE_FTS5"",
""ENABLE_GEOPOLY"",
""ENABLE_JSON1"",
""ENABLE_PREUPDATE_HOOK"",
""ENABLE_RTREE"",
""ENABLE_SESSION"",
""MAX_VARIABLE_NUMBER=250000"",
""THREADSAFE=1""
]
}
}
You can use the --token TOKEN option to send an API token with the simulated request.
Or you can make a request as a specific actor by passing a JSON representation of that actor to --actor :
datasette --memory --actor '{""id"": ""root""}' --get '/-/actor.json'
The exit code of datasette --get will be 0 if the request succeeds and 1 if the request produced an HTTP status code other than 200 - e.g. a 404 or 500 error.
This lets you use datasette --get / to run tests against a Datasette application in a continuous integration environment such as GitHub Actions.","[""CLI reference"", ""datasette serve""]",[]
changelog:write-api,changelog,write-api,Write API,"New API explorer at /-/api for trying out the API. ( #1871 )
/db/-/create API for Creating a table . ( #1882 )
/db/table/-/insert API for Inserting rows . ( #1851 )
/db/table/-/drop API for Dropping tables . ( #1874 )
/db/table/pk/-/update API for Updating a row . ( #1863 )
/db/table/pk/-/delete API for Deleting a row . ( #1864 )","[""Changelog"", ""1.0a0 (2022-11-29)""]","[{""href"": ""https://github.com/simonw/datasette/issues/1871"", ""label"": ""#1871""}, {""href"": ""https://github.com/simonw/datasette/issues/1882"", ""label"": ""#1882""}, {""href"": ""https://github.com/simonw/datasette/issues/1851"", ""label"": ""#1851""}, {""href"": ""https://github.com/simonw/datasette/issues/1874"", ""label"": ""#1874""}, {""href"": ""https://github.com/simonw/datasette/issues/1863"", ""label"": ""#1863""}, {""href"": ""https://github.com/simonw/datasette/issues/1864"", ""label"": ""#1864""}]"
changelog:writable-canned-queries,changelog,writable-canned-queries,Writable canned queries,"Datasette's Canned queries feature lets you define SQL queries in metadata.json which can then be executed by users visiting a specific URL. https://latest.datasette.io/fixtures/neighborhood_search for example.
Canned queries were previously restricted to SELECT , but Datasette 0.44 introduces the ability for canned queries to execute INSERT or UPDATE queries as well, using the new ""write"": true property ( #800 ):
{
""databases"": {
""dogs"": {
""queries"": {
""add_name"": {
""sql"": ""INSERT INTO names (name) VALUES (:name)"",
""write"": true
}
}
}
}
}
See Writable canned queries for more details.","[""Changelog"", ""0.44 (2020-06-11)""]","[{""href"": ""https://latest.datasette.io/fixtures/neighborhood_search"", ""label"": ""https://latest.datasette.io/fixtures/neighborhood_search""}, {""href"": ""https://github.com/simonw/datasette/issues/800"", ""label"": ""#800""}]"
changelog:v1-0-a9,changelog,v1-0-a9,1.0a9 (2024-02-16),This alpha release adds basic alter table support to the Datasette Write API and fixes a permissions bug relating to the /upsert API endpoint.,"[""Changelog""]",[]
changelog:v1-0-a8,changelog,v1-0-a8,1.0a8 (2024-02-07),"This alpha release continues the migration of Datasette's configuration from metadata.yaml to the new datasette.yaml configuration file, introduces a new system for JavaScript plugins and adds several new plugin hooks.
See Datasette 1.0a8: JavaScript plugins, new plugin hooks and plugin configuration in datasette.yaml for an annotated version of these release notes.","[""Changelog""]","[{""href"": ""https://simonwillison.net/2024/Feb/7/datasette-1a8/"", ""label"": ""Datasette 1.0a8: JavaScript plugins, new plugin hooks and plugin configuration in datasette.yaml""}]"
changelog:v1-0-a7,changelog,v1-0-a7,1.0a7 (2023-09-21),Fix for a crashing bug caused by viewing the table page for a named in-memory database. ( #2189 ),"[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/2189"", ""label"": ""#2189""}]"
changelog:v1-0-a6,changelog,v1-0-a6,1.0a6 (2023-09-07),"New plugin hook: actors_from_ids(datasette, actor_ids) and an internal method to accompany it, await .actors_from_ids(actor_ids) . This mechanism is intended to be used by plugins that may need to display the actor who was responsible for something managed by that plugin: they can now resolve the recorded IDs of actors into the full actor objects. ( #2181 )
DATASETTE_LOAD_PLUGINS environment variable for controlling which plugins are loaded by Datasette. ( #2164 )
Datasette now checks if the user has permission to view a table linked to by a foreign key before turning that foreign key into a clickable link. ( #2178 )
The execute-sql permission now implies that the actor can also view the database and instance. ( #2169 )
Documentation describing a pattern for building plugins that themselves define further hooks for other plugins. ( #1765 )
Datasette is now tested against the Python 3.12 preview. ( #2175 )","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/2181"", ""label"": ""#2181""}, {""href"": ""https://github.com/simonw/datasette/issues/2164"", ""label"": ""#2164""}, {""href"": ""https://github.com/simonw/datasette/issues/2178"", ""label"": ""#2178""}, {""href"": ""https://github.com/simonw/datasette/issues/2169"", ""label"": ""#2169""}, {""href"": ""https://github.com/simonw/datasette/issues/1765"", ""label"": ""#1765""}, {""href"": ""https://github.com/simonw/datasette/pull/2175"", ""label"": ""#2175""}]"
changelog:v1-0-a5,changelog,v1-0-a5,1.0a5 (2023-08-29),"When restrictions are applied to API tokens , those restrictions now behave slightly differently: applying the view-table restriction will imply the ability to view-database for the database containing that table, and both view-table and view-database will imply view-instance . Previously you needed to create a token with restrictions that explicitly listed view-instance and view-database and view-table in order to view a table without getting a permission denied error. ( #2102 )
New datasette.yaml (or .json ) configuration file, which can be specified using datasette -c path-to-file . The goal here to consolidate settings, plugin configuration, permissions, canned queries, and other Datasette configuration into a single single file, separate from metadata.yaml . The legacy settings.json config file used for Configuration directory mode has been removed, and datasette.yaml has a ""settings"" section where the same settings key/value pairs can be included. In the next future alpha release, more configuration such as plugins/permissions/canned queries will be moved to the datasette.yaml file. See #2093 for more details. Thanks, Alex Garcia.
The -s/--setting option can now take dotted paths to nested settings. These will then be used to set or over-ride the same options as are present in the new configuration file. ( #2156 )
New --actor '{""id"": ""json-goes-here""}' option for use with datasette --get to treat the simulated request as being made by a specific actor, see datasette --get . ( #2153 )
The Datasette _internal database has had some changes. It no longer shows up in the datasette.databases list by default, and is now instead available to plugins using the datasette.get_internal_database() . Plugins are invited to use this as a private database to store configuration and settings and secrets that should not be made visible through the default Datasette interface. Users can pass the new --internal internal.db option to persist that internal database to disk. Thanks, Alex Garcia. ( #2157 ).","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/2102"", ""label"": ""#2102""}, {""href"": ""https://github.com/simonw/datasette/issues/2093"", ""label"": ""#2093""}, {""href"": ""https://github.com/simonw/datasette/issues/2156"", ""label"": ""#2156""}, {""href"": ""https://github.com/simonw/datasette/issues/2153"", ""label"": ""#2153""}, {""href"": ""https://github.com/simonw/datasette/issues/2157"", ""label"": ""#2157""}]"
changelog:v1-0-a4,changelog,v1-0-a4,1.0a4 (2023-08-21),"This alpha fixes a security issue with the /-/api API explorer. On authenticated Datasette instances (instances protected using plugins such as datasette-auth-passwords ) the API explorer interface could reveal the names of databases and tables within the protected instance. The data stored in those tables was not revealed.
For more information and workarounds, read the security advisory . The issue has been present in every previous alpha version of Datasette 1.0: versions 1.0a0, 1.0a1, 1.0a2 and 1.0a3.
Also in this alpha:
The new datasette plugins --requirements option outputs a list of currently installed plugins in Python requirements.txt format, useful for duplicating that installation elsewhere. ( #2133 )
Writable canned queries can now define a on_success_message_sql field in their configuration, containing a SQL query that should be executed upon successful completion of the write operation in order to generate a message to be shown to the user. ( #2138 )
The automatically generated border color for a database is now shown in more places around the application. ( #2119 )
Every instance of example shell script code in the documentation should now include a working copy button, free from additional syntax. ( #2140 )","[""Changelog""]","[{""href"": ""https://datasette.io/plugins/datasette-auth-passwords"", ""label"": ""datasette-auth-passwords""}, {""href"": ""https://github.com/simonw/datasette/security/advisories/GHSA-7ch3-7pp7-7cpq"", ""label"": ""the security advisory""}, {""href"": ""https://github.com/simonw/datasette/issues/2133"", ""label"": ""#2133""}, {""href"": ""https://github.com/simonw/datasette/issues/2138"", ""label"": ""#2138""}, {""href"": ""https://github.com/simonw/datasette/issues/2119"", ""label"": ""#2119""}, {""href"": ""https://github.com/simonw/datasette/issues/2140"", ""label"": ""#2140""}]"
changelog:v1-0-a3,changelog,v1-0-a3,1.0a3 (2023-08-09),"This alpha release previews the updated design for Datasette's default JSON API. ( #782 )
The new default JSON representation for both table pages ( /dbname/table.json ) and arbitrary SQL queries ( /dbname.json?sql=... ) is now shaped like this:
{
""ok"": true,
""rows"": [
{
""id"": 3,
""name"": ""Detroit""
},
{
""id"": 2,
""name"": ""Los Angeles""
},
{
""id"": 4,
""name"": ""Memnonia""
},
{
""id"": 1,
""name"": ""San Francisco""
}
],
""truncated"": false
}
Tables will include an additional ""next"" key for pagination, which can be passed to ?_next= to fetch the next page of results.
The various ?_shape= options continue to work as before - see Different shapes for details.
A new ?_extra= mechanism is available for tables, but has not yet been stabilized or documented. Details on that are available in #262 .","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/782"", ""label"": ""#782""}, {""href"": ""https://github.com/simonw/datasette/issues/262"", ""label"": ""#262""}]"
changelog:v1-0-a2,changelog,v1-0-a2,1.0a2 (2022-12-14),"The third Datasette 1.0 alpha release adds upsert support to the JSON API, plus the ability to specify finely grained permissions when creating an API token.
See Datasette 1.0a2: Upserts and finely grained permissions for an extended, annotated version of these release notes.
New /db/table/-/upsert API, documented here . upsert is an update-or-insert: existing rows will have specified keys updated, but if no row matches the incoming primary key a brand new row will be inserted instead. ( #1878 )
New register_permissions(datasette) plugin hook. Plugins can now register named permissions, which will then be listed in various interfaces that show available permissions. ( #1940 )
The /db/-/create API for creating a table now accepts ""ignore"": true and ""replace"": true options when called with the ""rows"" property that creates a new table based on an example set of rows. This means the API can be called multiple times with different rows, setting rules for what should happen if a primary key collides with an existing row. ( #1927 )
Arbitrary permissions can now be configured at the instance, database and resource (table, SQL view or canned query) level in Datasette's Metadata JSON and YAML files. The new ""permissions"" key can be used to specify which actors should have which permissions. See Other permissions in datasette.yaml for details. ( #1636 )
The /-/create-token page can now be used to create API tokens which are restricted to just a subset of actions, including against specific databases or resources. See API Tokens for details. ( #1947 )
Likewise, the datasette create-token CLI command can now create tokens with a subset of permissions . ( #1855 )
New datasette.create_token() API method for programmatically creating signed API tokens. ( #1951 )
/db/-/create API now requires actor to have insert-row permission in order to use the ""row"" or ""rows"" properties. ( #1937 )","[""Changelog""]","[{""href"": ""https://simonwillison.net/2022/Dec/15/datasette-1a2/"", ""label"": ""Datasette 1.0a2: Upserts and finely grained permissions""}, {""href"": ""https://github.com/simonw/datasette/issues/1878"", ""label"": ""#1878""}, {""href"": ""https://github.com/simonw/datasette/issues/1940"", ""label"": ""#1940""}, {""href"": ""https://github.com/simonw/datasette/issues/1927"", ""label"": ""#1927""}, {""href"": ""https://github.com/simonw/datasette/issues/1636"", ""label"": ""#1636""}, {""href"": ""https://github.com/simonw/datasette/issues/1947"", ""label"": ""#1947""}, {""href"": ""https://github.com/simonw/datasette/issues/1855"", ""label"": ""#1855""}, {""href"": ""https://github.com/simonw/datasette/issues/1951"", ""label"": ""#1951""}, {""href"": ""https://github.com/simonw/datasette/issues/1937"", ""label"": ""#1937""}]"
changelog:v1-0-a13,changelog,v1-0-a13,1.0a13 (2024-03-12),"Each of the key concepts in Datasette now has an actions menu , which plugins can use to add additional functionality targeting that entity.
Plugin hook: view_actions() for actions that can be applied to a SQL view. ( #2297 )
Plugin hook: homepage_actions() for actions that apply to the instance homepage. ( #2298 )
Plugin hook: row_actions() for actions that apply to the row page. ( #2299 )
Action menu items for all of the *_actions() plugin hooks can now return an optional ""description"" key, which will be displayed in the menu below the action label. ( #2294 )
Plugin hooks documentation page is now organized with additional headings. ( #2300 )
Improved the display of action buttons on pages that also display metadata. ( #2286 )
The header and footer of the page now uses a subtle gradient effect, and options in the navigation menu are better visually defined. ( #2302 )
Table names that start with an underscore now default to hidden. ( #2104 )
pragma_table_list has been added to the allow-list of SQLite pragma functions supported by Datasette. select * from pragma_table_list() is no longer blocked. ( #2104 )","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/2297"", ""label"": ""#2297""}, {""href"": ""https://github.com/simonw/datasette/issues/2298"", ""label"": ""#2298""}, {""href"": ""https://github.com/simonw/datasette/issues/2299"", ""label"": ""#2299""}, {""href"": ""https://github.com/simonw/datasette/issues/2294"", ""label"": ""#2294""}, {""href"": ""https://github.com/simonw/datasette/issues/2300"", ""label"": ""#2300""}, {""href"": ""https://github.com/simonw/datasette/issues/2286"", ""label"": ""#2286""}, {""href"": ""https://github.com/simonw/datasette/issues/2302"", ""label"": ""#2302""}, {""href"": ""https://github.com/simonw/datasette/issues/2104"", ""label"": ""#2104""}, {""href"": ""https://github.com/simonw/datasette/issues/2104#issuecomment-1982352475"", ""label"": ""#2104""}]"
changelog:v1-0-a12,changelog,v1-0-a12,1.0a12 (2024-02-29),"New query_actions() plugin hook, similar to table_actions() and database_actions() . Can be used to add a menu of actions to the canned query or arbitrary SQL query page. ( #2283 )
New design for the button that opens the query, table and database actions menu. ( #2281 )
""does not contain"" table filter for finding rows that do not contain a string. ( #2287 )
Fixed a bug in the makeColumnActions(columnDetails) JavaScript plugin mechanism where the column action menu was not fully reset in between each interaction. ( #2289 )","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/2283"", ""label"": ""#2283""}, {""href"": ""https://github.com/simonw/datasette/issues/2281"", ""label"": ""#2281""}, {""href"": ""https://github.com/simonw/datasette/issues/2287"", ""label"": ""#2287""}, {""href"": ""https://github.com/simonw/datasette/issues/2289"", ""label"": ""#2289""}]"
changelog:v1-0-a11,changelog,v1-0-a11,1.0a11 (2024-02-19),"The ""replace"": true argument to the /db/table/-/insert API now requires the actor to have the update-row permission. ( #2279 )
Fixed some UI bugs in the interactive permissions debugging tool. ( #2278 )
The column action menu now aligns better with the cog icon, and positions itself taking into account the width of the browser window. ( #2263 )","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/2279"", ""label"": ""#2279""}, {""href"": ""https://github.com/simonw/datasette/issues/2278"", ""label"": ""#2278""}, {""href"": ""https://github.com/simonw/datasette/issues/2263"", ""label"": ""#2263""}]"
changelog:v1-0-a10,changelog,v1-0-a10,1.0a10 (2024-02-17),"The only changes in this alpha correspond to the way Datasette handles database transactions. ( #2277 )
The database.execute_write_fn() method has a new transaction=True parameter. This defaults to True which means all functions executed using this method are now automatically wrapped in a transaction - previously the functions needed to roll transaction handling on their own, and many did not.
Pass transaction=False to execute_write_fn() if you want to manually handle transactions in your function.
Several internal Datasette features, including parts of the JSON write API , had been failing to wrap their operations in a transaction. This has been fixed by the new transaction=True default.","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/2277"", ""label"": ""#2277""}]"
changelog:v1-0-a1,changelog,v1-0-a1,1.0a1 (2022-12-01),"Write APIs now serve correct CORS headers if Datasette is started in --cors mode. See the full list of CORS headers in the documentation. ( #1922 )
Fixed a bug where the _memory database could be written to even though writes were not persisted. ( #1917 )
The https://latest.datasette.io/ demo instance now includes an ephemeral database which can be used to test Datasette's write APIs, using the new datasette-ephemeral-tables plugin to drop any created tables after five minutes. This database is only available if you sign in as the root user using the link on the homepage. ( #1915 )
Fixed a bug where hitting the write endpoints with a GET request returned a 500 error. It now returns a 405 (method not allowed) error instead. ( #1916 )
The list of endpoints in the API explorer now lists mutable databases first. ( #1918 )
The ""ignore"": true and ""replace"": true options for the insert API are now documented . ( #1924 )","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/1922"", ""label"": ""#1922""}, {""href"": ""https://github.com/simonw/datasette/issues/1917"", ""label"": ""#1917""}, {""href"": ""https://latest.datasette.io/"", ""label"": ""https://latest.datasette.io/""}, {""href"": ""https://datasette.io/plugins/datasette-ephemeral-tables"", ""label"": ""datasette-ephemeral-tables""}, {""href"": ""https://github.com/simonw/datasette/issues/1915"", ""label"": ""#1915""}, {""href"": ""https://github.com/simonw/datasette/issues/1916"", ""label"": ""#1916""}, {""href"": ""https://github.com/simonw/datasette/issues/1918"", ""label"": ""#1918""}, {""href"": ""https://github.com/simonw/datasette/issues/1924"", ""label"": ""#1924""}]"
changelog:v1-0-a0,changelog,v1-0-a0,1.0a0 (2022-11-29),"This first alpha release of Datasette 1.0 introduces a brand new collection of APIs for writing to the database ( #1850 ), as well as a new API token mechanism baked into Datasette core. Previously, API tokens have only been supported by installing additional plugins.
This is very much a preview: expect many more backwards incompatible API changes prior to the full 1.0 release.
Feedback enthusiastically welcomed, either through issue comments or via the Datasette Discord community.","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/1850"", ""label"": ""#1850""}, {""href"": ""https://github.com/simonw/datasette/issues/1850"", ""label"": ""issue comments""}, {""href"": ""https://datasette.io/discord"", ""label"": ""Datasette Discord""}]"
changelog:v0-29-medium-changes,changelog,v0-29-medium-changes,Easier custom templates for table rows,"If you want to customize the display of individual table rows, you can do so using a _table.html template include that looks something like this:
{% for row in display_rows %}
{{ row[""title""] }}
{{ row[""description""] }}
Category: {{ row.display(""category_id"") }}
{% endfor %}
This is a backwards incompatible change . If you previously had a custom template called _rows_and_columns.html you need to rename it to _table.html .
See Custom templates for full details.","[""Changelog"", ""0.29 (2019-07-07)""]",[]
changelog:v0-28-register-output-renderer,changelog,v0-28-register-output-renderer,register_output_renderer plugins,"Russ Garrett implemented a new Datasette plugin hook called register_output_renderer ( #441 ) which allows plugins to create additional output renderers in addition to Datasette's default .json and .csv .
Russ's in-development datasette-geo plugin includes an example of this hook being used to output .geojson automatically converted from SpatiaLite.","[""Changelog"", ""0.28 (2019-05-19)""]","[{""href"": ""https://github.com/simonw/datasette/pull/441"", ""label"": ""#441""}, {""href"": ""https://github.com/russss/datasette-geo"", ""label"": ""datasette-geo""}, {""href"": ""https://github.com/russss/datasette-geo/blob/d4cecc020848bbde91e9e17bf352f7c70bc3dccf/datasette_plugin_geo/geojson.py"", ""label"": ""an example""}]"
changelog:v0-28-publish-cloudrun,changelog,v0-28-publish-cloudrun,datasette publish cloudrun,"Google Cloud Run is a brand new serverless hosting platform from Google, which allows you to build a Docker container which will run only when HTTP traffic is received and will shut down (and hence cost you nothing) the rest of the time. It's similar to Zeit's Now v1 Docker hosting platform which sadly is no longer accepting signups from new users.
The new datasette publish cloudrun command was contributed by Romain Primet ( #434 ) and publishes selected databases to a new Datasette instance running on Google Cloud Run.
See Publishing to Google Cloud Run for full documentation.","[""Changelog"", ""0.28 (2019-05-19)""]","[{""href"": ""https://cloud.google.com/run/"", ""label"": ""Google Cloud Run""}, {""href"": ""https://hyperion.alpha.spectrum.chat/zeit/now/cannot-create-now-v1-deployments~d206a0d4-5835-4af5-bb5c-a17f0171fb25?m=MTU0Njk2NzgwODM3OA=="", ""label"": ""no longer accepting signups""}, {""href"": ""https://github.com/simonw/datasette/pull/434"", ""label"": ""#434""}]"
changelog:v0-28-medium-changes,changelog,v0-28-medium-changes,Medium changes,"Datasette now conforms to the Black coding style ( #449 ) - and has a unit test to enforce this in the future
New Special table arguments :
?columnname__in=value1,value2,value3 filter for executing SQL IN queries against a table, see Table arguments ( #433 )
?columnname__date=yyyy-mm-dd filter which returns rows where the spoecified datetime column falls on the specified date ( 583b22a )
?tags__arraycontains=tag filter which acts against a JSON array contained in a column ( 78e45ea )
?_where=sql-fragment filter for the table view ( #429 )
?_fts_table=mytable and ?_fts_pk=mycolumn query string options can be used to specify which FTS table to use for a search query - see Configuring full-text search for a table or view ( #428 )
You can now pass the same table filter multiple times - for example, ?content__not=world&content__not=hello will return all rows where the content column is neither hello or world ( #288 )
You can now specify about and about_url metadata (in addition to source and license ) linking to further information about a project - see Source, license and about
New ?_trace=1 parameter now adds debug information showing every SQL query that was executed while constructing the page ( #435 )
datasette inspect now just calculates table counts, and does not introspect other database metadata ( #462 )
Removed /-/inspect page entirely - this will be replaced by something similar in the future, see #465
Datasette can now run against an in-memory SQLite database. You can do this by starting it without passing any files or by using the new --memory option to datasette serve . This can be useful for experimenting with SQLite queries that do not access any data, such as SELECT 1+1 or SELECT sqlite_version() .","[""Changelog"", ""0.28 (2019-05-19)""]","[{""href"": ""https://github.com/python/black"", ""label"": ""Black coding style""}, {""href"": ""https://github.com/simonw/datasette/pull/449"", ""label"": ""#449""}, {""href"": ""https://github.com/simonw/datasette/issues/433"", ""label"": ""#433""}, {""href"": ""https://github.com/simonw/datasette/commit/583b22aa28e26c318de0189312350ab2688c90b1"", ""label"": ""583b22a""}, {""href"": ""https://github.com/simonw/datasette/commit/78e45ead4d771007c57b307edf8fc920101f8733"", ""label"": ""78e45ea""}, {""href"": ""https://github.com/simonw/datasette/issues/429"", ""label"": ""#429""}, {""href"": ""https://github.com/simonw/datasette/issues/428"", ""label"": ""#428""}, {""href"": ""https://github.com/simonw/datasette/issues/288"", ""label"": ""#288""}, {""href"": ""https://github.com/simonw/datasette/issues/435"", ""label"": ""#435""}, {""href"": ""https://github.com/simonw/datasette/issues/462"", ""label"": ""#462""}, {""href"": ""https://github.com/simonw/datasette/issues/465"", ""label"": ""#465""}]"
changelog:v0-28-faceting,changelog,v0-28-faceting,"Faceting improvements, and faceting plugins","Datasette Facets provide an intuitive way to quickly summarize and interact with data. Previously the only supported faceting technique was column faceting, but 0.28 introduces two powerful new capabilities: facet-by-JSON-array and the ability to define further facet types using plugins.
Facet by array ( #359 ) is only available if your SQLite installation provides the json1 extension. Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns - useful for modelling things like tags without needing to break them out into a new table. See Facet by JSON array for more.
The new register_facet_classes() plugin hook ( #445 ) can be used to register additional custom facet classes. Each facet class should provide two methods: suggest() which suggests facet selections that might be appropriate for a provided SQL query, and facet_results() which executes a facet operation and returns results. Datasette's own faceting implementations have been refactored to use the same API as these plugins.","[""Changelog"", ""0.28 (2019-05-19)""]","[{""href"": ""https://github.com/simonw/datasette/issues/359"", ""label"": ""#359""}, {""href"": ""https://github.com/simonw/datasette/pull/445"", ""label"": ""#445""}]"
changelog:v0-28-databases-that-change,changelog,v0-28-databases-that-change,Supporting databases that change,"From the beginning of the project, Datasette has been designed with read-only databases in mind. If a database is guaranteed not to change it opens up all kinds of interesting opportunities - from taking advantage of SQLite immutable mode and HTTP caching to bundling static copies of the database directly in a Docker container. The interesting ideas in Datasette explores this idea in detail.
As my goals for the project have developed, I realized that read-only databases are no longer the right default. SQLite actually supports concurrent access very well provided only one thread attempts to write to a database at a time, and I keep encountering sensible use-cases for running Datasette on top of a database that is processing inserts and updates.
So, as-of version 0.28 Datasette no longer assumes that a database file will not change. It is now safe to point Datasette at a SQLite database which is being updated by another process.
Making this change was a lot of work - see tracking tickets #418 , #419 and #420 . It required new thinking around how Datasette should calculate table counts (an expensive operation against a large, changing database) and also meant reconsidering the ""content hash"" URLs Datasette has used in the past to optimize the performance of HTTP caches.
Datasette can still run against immutable files and gains numerous performance benefits from doing so, but this is no longer the default behaviour. Take a look at the new Performance and caching documentation section for details on how to make the most of Datasette against data that you know will be staying read-only and immutable.","[""Changelog"", ""0.28 (2019-05-19)""]","[{""href"": ""https://simonwillison.net/2018/Oct/4/datasette-ideas/"", ""label"": ""The interesting ideas in Datasette""}, {""href"": ""https://github.com/simonw/datasette/issues/418"", ""label"": ""#418""}, {""href"": ""https://github.com/simonw/datasette/issues/419"", ""label"": ""#419""}, {""href"": ""https://github.com/simonw/datasette/issues/420"", ""label"": ""#420""}]"
changelog:url-building,changelog,url-building,URL building,"The new datasette.urls family of methods can be used to generate URLs to key pages within the Datasette interface, both within custom templates and Datasette plugins. See Building URLs within plugins for more details. ( #904 )","[""Changelog"", ""0.51 (2020-10-31)""]","[{""href"": ""https://github.com/simonw/datasette/issues/904"", ""label"": ""#904""}]"
changelog:through-for-joins-through-many-to-many-tables,changelog,through-for-joins-through-many-to-many-tables,?_through= for joins through many-to-many tables,"The new ?_through={json} argument to the Table view allows records to be filtered based on a many-to-many relationship. See Special table arguments for full documentation - here's an example . ( #355 )
This feature was added to help support facet by many-to-many , which isn't quite ready yet but will be coming in the next Datasette release.","[""Changelog"", ""0.29 (2019-07-07)""]","[{""href"": ""https://latest.datasette.io/fixtures/roadside_attractions?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22}"", ""label"": ""an example""}, {""href"": ""https://github.com/simonw/datasette/issues/355"", ""label"": ""#355""}, {""href"": ""https://github.com/simonw/datasette/issues/551"", ""label"": ""facet by many-to-many""}]"
changelog:the-road-to-datasette-1-0,changelog,the-road-to-datasette-1-0,The road to Datasette 1.0,"I've assembled a milestone for Datasette 1.0 . The focus of the 1.0 release will be the following:
Signify confidence in the quality/stability of Datasette
Give plugin authors confidence that their plugins will work for the whole 1.x release cycle
Provide the same confidence to developers building against Datasette JSON APIs
If you have thoughts about what you would like to see for Datasette 1.0 you can join the conversation on issue #519 .","[""Changelog"", ""0.44 (2020-06-11)""]","[{""href"": ""https://github.com/simonw/datasette/milestone/7"", ""label"": ""milestone for Datasette 1.0""}, {""href"": ""https://github.com/simonw/datasette/issues/519"", ""label"": ""the conversation on issue #519""}]"
changelog:the-internal-database,changelog,the-internal-database,The _internal database,"As part of ongoing work to help Datasette handle much larger numbers of connected databases and tables (see Datasette Library ) Datasette now maintains an in-memory SQLite database with details of all of the attached databases, tables, columns, indexes and foreign keys. ( #1150 )
This will support future improvements such as a searchable, paginated homepage of all available tables.
You can explore an example of this database by signing in as root to the latest.datasette.io demo instance and then navigating to latest.datasette.io/_internal .
Plugins can use these tables to introspect attached data in an efficient way. Plugin authors should note that this is not yet considered a stable interface, so any plugins that use this may need to make changes prior to Datasette 1.0 if the _internal table schemas change.","[""Changelog"", ""0.54 (2021-01-25)""]","[{""href"": ""https://github.com/simonw/datasette/issues/417"", ""label"": ""Datasette Library""}, {""href"": ""https://github.com/simonw/datasette/issues/1150"", ""label"": ""#1150""}, {""href"": ""https://latest.datasette.io/login-as-root"", ""label"": ""signing in as root""}, {""href"": ""https://latest.datasette.io/_internal"", ""label"": ""latest.datasette.io/_internal""}]"
changelog:smaller-changes,changelog,smaller-changes,Smaller changes,"Datasette documentation now shows YAML examples for Metadata by default, with a tab interface for switching to JSON. ( #1153 )
register_output_renderer(datasette) plugins now have access to error and truncated arguments, allowing them to display error messages and take into account truncated results. ( #2130 )
render_cell() plugin hook now also supports an optional request argument. ( #2007 )
New Justfile to support development workflows for Datasette using Just .
datasette.render_template() can now accepts a datasette.views.Context subclass as an alternative to a dictionary. ( #2127 )
datasette install -e path option for editable installations, useful while developing plugins. ( #2106 )
When started with the --cors option Datasette now serves an Access-Control-Max-Age: 3600 header, ensuring CORS OPTIONS requests are repeated no more than once an hour. ( #2079 )
Fixed a bug where the _internal database could display None instead of null for in-memory databases. ( #1970 )","[""Changelog"", ""1.0a3 (2023-08-09)""]","[{""href"": ""https://github.com/simonw/datasette/issues/1153"", ""label"": ""#1153""}, {""href"": ""https://github.com/simonw/datasette/issues/2130"", ""label"": ""#2130""}, {""href"": ""https://github.com/simonw/datasette/issues/2007"", ""label"": ""#2007""}, {""href"": ""https://github.com/casey/just"", ""label"": ""Just""}, {""href"": ""https://github.com/simonw/datasette/issues/2127"", ""label"": ""#2127""}, {""href"": ""https://github.com/simonw/datasette/issues/2106"", ""label"": ""#2106""}, {""href"": ""https://github.com/simonw/datasette/issues/2079"", ""label"": ""#2079""}, {""href"": ""https://github.com/simonw/datasette/issues/1970"", ""label"": ""#1970""}]"
changelog:small-changes,changelog,small-changes,Small changes,"Databases published using datasette publish now open in Immutable mode . ( #469 )
?col__date= now works for columns containing spaces
Automatic label detection (for deciding which column to show when linking to a foreign key) has been improved. ( #485 )
Fixed bug where pagination broke when combined with an expanded foreign key. ( #489 )
Contributors can now run pip install -e .[docs] to get all of the dependencies needed to build the documentation, including cd docs && make livehtml support.
Datasette's dependencies are now all specified using the ~= match operator. ( #532 )
white-space: pre-wrap now used for table creation SQL. ( #505 )
Full list of commits between 0.28 and 0.29.","[""Changelog"", ""0.29 (2019-07-07)""]","[{""href"": ""https://github.com/simonw/datasette/issues/469"", ""label"": ""#469""}, {""href"": ""https://github.com/simonw/datasette/issues/485"", ""label"": ""#485""}, {""href"": ""https://github.com/simonw/datasette/issues/489"", ""label"": ""#489""}, {""href"": ""https://github.com/simonw/datasette/issues/532"", ""label"": ""#532""}, {""href"": ""https://github.com/simonw/datasette/issues/505"", ""label"": ""#505""}, {""href"": ""https://github.com/simonw/datasette/compare/0.28...0.29"", ""label"": ""Full list of commits""}]"
changelog:signed-values-and-secrets,changelog,signed-values-and-secrets,Signed values and secrets,"Both flash messages and user authentication needed a way to sign values and set signed cookies. Two new methods are now available for plugins to take advantage of this mechanism: .sign(value, namespace=""default"") and .unsign(value, namespace=""default"") .
Datasette will generate a secret automatically when it starts up, but to avoid resetting the secret (and hence invalidating any cookies) every time the server restarts you should set your own secret. You can pass a secret to Datasette using the new --secret option or with a DATASETTE_SECRET environment variable. See Configuring the secret for more details.
You can also set a secret when you deploy Datasette using datasette publish or datasette package - see Using secrets with datasette publish .
Plugins can now sign values and verify their signatures using the datasette.sign() and datasette.unsign() methods.","[""Changelog"", ""0.44 (2020-06-11)""]",[]
changelog:signed-api-tokens,changelog,signed-api-tokens,Signed API tokens,"New /-/create-token page allowing authenticated users to create signed API tokens that can act on their behalf, see API Tokens . ( #1852 )
New datasette create-token command for creating tokens from the command line: datasette create-token .
New allow_signed_tokens setting which can be used to turn off signed token support. ( #1856 )
New max_signed_tokens_ttl setting for restricting the maximum allowed duration of a signed token. ( #1858 )","[""Changelog"", ""1.0a0 (2022-11-29)""]","[{""href"": ""https://github.com/simonw/datasette/issues/1852"", ""label"": ""#1852""}, {""href"": ""https://github.com/simonw/datasette/issues/1856"", ""label"": ""#1856""}, {""href"": ""https://github.com/simonw/datasette/issues/1858"", ""label"": ""#1858""}]"
changelog:secret-plugin-configuration-options,changelog,secret-plugin-configuration-options,Secret plugin configuration options,"Plugins like datasette-auth-github need a safe way to set secret configuration options. Since the default mechanism for configuring plugins exposes those settings in /-/metadata a new mechanism was needed. Secret configuration values describes how plugins can now specify that their settings should be read from a file or an environment variable:
{
""plugins"": {
""datasette-auth-github"": {
""client_secret"": {
""$env"": ""GITHUB_CLIENT_SECRET""
}
}
}
}
These plugin secrets can be set directly using datasette publish . See Custom metadata and plugins for details. ( #538 and #543 )","[""Changelog"", ""0.29 (2019-07-07)""]","[{""href"": ""https://github.com/simonw/datasette-auth-github"", ""label"": ""datasette-auth-github""}, {""href"": ""https://github.com/simonw/datasette/issues/538"", ""label"": ""#538""}, {""href"": ""https://github.com/simonw/datasette/issues/543"", ""label"": ""#543""}]"
changelog:running-datasette-behind-a-proxy,changelog,running-datasette-behind-a-proxy,Running Datasette behind a proxy,"The base_url configuration option is designed to help run Datasette on a specific path behind a proxy - for example if you want to run an instance of Datasette at /my-datasette/ within your existing site's URL hierarchy, proxied behind nginx or Apache.
Support for this configuration option has been greatly improved ( #1023 ), and guidelines for using it are now available in a new documentation section on Running Datasette behind a proxy . ( #1027 )","[""Changelog"", ""0.51 (2020-10-31)""]","[{""href"": ""https://github.com/simonw/datasette/issues/1023"", ""label"": ""#1023""}, {""href"": ""https://github.com/simonw/datasette/issues/1027"", ""label"": ""#1027""}]"
changelog:register-routes-plugin-hooks,changelog,register-routes-plugin-hooks,register_routes() plugin hooks,"Plugins can now register new views and routes via the register_routes(datasette) plugin hook ( #819 ). View functions can be defined that accept any of the current datasette object, the current request , or the ASGI scope , send and receive objects.","[""Changelog"", ""0.44 (2020-06-11)""]","[{""href"": ""https://github.com/simonw/datasette/issues/819"", ""label"": ""#819""}]"
changelog:plugins-can-now-add-links-within-datasette,changelog,plugins-can-now-add-links-within-datasette,Plugins can now add links within Datasette,"A number of existing Datasette plugins add new pages to the Datasette interface, providig tools for things like uploading CSVs , editing table schemas or configuring full-text search .
Plugins like this can now link to themselves from other parts of Datasette interface. The menu_links(datasette, actor, request) hook ( #1064 ) lets plugins add links to Datasette's new top-right application menu, and the table_actions(datasette, actor, database, table, request) hook ( #1066 ) adds links to a new ""table actions"" menu on the table page.
The demo at latest.datasette.io now includes some example plugins. To see the new table actions menu first sign into that demo as root and then visit the facetable table to see the new cog icon menu at the top of the page.","[""Changelog"", ""0.51 (2020-10-31)""]","[{""href"": ""https://github.com/simonw/datasette-upload-csvs"", ""label"": ""uploading CSVs""}, {""href"": ""https://github.com/simonw/datasette-edit-schema"", ""label"": ""editing table schemas""}, {""href"": ""https://github.com/simonw/datasette-configure-fts"", ""label"": ""configuring full-text search""}, {""href"": ""https://github.com/simonw/datasette/issues/1064"", ""label"": ""#1064""}, {""href"": ""https://github.com/simonw/datasette/issues/1066"", ""label"": ""#1066""}, {""href"": ""https://latest.datasette.io/"", ""label"": ""latest.datasette.io""}, {""href"": ""https://latest.datasette.io/login-as-root"", ""label"": ""sign into that demo as root""}, {""href"": ""https://latest.datasette.io/fixtures/facetable"", ""label"": ""facetable""}]"
changelog:plugins-and-internals,changelog,plugins-and-internals,Plugins and internals,"New plugin hook: filters_from_request(request, database, table, datasette) , which runs on the table page and can be used to support new custom query string parameters that modify the SQL query. ( #473 )
Added two additional methods for writing to the database: await db.execute_write_script(sql, block=True) and await db.execute_write_many(sql, params_seq, block=True) . ( #1570 )
The db.execute_write() internal method now defaults to blocking until the write operation has completed. Previously it defaulted to queuing the write and then continuing to run code while the write was in the queue. ( #1579 )
Database write connections now execute the prepare_connection(conn, database, datasette) plugin hook. ( #1564 )
The Datasette() constructor no longer requires the files= argument, and is now documented at Datasette class . ( #1563 )
The tracing feature now traces write queries, not just read queries. ( #1568 )
The query string variables exposed by request.args will now include blank strings for arguments such as foo in ?foo=&bar=1 rather than ignoring those parameters entirely. ( #1551 )","[""Changelog"", ""0.60 (2022-01-13)""]","[{""href"": ""https://github.com/simonw/datasette/issues/473"", ""label"": ""#473""}, {""href"": ""https://github.com/simonw/datasette/issues/1570"", ""label"": ""#1570""}, {""href"": ""https://github.com/simonw/datasette/issues/1579"", ""label"": ""#1579""}, {""href"": ""https://github.com/simonw/datasette/issues/1564"", ""label"": ""#1564""}, {""href"": ""https://github.com/simonw/datasette/issues/1563"", ""label"": ""#1563""}, {""href"": ""https://github.com/simonw/datasette/issues/1568"", ""label"": ""#1568""}, {""href"": ""https://github.com/simonw/datasette/issues/1551"", ""label"": ""#1551""}]"
changelog:plugin-hooks-and-internals,changelog,plugin-hooks-and-internals,Plugin hooks and internals,"The prepare_jinja2_environment(env, datasette) plugin hook now accepts an optional datasette argument. Hook implementations can also now return an async function which will be awaited automatically. ( #1809 )
Database(is_mutable=) now defaults to True . ( #1808 )
The datasette.check_visibility() method now accepts an optional permissions= list, allowing it to take multiple permissions into account at once when deciding if something should be shown as public or private. This has been used to correctly display padlock icons in more places in the Datasette interface. ( #1829 )
Datasette no longer enforces upper bounds on its dependencies. ( #1800 )","[""Changelog"", ""0.63 (2022-10-27)""]","[{""href"": ""https://github.com/simonw/datasette/issues/1809"", ""label"": ""#1809""}, {""href"": ""https://github.com/simonw/datasette/issues/1808"", ""label"": ""#1808""}, {""href"": ""https://github.com/simonw/datasette/issues/1829"", ""label"": ""#1829""}, {""href"": ""https://github.com/simonw/datasette/issues/1800"", ""label"": ""#1800""}]"
changelog:plugin-hooks,changelog,plugin-hooks,Plugin hooks,"New jinja2_environment_from_request(datasette, request, env) plugin hook, which can be used to customize the current Jinja environment based on the incoming request. This can be used to modify the template lookup path based on the incoming request hostname, among other things. ( #2225 )
New family of template slot plugin hooks : top_homepage , top_database , top_table , top_row , top_query , top_canned_query . Plugins can use these to provide additional HTML to be injected at the top of the corresponding pages. ( #1191 )
New track_event() mechanism for plugins to emit and receive events when certain events occur within Datasette. ( #2240 )
Plugins can register additional event classes using register_events(datasette) .
They can then trigger those events with the datasette.track_event(event) internal method.
Plugins can subscribe to notifications of events using the track_event(datasette, event) plugin hook.
Datasette core now emits login , logout , create-token , create-table , drop-table , insert-rows , upsert-rows , update-row , delete-row events, documented here .
New internal function for plugin authors: await db.execute_isolated_fn(fn) , for creating a new SQLite connection, executing code and then closing that connection, all while preventing other code from writing to that particular database. This connection will not have the prepare_connection() plugin hook executed against it, allowing plugins to perform actions that might otherwise be blocked by existing connection configuration. ( #2218 )","[""Changelog"", ""1.0a8 (2024-02-07)""]","[{""href"": ""https://github.com/simonw/datasette/issues/2225"", ""label"": ""#2225""}, {""href"": ""https://github.com/simonw/datasette/issues/1191"", ""label"": ""#1191""}, {""href"": ""https://github.com/simonw/datasette/issues/2240"", ""label"": ""#2240""}, {""href"": ""https://github.com/simonw/datasette/issues/2218"", ""label"": ""#2218""}]"
changelog:permissions-fix-for-the-upsert-api,changelog,permissions-fix-for-the-upsert-api,Permissions fix for the upsert API,"The /database/table/-/upsert API had a minor permissions bug, only affecting Datasette instances that had configured the insert-row and update-row permissions to apply to a specific table rather than the database or instance as a whole. Full details in issue #2262 .
To avoid similar mistakes in the future the datasette.permission_allowed() method now specifies default= as a keyword-only argument.","[""Changelog"", ""1.0a9 (2024-02-16)""]","[{""href"": ""https://github.com/simonw/datasette/issues/2262"", ""label"": ""#2262""}]"
changelog:permissions,changelog,permissions,Permissions,"Datasette also now has a built-in concept of Permissions . The permissions system answers the following question:
Is this actor allowed to perform this action , optionally against this particular resource ?
You can use the new ""allow"" block syntax in metadata.json (or metadata.yaml ) to set required permissions at the instance, database, table or canned query level. For example, to restrict access to the fixtures.db database to the ""root"" user:
{
""databases"": {
""fixtures"": {
""allow"": {
""id"" ""root""
}
}
}
}
See Defining permissions with ""allow"" blocks for more details.
Plugins can implement their own custom permission checks using the new permission_allowed(datasette, actor, action, resource) hook.
A new debug page at /-/permissions shows recent permission checks, to help administrators and plugin authors understand exactly what checks are being performed. This tool defaults to only being available to the root user, but can be exposed to other users by plugins that respond to the permissions-debug permission. ( #788 )","[""Changelog"", ""0.44 (2020-06-11)""]","[{""href"": ""https://github.com/simonw/datasette/issues/788"", ""label"": ""#788""}]"
changelog:permission-checks-now-consider-opinions-from-every-plugin,changelog,permission-checks-now-consider-opinions-from-every-plugin,Permission checks now consider opinions from every plugin,"The datasette.permission_allowed() method previously consulted every plugin that implemented the permission_allowed() plugin hook and obeyed the opinion of the last plugin to return a value. ( #2275 )
Datasette now consults every plugin and checks to see if any of them returned False (the veto rule), and if none of them did, it then checks to see if any of them returned True .
This is explained at length in the new documentation covering How permissions are resolved .","[""Changelog"", ""1.0a9 (2024-02-16)""]","[{""href"": ""https://github.com/simonw/datasette/issues/2275"", ""label"": ""#2275""}]"
changelog:other-small-fixes,changelog,other-small-fixes,Other small fixes,"Made several performance improvements to the database schema introspection code that runs when Datasette first starts up. ( #1555 )
Label columns detected for foreign keys are now case-insensitive, so Name or TITLE will be detected in the same way as name or title . ( #1544 )
Upgraded Pluggy dependency to 1.0. ( #1575 )
Now using Plausible analytics for the Datasette documentation.
explain query plan is now allowed with varying amounts of whitespace in the query. ( #1588 )
New CLI reference page showing the output of --help for each of the datasette sub-commands. This lead to several small improvements to the help copy. ( #1594 )
Fixed bug where writable canned queries could not be used with custom templates. ( #1547 )
Improved fix for a bug where columns with a underscore prefix could result in unnecessary hidden form fields. ( #1527 )","[""Changelog"", ""0.60 (2022-01-13)""]","[{""href"": ""https://github.com/simonw/datasette/issues/1555"", ""label"": ""#1555""}, {""href"": ""https://github.com/simonw/datasette/issues/1544"", ""label"": ""#1544""}, {""href"": ""https://github.com/simonw/datasette/issues/1575"", ""label"": ""#1575""}, {""href"": ""https://plausible.io/"", ""label"": ""Plausible analytics""}, {""href"": ""https://github.com/simonw/datasette/issues/1588"", ""label"": ""#1588""}, {""href"": ""https://github.com/simonw/datasette/issues/1594"", ""label"": ""#1594""}, {""href"": ""https://github.com/simonw/datasette/issues/1547"", ""label"": ""#1547""}, {""href"": ""https://github.com/simonw/datasette/issues/1527"", ""label"": ""#1527""}]"
changelog:other-changes,changelog,other-changes,Other changes,"The new DATASETTE_TRACE_PLUGINS=1 environment variable turns on detailed trace output for every executed plugin hook, useful for debugging and understanding how the plugin system works at a low level. ( #2274 )
Datasette on Python 3.9 or above marks its non-cryptographic uses of the MD5 hash function as usedforsecurity=False , for compatibility with FIPS systems. ( #2270 )
SQL relating to Datasette's internal database now executes inside a transaction, avoiding a potential database locked error. ( #2273 )
The /-/threads debug page now identifies the database in the name associated with each dedicated write thread. ( #2265 )
The /db/-/create API now fires a insert-rows event if rows were inserted after the table was created. ( #2260 )","[""Changelog"", ""1.0a9 (2024-02-16)""]","[{""href"": ""https://github.com/simonw/datasette/issues/2274"", ""label"": ""#2274""}, {""href"": ""https://github.com/simonw/datasette/issues/2270"", ""label"": ""#2270""}, {""href"": ""https://github.com/simonw/datasette/issues/2273"", ""label"": ""#2273""}, {""href"": ""https://github.com/simonw/datasette/issues/2265"", ""label"": ""#2265""}, {""href"": ""https://github.com/simonw/datasette/issues/2260"", ""label"": ""#2260""}]"
changelog:new-visual-design,changelog,new-visual-design,New visual design,"Datasette is no longer white and grey with blue and purple links! Natalie Downe has been working on a visual refresh, the first iteration of which is included in this release. ( #1056 )","[""Changelog"", ""0.51 (2020-10-31)""]","[{""href"": ""https://twitter.com/natbat"", ""label"": ""Natalie Downe""}, {""href"": ""https://github.com/simonw/datasette/pull/1056"", ""label"": ""#1056""}]"
changelog:new-plugin-hooks,changelog,new-plugin-hooks,New plugin hooks,"register_magic_parameters(datasette) can be used to define new types of magic canned query parameters.
startup(datasette) can run custom code when Datasette first starts up. datasette-init is a new plugin that uses this hook to create database tables and views on startup if they have not yet been created. ( #834 )
canned_queries(datasette, database, actor) lets plugins provide additional canned queries beyond those defined in Datasette's metadata. See datasette-saved-queries for an example of this hook in action. ( #852 )
forbidden(datasette, request, message) is a hook for customizing how Datasette responds to 403 forbidden errors. ( #812 )","[""Changelog"", ""0.45 (2020-07-01)""]","[{""href"": ""https://github.com/simonw/datasette-init"", ""label"": ""datasette-init""}, {""href"": ""https://github.com/simonw/datasette/issues/834"", ""label"": ""#834""}, {""href"": ""https://github.com/simonw/datasette-saved-queries"", ""label"": ""datasette-saved-queries""}, {""href"": ""https://github.com/simonw/datasette/issues/852"", ""label"": ""#852""}, {""href"": ""https://github.com/simonw/datasette/issues/812"", ""label"": ""#812""}]"
changelog:new-plugin-hook-extra-template-vars,changelog,new-plugin-hook-extra-template-vars,New plugin hook: extra_template_vars,"The extra_template_vars(template, database, table, columns, view_name, request, datasette) plugin hook allows plugins to inject their own additional variables into the Datasette template context. This can be used in conjunction with custom templates to customize the Datasette interface. datasette-auth-github uses this hook to add custom HTML to the new top navigation bar (which is designed to be modified by plugins, see #540 ).","[""Changelog"", ""0.29 (2019-07-07)""]","[{""href"": ""https://github.com/simonw/datasette-auth-github"", ""label"": ""datasette-auth-github""}, {""href"": ""https://github.com/simonw/datasette/issues/540"", ""label"": ""#540""}]"
changelog:new-plugin-hook-asgi-wrapper,changelog,new-plugin-hook-asgi-wrapper,New plugin hook: asgi_wrapper,"The asgi_wrapper(datasette) plugin hook allows plugins to entirely wrap the Datasette ASGI application in their own ASGI middleware. ( #520 )
Two new plugins take advantage of this hook:
datasette-auth-github adds a authentication layer: users will have to sign in using their GitHub account before they can view data or interact with Datasette. You can also use it to restrict access to specific GitHub users, or to members of specified GitHub organizations or teams .
datasette-cors allows you to configure CORS headers for your Datasette instance. You can use this to enable JavaScript running on a whitelisted set of domains to make fetch() calls to the JSON API provided by your Datasette instance.","[""Changelog"", ""0.29 (2019-07-07)""]","[{""href"": ""https://github.com/simonw/datasette/issues/520"", ""label"": ""#520""}, {""href"": ""https://github.com/simonw/datasette-auth-github"", ""label"": ""datasette-auth-github""}, {""href"": ""https://help.github.com/en/articles/about-organizations"", ""label"": ""organizations""}, {""href"": ""https://help.github.com/en/articles/organizing-members-into-teams"", ""label"": ""teams""}, {""href"": ""https://github.com/simonw/datasette-cors"", ""label"": ""datasette-cors""}, {""href"": ""https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS"", ""label"": ""CORS headers""}]"
changelog:new-features,changelog,new-features,New features,"If an error occurs while executing a user-provided SQL query, that query is now re-displayed in an editable form along with the error message. ( #619 )
New ?_col= and ?_nocol= parameters to show and hide columns in a table, plus an interface for hiding and showing columns in the column cog menu. ( #615 )
A new ?_facet_size= parameter for customizing the number of facet results returned on a table or view page. ( #1332 )
?_facet_size=max sets that to the maximum, which defaults to 1,000 and is controlled by the the max_returned_rows setting. If facet results are truncated the … at the bottom of the facet list now links to this parameter. ( #1337 )
?_nofacet=1 option to disable all facet calculations on a page, used as a performance optimization for CSV exports and ?_shape=array/object . ( #1349 , #263 )
?_nocount=1 option to disable full query result counts. ( #1353 )
?_trace=1 debugging option is now controlled by the new trace_debug setting, which is turned off by default. ( #1359 )","[""Changelog"", ""0.57 (2021-06-05)""]","[{""href"": ""https://github.com/simonw/datasette/issues/619"", ""label"": ""#619""}, {""href"": ""https://github.com/simonw/datasette/issues/615"", ""label"": ""#615""}, {""href"": ""https://github.com/simonw/datasette/issues/1332"", ""label"": ""#1332""}, {""href"": ""https://github.com/simonw/datasette/issues/1337"", ""label"": ""#1337""}, {""href"": ""https://github.com/simonw/datasette/issues/1349"", ""label"": ""#1349""}, {""href"": ""https://github.com/simonw/datasette/issues/263"", ""label"": ""#263""}, {""href"": ""https://github.com/simonw/datasette/issues/1353"", ""label"": ""#1353""}, {""href"": ""https://github.com/simonw/datasette/issues/1359"", ""label"": ""#1359""}]"
changelog:new-configuration-settings,changelog,new-configuration-settings,New configuration settings,"Datasette's Settings now also supports boolean settings. A number of new
configuration options have been added:
num_sql_threads - the number of threads used to execute SQLite queries. Defaults to 3.
allow_facet - enable or disable custom Facets using the _facet= parameter. Defaults to on.
suggest_facets - should Datasette suggest facets? Defaults to on.
allow_download - should users be allowed to download the entire SQLite database? Defaults to on.
allow_sql - should users be allowed to execute custom SQL queries? Defaults to on.
default_cache_ttl - Default HTTP caching max-age header in seconds. Defaults to 365 days - caching can be disabled entirely by settings this to 0.
cache_size_kb - Set the amount of memory SQLite uses for its per-connection cache , in KB.
allow_csv_stream - allow users to stream entire result sets as a single CSV file. Defaults to on.
max_csv_mb - maximum size of a returned CSV file in MB. Defaults to 100MB, set to 0 to disable this limit.","[""Changelog"", ""0.23 (2018-06-18)""]","[{""href"": ""https://www.sqlite.org/pragma.html#pragma_cache_size"", ""label"": ""per-connection cache""}]"
changelog:named-in-memory-database-support,changelog,named-in-memory-database-support,Named in-memory database support,"As part of the work building the _internal database, Datasette now supports named in-memory databases that can be shared across multiple connections. This allows plugins to create in-memory databases which will persist data for the lifetime of the Datasette server process. ( #1151 )
The new memory_name= parameter to the Database class can be used to create named, shared in-memory databases.","[""Changelog"", ""0.54 (2021-01-25)""]","[{""href"": ""https://github.com/simonw/datasette/issues/1151"", ""label"": ""#1151""}]"
changelog:miscellaneous,changelog,miscellaneous,Miscellaneous,"Got JSON data in one of your columns? Use the new ?_json=COLNAME argument
to tell Datasette to return that JSON value directly rather than encoding it
as a string.
If you just want an array of the first value of each row, use the new
?_shape=arrayfirst option - example .","[""Changelog"", ""0.23 (2018-06-18)""]","[{""href"": ""https://latest.datasette.io/fixtures.json?sql=select+neighborhood+from+facetable+order+by+pk+limit+101&_shape=arrayfirst"", ""label"": ""example""}]"
changelog:minor-fixes,changelog,minor-fixes,Minor fixes,"Datasette no longer attempts to run SQL queries in parallel when rendering a table page, as this was leading to some rare crashing bugs. ( #2189 )
Fixed warning: DeprecationWarning: pkg_resources is deprecated as an API ( #2057 )
Fixed bug where ?_extra=columns parameter returned an incorrectly shaped response. ( #2230 )","[""Changelog"", ""1.0a8 (2024-02-07)""]","[{""href"": ""https://github.com/simonw/datasette/issues/2189"", ""label"": ""#2189""}, {""href"": ""https://github.com/simonw/datasette/issues/2057"", ""label"": ""#2057""}, {""href"": ""https://github.com/simonw/datasette/issues/2230"", ""label"": ""#2230""}]"
changelog:magic-parameters-for-canned-queries,changelog,magic-parameters-for-canned-queries,Magic parameters for canned queries,"Canned queries now support Magic parameters , which can be used to insert or select automatically generated values. For example:
insert into logs
(user_id, timestamp)
values
(:_actor_id, :_now_datetime_utc)
This inserts the currently authenticated actor ID and the current datetime. ( #842 )","[""Changelog"", ""0.45 (2020-07-01)""]","[{""href"": ""https://github.com/simonw/datasette/issues/842"", ""label"": ""#842""}]"
changelog:log-out,changelog,log-out,Log out,"The ds_actor cookie can be used by plugins (or by Datasette's --root mechanism ) to authenticate users. The new /-/logout page provides a way to clear that cookie.
A ""Log out"" button now shows in the global navigation provided the user is authenticated using the ds_actor cookie. ( #840 )","[""Changelog"", ""0.45 (2020-07-01)""]","[{""href"": ""https://github.com/simonw/datasette/issues/840"", ""label"": ""#840""}]"
changelog:latest-datasette-io,changelog,latest-datasette-io,latest.datasette.io,"Every commit to Datasette master is now automatically deployed by Travis CI to
https://latest.datasette.io/ - ensuring there is always a live demo of the
latest version of the software.
The demo uses the fixtures from our
unit tests, ensuring it demonstrates the same range of functionality that is
covered by the tests.
You can see how the deployment mechanism works in our .travis.yml file.","[""Changelog"", ""0.23 (2018-06-18)""]","[{""href"": ""https://latest.datasette.io/"", ""label"": ""https://latest.datasette.io/""}, {""href"": ""https://github.com/simonw/datasette/blob/master/tests/fixtures.py"", ""label"": ""the fixtures""}, {""href"": ""https://github.com/simonw/datasette/blob/master/.travis.yml"", ""label"": "".travis.yml""}]"
changelog:javascript-plugins,changelog,javascript-plugins,JavaScript plugins,"Datasette now includes a JavaScript plugins mechanism , allowing JavaScript to customize Datasette in a way that can collaborate with other plugins.
This provides two initial hooks, with more to come in the future:
makeAboveTablePanelConfigs() can add additional panels to the top of the table page.
makeColumnActions() can add additional actions to the column menu.
Thanks Cameron Yick for contributing this feature. ( #2052 )","[""Changelog"", ""1.0a8 (2024-02-07)""]","[{""href"": ""https://github.com/hydrosquall"", ""label"": ""Cameron Yick""}, {""href"": ""https://github.com/simonw/datasette/pull/2052"", ""label"": ""#2052""}]"
changelog:javascript-modules,changelog,javascript-modules,JavaScript modules,"JavaScript modules were introduced in ECMAScript 2015 and provide native browser support for the import and export keywords.
To use modules, JavaScript needs to be included in