Many :ref:`plugin_hooks` are passed objects that provide access to internal Datasette functionality. The interface to these objects should not be considered stable with the exception of methods that are documented here.
The request object is passed to various plugin hooks. It represents an incoming HTTP request. It has the following properties:
.scope- dictionary- The ASGI scope that was used to construct this request, described in the ASGI HTTP connection scope specification.
.method- string- The HTTP method for this request, usually
GETorPOST. .url- string- The full URL for this request, e.g.
https://latest.datasette.io/fixtures. .scheme- string- The request scheme - usually
httpsorhttp. .headers- dictionary (str -> str)- A dictionary of incoming HTTP request headers. Header names have been converted to lowercase.
.cookies- dictionary (str -> str)- A dictionary of incoming cookies
.host- string- The host header from the incoming request, e.g.
latest.datasette.ioorlocalhost. .path- string- The path of the request excluding the query string, e.g.
/fixtures. .full_path- string- The path of the request including the query string if one is present, e.g.
/fixtures?sql=select+sqlite_version(). .query_string- string- The query string component of the request, without the
?- e.g.name__contains=sam&age__gt=10. .args- MultiParams- An object representing the parsed query string parameters, see below.
.url_vars- dictionary (str -> str)- Variables extracted from the URL path, if that path was defined using a regular expression. See :ref:`plugin_register_routes`.
.actor- dictionary (str -> Any) or None- The currently authenticated actor (see :ref:`actors <authentication_actor>`), or
Noneif the request is unauthenticated.
The object also has two awaitable methods:
await request.post_vars()- dictionary- Returns a dictionary of form variables that were submitted in the request body via
POST. Don't forget to read about :ref:`internals_csrf`! await request.post_body()- bytes- Returns the un-parsed body of a request submitted by
POST- useful for things like incoming JSON data.
And a class method that can be used to create fake request objects for use in tests:
fake(path_with_query_string, method="GET", scheme="http", url_vars=None)Returns a
Requestinstance for the specified path and method. For example:from datasette import Request from pprint import pprint request = Request.fake( "/fixtures/facetable/", url_vars={"database": "fixtures", "table": "facetable"}, ) pprint(request.scope)
This outputs:
{'http_version': '1.1', 'method': 'GET', 'path': '/fixtures/facetable/', 'query_string': b'', 'raw_path': b'/fixtures/facetable/', 'scheme': 'http', 'type': 'http', 'url_route': {'kwargs': {'database': 'fixtures', 'table': 'facetable'}}}
request.args is a MultiParams object - a dictionary-like object which provides access to query string parameters that may have multiple values.
Consider the query string ?foo=1&foo=2&bar=3 - with two values for foo and one value for bar.
request.args[key]- string- Returns the first value for that key, or raises a
KeyErrorif the key is missing. For the above examplerequest.args["foo"]would return"1". request.args.get(key)- string or None- Returns the first value for that key, or
Noneif the key is missing. Pass a second argument to specify a different default, e.g.q = request.args.get("q", ""). request.args.getlist(key)- list of strings- Returns the list of strings for that key.
request.args.getlist("foo")would return["1", "2"]in the above example.request.args.getlist("bar")would return["3"]. If the key is missing an empty list will be returned. request.args.keys()- list of strings- Returns the list of available keys - for the example this would be
["foo", "bar"]. key in request.args- True or False- You can use
if key in request.argsto check if a key is present. for key in request.args- iterator- This lets you loop through every available key.
len(request.args)- integer- Returns the number of keys.
The Response class can be returned from view functions that have been registered using the :ref:`plugin_register_routes` hook.
The Response() constructor takes the following arguments:
body- string- The body of the response.
status- integer (optional)- The HTTP status - defaults to 200.
headers- dictionary (optional)- A dictionary of extra HTTP headers, e.g.
{"x-hello": "world"}. content_type- string (optional)- The content-type for the response. Defaults to
text/plain.
For example:
from datasette.utils.asgi import Response
response = Response(
"<xml>This is XML</xml>",
content_type="application/xml; charset=utf-8",
)The quickest way to create responses is using the Response.text(...), Response.html(...), Response.json(...) or Response.redirect(...) helper methods:
from datasette.utils.asgi import Response
html_response = Response.html("This is HTML")
json_response = Response.json({"this_is": "json"})
text_response = Response.text(
"This will become utf-8 encoded text"
)
# Redirects are served as 302, unless you pass status=301:
redirect_response = Response.redirect(
"https://latest.datasette.io/"
)Each of these responses will use the correct corresponding content-type - text/html; charset=utf-8, application/json; charset=utf-8 or text/plain; charset=utf-8 respectively.
Each of the helper methods take optional status= and headers= arguments, documented above.
In most cases you will return Response objects from your own view functions. You can also use a Response instance to respond at a lower level via ASGI, for example if you are writing code that uses the :ref:`plugin_asgi_wrapper` hook.
Create a Response object and then use await response.asgi_send(send), passing the ASGI send function. For example:
async def require_authorization(scope, receive, send):
response = Response.text(
"401 Authorization Required",
headers={
"www-authenticate": 'Basic realm="Datasette", charset="UTF-8"'
},
status=401,
)
await response.asgi_send(send)To set cookies on the response, use the response.set_cookie(...) method. The method signature looks like this:
def set_cookie(
self,
key,
value="",
max_age=None,
expires=None,
path="/",
domain=None,
secure=False,
httponly=False,
samesite="lax",
):
...You can use this with :ref:`datasette.sign() <datasette_sign>` to set signed cookies. Here's how you would set the :ref:`ds_actor cookie <authentication_ds_actor>` for use with Datasette :ref:`authentication <authentication>`:
response = Response.redirect("/")
response.set_cookie(
"ds_actor",
datasette.sign({"a": {"id": "cleopaws"}}, "actor"),
)
return responseThis object is an instance of the Datasette class, passed to many plugin hooks as an argument called datasette.
You can create your own instance of this - for example to help write tests for a plugin - like so:
from datasette.app import Datasette
# With no arguments a single in-memory database will be attached
datasette = Datasette()
# The files= argument can load files from disk
datasette = Datasette(files=["/path/to/my-database.db"])
# Pass metadata as a JSON dictionary like this
datasette = Datasette(
files=["/path/to/my-database.db"],
metadata={
"databases": {
"my-database": {
"description": "This is my database"
}
}
},
)Constructor parameters include:
files=[...]- a list of database files to openimmutables=[...]- a list of database files to open in immutable modemetadata={...}- a dictionary of :ref:`metadata`config_dir=...- the :ref:`configuration directory <config_dir>` to use, stored indatasette.config_dir
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 :ref:`internals_database` instances.
All databases are listed, irrespective of user permissions. This means that the _internal database will always be listed here.
plugin_name- string- The name of the plugin to look up configuration for. Usually this is something similar to
datasette-cluster-map. database- None or string- The database the user is interacting with.
table- None or string- The table the user is interacting with.
This method lets you read plugin configuration values that were set in metadata.json. See :ref:`writing_plugins_configuration` for full details of how this method should be used.
The return value will be the value from the configuration file - usually a dictionary.
If the plugin is not configured the return value will be None.
template- string, list of strings or jinja2.TemplateThe template file to be rendered, e.g.
my_plugin.html. Datasette will search for this file first in the--template-dir=location, if it was specified - then in the plugin's bundled templates and finally in Datasette's set of default templates.If this is a list of template file names then the first one that exists will be loaded and rendered.
If this is a Jinja Template object it will be used directly.
context- None or a Python dictionary- The context variables to pass to the template.
request- request object or None- If you pass a Datasette request object here it will be made available to the template.
Renders a Jinja template using Datasette's preconfigured instance of Jinja and returns the resulting string. The template will have access to Datasette's default template functions and any functions that have been made available by other plugins.
actor- dictionary- The authenticated actor. This is usually
request.actor. action- string- 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.
default- optional, True or False- Should this permission check be default allow or default deny.
Check if the given actor has :ref:`permission <authentication_permissions>` to perform the given action on the given resource.
Some permission checks are carried out against :ref:`rules defined in metadata.json <authentication_permissions_metadata>`, while other custom permissions may be decided by plugins that implement the :ref:`plugin_hook_permission_allowed` plugin hook.
If neither metadata.json nor any of the plugins provide an answer to the permission query the default argument will be returned.
See :ref:`permissions` for a full list of permission actions included in Datasette core.
actor- dictionary- The authenticated actor. This is usually
request.actor. permissions- list- A list of permissions to check. Each permission in that list can be a string
actionname or a 2-tuple of(action, resource).
This method allows multiple permissions to be checked at once. It raises a datasette.Forbidden exception if any of the checks are denied before one of them is explicitly granted.
This is useful when you need to check multiple permissions at once. For example, an actor should be able to view a table if either one of the following checks returns True or not a single one of them returns False:
await self.ds.ensure_permissions(
request.actor,
[
("view-table", (database, table)),
("view-database", database),
"view-instance",
],
)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 ofactionstrings or(action, resource)tuples, optional- Provide this instead of
actionandresourceto 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 self.ds.check_visibility(
request.actor,
action="view-table",
resource=(database, table),
)The following example runs three checks in a row, similar to :ref:`datasette_ensure_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 self.ds.check_visibility(
request.actor,
permissions=[
("view-table", (database, table)),
("view-database", database),
"view-instance",
],
)name- string, optional- The name of the database - optional.
Returns the specified database object. Raises a KeyError if the database does not exist. Call this method without an argument to return the first connected database.
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)"
)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.
name- string- The name of the database to be removed.
This removes a database that has been previously added. name= is the unique name of that database.
value- any serializable type- The value to be signed.
namespace- string, optional- An alternative namespace, see the itsdangerous salt documentation.
Utility method for signing values, such that you can safely pass data to and from an untrusted environment. This is a wrapper around the itsdangerous library.
This method returns a signed string, which can be decoded and verified using :ref:`datasette_unsign`.
signed- any serializable type- The signed string that was created using :ref:`datasette_sign`.
namespace- string, optional- The alternative namespace, if one was used.
Returns the original, decoded object that was passed to :ref:`datasette_sign`. If the signature is not valid this raises a itsdangerous.BadSignature exception.
request- Request- The current Request object
message- string- The message string
type- constant, optional- The message type -
datasette.INFO,datasette.WARNINGordatasette.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.
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 :ref:`setting_force_https_urls` configuration setting is taken into account.
key- string- The name of the setting, e.g.
base_url.
Returns the configured value for the specified :ref:`setting <settings>`. This can be a string, boolean or integer depending on the requested setting.
For example:
downloads_are_allowed = datasette.setting("allow_download")Plugins can make internal simulated HTTP requests to the Datasette instance within which they are running. This ensures that all of Datasette's external JSON APIs are also available to plugins, while avoiding the overhead of making an external HTTP call to access those APIs.
The datasette.client object is a wrapper around the HTTPX Python library, providing an async-friendly API that is similar to the widely used Requests library.
It offers the following methods:
await datasette.client.get(path, **kwargs)- returns HTTPX Response- Execute an internal GET request against that path.
await datasette.client.post(path, **kwargs)- returns HTTPX Response- Execute an internal POST request. Use
data={"name": "value"}to pass form parameters. await datasette.client.options(path, **kwargs)- returns HTTPX Response- Execute an internal OPTIONS request.
await datasette.client.head(path, **kwargs)- returns HTTPX Response- Execute an internal HEAD request.
await datasette.client.put(path, **kwargs)- returns HTTPX Response- Execute an internal PUT request.
await datasette.client.patch(path, **kwargs)- returns HTTPX Response- Execute an internal PATCH request.
await datasette.client.delete(path, **kwargs)- returns HTTPX Response- Execute an internal DELETE request.
await datasette.client.request(method, path, **kwargs)- returns HTTPX Response- Execute an internal request with the given HTTP method against that path.
These methods can be used with :ref:`internals_datasette_urls` - for example:
table_json = (
await datasette.client.get(
datasette.urls.table(
"fixtures", "facetable", format="json"
)
)
).json()datasette.client methods automatically take the current :ref:`setting_base_url` setting into account, whether or not you use the datasette.urls family of methods to construct the path.
For documentation on available **kwargs options and the shape of the HTTPX Response object refer to the HTTPX Async documentation.
The datasette.urls object contains methods for building URLs to pages within Datasette. Plugins should use this to link to pages, since these methods take into account any :ref:`setting_base_url` configuration setting that might be in effect.
datasette.urls.instance(format=None)- Returns the URL to the Datasette instance root page. This is usually
"/". datasette.urls.path(path, format=None)Takes a path and returns the full path, taking
base_urlinto account.For example,
datasette.urls.path("-/logout")will return the path to the logout page, which will be"/-/logout"by default or/prefix-path/-/logoutifbase_urlis set to/prefix-path/datasette.urls.logout()- Returns the URL to the logout page, usually
"/-/logout" datasette.urls.static(path)- Returns the URL of one of Datasette's default static assets, for example
"/-/static/app.css" datasette.urls.static_plugins(plugin_name, path)Returns the URL of one of the static assets belonging to a plugin.
datasette.urls.static_plugins("datasette_cluster_map", "datasette-cluster-map.js")would return"/-/static-plugins/datasette_cluster_map/datasette-cluster-map.js"datasette.urls.static(path)- Returns the URL of one of Datasette's default static assets, for example
"/-/static/app.css" datasette.urls.database(database_name, format=None)- Returns the URL to a database page, for example
"/fixtures" datasette.urls.table(database_name, table_name, format=None)- Returns the URL to a table page, for example
"/fixtures/facetable" datasette.urls.query(database_name, query_name, format=None)- Returns the URL to a query page, for example
"/fixtures/pragma_cache_size"
These functions can be accessed via the {{ urls }} object in Datasette templates, for example:
<a href="{{ urls.instance() }}">Homepage</a>
<a href="{{ urls.database("fixtures") }}">Fixtures database</a>
<a href="{{ urls.table("fixtures", "facetable") }}">facetable table</a>
<a href="{{ urls.query("fixtures", "pragma_cache_size") }}">pragma_cache_size query</a>Use the format="json" (or "csv" or other formats supported by plugins) arguments to get back URLs to the JSON representation. This is the path with .json added on the end.
These methods each return a datasette.utils.PrefixedUrlString object, which is a subclass of the Python str type. This allows the logic that considers the base_url setting to detect if that prefix has already been applied to the path.
Instances of the Database class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas.
The Database() constructor can be used by plugins, in conjunction with :ref:`datasette_add_database`, to create and register new databases.
The arguments are as follows:
ds- :ref:`internals_datasette` (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
Falseto cause Datasette to open the file in immutable mode. is_memory- boolean- Use this to create non-shared memory connections.
memory_name- string orNone- 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.
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.
Executes a SQL query against the database and returns the resulting rows (see :ref:`database_results`).
sql- string (required)- The SQL query to execute. This can include
?or:namedparameters. 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 toFalseto 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.QueryInterruptedexception. 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.
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 firstpage_sizerows in the query result. You can disable truncation by passingtruncate=Falseto thedb.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
Noneif 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.MultipleValuesexception otherwise. .__len__()- Calling
len(results)returns the (truncated) number of returned results.
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)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 behaviour 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.
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.
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")],
)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.
Warning
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.
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.
The Database class also provides properties and methods for introspecting the database.
db.name- string- The name of the database - usually the filename without the
.dbprefix. db.size- integer- The size of the database file in bytes. 0 for
:memory:databases. db.mtime_ns- integer or None- The last modification time of the database file in nanoseconds since the epoch.
Nonefor:memory:databases. db.is_mutable- boolean- Is this database mutable, and allowed to accept writes?
db.is_memory- boolean- Is this database an in-memory database?
await db.attached_databases()- list of named tuples- Returns a list of additional databases that have been connected to this database using the SQLite ATTACH command. Each named tuple has fields
seq,nameandfile. await db.table_exists(table)- boolean- Check if a table called
tableexists. await db.table_names()- list of strings- List of names of tables in the database.
await db.view_names()- list of strings- List of names of views in the database.
await db.table_columns(table)- list of strings- Names of columns in a specific table.
await db.table_column_details(table)- list of named tuples- Full details of the columns in a specific table. Each column is represented by a
Columnnamed tuple with fieldscid(integer representing the column position),name(string),type(string, e.g.REALorVARCHAR(30)),notnull(integer 1 or 0),default_value(string or None),is_pk(integer 1 or 0). await db.primary_keys(table)- list of strings- Names of the columns that are part of the primary key for this table.
await db.fts_table(table)- string or None- The name of the FTS table associated with this table, if one exists.
await db.label_column_for_table(table)- string or None- The label column that is associated with this table - either automatically detected or using the
"label_column"key from :ref:`metadata`, see :ref:`label_columns`. await db.foreign_keys_for_table(table)- list of dictionaries- Details of columns in this table which are foreign keys to other tables. A list of dictionaries where each dictionary is shaped like this:
{"column": string, "other_table": string, "other_column": string}. await db.hidden_table_names()- list of strings- List of tables which Datasette "hides" by default - usually these are tables associated with SQLite's full-text search feature, the SpatiaLite extension or tables hidden using the :ref:`metadata_hiding_tables` feature.
await db.get_table_definition(table)- string- Returns the SQL definition for the table - the
CREATE TABLEstatement and any associatedCREATE INDEXstatements. await db.get_view_definition(view)- string- Returns the SQL definition of the named view.
await db.get_all_foreign_keys()- dictionaryDictionary representing both incoming and outgoing foreign keys for this table. It has two keys,
"incoming"and"outgoing", each of which is a list of dictionaries with keys"column","other_table"and"other_column". For example:{ "incoming": [], "outgoing": [ { "other_table": "attraction_characteristic", "column": "characteristic_id", "other_column": "pk", }, { "other_table": "roadside_attractions", "column": "attraction_id", "other_column": "pk", } ] }
Datasette uses asgi-csrf to guard against CSRF attacks on form POST submissions. Users receive a ds_csrftoken cookie which is compared against the csrftoken form field (or x-csrftoken HTTP header) for every incoming request.
If your plugin implements a <form method="POST"> anywhere you will need to include that token. You can do so with the following template snippet:
<input type="hidden" name="csrftoken" value="{{ csrftoken() }}">If you are rendering templates using the :ref:`datasette_render_template` method the csrftoken() helper will only work if you provide the request= argument to that method. If you forget to do this you will see the following error:
form-urlencoded POST field did not match cookie
You can selectively disable CSRF protection using the :ref:`plugin_hook_skip_csrf` hook.
Warning
This API should be considered unstable - the structure of these tables may change prior to the release of Datasette 1.0.
Datasette maintains an in-memory SQLite database with details of the the databases, tables and columns for all of the attached databases.
By default all actors are denied access to the view-database permission for the _internal database, so the database is not visible to anyone unless they :ref:`sign in as root <authentication_root>`.
Plugins can access this database by calling db = datasette.get_database("_internal") and then executing queries using the :ref:`Database API <internals_database>`.
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.
The datasette.utils module contains various utility functions used by Datasette. As a general rule you should consider anything in this module to be unstable - functions and classes here could change without warning or be removed entirely between Datasette releases, without being mentioned in the release notes.
The exception to this rule is anythang that is documented here. If you find a need for an undocumented utility function in your own work, consider opening an issue requesting that the function you are using be upgraded to documented and supported status.
This function accepts a string containing either JSON or YAML, expected to be of the format described in :ref:`metadata`. It returns a nested Python dictionary representing the parsed data from that string.
If the metadata cannot be parsed as either JSON or YAML the function will raise a utils.BadMetadataError exception.
.. autofunction:: datasette.utils.parse_metadata
Utility function for calling await on a return value if it is awaitable, otherwise returning the value. This is used by Datasette to support plugin hooks that can optionally return awaitable functions. Read more about this function in The “await me maybe” pattern for Python asyncio.
.. autofunction:: datasette.utils.await_me_maybe
Datasette uses a custom encoding scheme in some places, called tilde encoding. This is primarily used for table names and row primary keys, to avoid any confusion between / characters in those values and the Datasette URLs that reference them.
Tilde encoding uses the same algorithm as URL percent-encoding, but with the ~ tilde character used in place of %.
Any character other than ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz0123456789_- will be replaced by the numeric equivalent preceded by a tilde. For example:
/becomes~2F.becomes~2E%becomes~25~becomes~7E- Space becomes
+ polls/2022.primarybecomespolls~2F2022~2Eprimary
Note that the space character is a special case: it will be replaced with a + symbol.
.. autofunction:: datasette.utils.tilde_encode
.. autofunction:: datasette.utils.tilde_decode
Running Datasette with --setting trace_debug 1 enables trace debug output, which can then be viewed by adding ?_trace=1 to the query string for any page.
You can see an example of this at the bottom of latest.datasette.io/fixtures/facetable?_trace=1. The JSON output shows full details of every SQL query that was executed to generate the page.
The datasette-pretty-traces plugin can be installed to provide a more readable display of this information. You can see a demo of that here.
You can add your own custom traces to the JSON output using the trace() context manager. This takes a string that identifies the type of trace being recorded, and records any keyword arguments as additional JSON keys on the resulting trace object.
The start and end time, duration and a traceback of where the trace was executed will be automatically attached to the JSON object.
This example uses trace to record the start, end and duration of any HTTP GET requests made using the function:
from datasette.tracer import trace
import httpx
async def fetch_url(url):
with trace("fetch-url", url=url):
async with httpx.AsyncClient() as client:
return await client.get(url)If your code uses a mechanism such as asyncio.gather() to execute code in additional tasks you may find that some of the traces are missing from the display.
You can use the trace_child_tasks() context manager to ensure these child tasks are correctly handled.
from datasette import tracer
with tracer.trace_child_tasks():
results = await asyncio.gather(
# ... async tasks here
)This example uses the :ref:`register_routes() <plugin_register_routes>` plugin hook to add a page at /parallel-queries which executes two SQL queries in parallel using asyncio.gather() and returns their results.
from datasette import hookimpl
from datasette import tracer
@hookimpl
def register_routes():
async def parallel_queries(datasette):
db = datasette.get_database()
with tracer.trace_child_tasks():
one, two = await asyncio.gather(
db.execute("select 1"),
db.execute("select 2"),
)
return Response.json(
{
"one": one.single_value(),
"two": two.single_value(),
}
)
return [
(r"/parallel-queries$", parallel_queries),
]Adding ?_trace=1 will show that the trace covers both of those child tasks.
The following commonly used symbols can be imported directly from the datasette module:
from datasette import Response
from datasette import Forbidden
from datasette import NotFound
from datasette import hookimpl
from datasette import actor_matches_allow