rowid,title,content,sections_fts,rank 792,Import shortcuts,"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",95, 791,Tracing child tasks,"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 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), ] Note that running parallel SQL queries in this way has been known to cause problems in the past , so treat this example with caution. Adding ?_trace=1 will show that the trace covers both of those child tasks.",95, 790,datasette.tracer,"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)",95, 789,JSON encoding,"class datasette.utils. CustomJSONEncoder * skipkeys = False ensure_ascii = True check_circular = True allow_nan = True sort_keys = False indent = None separators = None default = None The CustomJSONEncoder class handles serialization for objects commonly used by Datasette, including SQLite cursors and binary blobs. Datasette uses it internally to serve .json endpoints, and plugins that return JSON can use it to match Datasette's own handling. Built-in types (text, numbers, lists, etc) are encoded the same as Python's built-in json module. sqlite3.Row becomes a tuple sqlite3.Cursor becomes a list Binary blobs are encoded as an object, with the actual data base64-encoded, like so: { ""$base64"": True, ""encoded"": ..., } Example: https://latest.datasette.io/fixtures/binary_data.json",95, 788,"await async_call_with_supported_arguments(fn, **kwargs)","Async version of call_with_supported_arguments . Use this for async def callback functions. async datasette.utils. async_call_with_supported_arguments fn ** kwargs Async version of call_with_supported_arguments() . Calls await fn(...) with the subset of **kwargs matching its signature. Parameters fn -- An async callable kwargs -- All available keyword arguments Returns The return value of await fn(...)",95, 787,"call_with_supported_arguments(fn, **kwargs)","Call fn , passing it only those keyword arguments that match its function signature. This implements a dependency injection pattern - the caller provides all available arguments, and the function receives only the ones it declares as parameters. This is useful in plugins that want to define callback functions that only declare the arguments they need. For example: from datasette.utils import call_with_supported_arguments def my_callback(request, datasette): ... # This will pass only request and datasette, ignoring other kwargs: call_with_supported_arguments( my_callback, request=request, datasette=datasette, database=database, table=table, ) datasette.utils. call_with_supported_arguments fn ** kwargs Call fn with the subset of **kwargs matching its signature. This implements dependency injection: the caller provides all available keyword arguments and the function receives only the ones it declares as parameters. Parameters fn -- A callable (sync function) kwargs -- All available keyword arguments Returns The return value of fn",95, 786,Tilde encoding,"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.primary becomes polls~2F2022~2Eprimary Note that the space character is a special case: it will be replaced with a + symbol. datasette.utils. tilde_encode s : str str Returns tilde-encoded string - for example /foo/bar -> ~2Ffoo~2Fbar datasette.utils. tilde_decode s : str str Decodes a tilde-encoded string, so ~2Ffoo~2Fbar -> /foo/bar",95, 785,named_parameters(sql),"Derive the list of :named parameters referenced in a SQL query. datasette.utils. named_parameters sql : str list [ str ] Given a SQL statement, return a list of named parameters that are used in the statement e.g. for select * from foo where id=:id this would return [""id""]",95, 784,await_me_maybe(value),"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 . async datasette.utils. await_me_maybe value : Any Any If value is callable, call it. If awaitable, await it. Otherwise return it.",95, 783,parse_metadata(content),"This function accepts a string containing either JSON or YAML, expected to be of the format described in 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. datasette.utils. parse_metadata content : str dict Detects if content is JSON or YAML and parses it appropriately.",95, 782,The datasette.utils module,"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 anything 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.",95, 781,Internal database schema,"The internal database schema is as follows: [[[cog from metadata_doc import internal_schema internal_schema(cog) ]]] CREATE TABLE ""_sqlite_migrations"" ( ""id"" INTEGER PRIMARY KEY, ""migration_set"" TEXT, ""name"" TEXT, ""applied_at"" TEXT ); CREATE UNIQUE INDEX ""idx__sqlite_migrations_migration_set_name"" ON ""_sqlite_migrations"" (""migration_set"", ""name""); CREATE TABLE catalog_databases ( database_name TEXT PRIMARY KEY, path TEXT, is_memory INTEGER, schema_version INTEGER ); CREATE TABLE catalog_tables ( database_name TEXT, table_name TEXT, rootpage INTEGER, sql TEXT, PRIMARY KEY (database_name, table_name), FOREIGN KEY (database_name) REFERENCES catalog_databases(database_name) ); CREATE TABLE catalog_views ( database_name TEXT, view_name TEXT, rootpage INTEGER, sql TEXT, PRIMARY KEY (database_name, view_name), FOREIGN KEY (database_name) REFERENCES catalog_databases(database_name) ); CREATE TABLE catalog_columns ( database_name TEXT, table_name TEXT, cid INTEGER, name TEXT, type TEXT, ""notnull"" INTEGER, default_value TEXT, -- renamed from dflt_value is_pk INTEGER, -- renamed from pk hidden INTEGER, PRIMARY KEY (database_name, table_name, name), FOREIGN KEY (database_name) REFERENCES catalog_databases(database_name), FOREIGN KEY (database_name, table_name) REFERENCES catalog_tables(database_name, table_name) ); CREATE TABLE catalog_indexes ( database_name TEXT, table_name TEXT, seq INTEGER, name TEXT, ""unique"" INTEGER, origin TEXT, partial INTEGER, PRIMARY KEY (database_name, table_name, name), FOREIGN KEY (database_name) REFERENCES catalog_databases(database_name), FOREIGN KEY (database_name, table_name) REFERENCES catalog_tables(database_name, table_name) ); CREATE TABLE catalog_foreign_keys ( database_name TEXT, table_name TEXT, id INTEGER, seq INTEGER, ""table"" TEXT, ""from"" TEXT, ""to"" TEXT, on_update TEXT, on_delete TEXT, match TEXT, PRIMARY KEY (database_name, table_name, id, seq), FOREIGN KEY (database_name) REFERENCES catalog_databases(database_name), FOREIGN KEY (database_name, table_name) REFERENCES catalog_tables(database_name, table_name) ); CREATE TABLE metadata_instance ( key text, value text, unique(key) ); CREATE TABLE metadata_databases ( database_name text, key text, value text, unique(database_name, key) ); CREATE TABLE metadata_resources ( database_name text, resource_name text, key text, value text, unique(database_name, resource_name, key) ); CREATE TABLE metadata_columns ( database_name text, resource_name text, column_name text, key text, value text, unique(database_name, resource_name, column_name, key) ); CREATE TABLE column_types ( database_name TEXT NOT NULL, resource_name TEXT NOT NULL, column_name TEXT NOT NULL, column_type TEXT NOT NULL, config TEXT, PRIMARY KEY (database_name, resource_name, column_name) ); CREATE TABLE queries ( database_name TEXT NOT NULL, name TEXT NOT NULL, sql TEXT NOT NULL, title TEXT, description TEXT, description_html TEXT, options TEXT NOT NULL DEFAULT '{}', parameters TEXT NOT NULL DEFAULT '[]', is_write INTEGER NOT NULL DEFAULT 0 CHECK (is_write IN (0, 1)), is_private INTEGER NOT NULL DEFAULT 0 CHECK (is_private IN (0, 1)), is_trusted INTEGER NOT NULL DEFAULT 0 CHECK (is_trusted IN (0, 1)), source TEXT NOT NULL DEFAULT 'user', owner_id TEXT, created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP, updated_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP, PRIMARY KEY (database_name, name) ); CREATE INDEX queries_owner_idx ON queries(owner_id); [[[end]]]",95, 780,Datasette's internal database,"Datasette maintains an ""internal"" SQLite database used for configuration, caching, and storage. Plugins can store configuration, settings, and other data inside this database. By default, Datasette will use a temporary in-memory SQLite database as the internal database, which is created at startup and destroyed at shutdown. Users of Datasette can optionally pass in a --internal flag to specify the path to a SQLite database to use as the internal database, which will persist internal data across Datasette instances. Datasette maintains tables called catalog_databases , catalog_tables , catalog_views , catalog_columns , catalog_indexes , catalog_foreign_keys with details of the attached databases and their schemas. These tables should not be considered a stable API - they may change between Datasette releases. Metadata is stored in tables metadata_instance , metadata_databases , metadata_resources and metadata_columns . Plugins can interact with these tables via the get_*_metadata() and set_*_metadata() methods . The internal database is not exposed in the Datasette application by default, which means private data can safely be stored without worry of accidentally leaking information through the default Datasette interface and API. However, other plugins do have full read and write access to the internal database. Plugins can access this database by calling internal_db = datasette.get_internal_database() and then executing queries using the Database API . Plugin authors are asked to practice good etiquette when using the internal database, as all plugins use the same database to store data. For example: Use a unique prefix when creating tables, indices, and triggers in the internal database. If your plugin is called datasette-xyz , then prefix names with datasette_xyz_* . Avoid long-running write statements that may stall or block other plugins that are trying to write at the same time. Use temporary tables or shared in-memory attached databases when possible. Avoid implementing features that could expose private data stored in the internal database by other plugins.",95, 779,CSRF protection,"Datasette protects against Cross-Site Request Forgery by inspecting the browser-set Sec-Fetch-Site and Origin headers on every unsafe (non- GET / HEAD / OPTIONS ) request, following the approach described in Filippo Valsorda's article and implemented in Go 1.25's http.CrossOriginProtection . A request is rejected with a 403 response if: It carries Sec-Fetch-Site with any value other than same-origin or none , or It has no Sec-Fetch-Site header but does carry an Origin header whose host does not match the request Host . Requests from non-browser clients ( curl , server-to-server scripts, etc.) do not send Sec-Fetch-Site or Origin and pass through unchanged - CSRF is a browser-only attack. No token, cookie, or hidden form field is needed. Any
inside Datasette or a plugin will be accepted from the same origin without modification.",95, 778,Database introspection,"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 .db prefix. 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. None for :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? db.is_temp_disk - boolean Is this database a temporary file-backed database? See Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None, is_temp_disk=False) for details. Temporary disk databases report hash as None but have real values for size and mtime_ns since they are backed by a file on disk. 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 , name and file . await db.table_exists(table) - boolean Check if a table called table exists. await db.view_exists(view) - boolean Check if a view called view exists. 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 Column named tuple with fields cid (integer representing the column position), name (string), type (string, e.g. REAL or VARCHAR(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 in configuration, see label_column . 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 hidden feature. await db.get_table_definition(table) - string Returns the SQL definition for the table - the CREATE TABLE statement and any associated CREATE INDEX statements. await db.get_view_definition(view) - string Returns the SQL definition of the named view. await db.get_all_foreign_keys() - dictionary Dictionary representing both incoming and outgoing foreign keys for every table in this database. Each key is a table name that points to a dictionary with two keys, ""incoming"" and ""outgoing"" , each of which is a list of dictionaries with keys ""column"" , ""other_table"" and ""other_column"" . For example: { ""documents"": { ""incoming"": [ { ""other_table"": ""pages"", ""column"": ""id"", ""other_column"": ""document_id"" } ], ""outgoing"": [] }, ""pages"": { ""incoming"": [ { ""other_table"": ""organization_pages"", ""column"": ""id"", ""other_column"": ""page_id"" } ], ""outgoing"": [ { ""other_table"": ""documents"", ""column"": ""document_id"", ""other_column"": ""id"" } ] }, ""organization"": { ""incoming"": [ { ""other_table"": ""organization_pages"", ""column"": ""id"", ""other_column"": ""organization_id"" } ], ""outgoing"": [] }, ""organization_pages"": { ""incoming"": [], ""outgoing"": [ { ""other_table"": ""pages"", ""column"": ""page_id"", ""other_column"": ""id"" }, { ""other_table"": ""organization"", ""column"": ""organization_id"", ""other_column"": ""id"" } ] } }",95, 777,db.close(),"Release all resources held by this Database instance. This shuts down the background write thread (if one was started by a previous call to await db.execute_write_fn(fn, block=True, transaction=True) or similar), closes the write connection, and closes any cached read connections. After db.close() has been called, any further call to await db.execute(sql, ...) , await db.execute_fn(fn) , await db.execute_write(sql, params=None, block=True, request=None, return_all=False, returning_limit=10, transaction=True) , await db.execute_write_fn(fn, block=True, transaction=True) , await db.execute_write_many(sql, params_seq, block=True) , await db.execute_write_script(sql, block=True) or await db.execute_isolated_fn(fn) will raise a datasette.database.DatasetteClosedError exception. close() is idempotent — calling it a second time is a no-op. It is one-way: a closed Database cannot be reopened.",95, 776,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. Running VACUUM using this method also ensures it won't trigger incorrect RenameTableEvent events, since execute_isolated_fn() does not trigger the Datasette mechanism that detects renamed tables in a way that can be confused by a VACUUM . 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.",95, 775,"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) Your function can optionally accept a track_event parameter in addition to conn . If it does, it will be passed a callable that can be used to queue events for dispatch after the write transaction commits successfully. Events queued this way are discarded if the write raises an exception. from datasette.events import AlterTableEvent def my_write(conn, track_event): before_schema = conn.execute( ""select sql from sqlite_master where name = 'my_table'"" ).fetchone()[0] conn.execute( ""alter table my_table add column new_col text"" ) after_schema = conn.execute( ""select sql from sqlite_master where name = 'my_table'"" ).fetchone()[0] track_event( AlterTableEvent( actor=None, database=""mydb"", table=""my_table"", before_schema=before_schema, after_schema=after_schema, ) ) await database.execute_write_fn(my_write) 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.",95, 774,"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.",95, 773,"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.",95, 772,"await db.execute_write(sql, params=None, block=True, request=None, return_all=False, returning_limit=10, transaction=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 optional request= argument is used internally by Datasette to pass request context to write_wrapper plugin hooks . The method will block until the operation is completed, and the return value will be an ExecuteWriteResult object. This imitates a subset of the sqlite3.Cursor object: .rowcount The number of rows modified by the statement, or -1 if that number is unavailable. .lastrowid The row ID of the last modified row, as returned by sqlite3.Cursor.lastrowid . .description The same column metadata exposed by Python's sqlite3.Cursor.description : one tuple per returned column, or None if the statement does not return rows. .truncated True if the statement returned more rows than returning_limit . .fetchall() Returns any rows buffered by Datasette from the statement, such as rows from SQLite's RETURNING clause. This may be limited by returning_limit unless return_all=True was used. This method empties the buffer, so calling it again will return an empty list. SQLite statements using RETURNING must have their rows consumed before the transaction can commit. Datasette will fetch up to returning_limit + 1 rows before committing, store up to returning_limit rows on the result object and set .truncated if there were more. The default returning_limit is 10 . When .truncated is True , .rowcount will be -1 . SQLite only reports the final row count for a RETURNING statement after every returned row has been fetched, and Datasette has deliberately stopped fetching rows after returning_limit to avoid buffering a potentially large result in memory. If you need to retrieve every row returned by a statement, pass return_all=True . This will buffer all returned rows in memory before committing. 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. Pass transaction=False for statements such as VACUUM that cannot run inside a transaction.",95, 771,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)",95, 770,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] . .dicts() - list of dict This method returns a list of Python dictionaries, one for each row. .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.",95, 769,"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 .",95, 768,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 .",95, 767,"Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None, is_temp_disk=False)","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. is_temp_disk - boolean Set this to True to create a temporary file-backed database. This creates a SQLite database in a temporary file on disk (using Python's tempfile.mkstemp() ) with WAL mode enabled for better concurrent read/write performance. The temporary file is automatically cleaned up when the database is closed or when the process exits. Unlike named in-memory databases ( memory_name ), temporary disk databases support concurrent readers and writers without locking errors, because WAL mode allows readers and writers to operate simultaneously. This makes them suitable for use cases like the internal database where concurrent access is common. When is_temp_disk=True , the path , is_mutable , and mode parameters are set automatically and should not be provided. 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.",95, 766,Database class,"Instances of the Database class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas.",95, 765,Available SQL parameters,"When writing SQL for PermissionSQL , the following parameters are automatically available: :actor - JSON string or NULL The full actor dictionary serialized as JSON. Use SQLite's json_extract() function to access fields: json_extract(:actor, '$.role') = 'admin' json_extract(:actor, '$.team') = 'engineering' :actor_id - string or NULL The actor's id field, for simple equality comparisons: :actor_id = 'alice' :action - string The action being checked (e.g., ""view-table"" , ""insert-row"" , ""execute-sql"" ). Example usage: Here's an example plugin that grants view-table permissions to users with an ""analyst"" role for tables in the ""analytics"" database: from datasette import hookimpl from datasette.permissions import PermissionSQL @hookimpl def permission_resources_sql(datasette, actor, action): if action != ""view-table"": return None return PermissionSQL( source=""my_analytics_plugin"", sql="""""" SELECT 'analytics' AS parent, NULL AS child, 1 AS allow, 'Analysts can view analytics database' AS reason WHERE json_extract(:actor, '$.role') = 'analyst' AND :action = 'view-table' """""", params={}, ) A more complex example that uses custom parameters: @hookimpl def permission_resources_sql(datasette, actor, action): if not actor: return None user_teams = actor.get(""teams"", []) return PermissionSQL( source=""team_permissions_plugin"", sql="""""" SELECT team_database AS parent, team_table AS child, 1 AS allow, 'User is member of team: ' || team_name AS reason FROM team_permissions WHERE user_id = :user_id AND :action IN ('view-table', 'insert-row', 'update-row') """""", params={""user_id"": actor.get(""id"")}, ) Permission resolution rules: When multiple PermissionSQL objects return conflicting rules for the same resource, Datasette applies the following precedence: Specificity : Child-level rules (with both parent and child ) override parent-level rules (with only parent ), which override root-level rules (with neither parent nor child ) Deny over allow : At the same specificity level, deny ( allow=0 ) takes precedence over allow ( allow=1 ) Implicit deny : If no rules match a resource, access is denied by default",95, 764,PermissionSQL class,"The PermissionSQL class is used by plugins to contribute SQL-based permission rules through the permission_resources_sql(datasette, actor, action) hook. This enables efficient permission checking across multiple resources by leveraging SQLite's query engine. from datasette.permissions import PermissionSQL @dataclass class PermissionSQL: source: str # Plugin name for auditing sql: str # SQL query returning permission rules params: Dict[str, Any] # Parameters for the SQL query Attributes: source - string An identifier for the source of these permission rules, typically the plugin name. This is used for debugging and auditing. sql - string A SQL query that returns permission rules. The query must return rows with the following columns: parent (TEXT or NULL) - The parent resource identifier (e.g., database name) child (TEXT or NULL) - The child resource identifier (e.g., table name) allow (INTEGER) - 1 for allow, 0 for deny reason (TEXT) - A human-readable explanation of why this permission was granted or denied params - dictionary A dictionary of parameters to bind into the SQL query. Parameter names should not include the : prefix.",95, 763,Permission classes and utilities,,95, 762,datasette.urls,"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 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_url into account. For example, datasette.urls.path(""-/logout"") will return the path to the logout page, which will be ""/-/logout"" by default or /prefix-path/-/logout if base_url is 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: Homepage Fixtures database facetable table pragma_cache_size query 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.",95, 761,Detecting internal client requests,"datasette.in_client() - returns bool Returns True if the current code is executing within a datasette.client request, False otherwise. This method is useful for plugins that need to behave differently when called through datasette.client versus when handling external HTTP requests. Example usage: async def fetch_documents(datasette): if not datasette.in_client(): return Response.text( ""Only available via internal client requests"", status=403, ) ... Note that datasette.in_client() is independent of skip_permission_checks . A request made through datasette.client will always have in_client() return True , regardless of whether skip_permission_checks is set.",95, 760,Bypassing permission checks,"All datasette.client methods accept an optional skip_permission_checks=True parameter. When set, all permission checks will be bypassed for that request, allowing access to any resource regardless of the configured permissions. This is useful for plugins and internal operations that need to access all resources without being subject to permission restrictions. Example usage: # Regular request - respects permissions response = await datasette.client.get( ""/private-db/secret-table.json"" ) # May return 403 Forbidden if access is denied # With skip_permission_checks - bypasses all permission checks response = await datasette.client.get( ""/private-db/secret-table.json"", skip_permission_checks=True, ) # Will return 200 OK and the data, regardless of permissions This parameter works with all HTTP methods ( get , post , put , patch , delete , options , head ) and the generic request method. Use skip_permission_checks=True with caution. It completely bypasses Datasette's permission system and should only be used in trusted plugin code or internal operations where you need guaranteed access to resources.",95, 759,Authenticating as an actor,"All datasette.client methods accept an optional actor= parameter. When set to a dictionary describing an actor, the request is made with a signed ds_actor cookie identifying that actor — as if the request had been made by a user who is signed in as that actor. This is a convenient shorthand equivalent to signing the cookie manually using datasette.client.actor_cookie() . Example usage: response = await datasette.client.get( ""/-/actor.json"", actor={""id"": ""root""} ) assert response.json() == {""actor"": {""id"": ""root""}} This parameter works with all HTTP methods ( get , post , put , patch , delete , options , head ) and the generic request method. Passing both actor= and a ds_actor cookie via cookies= raises a TypeError . Other unrelated cookies can be combined with actor= .",95, 758,datasette.client,"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 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 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 .",95, 757,.resolve_row(request),"request - Request object A request object This method assumes your route declares named groups for database , table and pks . It returns a ResolvedRow named tuple instance with the following fields: db - Database The database object table - string The name of the table sql - string SQL snippet that can be used in a WHERE clause to select the row params - dict Parameters that should be passed to the SQL query pks - list List of primary key column names pk_values - list List of primary key values decoded from the URL row - sqlite3.Row The row itself If the database or table cannot be found it raises a datasette.utils.asgi.DatabaseNotFound exception. If the table does not exist it raises a datasette.utils.asgi.TableNotFound exception. If the row cannot be found it raises a datasette.utils.asgi.RowNotFound exception. This has .database_name , .table and .pk_values attributes, extracted from the request path.",95, 756,.resolve_table(request),"request - Request object A request object This assumes that the regular expression for your route declares both a database and a table named group. It returns a ResolvedTable named tuple instance with the following fields: db - Database The database object table - string The name of the table (or view) is_view - boolean True if this is a view, False if it is a table If the database or table cannot be found it raises a datasette.utils.asgi.DatabaseNotFound exception. If the table does not exist it raises a datasette.utils.asgi.TableNotFound exception - a subclass of datasette.utils.asgi.NotFound with .database_name and .table attributes.",95, 755,.resolve_database(request),"request - Request object A request object If you are implementing your own custom views, you may need to resolve the database that the user is requesting based on a URL path. If the regular expression for your route declares a database named group, you can use this method to resolve the database object. This returns a Database instance. If the database cannot be found, it raises a datasette.utils.asgi.DatabaseNotFound exception - which is a subclass of datasette.utils.asgi.NotFound with a .database_name attribute set to the name of the database that was requested.",95, 754,.setting(key),"key - string The name of the setting, e.g. base_url . Returns the configured value for the specified setting . This can be a string, boolean or integer depending on the requested setting. For example: downloads_are_allowed = datasette.setting(""allow_download"")",95, 753,".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.",95, 752,".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.",95, 751,".unsign(value, namespace=""default"")","signed - any serializable type The signed string that was created using .sign(value, namespace=""default"") . namespace - string, optional The alternative namespace, if one was used. Returns the original, decoded object that was passed to .sign(value, namespace=""default"") . If the signature is not valid this raises a itsdangerous.BadSignature exception.",95, 750,".sign(value, namespace=""default"")","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 .unsign(value, namespace=""default"") .",95, 749,await .track_event(event),"event - Event An instance of a subclass of datasette.events.Event . Plugins can call this to track events, using classes they have previously registered. See Event tracking for details. The event will then be passed to all plugins that have registered to receive events using the track_event(datasette, event) hook. Example usage, assuming the plugin has previously registered the BanUserEvent class: await datasette.track_event( BanUserEvent(user={""id"": 1, ""username"": ""cleverbot""}) )",95, 748,.close(),"Release all resources held by this Datasette instance. This calls db.close() on every attached database (including the internal database), shuts down the thread pool executor used to run SQL queries, and unlinks the temporary file used to back the internal database if one was created. close() is synchronous, idempotent and one-way: after a call to close() any attempt to use the Datasette instance to execute SQL will raise a datasette.database.DatasetteClosedError exception. A closed Datasette cannot be reopened — callers that need a fresh instance should construct a new one. If a call to Database.close() on one of the attached databases raises an exception, Datasette.close() will continue trying to close the remaining databases and will re-raise the first exception after every database has been processed. When Datasette is being served over ASGI the close() method is wired up to the lifespan shutdown event, so resources are released cleanly on SIGTERM / SIGINT .",95, 747,.remove_database(name),"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.",95, 746,".add_memory_database(memory_name, name=None, route=None)","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 patterns will result in the in-memory database being served at /statistics . The name and route parameters are optional and work the same way as they do for .add_database(db, name=None, route=None) .",95, 745,".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)"" )",95, 744,"await .remove_column_type(database, resource, column)","database - string The name of the database. resource - string The name of the table or view. column - string The name of the column. Removes the column type assignment for the specified column. await datasette.remove_column_type( ""mydb"", ""mytable"", ""location"" )",95, 743,"await .set_column_type(database, resource, column, column_type, config=None)","database - string The name of the database. resource - string The name of the table or view. column - string The name of the column. column_type - string The column type name to assign, e.g. ""email"" . config - dict, optional Optional configuration dict for the column type. Assigns a column type to a column. Overwrites any existing assignment for that column. Raises ValueError if the column type declares sqlite_types and the target column does not match one of those SQLite types. await datasette.set_column_type( ""mydb"", ""mytable"", ""location"", ""point"", config={""srid"": 4326}, )",95, 742,"await .get_column_types(database, resource)","database - string The name of the database. resource - string The name of the table or view. Returns a dictionary mapping column names to ColumnType subclass instances (with .config populated) for all columns that have assigned types on the given resource. ct_map = await datasette.get_column_types(""mydb"", ""mytable"") for col_name, ct in ct_map.items(): print(col_name, ct.name, ct.config)",95, 741,"await .get_column_type(database, resource, column)","database - string The name of the database. resource - string The name of the table or view. column - string The name of the column. Returns a ColumnType subclass instance with .config populated for the specified column, or None if no column type is assigned. ct = await datasette.get_column_type( ""mydb"", ""mytable"", ""email_col"" ) if ct: print(ct.name) # ""email"" print(ct.config) # None or {...}",95, 740,Column types,Column types are stored in the column_types table in the internal database . The following methods provide the API for reading and modifying column type assignments.,95, 739,"await .remove_query(database, name, source=None)","Removes a stored query. database - string The database the query belongs to. name - string The query name. source - string, optional If provided, only a query with this source will be removed.",95, 738,"await .count_queries(database=None, ...)","Counts stored queries visible to the specified actor. This accepts the same filtering keyword arguments as await .list_queries(database=None, ...) , except for limit , cursor and include_private .",95, 737,"await .list_queries(database=None, ...)","Lists stored queries visible to the specified actor. async def list_queries( self, database=None, *, actor=None, limit=50, cursor=None, q=None, is_write=None, is_private=None, is_trusted=None, source=None, owner_id=None, include_private=False, ): ... database - string, optional Restrict results to a specific database. Omit this to list queries across all databases. actor - dictionary, optional The authenticated actor. Results are filtered using that actor's view-query permission. limit - integer, optional Number of queries to return. Values are clamped to the range 1-1000. cursor - string, optional Pagination cursor from the previous page's next value. q - string, optional Search string matched against query name, title, description and SQL. is_write , is_private , is_trusted - boolean, optional Filter by those stored query flags. source - string, optional Filter by query source. owner_id - string, optional Filter by owner actor ID. include_private - boolean, optional Set to True to populate a private boolean on each returned StoredQuery indicating if anonymous users would be unable to view that query. The return value is a StoredQueryPage dataclass instance with these attributes: queries - list of StoredQuery instances Stored queries in the same format returned by await .get_query(database, name) . next - string or None Pagination cursor for the next page, if one exists. has_more - boolean True if another page of results is available. limit - integer The limit used for this page.",95, 736,"await .get_query(database, name)","Returns a StoredQuery dataclass instance, or None if the query does not exist. StoredQuery has the following attributes: database , name , sql , title , description , description_html , hide_sql , fragment , parameters , is_write , is_private , is_trusted , source , owner_id , on_success_message , on_success_message_sql , on_success_redirect , on_error_message and on_error_redirect . parameters is a list of explicit parameter names.",95, 735,"await .update_query(database, name, ...)","Updates fields for an existing stored query. Only keyword arguments that are provided will be changed. The available keyword arguments are the same as those for await .add_query(database, name, sql, ...) , except for replace . Pass None to clear optional text fields and options such as on_success_redirect . Passing hide_sql=False removes the hide_sql option. Example: await datasette.update_query( database=""fixtures"", name=""recent_rows"", title=""Latest rows"", is_private=True, owner_id=""alice"", )",95, 734,"await .add_query(database, name, sql, ...)","Adds a stored query. async def add_query( self, database, name, sql, *, title=None, description=None, description_html=None, hide_sql=False, fragment=None, parameters=None, is_write=False, is_private=False, is_trusted=False, source=""plugin"", owner_id=None, on_success_message=None, on_success_message_sql=None, on_success_redirect=None, on_error_message=None, on_error_redirect=None, replace=True, ): ... database - string The name of the database this query should belong to. name - string The name of the stored query, used in the URL for that query. sql - string The SQL for the stored query. title - string, optional A display title for the query. description - string, optional A plain text description. description_html - string, optional An HTML description. hide_sql - boolean, optional Set to True to hide the SQL by default on the query page. fragment - string, optional A URL fragment to append to query links, for example ""chart"" . parameters - list of strings, optional Explicit parameter names for the query form. If omitted, Datasette derives parameters from the SQL. is_write - boolean, optional Set to True for writable queries. They will the run against the SQLite write connection for the database. is_private - boolean, optional Set to True for private queries. Private queries can only be viewed, updated or deleted by their owner. is_trusted - boolean, optional Set to True for trusted stored queries . source - string, optional Identifies where the query came from. Defaults to ""plugin"" . owner_id - string, optional Actor ID of the query owner, used by private query permissions. on_success_message , on_success_message_sql , on_success_redirect , on_error_message , on_error_redirect - strings, optional Options for writable queries . replace - boolean, optional Defaults to True , which replaces any existing stored query with the same database and name . Set this to False to raise a SQLite integrity error if the query already exists. Example: await datasette.add_query( database=""fixtures"", name=""recent_rows"", sql=""select * from facetable order by created desc limit 10"", title=""Recent rows"", source=""my-plugin"", )",95, 733,Stored queries,"Stored queries are stored in the queries table in the internal database . Plugins can use the following methods to add, update, list and remove stored queries.",95, 732,"await .set_column_metadata(self, database_name, resource_name, column_name, key, value)","database_name - string The database the metadata entry belongs to. resource_name - string The resource (table, view, or stored query) the metadata entry belongs to. column-name - string The column the metadata entry belongs to. key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the specified column. Any previous column-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_columns table inside the internal database .",95, 731,"await .set_resource_metadata(self, database_name, resource_name, key, value)","database_name - string The database the metadata entry belongs to. resource_name - string The resource (table, view, or stored query) the metadata entry belongs to. key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the specified ""resource"". Any previous resource-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_resources table inside the internal database .",95, 730,"await .set_database_metadata(self, database_name, key, value)","database_name - string The database the metadata entry belongs to. key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the specified database. Any previous database-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_databases table inside the internal database .",95, 729,"await .set_instance_metadata(self, key, value)","key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the entire Datasette instance. Any previous instance-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_instance table inside the internal database .",95, 728,"await .get_column_metadata(self, database_name, resource_name, column_name)","database_name - string The name of the database to query. resource_name - string The name of the resource (table, view, or stored query) inside database_name to query. column_name - string The name of the column inside resource_name to query. Returns metadata keys and values for the specified column, resource, and table as a dictionary. Internally queries the metadata_columns table inside the internal database .",95, 727,"await .get_resource_metadata(self, database_name, resource_name)","database_name - string The name of the database to query. resource_name - string The name of the resource (table, view, or stored query) inside database_name to query. Returns metadata keys and values for the specified ""resource"" as a dictionary. A ""resource"" in this context can be a table, view, or stored query. Internally queries the metadata_resources table inside the internal database .",95, 726,"await .get_database_metadata(self, database_name)","database_name - string The name of the database to query. Returns metadata keys and values for the specified database as a dictionary. Internally queries the metadata_databases table inside the internal database .",95, 725,await .get_instance_metadata(self),"Returns metadata keys and values for the entire Datasette instance as a dictionary. Internally queries the metadata_instance table inside the internal database .",95, 724,Getting and setting metadata,"Metadata about the instance, databases, tables and columns is stored in tables in Datasette's internal database . The following methods are the supported API for plugins to read and update that stored metadata.",95, 723,.get_internal_database(),Returns a database object for reading and writing to the private internal database .,95, 722,.get_database(name),"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.",95, 721,await .verify_token(token),"token - string The token string to verify. This is an async method that verifies an API token by trying each registered token handler in order. Returns an actor dictionary from the first handler that recognizes the token, or None if no handler accepts it. actor = await datasette.verify_token(token) if actor: # Token was valid print(actor[""id""])",95, 720,TokenRestrictions,"The TokenRestrictions class uses a builder pattern to specify which actions a token is allowed to perform. Import it from datasette.tokens : from datasette.tokens import TokenRestrictions restrictions = ( TokenRestrictions() .allow_all(""view-instance"") .allow_all(""view-table"") .allow_database(""docs"", ""view-query"") .allow_resource(""docs"", ""attachments"", ""insert-row"") .allow_resource(""docs"", ""attachments"", ""update-row"") ) The builder methods are: allow_all(action) - allow an action across all databases and resources allow_database(database, action) - allow an action on a specific database allow_resource(database, resource, action) - allow an action on a specific resource (table, SQL view or stored query ) within a database Each method returns the TokenRestrictions instance so calls can be chained. TokenRestrictions also provides an abbreviated(datasette) method which returns the restrictions as a dictionary using the compact format described in Restricting the actions that a token can perform , with action names replaced by their registered abbreviations. It returns the inner dictionary only - the ""_r"" wrapping key shown in that section is not included. Returns None if no restrictions are set. This is useful when writing a custom register_token_handler(datasette) that needs to embed restrictions in a token payload. For example, the following restrictions: restrictions = ( TokenRestrictions() .allow_all(""view-instance"") .allow_database(""docs"", ""view-query"") .allow_resource(""docs"", ""attachments"", ""insert-row"") ) restrictions.abbreviated(datasette) Returns this dictionary, using the abbreviations registered for each action: { ""a"": [""vi""], ""d"": {""docs"": [""vq""]}, ""r"": {""docs"": {""attachments"": [""ir""]}}, } 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 = await datasette.create_token( actor_id=""user1"", restrictions=( TokenRestrictions() .allow_all(""view-instance"") .allow_all(""view-table"") .allow_database(""docs"", ""view-query"") .allow_resource(""docs"", ""attachments"", ""insert-row"") .allow_resource(""docs"", ""attachments"", ""update-row"") ), )",95, 719,"await .create_token(actor_id, expires_after=None, restrictions=None, handler=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. restrictions - TokenRestrictions , optional A TokenRestrictions object limiting which actions the token can perform. handler - string, optional The name of a specific token handler to use. If omitted, the first registered handler is used. See register_token_handler(datasette) . This is an async method that returns an API token string which can be used to authenticate requests to the Datasette API. The default SignedTokenHandler returns tokens of the format dstok_... . 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 = await datasette.create_token( actor_id=""user1"", expires_after=3600, )",95, 718,"await .check_visibility(actor, action, resource=None)","actor - dictionary The authenticated actor. This is usually request.actor . action - string The name of the action that is being permission checked. resource - Resource object, optional The resource being checked, as a Resource object such as DatabaseResource(database=...) , TableResource(database=..., table=...) , or QueryResource(database=..., query=...) . Only some permissions apply to a resource. 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: from datasette.resources import TableResource visible, private = await datasette.check_visibility( request.actor, action=""view-table"", resource=TableResource(database=database, table=table), )",95, 717,"await .ensure_permission(action, resource=None, actor=None)","action - string The action to check. See Built-in actions for a list of available actions. resource - Resource object (optional) The resource to check the permission against. Must be an instance of InstanceResource , DatabaseResource , or TableResource from the datasette.resources module. If omitted, defaults to InstanceResource() for instance-level permissions. actor - dictionary (optional) The authenticated actor. This is usually request.actor . This is a convenience wrapper around await .allowed(*, action, resource, actor=None) that raises a datasette.Forbidden exception if the permission check fails. Use this when you want to enforce a permission check and halt execution if the actor is not authorized. Example: from datasette.resources import TableResource # Will raise Forbidden if actor cannot view the table await datasette.ensure_permission( action=""view-table"", resource=TableResource( database=""fixtures"", table=""cities"" ), actor=request.actor, ) # For instance-level actions, resource can be omitted: await datasette.ensure_permission( action=""permissions-debug"", actor=request.actor )",95, 716,"await .allowed_resources_sql(*, action, actor=None, parent=None, include_is_private=False)","Builds the SQL query that Datasette uses to determine which resources an actor may access for a specific action. Returns a (sql: str, params: dict) namedtuple that can be executed against the internal catalog_* database tables. parent can be used to limit results to a specific database, and include_is_private adds a column indicating whether anonymous users would be denied access to that resource. Plugins that need to execute custom analysis over the raw allow/deny rules can use this helper to run the same query that powers the /-/allowed debugging interface. The SQL query built by this method will return the following columns: parent : The parent resource identifier (or NULL) child : The child resource identifier (or NULL) reason : The reason from the rule that granted access is_private : (if include_is_private ) 1 if anonymous users cannot access, 0 otherwise",95, 715,"await .allowed_resources(action, actor=None, *, parent=None, include_is_private=False, include_reasons=False, limit=100, next=None)","Returns a PaginatedResources object containing resources that the actor can access for the specified action, with support for keyset pagination. action - string The action name (e.g., ""view-table"", ""view-database"") actor - dictionary, optional The authenticated actor. Defaults to None for unauthenticated requests. parent - string, optional Optional parent filter (e.g., database name) to limit results include_is_private - boolean, optional If True, adds a .private attribute to each Resource indicating whether anonymous users can access it include_reasons - boolean, optional If True, adds a .reasons attribute with a list of strings describing why access was granted (useful for debugging) limit - integer, optional Maximum number of results to return per page (1-1000, default 100) next - string, optional Keyset token from a previous page for pagination The method returns a PaginatedResources object (from datasette.utils ) with the following attributes: resources - list List of Resource objects for the current page next - string or None Token for the next page, or None if no more results exist Example usage: # Get first page of tables page = await datasette.allowed_resources( ""view-table"", actor=request.actor, parent=""fixtures"", limit=50, ) for table in page.resources: print(table.parent, table.child) if hasattr(table, ""private""): print(f"" Private: {table.private}"") # Get next page if available if page.next: next_page = await datasette.allowed_resources( ""view-table"", actor=request.actor, next=page.next ) # Iterate through all results automatically page = await datasette.allowed_resources( ""view-table"", actor=request.actor ) async for table in page.all(): print(table.parent, table.child) # With reasons for debugging page = await datasette.allowed_resources( ""view-table"", actor=request.actor, include_reasons=True ) for table in page.resources: print(f""{table.child}: {table.reasons}"") The page.all() async generator automatically handles pagination, fetching additional pages and yielding all resources one at a time. This method uses await .allowed_resources_sql(*, action, actor=None, parent=None, include_is_private=False) under the hood and is an efficient way to list the databases, tables or other resources that an actor can access for a specific action.",95, 714,"await .allowed_many(*, actions, resource, actor=None)","actions - list of strings The names of the actions to permission check. resource - Resource object A Resource object representing the database, table, or other resource that each action is checked against. Omit for global actions. actor - dictionary, optional The authenticated actor. This is usually request.actor . Defaults to None for unauthenticated requests. Checks several actions against the same resource for the same actor, returning a dictionary mapping each action name to True or False . The whole batch - including any actions pulled in through also_requires dependencies - is resolved with a single SQL query against the internal database, so this is much faster than calling datasette.allowed() once per action. Example usage: from datasette.resources import TableResource results = await datasette.allowed_many( actions=[""insert-row"", ""delete-row"", ""drop-table""], resource=TableResource( database=""fixtures"", table=""facetable"" ), actor=request.actor, ) # {""insert-row"": True, ""delete-row"": True, ""drop-table"": False} Each result is stored in the per-request permission check cache, so subsequent datasette.allowed() calls for the same checks within the same request are served from that cache. Datasette uses this before running the table_actions and database_actions plugin hooks: it resolves every registered table-level action against the current table and every database-level action against its database first, which means allowed() calls made by those plugin hooks are usually served from the cache instead of triggering additional queries. Actions for which no plugin provides any permission rules are resolved to False directly, without being included in the SQL query at all.",95, 713,"await .allowed(*, action, resource, actor=None)","action - string The name of the action that is being permission checked. resource - Resource object A Resource object representing the database, table, or other resource. Must be an instance of a Resource class such as TableResource , DatabaseResource , QueryResource , or InstanceResource . actor - dictionary, optional The authenticated actor. This is usually request.actor . Defaults to None for unauthenticated requests. This method checks if the given actor has permission to perform the given action on the given resource. All parameters must be passed as keyword arguments. Example usage: from datasette.resources import ( TableResource, DatabaseResource, ) # Check if actor can view a specific table can_view = await datasette.allowed( action=""view-table"", resource=TableResource( database=""fixtures"", table=""facetable"" ), actor=request.actor, ) # Check if actor can execute SQL on a database can_execute = await datasette.allowed( action=""execute-sql"", resource=DatabaseResource(database=""fixtures""), actor=request.actor, ) The method returns True if the permission is granted, False if denied. Results are cached for the duration of the current request, so checking the same (actor, action, resource) combination twice within one request only does the underlying permission resolution work once.",95, 712,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""} }",95, 711,".static(path, plugin=None, mount=None)","path - string The path to the static asset, relative to the selected static directory. plugin - string, optional The plugin name, for linking to an asset in that plugin's static/ directory. mount - string, optional The --static mount name, for linking to an asset in a directory mounted using datasette --static mount_name:directory . Returns a URL for a static asset with a ?_hash= parameter based on the file contents. That URL takes the base_url setting into account. When the ?_hash= parameter matches the current file contents, Datasette will serve the asset with Cache-Control: max-age=31536000, immutable, public . Call this with just path for one of Datasette's bundled static assets: datasette.static(""app.css"") Use plugin= for plugin static assets: datasette.static( ""plugin.js"", plugin=""datasette_plugin_name"" ) Use mount= for static directories mounted using the --static option: datasette.static(""styles.css"", mount=""assets"") plugin and mount are mutually exclusive. The same feature is available to Jinja templates as the static() template function, described in Linking to static assets .",95, 710,"await .render_template(template, context=None, request=None)","template - string, list of strings or jinja2.Template The 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.",95, 709,".plugin_config(plugin_name, database=None, table=None)","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 datasette.yaml . See Writing plugins that accept 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 .",95, 708,.actions,"Property exposing a dictionary of actions that have been registered using the register_actions(datasette) plugin hook. The dictionary keys are the action names - e.g. view-instance - and the values are Action() objects describing the permission.",95, 707,.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.",95, 706,Datasette class,"This 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 open immutables=[...] - a list of database files to open in immutable mode metadata={...} - a dictionary of Metadata config_dir=... - the configuration directory to use, stored in datasette.config_dir",95, 705,Setting cookies with response.set_cookie(),"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 datasette.sign() to set signed cookies. Here's how you would set the ds_actor cookie for use with Datasette authentication : response = Response.redirect(""/"") response.set_cookie( ""ds_actor"", datasette.sign({""a"": {""id"": ""cleopaws""}}, ""actor""), ) return response",95, 704,Returning a response with .asgi_send(send),"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 asgi_wrapper(datasette) 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)",95, 703,Response class,"The Response class can be returned from view functions that have been registered using the register_routes(datasette) 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( ""This is XML"", content_type=""application/xml; charset=utf-8"", ) The quickest way to create responses is using the Response.text(...) , Response.html(...) , Response.json(...) , Response.error(...) 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"" ) # A JSON error in Datasette's standard error format: error_response = Response.error(""Cannot do that"", 400) # 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. Response.error(messages, status=400) returns a JSON error in the standard Datasette error format . messages can be a single string or a list of strings. Use this for JSON-only endpoints; if your error should content-negotiate between JSON and HTML, raise Forbidden , NotFound , BadRequest or DatasetteError instead and Datasette's error handling will build the appropriate response.",95, 702,The UploadedFile class,"When parsing multipart form data with files=True , file uploads are returned as UploadedFile objects with the following properties and methods: uploaded_file.name - string The form field name. uploaded_file.filename - string The original filename provided by the client. Note: This is sanitized to remove path components for security. uploaded_file.content_type - string or None The MIME type of the uploaded file, if provided by the client. uploaded_file.size - integer The size of the uploaded file in bytes. await uploaded_file.read(size=-1) - bytes Read and return up to size bytes from the file. If size is -1 (default), read the entire file. await uploaded_file.seek(offset, whence=0) - integer Seek to the given position in the file. Returns the new position. await uploaded_file.close() Close the underlying file. This is called automatically when the object is garbage collected. Files smaller than 1MB are stored in memory. Larger files are automatically spilled to temporary files on disk and cleaned up when the request completes. Example: form = await request.form(files=True) uploaded = form[""document""] # Check file metadata print(f""Filename: {uploaded.filename}"") print(f""Content-Type: {uploaded.content_type}"") print(f""Size: {uploaded.size} bytes"") # Read file content content = await uploaded.read() # Or read in chunks await uploaded.seek(0) while chunk := await uploaded.read(8192): process_chunk(chunk)",95, 701,The FormData class,"await request.form() returns a FormData object - a dictionary-like object which provides access to form fields and uploaded files. It has a similar interface to MultiParams . form[key] - string or UploadedFile Returns the first value for that key, or raises a KeyError if the key is missing. form.get(key) - string, UploadedFile, or None Returns the first value for that key, or None if the key is missing. Pass a second argument to specify a different default. form.getlist(key) - list Returns the list of values for that key. If the key is missing an empty list will be returned. form.keys() - list of strings Returns the list of available keys. key in form - True or False You can use if key in form to check if a key is present. for key in form - iterator This lets you loop through every available key. len(form) - integer Returns the total number of submitted values.",95, 700,The MultiParams class,"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 KeyError if the key is missing. For the above example request.args[""foo""] would return ""1"" . request.args.get(key) - string or None Returns the first value for that key, or None if 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.args to 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.",95, 699,Request object,"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 GET or POST . .url - string The full URL for this request, e.g. https://latest.datasette.io/fixtures . .scheme - string The request scheme - usually https or http . .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.io or localhost . .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 register_routes(datasette) . .actor - dictionary (str -> Any) or None The currently authenticated actor (see actors ), or None if the request is unauthenticated. .max_post_body_bytes - integer The maximum number of bytes await request.post_body() will read into memory, or 0 for no limit. Set from the max_post_body_bytes setting (default 2MB) for requests created by Datasette. Can be passed to the Request constructor as a keyword argument. The object also has the following awaitable methods: await request.form(files=False, ...) - FormData Parses form data from the request body. Supports both application/x-www-form-urlencoded and multipart/form-data content types. Returns a The FormData class object with dict-like access to form fields and uploaded files. Requirements and errors: A Content-Type header is required. Missing or unsupported content types raise BadRequest . For multipart/form-data , the boundary=... parameter is required. Parameters: files (bool, default False ): If True , uploaded files are stored and accessible. If False (default), file content is discarded but form fields are still available. max_file_size (int, default 50MB): Maximum size per uploaded file in bytes. max_request_size (int, default 100MB): Maximum total request body size in bytes. max_fields (int, default 1000): Maximum number of form fields. max_files (int, default 100): Maximum number of uploaded files. max_parts (int, default max_fields + max_files ): Maximum number of multipart parts in total. max_field_size (int, default 100KB): Maximum size of a text field value in bytes. max_memory_file_size (int, default 1MB): File size threshold before uploads spill to disk. max_part_header_bytes (int, default 16KB): Maximum total bytes allowed in part headers. max_part_header_lines (int, default 100): Maximum header lines per part. min_free_disk_bytes (int, default 50MB): Minimum free bytes required in the temp directory before accepting file uploads. Example usage: # Parse form fields only (files are discarded) form = await request.form() username = form[""username""] tags = form.getlist(""tags"") # For multiple values # Parse form fields AND files form = await request.form(files=True) uploaded = form[""avatar""] content = await uploaded.read() print( uploaded.filename, uploaded.content_type, uploaded.size ) Cleanup note: When using files=True , call await form.aclose() once you are done with the uploads to ensure spooled temporary files are closed promptly. You can also use async with form: ... for automatic cleanup. Don't forget to read about CSRF protection ! await request.post_vars() - dictionary Returns a dictionary of form variables that were submitted in the request body via POST using application/x-www-form-urlencoded encoding. For multipart forms or file uploads, use request.form() instead. await request.json() - Any Returns the parsed JSON body of a request submitted by POST . await request.post_body(max_bytes=None) - bytes Returns the un-parsed body of a request submitted by POST - useful for things like incoming JSON data. The body is read fully into memory, capped at request.max_post_body_bytes - which Datasette sets from the max_post_body_bytes setting (default 2MB). Bodies that exceed the limit raise a datasette.PayloadTooLarge exception, which Datasette turns into an HTTP 413 error response. Pass max_bytes= to override the limit for a specific call, or max_bytes=0 to disable it. request.post_vars() and request.json() read the body through this method, so the same limit applies to them. 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 Request instance 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'}}}",95, 698,Internals for plugins,Many 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.,95, 697,0.8 (2017-11-13),"V0.8 - added PyPI metadata, ready to ship. Implemented offset/limit pagination for views ( #70 ). Improved pagination. ( #78 ) Limit on max rows returned, controlled by --max_returned_rows option. ( #69 ) If someone executes 'select * from table' against a table with a million rows in it, we could run into problems: just serializing that much data as JSON is likely to lock up the server. Solution: we now have a hard limit on the maximum number of rows that can be returned by a query. If that limit is exceeded, the server will return a ""truncated"": true field in the JSON. This limit can be optionally controlled by the new --max_returned_rows option. Setting that option to 0 disables the limit entirely.",95, 696,0.9 (2017-11-13),"Added --sql_time_limit_ms and --extra-options . The serve command now accepts --sql_time_limit_ms for customizing the SQL time limit. The publish and package commands now accept --extra-options which can be used to specify additional options to be passed to the datasite serve command when it executes inside the resulting Docker containers.",95, 695,0.10 (2017-11-14),"Fixed #83 - 500 error on individual row pages. Stop using sqlite WITH RECURSIVE in our tests. The version of Python 3 running in Travis CI doesn't support this.",95, 694,0.11 (2017-11-14),"Added datasette publish now --force option. This calls now with --force - useful as it means you get a fresh copy of datasette even if Now has already cached that docker layer. Enable --cors by default when running in a container.",95, 693,0.12 (2017-11-16),"Added __version__ , now displayed as tooltip in page footer ( #108 ). Added initial docs, including a changelog ( #99 ). Turned on auto-escaping in Jinja. Added a UI for editing named parameters ( #96 ). You can now construct a custom SQL statement using SQLite named parameters (e.g. :name ) and datasette will display form fields for editing those parameters. Here’s an example which lets you see the most popular names for dogs of different species registered through various dog registration schemes in Australia. Pin to specific Jinja version. ( #100 ). Default to 127.0.0.1 not 0.0.0.0. ( #98 ). Added extra metadata options to publish and package commands. ( #92 ). You can now run these commands like so: datasette now publish mydb.db \ --title=""My Title"" \ --source=""Source"" \ --source_url=""http://www.example.com/"" \ --license=""CC0"" \ --license_url=""https://creativecommons.org/publicdomain/zero/1.0/"" This will write those values into the metadata.json that is packaged with the app. If you also pass --metadata=metadata.json that file will be updated with the extra values before being written into the Docker image. Added production-ready Dockerfile ( #94 ) [Andrew Cutler] New ?_sql_time_limit_ms=10 argument to database and table page ( #95 ) SQL syntax highlighting with Codemirror ( #89 ) [Tom Dyson]",95, 692,0.13 (2017-11-24),"Search now applies to current filters. Combined search into the same form as filters. Closes #133 Much tidier design for table view header. Closes #147 Added ?column__not=blah filter. Closes #148 Row page now resolves foreign keys. Closes #132 Further tweaks to select/input filter styling. Refs #86 - thanks for the help, @natbat! Show linked foreign key in table cells. Added UI for editing table filters. Refs #86 Hide FTS-created tables on index pages. Closes #129 Add publish to heroku support [Jacob Kaplan-Moss] datasette publish heroku mydb.db Pull request #104 Initial implementation of ?_group_count=column . URL shortcut for counting rows grouped by one or more columns. ?_group_count=column1&_group_count=column2 works as well. SQL generated looks like this: select ""qSpecies"", count(*) as ""count"" from Street_Tree_List group by ""qSpecies"" order by ""count"" desc limit 100 Or for two columns like this: select ""qSpecies"", ""qSiteInfo"", count(*) as ""count"" from Street_Tree_List group by ""qSpecies"", ""qSiteInfo"" order by ""count"" desc limit 100 Refs #44 Added --build=master option to datasette publish and package. The datasette publish and datasette package commands both now accept an optional --build argument. If provided, this can be used to specify a branch published to GitHub that should be built into the container. This makes it easier to test code that has not yet been officially released to PyPI, e.g.: datasette publish now mydb.db --branch=master Implemented ?_search=XXX + UI if a FTS table is detected. Closes #131 Added datasette --version support. Table views now show expanded foreign key references, if possible. If a table has foreign key columns, and those foreign key tables have label_columns , the TableView will now query those other tables for the corresponding values and display those values as links in the corresponding table cells. label_columns are currently detected by the inspect() function, which looks for any table that has just two columns - an ID column and one other - and sets the label_column to be that second non-ID column. Don't prevent tabbing to ""Run SQL"" button ( #117 ) [Robert Gieseke] See comment in #115 Add keyboard shortcut to execute SQL query ( #115 ) [Robert Gieseke] Allow --load-extension to be set via environment variable. Add support for ?field__isnull=1 ( #107 ) [Ray N] Add spatialite, switch to debian and local build ( #114 ) [Ariel Núñez] Added --load-extension argument to datasette serve. Allows loading of SQLite extensions. Refs #110 .",95,