{"id": "getting_started:getting-started-tutorial", "page": "getting_started", "ref": "getting-started-tutorial", "title": "Follow a tutorial", "content": "Datasette has several tutorials to help you get started with the tool. Try one of the following: \n \n \n Exploring a database with Datasette shows how to use the Datasette web interface to explore a new database. \n \n \n Learn SQL with Datasette introduces SQL, and shows how to use that query language to ask questions of your data. \n \n \n Cleaning data with sqlite-utils and Datasette guides you through using sqlite-utils to turn a CSV file into a database that you can explore using Datasette.", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://datasette.io/tutorials\", \"label\": \"tutorials\"}, {\"href\": \"https://datasette.io/tutorials/explore\", \"label\": \"Exploring a database with Datasette\"}, {\"href\": \"https://datasette.io/tutorials/learn-sql\", \"label\": \"Learn SQL with Datasette\"}, {\"href\": \"https://datasette.io/tutorials/clean-data\", \"label\": \"Cleaning data with sqlite-utils and Datasette\"}, {\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}]"} {"id": "getting_started:getting-started-glitch", "page": "getting_started", "ref": "getting-started-glitch", "title": "Try Datasette without installing anything using Glitch", "content": "Glitch is a free online tool for building web apps directly from your web browser. You can use Glitch to try out Datasette without needing to install any software on your own computer. \n Here's a demo project on Glitch which you can use as the basis for your own experiments: \n glitch.com/~datasette-csvs \n Glitch allows you to \"remix\" any project to create your own copy and start editing it in your browser. You can remix the datasette-csvs project by clicking this button: \n \n Find a CSV file and drag it onto the Glitch file explorer panel - datasette-csvs will automatically convert it to a SQLite database (using sqlite-utils ) and allow you to start exploring it using Datasette. \n If your CSV file has a latitude and longitude column you can visualize it on a map by uncommenting the datasette-cluster-map line in the requirements.txt file using the Glitch file editor. \n Need some data? Try this Public Art Data for the city of Seattle - hit \"Export\" and select \"CSV\" to download it as a CSV file. \n For more on how this works, see Running Datasette on Glitch .", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://glitch.com/\", \"label\": \"Glitch\"}, {\"href\": \"https://glitch.com/~datasette-csvs\", \"label\": \"glitch.com/~datasette-csvs\"}, {\"href\": \"https://glitch.com/edit/#!/remix/datasette-csvs\", \"label\": null}, {\"href\": \"https://github.com/simonw/sqlite-utils\", \"label\": \"sqlite-utils\"}, {\"href\": \"https://data.seattle.gov/Community/Public-Art-Data/j7sn-tdzk\", \"label\": \"Public Art Data\"}, {\"href\": \"https://simonwillison.net/2019/Apr/23/datasette-glitch/\", \"label\": \"Running Datasette on Glitch\"}]"} {"id": "getting_started:getting-started-demo", "page": "getting_started", "ref": "getting-started-demo", "title": "Play with a live demo", "content": "The best way to experience Datasette for the first time is with a demo: \n \n \n global-power-plants.datasettes.com provides a searchable database of power plants around the world, using data from the World Resources Institude rendered using the datasette-cluster-map plugin. \n \n \n fivethirtyeight.datasettes.com shows Datasette running against over 400 datasets imported from the FiveThirtyEight GitHub repository .", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://global-power-plants.datasettes.com/global-power-plants/global-power-plants\", \"label\": \"global-power-plants.datasettes.com\"}, {\"href\": \"https://www.wri.org/publication/global-power-plant-database\", \"label\": \"World Resources Institude\"}, {\"href\": \"https://github.com/simonw/datasette-cluster-map\", \"label\": \"datasette-cluster-map\"}, {\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight\", \"label\": \"fivethirtyeight.datasettes.com\"}, {\"href\": \"https://github.com/fivethirtyeight/data\", \"label\": \"FiveThirtyEight GitHub repository\"}]"} {"id": "getting_started:getting-started-datasette-lite", "page": "getting_started", "ref": "getting-started-datasette-lite", "title": "Datasette in your browser with Datasette Lite", "content": "Datasette Lite is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. \n You can pass a URL to a CSV, SQLite or raw SQL file directly to Datasette Lite to explore that data in your browser. \n This example link opens Datasette Lite and loads the SQL Murder Mystery example database from Northwestern University Knight Lab .", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://lite.datasette.io/\", \"label\": \"Datasette Lite\"}, {\"href\": \"https://lite.datasette.io/?url=https%3A%2F%2Fraw.githubusercontent.com%2FNUKnightLab%2Fsql-mysteries%2Fmaster%2Fsql-murder-mystery.db#/sql-murder-mystery\", \"label\": \"example link\"}, {\"href\": \"https://github.com/NUKnightLab/sql-mysteries\", \"label\": \"Northwestern University Knight Lab\"}]"} {"id": "getting_started:getting-started", "page": "getting_started", "ref": "getting-started", "title": "Getting started", "content": "", "breadcrumbs": "[]", "references": "[]"} {"id": "full_text_search:id1", "page": "full_text_search", "ref": "id1", "title": "Full-text search", "content": "SQLite includes a powerful mechanism for enabling full-text search against SQLite records. Datasette can detect if a table has had full-text search configured for it in the underlying database and display a search interface for filtering that table. \n Here's an example search : \n \n Datasette automatically detects which tables have been configured for full-text search.", "breadcrumbs": "[]", "references": "[{\"href\": \"https://www.sqlite.org/fts3.html\", \"label\": \"a powerful mechanism for enabling full-text search\"}, {\"href\": \"https://register-of-members-interests.datasettes.com/regmem/items?_search=hamper&_sort_desc=date\", \"label\": \"an example search\"}]"} {"id": "full_text_search:full-text-search-table-view-api", "page": "full_text_search", "ref": "full-text-search-table-view-api", "title": "The table page and table view API", "content": "Table views that support full-text search can be queried using the ?_search=TERMS query string parameter. This will run the search against content from all of the columns that have been included in the index. \n Try this example: fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort \n SQLite full-text search supports wildcards. This means you can easily implement prefix auto-complete by including an asterisk at the end of the search term - for example: \n /dbname/tablename/?_search=rob* \n This will return all records containing at least one word that starts with the letters rob . \n You can also run searches against just the content of a specific named column by using _search_COLNAME=TERMS - for example, this would search for just rows where the name column in the FTS index mentions Sarah : \n /dbname/tablename/?_search_name=Sarah", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort\", \"label\": \"fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort\"}]"} {"id": "full_text_search:full-text-search-table-or-view", "page": "full_text_search", "ref": "full-text-search-table-or-view", "title": "Configuring full-text search for a table or view", "content": "If a table has a corresponding FTS table set up using the content= argument to CREATE VIRTUAL TABLE shown below, Datasette will detect it automatically and add a search interface to the table page for that table. \n You can also manually configure which table should be used for full-text search using query string parameters or Metadata . You can set the associated FTS table for a specific table and you can also set one for a view - if you do that, the page for that SQL view will offer a search option. \n Use ?_fts_table=x to over-ride the FTS table for a specific page. If the primary key was something other than rowid you can use ?_fts_pk=col to set that as well. This is particularly useful for views, for example: \n https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk \n The fts_table metadata property can be used to specify an associated FTS table. If the primary key column in your table which was used to populate the FTS table is something other than rowid , you can specify the column to use with the fts_pk property. \n The \"searchmode\": \"raw\" property can be used to default the table to accepting SQLite advanced search operators, as described in Advanced SQLite search queries . \n Here is an example which enables full-text search (with SQLite advanced search operators) for a display_ads view which is defined against the ads table and hence needs to run FTS against the ads_fts table, using the id as the primary key: \n {\n \"databases\": {\n \"russian-ads\": {\n \"tables\": {\n \"display_ads\": {\n \"fts_table\": \"ads_fts\",\n \"fts_pk\": \"id\",\n \"searchmode\": \"raw\"\n }\n }\n }\n }\n}", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk\", \"label\": \"https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk\"}]"} {"id": "full_text_search:full-text-search-fts-versions", "page": "full_text_search", "ref": "full-text-search-fts-versions", "title": "FTS versions", "content": "There are three different versions of the SQLite FTS module: FTS3, FTS4 and FTS5. You can tell which versions are supported by your instance of Datasette by checking the /-/versions page. \n FTS5 is the most advanced module but may not be available in the SQLite version that is bundled with your Python installation. Most importantly, FTS5 is the only version that has the ability to order by search relevance without needing extra code. \n If you can't be sure that FTS5 will be available, you should use FTS4.", "breadcrumbs": "[\"Full-text search\"]", "references": "[]"} {"id": "full_text_search:full-text-search-enabling", "page": "full_text_search", "ref": "full-text-search-enabling", "title": "Enabling full-text search for a SQLite table", "content": "Datasette takes advantage of the external content mechanism in SQLite, which allows a full-text search virtual table to be associated with the contents of another SQLite table. \n To set up full-text search for a table, you need to do two things: \n \n \n Create a new FTS virtual table associated with your table \n \n \n Populate that FTS table with the data that you would like to be able to run searches against", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://www.sqlite.org/fts3.html#_external_content_fts4_tables_\", \"label\": \"external content\"}]"} {"id": "full_text_search:full-text-search-custom-sql", "page": "full_text_search", "ref": "full-text-search-custom-sql", "title": "Searches using custom SQL", "content": "You can include full-text search results in custom SQL queries. The general pattern with SQLite search is to run the search as a sub-select that returns rowid values, then include those rowids in another part of the query. \n You can see the syntax for a basic search by running that search on a table page and then clicking \"View and edit SQL\" to see the underlying SQL. For example, consider this search for manafort is the US FARA database : \n /fara/FARA_All_ShortForms?_search=manafort \n If you click View and edit SQL you'll see that the underlying SQL looks like this: \n select\n rowid,\n Short_Form_Termination_Date,\n Short_Form_Date,\n Short_Form_Last_Name,\n Short_Form_First_Name,\n Registration_Number,\n Registration_Date,\n Registrant_Name,\n Address_1,\n Address_2,\n City,\n State,\n Zip\nfrom\n FARA_All_ShortForms\nwhere\n rowid in (\n select\n rowid\n from\n FARA_All_ShortForms_fts\n where\n FARA_All_ShortForms_fts match escape_fts(:search)\n )\norder by\n rowid\nlimit\n 101", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort\", \"label\": \"manafort is the US FARA database\"}, {\"href\": \"https://fara.datasettes.com/fara?sql=select%0D%0A++rowid%2C%0D%0A++Short_Form_Termination_Date%2C%0D%0A++Short_Form_Date%2C%0D%0A++Short_Form_Last_Name%2C%0D%0A++Short_Form_First_Name%2C%0D%0A++Registration_Number%2C%0D%0A++Registration_Date%2C%0D%0A++Registrant_Name%2C%0D%0A++Address_1%2C%0D%0A++Address_2%2C%0D%0A++City%2C%0D%0A++State%2C%0D%0A++Zip%0D%0Afrom%0D%0A++FARA_All_ShortForms%0D%0Awhere%0D%0A++rowid+in+%28%0D%0A++++select%0D%0A++++++rowid%0D%0A++++from%0D%0A++++++FARA_All_ShortForms_fts%0D%0A++++where%0D%0A++++++FARA_All_ShortForms_fts+match+escape_fts%28%3Asearch%29%0D%0A++%29%0D%0Aorder+by%0D%0A++rowid%0D%0Alimit%0D%0A++101&search=manafort\", \"label\": \"View and edit SQL\"}]"} {"id": "full_text_search:full-text-search-advanced-queries", "page": "full_text_search", "ref": "full-text-search-advanced-queries", "title": "Advanced SQLite search queries", "content": "SQLite full-text search includes support for a variety of advanced queries , including AND , OR , NOT and NEAR . \n By default Datasette disables these features to ensure they do not cause errors or confusion for users who are not aware of them. You can disable this escaping and use the advanced queries by adding &_searchmode=raw to the table page query string. \n If you want to enable these operators by default for a specific table, you can do so by adding \"searchmode\": \"raw\" to the metadata configuration for that table, see Configuring full-text search for a table or view . \n If that option has been specified in the table metadata but you want to over-ride it and return to the default behavior you can append &_searchmode=escaped to the query string.", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://www.sqlite.org/fts5.html#full_text_query_syntax\", \"label\": \"a variety of advanced queries\"}]"} {"id": "full_text_search:configuring-fts-using-sqlite-utils", "page": "full_text_search", "ref": "configuring-fts-using-sqlite-utils", "title": "Configuring FTS using sqlite-utils", "content": "sqlite-utils is a CLI utility and Python library for manipulating SQLite databases. You can use it from Python code to configure FTS search, or you can achieve the same goal using the accompanying command-line tool . \n Here's how to use sqlite-utils to enable full-text search for an items table across the name and description columns: \n $ sqlite-utils enable-fts mydatabase.db items name description", "breadcrumbs": "[\"Full-text search\", \"Enabling full-text search for a SQLite table\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}, {\"href\": \"https://sqlite-utils.datasette.io/en/latest/python-api.html#enabling-full-text-search\", \"label\": \"it from Python code\"}, {\"href\": \"https://sqlite-utils.datasette.io/en/latest/cli.html#configuring-full-text-search\", \"label\": \"using the accompanying command-line tool\"}]"} {"id": "full_text_search:configuring-fts-using-csvs-to-sqlite", "page": "full_text_search", "ref": "configuring-fts-using-csvs-to-sqlite", "title": "Configuring FTS using csvs-to-sqlite", "content": "If your data starts out in CSV files, you can use Datasette's companion tool csvs-to-sqlite to convert that file into a SQLite database and enable full-text search on specific columns. For a file called items.csv where you want full-text search to operate against the name and description columns you would run the following: \n $ csvs-to-sqlite items.csv items.db -f name -f description", "breadcrumbs": "[\"Full-text search\", \"Enabling full-text search for a SQLite table\"]", "references": "[{\"href\": \"https://github.com/simonw/csvs-to-sqlite\", \"label\": \"csvs-to-sqlite\"}]"} {"id": "full_text_search:configuring-fts-by-hand", "page": "full_text_search", "ref": "configuring-fts-by-hand", "title": "Configuring FTS by hand", "content": "We recommend using sqlite-utils , but if you want to hand-roll a SQLite full-text search table you can do so using the following SQL. \n To enable full-text search for a table called items that works against the name and description columns, you would run this SQL to create a new items_fts FTS virtual table: \n CREATE VIRTUAL TABLE \"items_fts\" USING FTS4 (\n name,\n description,\n content=\"items\"\n); \n This creates a set of tables to power full-text search against items . The new items_fts table will be detected by Datasette as the fts_table for the items table. \n Creating the table is not enough: you also need to populate it with a copy of the data that you wish to make searchable. You can do that using the following SQL: \n INSERT INTO \"items_fts\" (rowid, name, description)\n SELECT rowid, name, description FROM items; \n If your table has columns that are foreign key references to other tables you can include that data in your full-text search index using a join. Imagine the items table has a foreign key column called category_id which refers to a categories table - you could create a full-text search table like this: \n CREATE VIRTUAL TABLE \"items_fts\" USING FTS4 (\n name,\n description,\n category_name,\n content=\"items\"\n); \n And then populate it like this: \n INSERT INTO \"items_fts\" (rowid, name, description, category_name)\n SELECT items.rowid,\n items.name,\n items.description,\n categories.name\n FROM items JOIN categories ON items.category_id=categories.id; \n You can use this technique to populate the full-text search index from any combination of tables and joins that makes sense for your project.", "breadcrumbs": "[\"Full-text search\", \"Enabling full-text search for a SQLite table\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}]"} {"id": "facets:suggested-facets", "page": "facets", "ref": "suggested-facets", "title": "Suggested facets", "content": "Datasette's table UI will suggest facets for the user to apply, based on the following criteria: \n For the currently filtered data are there any columns which, if applied as a facet... \n \n \n Will return 30 or less unique options \n \n \n Will return more than one unique option \n \n \n Will return less unique options than the total number of filtered rows \n \n \n And the query used to evaluate this criteria can be completed in under 50ms \n \n \n That last point is particularly important: Datasette runs a query for every column that is displayed on a page, which could get expensive - so to avoid slow load times it sets a time limit of just 50ms for each of those queries.\n This means suggested facets are unlikely to appear for tables with millions of records in them.", "breadcrumbs": "[\"Facets\"]", "references": "[]"} {"id": "facets:speeding-up-facets-with-indexes", "page": "facets", "ref": "speeding-up-facets-with-indexes", "title": "Speeding up facets with indexes", "content": "The performance of facets can be greatly improved by adding indexes on the columns you wish to facet by.\n Adding indexes can be performed using the sqlite3 command-line utility. Here's how to add an index on the state column in a table called Food_Trucks : \n $ sqlite3 mydatabase.db\nSQLite version 3.19.3 2017-06-27 16:48:08\nEnter \".help\" for usage hints.\nsqlite> CREATE INDEX Food_Trucks_state ON Food_Trucks(\"state\"); \n Or using the sqlite-utils command-line utility: \n $ sqlite-utils create-index mydatabase.db Food_Trucks state", "breadcrumbs": "[\"Facets\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/en/stable/cli.html#creating-indexes\", \"label\": \"sqlite-utils\"}]"} {"id": "facets:id3", "page": "facets", "ref": "id3", "title": "Facet by date", "content": "If Datasette finds any columns that contain dates in the first 100 values, it will offer a faceting interface against the dates of those values.\n This works especially well against timestamp values such as 2019-03-01 12:44:00 . \n Example here: latest.datasette.io/fixtures/facetable?_facet_date=created", "breadcrumbs": "[\"Facets\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/facetable?_facet_date=created\", \"label\": \"latest.datasette.io/fixtures/facetable?_facet_date=created\"}]"} {"id": "facets:id2", "page": "facets", "ref": "id2", "title": "Facet by JSON array", "content": "If your SQLite installation provides the json1 extension (you can check using /-/versions ) Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns. \n This is useful for modelling things like tags without needing to break them out into a new table. \n Example here: latest.datasette.io/fixtures/facetable?_facet_array=tags", "breadcrumbs": "[\"Facets\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/facetable?_facet_array=tags\", \"label\": \"latest.datasette.io/fixtures/facetable?_facet_array=tags\"}]"} {"id": "facets:id1", "page": "facets", "ref": "id1", "title": "Facets", "content": "Datasette facets can be used to add a faceted browse interface to any database table.\n With facets, tables are displayed along with a summary showing the most common values in specified columns.\n These values can be selected to further filter the table. \n Here's an example : \n \n Facets can be specified in two ways: using query string parameters, or in metadata.json configuration for the table.", "breadcrumbs": "[]", "references": "[{\"href\": \"https://congress-legislators.datasettes.com/legislators/legislator_terms?_facet=type&_facet=party&_facet=state&_facet_size=10\", \"label\": \"an example\"}]"} {"id": "facets:facets-metadata", "page": "facets", "ref": "facets-metadata", "title": "Facets in metadata.json", "content": "You can turn facets on by default for specific tables by adding them to a \"facets\" key in a Datasette Metadata file. \n Here's an example that turns on faceting by default for the qLegalStatus column in the Street_Tree_List table in the sf-trees database: \n {\n \"databases\": {\n \"sf-trees\": {\n \"tables\": {\n \"Street_Tree_List\": {\n \"facets\": [\"qLegalStatus\"]\n }\n }\n }\n }\n} \n Facets defined in this way will always be shown in the interface and returned in the API, regardless of the _facet arguments passed to the view. \n You can specify array or date facets in metadata using JSON objects with a single key of array or date and a value specifying the column, like this: \n {\n \"facets\": [\n {\"array\": \"tags\"},\n {\"date\": \"created\"}\n ]\n} \n You can change the default facet size (the number of results shown for each facet) for a table using facet_size : \n {\n \"databases\": {\n \"sf-trees\": {\n \"tables\": {\n \"Street_Tree_List\": {\n \"facets\": [\"qLegalStatus\"],\n \"facet_size\": 10\n }\n }\n }\n }\n}", "breadcrumbs": "[\"Facets\"]", "references": "[]"} {"id": "facets:facets-in-query-strings", "page": "facets", "ref": "facets-in-query-strings", "title": "Facets in query strings", "content": "To turn on faceting for specific columns on a Datasette table view, add one or more _facet=COLUMN parameters to the URL.\n For example, if you want to turn on facets for the city_id and state columns, construct a URL that looks like this: \n /dbname/tablename?_facet=state&_facet=city_id \n This works for both the HTML interface and the .json view.\n When enabled, facets will cause a facet_results block to be added to the JSON output, looking something like this: \n {\n \"state\": {\n \"name\": \"state\",\n \"results\": [\n {\n \"value\": \"CA\",\n \"label\": \"CA\",\n \"count\": 10,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=CA\",\n \"selected\": false\n },\n {\n \"value\": \"MI\",\n \"label\": \"MI\",\n \"count\": 4,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=MI\",\n \"selected\": false\n },\n {\n \"value\": \"MC\",\n \"label\": \"MC\",\n \"count\": 1,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=MC\",\n \"selected\": false\n }\n ],\n \"truncated\": false\n }\n \"city_id\": {\n \"name\": \"city_id\",\n \"results\": [\n {\n \"value\": 1,\n \"label\": \"San Francisco\",\n \"count\": 6,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=1\",\n \"selected\": false\n },\n {\n \"value\": 2,\n \"label\": \"Los Angeles\",\n \"count\": 4,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=2\",\n \"selected\": false\n },\n {\n \"value\": 3,\n \"label\": \"Detroit\",\n \"count\": 4,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=3\",\n \"selected\": false\n },\n {\n \"value\": 4,\n \"label\": \"Memnonia\",\n \"count\": 1,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=4\",\n \"selected\": false\n }\n ],\n \"truncated\": false\n }\n} \n If Datasette detects that a column is a foreign key, the \"label\" property will be automatically derived from the detected label column on the referenced table. \n The default number of facet results returned is 30, controlled by the default_facet_size setting.\n You can increase this on an individual page by adding ?_facet_size=100 to the query string, up to a maximum of max_returned_rows (which defaults to 1000).", "breadcrumbs": "[\"Facets\"]", "references": "[]"} {"id": "ecosystem:sqlite-utils", "page": "ecosystem", "ref": "sqlite-utils", "title": "sqlite-utils", "content": "sqlite-utils is a key building block for the wider Datasette ecosystem. It provides a collection of utilities for manipulating SQLite databases, both as a Python library and a command-line utility. Features include: \n \n \n Insert data into a SQLite database from JSON, CSV or TSV, automatically creating tables with the correct schema or altering existing tables to add missing columns. \n \n \n Configure tables for use with SQLite full-text search, including creating triggers needed to keep the search index up-to-date. \n \n \n Modify tables in ways that are not supported by SQLite's default ALTER TABLE syntax - for example changing the types of columns or selecting a new primary key for a table. \n \n \n Adding foreign keys to existing database tables. \n \n \n Extracting columns of data into a separate lookup table.", "breadcrumbs": "[\"The Datasette Ecosystem\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}]"} {"id": "ecosystem:ecosystem", "page": "ecosystem", "ref": "ecosystem", "title": "The Datasette Ecosystem", "content": "Datasette sits at the center of a growing ecosystem of open source tools aimed at making it as easy as possible to gather, analyze and publish interesting data. \n These tools are divided into two main groups: tools for building SQLite databases (for use with Datasette) and plugins that extend Datasette's functionality. \n The Datasette project website includes a directory of plugins and a directory of tools: \n \n \n Plugins directory on datasette.io \n \n \n Tools directory on datasette.io", "breadcrumbs": "[]", "references": "[{\"href\": \"https://datasette.io/\", \"label\": \"Datasette project website\"}, {\"href\": \"https://datasette.io/plugins\", \"label\": \"Plugins directory on datasette.io\"}, {\"href\": \"https://datasette.io/tools\", \"label\": \"Tools directory on datasette.io\"}]"} {"id": "ecosystem:dogsheep", "page": "ecosystem", "ref": "dogsheep", "title": "Dogsheep", "content": "Dogsheep is a collection of tools for personal analytics using SQLite and Datasette. The project provides tools like github-to-sqlite and twitter-to-sqlite that can import data from different sources in order to create a personal data warehouse. Personal Data Warehouses: Reclaiming Your Data is a talk that explains Dogsheep and demonstrates it in action.", "breadcrumbs": "[\"The Datasette Ecosystem\"]", "references": "[{\"href\": \"https://dogsheep.github.io/\", \"label\": \"Dogsheep\"}, {\"href\": \"https://datasette.io/tools/github-to-sqlite\", \"label\": \"github-to-sqlite\"}, {\"href\": \"https://datasette.io/tools/twitter-to-sqlite\", \"label\": \"twitter-to-sqlite\"}, {\"href\": \"https://simonwillison.net/2020/Nov/14/personal-data-warehouses/\", \"label\": \"Personal Data Warehouses: Reclaiming Your Data\"}]"} {"id": "deploying:nginx-proxy-configuration", "page": "deploying", "ref": "nginx-proxy-configuration", "title": "Nginx proxy configuration", "content": "Here is an example of an nginx configuration file that will proxy traffic to Datasette: \n daemon off;\n\nevents {\n worker_connections 1024;\n}\nhttp {\n server {\n listen 80;\n location /my-datasette {\n proxy_pass http://127.0.0.1:8009/my-datasette;\n proxy_set_header Host $host;\n }\n }\n} \n You can also use the --uds option to Datasette to listen on a Unix domain socket instead of a port, configuring the nginx upstream proxy like this: \n daemon off;\nevents {\n worker_connections 1024;\n}\nhttp {\n server {\n listen 80;\n location /my-datasette {\n proxy_pass http://datasette/my-datasette;\n proxy_set_header Host $host;\n }\n }\n upstream datasette {\n server unix:/tmp/datasette.sock;\n }\n} \n Then run Datasette with datasette --uds /tmp/datasette.sock path/to/database.db --setting base_url /my-datasette/ .", "breadcrumbs": "[\"Deploying Datasette\", \"Running Datasette behind a proxy\"]", "references": "[{\"href\": \"https://nginx.org/\", \"label\": \"nginx\"}]"} {"id": "deploying:deploying-systemd", "page": "deploying", "ref": "deploying-systemd", "title": "Running Datasette using systemd", "content": "You can run Datasette on Ubuntu or Debian systems using systemd . \n First, ensure you have Python 3 and pip installed. On Ubuntu you can use sudo apt-get install python3 python3-pip . \n You can install Datasette into a virtual environment, or you can install it system-wide. To install system-wide, use sudo pip3 install datasette . \n Now create a folder for your Datasette databases, for example using mkdir /home/ubuntu/datasette-root . \n You can copy a test database into that folder like so: \n cd /home/ubuntu/datasette-root\ncurl -O https://latest.datasette.io/fixtures.db \n Create a file at /etc/systemd/system/datasette.service with the following contents: \n [Unit]\nDescription=Datasette\nAfter=network.target\n\n[Service]\nType=simple\nUser=ubuntu\nEnvironment=DATASETTE_SECRET=\nWorkingDirectory=/home/ubuntu/datasette-root\nExecStart=datasette serve . -h 127.0.0.1 -p 8000\nRestart=on-failure\n\n[Install]\nWantedBy=multi-user.target \n Add a random value for the DATASETTE_SECRET - this will be used to sign Datasette cookies such as the CSRF token cookie. You can generate a suitable value like so: \n $ python3 -c 'import secrets; print(secrets.token_hex(32))' \n This configuration will run Datasette against all database files contained in the /home/ubuntu/datasette-root directory. If that directory contains a metadata.yml (or .json ) file or a templates/ or plugins/ sub-directory those will automatically be loaded by Datasette - see Configuration directory mode for details. \n You can start the Datasette process running using the following: \n sudo systemctl daemon-reload\nsudo systemctl start datasette.service \n You will need to restart the Datasette service after making changes to its metadata.json configuration or adding a new database file to that directory. You can do that using: \n sudo systemctl restart datasette.service \n Once the service has started you can confirm that Datasette is running on port 8000 like so: \n curl 127.0.0.1:8000/-/versions.json\n# Should output JSON showing the installed version \n Datasette will not be accessible from outside the server because it is listening on 127.0.0.1 . You can expose it by instead listening on 0.0.0.0 , but a better way is to set up a proxy such as nginx - see Running Datasette behind a proxy .", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[]"} {"id": "deploying:deploying-proxy", "page": "deploying", "ref": "deploying-proxy", "title": "Running Datasette behind a proxy", "content": "You may wish to run Datasette behind an Apache or nginx proxy, using a path within your existing site. \n You can use the base_url configuration setting to tell Datasette to serve traffic with a specific URL prefix. For example, you could run Datasette like this: \n datasette my-database.db --setting base_url /my-datasette/ -p 8009 \n This will run Datasette with the following URLs: \n \n \n http://127.0.0.1:8009/my-datasette/ - the Datasette homepage \n \n \n http://127.0.0.1:8009/my-datasette/my-database - the page for the my-database.db database \n \n \n http://127.0.0.1:8009/my-datasette/my-database/some_table - the page for the some_table table \n \n \n You can now set your nginx or Apache server to proxy the /my-datasette/ path to this Datasette instance.", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[]"} {"id": "deploying:deploying-openrc", "page": "deploying", "ref": "deploying-openrc", "title": "Running Datasette using OpenRC", "content": "OpenRC is the service manager on non-systemd Linux distributions like Alpine Linux and Gentoo . \n Create an init script at /etc/init.d/datasette with the following contents: \n #!/sbin/openrc-run\n\nname=\"datasette\"\ncommand=\"datasette\"\ncommand_args=\"serve -h 0.0.0.0 /path/to/db.db\"\ncommand_background=true\npidfile=\"/run/${RC_SVCNAME}.pid\" \n You then need to configure the service to run at boot and start it: \n rc-update add datasette\nrc-service datasette start", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[{\"href\": \"https://www.alpinelinux.org/\", \"label\": \"Alpine Linux\"}, {\"href\": \"https://www.gentoo.org/\", \"label\": \"Gentoo\"}]"} {"id": "deploying:deploying-fundamentals", "page": "deploying", "ref": "deploying-fundamentals", "title": "Deployment fundamentals", "content": "Datasette can be deployed as a single datasette process that listens on a port. Datasette is not designed to be run as root, so that process should listen on a higher port such as port 8000. \n If you want to serve Datasette on port 80 (the HTTP default port) or port 443 (for HTTPS) you should run it behind a proxy server, such as nginx, Apache or HAProxy. The proxy server can listen on port 80/443 and forward traffic on to Datasette.", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[]"} {"id": "deploying:deploying-buildpacks", "page": "deploying", "ref": "deploying-buildpacks", "title": "Deploying using buildpacks", "content": "Some hosting providers such as Heroku , DigitalOcean App Platform and Scalingo support the Buildpacks standard for deploying Python web applications. \n Deploying Datasette on these platforms requires two files: requirements.txt and Procfile . \n The requirements.txt file lets the platform know which Python packages should be installed. It should contain datasette at a minimum, but can also list any Datasette plugins you wish to install - for example: \n datasette\ndatasette-vega \n The Procfile lets the hosting platform know how to run the command that serves web traffic. It should look like this: \n web: datasette . -h 0.0.0.0 -p $PORT --cors \n The $PORT environment variable is provided by the hosting platform. --cors enables CORS requests from JavaScript running on other websites to your domain - omit this if you don't want to allow CORS. You can add additional Datasette Settings options here too. \n These two files should be enough to deploy Datasette on any host that supports buildpacks. Datasette will serve any SQLite files that are included in the root directory of the application. \n If you want to build SQLite files or download them as part of the deployment process you can do so using a bin/post_compile file. For example, the following bin/post_compile will download an example database that will then be served by Datasette: \n wget https://fivethirtyeight.datasettes.com/fivethirtyeight.db \n simonw/buildpack-datasette-demo is an example GitHub repository showing a Datasette configuration that can be deployed to a buildpack-supporting host.", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[{\"href\": \"https://www.heroku.com/\", \"label\": \"Heroku\"}, {\"href\": \"https://www.digitalocean.com/docs/app-platform/\", \"label\": \"DigitalOcean App Platform\"}, {\"href\": \"https://scalingo.com/\", \"label\": \"Scalingo\"}, {\"href\": \"https://buildpacks.io/\", \"label\": \"Buildpacks standard\"}, {\"href\": \"https://github.com/simonw/buildpack-datasette-demo\", \"label\": \"simonw/buildpack-datasette-demo\"}]"} {"id": "deploying:deploying", "page": "deploying", "ref": "deploying", "title": "Deploying Datasette", "content": "The quickest way to deploy a Datasette instance on the internet is to use the datasette publish command, described in Publishing data . This can be used to quickly deploy Datasette to a number of hosting providers including Heroku, Google Cloud Run and Vercel. \n You can deploy Datasette to other hosting providers using the instructions on this page.", "breadcrumbs": "[]", "references": "[]"} {"id": "deploying:apache-proxy-configuration", "page": "deploying", "ref": "apache-proxy-configuration", "title": "Apache proxy configuration", "content": "For Apache , you can use the ProxyPass directive. First make sure the following lines are uncommented: \n LoadModule proxy_module lib/httpd/modules/mod_proxy.so\nLoadModule proxy_http_module lib/httpd/modules/mod_proxy_http.so \n Then add these directives to proxy traffic: \n ProxyPass /my-datasette/ http://127.0.0.1:8009/my-datasette/\nProxyPreserveHost On \n A live demo of Datasette running behind Apache using this proxy setup can be seen at datasette-apache-proxy-demo.datasette.io/prefix/ . The code for that demo can be found in the demos/apache-proxy directory. \n Using --uds you can use Unix domain sockets similar to the nginx example: \n ProxyPass /my-datasette/ unix:/tmp/datasette.sock|http://localhost/my-datasette/ \n The ProxyPreserveHost On directive ensures that the original Host: header from the incoming request is passed through to Datasette. Datasette needs this to correctly assemble links to other pages using the .absolute_url(request, path) method.", "breadcrumbs": "[\"Deploying Datasette\", \"Running Datasette behind a proxy\"]", "references": "[{\"href\": \"https://httpd.apache.org/\", \"label\": \"Apache\"}, {\"href\": \"https://datasette-apache-proxy-demo.datasette.io/prefix/\", \"label\": \"datasette-apache-proxy-demo.datasette.io/prefix/\"}, {\"href\": \"https://github.com/simonw/datasette/tree/main/demos/apache-proxy\", \"label\": \"demos/apache-proxy\"}, {\"href\": \"https://httpd.apache.org/docs/2.4/mod/mod_proxy.html#proxypreservehost\", \"label\": \"ProxyPreserveHost On\"}]"} {"id": "custom_templates:publishing-static-assets", "page": "custom_templates", "ref": "publishing-static-assets", "title": "Publishing static assets", "content": "The datasette publish command can be used to publish your static assets,\n using the same syntax as above: \n $ datasette publish cloudrun mydb.db --static assets:static-files/ \n This will upload the contents of the static-files/ directory as part of the\n deployment, and configure Datasette to correctly serve the assets from /assets/ .", "breadcrumbs": "[\"Custom pages and templates\", \"Custom CSS and JavaScript\"]", "references": "[]"} {"id": "custom_templates:id1", "page": "custom_templates", "ref": "id1", "title": "Custom pages", "content": "You can add templated pages to your Datasette instance by creating HTML files in a pages directory within your templates directory. \n For example, to add a custom page that is served at http://localhost/about you would create a file in templates/pages/about.html , then start Datasette like this: \n $ datasette mydb.db --template-dir=templates/ \n You can nest directories within pages to create a nested structure. To create a http://localhost:8001/about/map page you would create templates/pages/about/map.html .", "breadcrumbs": "[\"Custom pages and templates\"]", "references": "[]"} {"id": "custom_templates:customization-static-files", "page": "custom_templates", "ref": "customization-static-files", "title": "Serving static files", "content": "Datasette can serve static files for you, using the --static option.\n Consider the following directory structure: \n metadata.json\nstatic-files/styles.css\nstatic-files/app.js \n You can start Datasette using --static assets:static-files/ to serve those\n files from the /assets/ mount point: \n $ datasette -m metadata.json --static assets:static-files/ --memory \n The following URLs will now serve the content from those CSS and JS files: \n http://localhost:8001/assets/styles.css\nhttp://localhost:8001/assets/app.js \n You can reference those files from metadata.json like so: \n {\n \"extra_css_urls\": [\n \"/assets/styles.css\"\n ],\n \"extra_js_urls\": [\n \"/assets/app.js\"\n ]\n}", "breadcrumbs": "[\"Custom pages and templates\", \"Custom CSS and JavaScript\"]", "references": "[]"} {"id": "custom_templates:customization-custom-templates", "page": "custom_templates", "ref": "customization-custom-templates", "title": "Custom templates", "content": "By default, Datasette uses default templates that ship with the package. \n You can over-ride these templates by specifying a custom --template-dir like\n this: \n datasette mydb.db --template-dir=mytemplates/ \n Datasette will now first look for templates in that directory, and fall back on\n the defaults if no matches are found. \n It is also possible to over-ride templates on a per-database, per-row or per-\n table basis. \n The lookup rules Datasette uses are as follows: \n Index page (/):\n index.html\n\nDatabase page (/mydatabase):\n database-mydatabase.html\n database.html\n\nCustom query page (/mydatabase?sql=...):\n query-mydatabase.html\n query.html\n\nCanned query page (/mydatabase/canned-query):\n query-mydatabase-canned-query.html\n query-mydatabase.html\n query.html\n\nTable page (/mydatabase/mytable):\n table-mydatabase-mytable.html\n table.html\n\nRow page (/mydatabase/mytable/id):\n row-mydatabase-mytable.html\n row.html\n\nTable of rows and columns include on table page:\n _table-table-mydatabase-mytable.html\n _table-mydatabase-mytable.html\n _table.html\n\nTable of rows and columns include on row page:\n _table-row-mydatabase-mytable.html\n _table-mydatabase-mytable.html\n _table.html \n If a table name has spaces or other unexpected characters in it, the template\n filename will follow the same rules as our custom
CSS classes - for\n example, a table called \"Food Trucks\" will attempt to load the following\n templates: \n table-mydatabase-Food-Trucks-399138.html\ntable.html \n You can find out which templates were considered for a specific page by viewing\n source on that page and looking for an HTML comment at the bottom. The comment\n will look something like this: \n \n This example is from the canned query page for a query called \"tz\" in the\n database called \"mydb\". The asterisk shows which template was selected - so in\n this case, Datasette found a template file called query-mydb-tz.html and\n used that - but if that template had not been found, it would have tried for\n query-mydb.html or the default query.html . \n It is possible to extend the default templates using Jinja template\n inheritance. If you want to customize EVERY row template with some additional\n content you can do so by creating a row.html template like this: \n {% extends \"default:row.html\" %}\n\n{% block content %}\nThis line renders the original block:
\n{{ super() }}\n{% endblock %} \n Note the default:row.html template name, which ensures Jinja will inherit\n from the default template. \n The _table.html template is included by both the row and the table pages,\n and a list of rows. The default _table.html template renders them as an\n HTML template and can be seen here . \n You can provide a custom template that applies to all of your databases and\n tables, or you can provide custom templates for specific tables using the\n template naming scheme described above. \n If you want to present your data in a format other than an HTML table, you\n can do so by looping through display_rows in your own _table.html \n template. You can use {{ row[\"column_name\"] }} to output the raw value\n of a specific column. \n If you want to output the rendered HTML version of a column, including any\n links to foreign keys, you can use {{ row.display(\"column_name\") }} . \n Here is an example of a custom _table.html template: \n {% for row in display_rows %}\n{{ row[\"description\"] }} Category: {{ row.display(\"category_id\") }}
id | \nname | \n
---|---|
1 | \nSMITH | \n
{{ row[\"description\"] }} Category: {{ row.display(\"category_id\") }}