id,page,ref,title,content,breadcrumbs,references installation:installation-homebrew,installation,installation-homebrew,Using Homebrew,"If you have a Mac and use Homebrew , you can install Datasette by running this command in your terminal: brew install datasette This should install the latest version. You can confirm by running: datasette --version You can upgrade to the latest Homebrew packaged version using: brew upgrade datasette Once you have installed Datasette you can install plugins using the following: datasette install datasette-vega If the latest packaged release of Datasette has not yet been made available through Homebrew, you can upgrade your Homebrew installation in-place using: datasette install -U datasette","[""Installation"", ""Basic installation""]","[{""href"": ""https://brew.sh/"", ""label"": ""Homebrew""}]" publish:cli-package,publish,cli-package,datasette package,"If you have docker installed (e.g. using Docker for Mac ) you can use the datasette package command to create a new Docker image in your local repository containing the datasette app bundled together with one or more SQLite databases: datasette package mydatabase.db Here's example output for the package command: datasette package parlgov.db --extra-options=""--setting sql_time_limit_ms 2500"" Sending build context to Docker daemon 4.459MB Step 1/7 : FROM python:3.11.0-slim-bullseye ---> 79e1dc9af1c1 Step 2/7 : COPY . /app ---> Using cache ---> cd4ec67de656 Step 3/7 : WORKDIR /app ---> Using cache ---> 139699e91621 Step 4/7 : RUN pip install datasette ---> Using cache ---> 340efa82bfd7 Step 5/7 : RUN datasette inspect parlgov.db --inspect-file inspect-data.json ---> Using cache ---> 5fddbe990314 Step 6/7 : EXPOSE 8001 ---> Using cache ---> 8e83844b0fed Step 7/7 : CMD datasette serve parlgov.db --port 8001 --inspect-file inspect-data.json --setting sql_time_limit_ms 2500 ---> Using cache ---> 1bd380ea8af3 Successfully built 1bd380ea8af3 You can now run the resulting container like so: docker run -p 8081:8001 1bd380ea8af3 This exposes port 8001 inside the container as port 8081 on your host machine, so you can access the application at http://localhost:8081/ You can customize the port that is exposed by the container using the --port option: datasette package mydatabase.db --port 8080 A full list of options can be seen by running datasette package --help : See datasette package for the full list of options for this command.","[""Publishing data""]","[{""href"": ""https://www.docker.com/docker-mac"", ""label"": ""Docker for Mac""}]" installation:id1,installation,id1,Installation,"If you just want to try Datasette out you don't need to install anything: see Try Datasette without installing anything using Glitch There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker. If you want to start making contributions to the Datasette project by installing a copy that lets you directly modify the code, take a look at our guide to Setting up a development environment . Basic installation Datasette Desktop for Mac Using Homebrew Using pip Advanced installation options Using pipx Installing plugins using pipx Upgrading packages using pipx Using Docker Loading SpatiaLite Installing plugins A note about extensions",[],[] performance:performance-hashed-urls,performance,performance-hashed-urls,datasette-hashed-urls,"If you open a database file in immutable mode using the -i option, you can be assured that the content of that database will not change for the lifetime of the Datasette server. The datasette-hashed-urls plugin implements an optimization where your database is served with part of the SHA-256 hash of the database contents baked into the URL. A database at /fixtures will instead be served at /fixtures-aa7318b , and a year-long cache expiry header will be returned with those pages. This will then be cached by both browsers and caching proxies such as Cloudflare or Fastly, providing a potentially significant performance boost. To install the plugin, run the following: datasette install datasette-hashed-urls Prior to Datasette 0.61 hashed URL mode was a core Datasette feature, enabled using the hash_urls setting. This implementation has now been removed in favor of the datasette-hashed-urls plugin. Prior to Datasette 0.28 hashed URL mode was the default behaviour for Datasette, since all database files were assumed to be immutable and unchanging. From 0.28 onwards the default has been to treat database files as mutable unless explicitly configured otherwise.","[""Performance and caching""]","[{""href"": ""https://datasette.io/plugins/datasette-hashed-urls"", ""label"": ""datasette-hashed-urls plugin""}]" json_api:json-api-cors,json_api,json-api-cors,Enabling CORS,"If you start Datasette with the --cors option, each JSON endpoint will be served with the following additional HTTP headers: [[[cog from datasette.utils import add_cors_headers import textwrap headers = {} add_cors_headers(headers) output = ""\n"".join(""{}: {}"".format(k, v) for k, v in headers.items()) cog.out(""\n::\n\n"") cog.out(textwrap.indent(output, ' ')) cog.out(""\n\n"") ]]] Access-Control-Allow-Origin: * Access-Control-Allow-Headers: Authorization, Content-Type Access-Control-Expose-Headers: Link Access-Control-Allow-Methods: GET, POST, HEAD, OPTIONS Access-Control-Max-Age: 3600 [[[end]]] This allows JavaScript running on any domain to make cross-origin requests to interact with the Datasette API. If you start Datasette without the --cors option only JavaScript running on the same domain as Datasette will be able to access the API. Here's how to serve data.db with CORS enabled: datasette data.db --cors","[""JSON API""]",[] sql_queries:sql-views,sql_queries,sql-views,Views,"If you want to bundle some pre-written SQL queries with your Datasette-hosted database you can do so in two ways. The first is to include SQL views in your database - Datasette will then list those views on your database index page. The quickest way to create views is with the SQLite command-line interface: sqlite3 sf-trees.db SQLite version 3.19.3 2017-06-27 16:48:08 Enter "".help"" for usage hints. sqlite> CREATE VIEW demo_view AS select qSpecies from Street_Tree_List; You can also use the sqlite-utils tool to create a view : sqlite-utils create-view sf-trees.db demo_view ""select qSpecies from Street_Tree_List""","[""Running SQL queries""]","[{""href"": ""https://sqlite-utils.datasette.io/"", ""label"": ""sqlite-utils""}, {""href"": ""https://sqlite-utils.datasette.io/en/stable/cli.html#creating-views"", ""label"": ""create a view""}]" changelog:v0-29-medium-changes,changelog,v0-29-medium-changes,Easier custom templates for table rows,"If you want to customize the display of individual table rows, you can do so using a _table.html template include that looks something like this: {% for row in display_rows %}

{{ row[""title""] }}

{{ row[""description""] }}

Category: {{ row.display(""category_id"") }}

{% endfor %} This is a backwards incompatible change . If you previously had a custom template called _rows_and_columns.html you need to rename it to _table.html . See Custom templates for full details.","[""Changelog"", ""0.29 (2019-07-07)""]",[] installation:installing-plugins,installation,installing-plugins,Installing plugins,"If you want to install plugins into your local Datasette Docker image you can do so using the following recipe. This will install the plugins and then save a brand new local image called datasette-with-plugins : docker run datasetteproject/datasette \ pip install datasette-vega docker commit $(docker ps -lq) datasette-with-plugins You can now run the new custom image like so: docker run -p 8001:8001 -v `pwd`:/mnt \ datasette-with-plugins \ datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db You can confirm that the plugins are installed by visiting http://127.0.0.1:8001/-/plugins Some plugins such as datasette-ripgrep may need additional system packages. You can install these by running apt-get install inside the container: docker run datasette-057a0 bash -c ' apt-get update && apt-get install ripgrep && pip install datasette-ripgrep' docker commit $(docker ps -lq) datasette-with-ripgrep","[""Installation"", ""Advanced installation options"", ""Using Docker""]","[{""href"": ""http://127.0.0.1:8001/-/plugins"", ""label"": ""http://127.0.0.1:8001/-/plugins""}, {""href"": ""https://datasette.io/plugins/datasette-ripgrep"", ""label"": ""datasette-ripgrep""}]" facets:id2,facets,id2,Facet by JSON array,"If your SQLite installation provides the json1 extension (you can check using /-/versions ) Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns. This is useful for modelling things like tags without needing to break them out into a new table. Example here: latest.datasette.io/fixtures/facetable?_facet_array=tags","[""Facets""]","[{""href"": ""https://latest.datasette.io/fixtures/facetable?_facet_array=tags"", ""label"": ""latest.datasette.io/fixtures/facetable?_facet_array=tags""}]" internals:internals-tracer-trace-child-tasks,internals,internals-tracer-trace-child-tasks,Tracing child tasks,"If your code uses a mechanism such as asyncio.gather() to execute code in additional tasks you may find that some of the traces are missing from the display. You can use the trace_child_tasks() context manager to ensure these child tasks are correctly handled. from datasette import tracer with tracer.trace_child_tasks(): results = await asyncio.gather( # ... async tasks here ) This example uses the register_routes() plugin hook to add a page at /parallel-queries which executes two SQL queries in parallel using asyncio.gather() and returns their results. from datasette import hookimpl from datasette import tracer @hookimpl def register_routes(): async def parallel_queries(datasette): db = datasette.get_database() with tracer.trace_child_tasks(): one, two = await asyncio.gather( db.execute(""select 1""), db.execute(""select 2""), ) return Response.json( { ""one"": one.single_value(), ""two"": two.single_value(), } ) return [ (r""/parallel-queries$"", 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.","[""Internals for plugins"", ""datasette.tracer""]","[{""href"": ""https://github.com/simonw/datasette/issues/2189"", ""label"": ""been known to cause problems in the past""}]" full_text_search:configuring-fts-using-csvs-to-sqlite,full_text_search,configuring-fts-using-csvs-to-sqlite,Configuring FTS using csvs-to-sqlite,"If your data starts out in CSV files, you can use Datasette's companion tool csvs-to-sqlite to convert that file into a SQLite database and enable full-text search on specific columns. For a file called items.csv where you want full-text search to operate against the name and description columns you would run the following: csvs-to-sqlite items.csv items.db -f name -f description","[""Full-text search"", ""Enabling full-text search for a SQLite table""]","[{""href"": ""https://github.com/simonw/csvs-to-sqlite"", ""label"": ""csvs-to-sqlite""}]" performance:http-caching,performance,http-caching,HTTP caching,"If your database is immutable and guaranteed not to change, you can gain major performance improvements from Datasette by enabling HTTP caching. This can work at two different levels. First, it can tell browsers to cache the results of queries and serve future requests from the browser cache. More significantly, it allows you to run Datasette behind a caching proxy such as Varnish or use a cache provided by a hosted service such as Fastly or Cloudflare . This can provide incredible speed-ups since a query only needs to be executed by Datasette the first time it is accessed - all subsequent hits can then be served by the cache. Using a caching proxy in this way could enable a Datasette-backed visualization to serve thousands of hits a second while running Datasette itself on extremely inexpensive hosting. Datasette's integration with HTTP caches can be enabled using a combination of configuration options and query string arguments. The default_cache_ttl setting sets the default HTTP cache TTL for all Datasette pages. This is 5 seconds unless you change it - you can set it to 0 if you wish to disable HTTP caching entirely. You can also change the cache timeout on a per-request basis using the ?_ttl=10 query string parameter. This can be useful when you are working with the Datasette JSON API - you may decide that a specific query can be cached for a longer time, or maybe you need to set ?_ttl=0 for some requests for example if you are running a SQL order by random() query.","[""Performance and caching""]","[{""href"": ""https://varnish-cache.org/"", ""label"": ""Varnish""}, {""href"": ""https://www.fastly.com/"", ""label"": ""Fastly""}, {""href"": ""https://www.cloudflare.com/"", ""label"": ""Cloudflare""}]" writing_plugins:writing-plugins-static-assets,writing_plugins,writing-plugins-static-assets,Static assets,"If your plugin has a static/ directory, Datasette will automatically configure itself to serve those static assets from the following path: /-/static-plugins/NAME_OF_PLUGIN_PACKAGE/yourfile.js Use the datasette.urls.static_plugins(plugin_name, path) method to generate URLs to that asset that take the base_url setting into account, see datasette.urls . To bundle the static assets for a plugin in the package that you publish to PyPI, add the following to the plugin's setup.py : package_data = ( { ""datasette_plugin_name"": [ ""static/plugin.js"", ], }, ) Where datasette_plugin_name is the name of the plugin package (note that it uses underscores, not hyphens) and static/plugin.js is the path within that package to the static file. datasette-cluster-map is a useful example of a plugin that includes packaged static assets in this way.","[""Writing plugins""]","[{""href"": ""https://github.com/simonw/datasette-cluster-map"", ""label"": ""datasette-cluster-map""}]" writing_plugins:writing-plugins-custom-templates,writing_plugins,writing-plugins-custom-templates,Custom templates,"If your plugin has a templates/ directory, Datasette will attempt to load templates from that directory before it uses its own default templates. The priority order for template loading is: templates from the --template-dir argument, if specified templates from the templates/ directory in any installed plugins default templates that ship with Datasette See Custom pages and templates for more details on how to write custom templates, including which filenames to use to customize which parts of the Datasette UI. Templates should be bundled for distribution using the same package_data mechanism in setup.py described for static assets above, for example: package_data = ( { ""datasette_plugin_name"": [ ""templates/my_template.html"", ], }, ) You can also use wildcards here such as templates/*.html . See datasette-edit-schema for an example of this pattern.","[""Writing plugins""]","[{""href"": ""https://github.com/simonw/datasette-edit-schema"", ""label"": ""datasette-edit-schema""}]" testing_plugins:testing-plugins-pytest-httpx,testing_plugins,testing-plugins-pytest-httpx,Testing outbound HTTP calls with pytest-httpx,"If your plugin makes outbound HTTP calls - for example datasette-auth-github or datasette-import-table - you may need to mock those HTTP requests in your tests. The pytest-httpx package is a useful library for mocking calls. It can be tricky to use with Datasette though since it mocks all HTTPX requests, and Datasette's own testing mechanism uses HTTPX internally. To avoid breaking your tests, you can return [""localhost""] from the non_mocked_hosts() fixture. As an example, here's a very simple plugin which executes an HTTP response and returns the resulting content: from datasette import hookimpl from datasette.utils.asgi import Response import httpx @hookimpl def register_routes(): return [ (r""^/-/fetch-url$"", fetch_url), ] async def fetch_url(datasette, request): if request.method == ""GET"": return Response.html( """"""
"""""".format( request.scope[""csrftoken""]() ) ) vars = await request.post_vars() url = vars[""url""] return Response.text(httpx.get(url).text) Here's a test for that plugin that mocks the HTTPX outbound request: from datasette.app import Datasette import pytest @pytest.fixture def non_mocked_hosts(): # This ensures httpx-mock will not affect Datasette's own # httpx calls made in the tests by datasette.client: return [""localhost""] async def test_outbound_http_call(httpx_mock): httpx_mock.add_response( url=""https://www.example.com/"", text=""Hello world"", ) datasette = Datasette([], memory=True) response = await datasette.client.post( ""/-/fetch-url"", data={""url"": ""https://www.example.com/""}, ) assert response.text == ""Hello world"" outbound_request = httpx_mock.get_request() assert ( outbound_request.url == ""https://www.example.com/"" )","[""Testing plugins""]","[{""href"": ""https://pypi.org/project/pytest-httpx/"", ""label"": ""pytest-httpx""}]" plugin_hooks:plugin-register-permissions,plugin_hooks,plugin-register-permissions,register_permissions(datasette),"If your plugin needs to register additional permissions unique to that plugin - upload-csvs for example - you can return a list of those permissions from this hook. from datasette import hookimpl, Permission @hookimpl def register_permissions(datasette): return [ Permission( name=""upload-csvs"", abbr=None, description=""Upload CSV files"", takes_database=True, takes_resource=False, default=False, ) ] The fields of the Permission class are as follows: name - string The name of the permission, e.g. upload-csvs . This should be unique across all plugins that the user might have installed, so choose carefully. abbr - string or None An abbreviation of the permission, e.g. uc . This is optional - you can set it to None if you do not want to pick an abbreviation. Since this needs to be unique across all installed plugins it's best not to specify an abbreviation at all. If an abbreviation is provided it will be used when creating restricted signed API tokens. description - string or None A human-readable description of what the permission lets you do. Should make sense as the second part of a sentence that starts ""A user with this permission can ..."". takes_database - boolean True if this permission can be granted on a per-database basis, False if it is only valid at the overall Datasette instance level. takes_resource - boolean True if this permission can be granted on a per-resource basis. A resource is a database table, SQL view or canned query . default - boolean The default value for this permission if it is not explicitly granted to a user. True means the permission is granted by default, False means it is not. This should only be True if you want anonymous users to be able to take this action.","[""Plugin hooks""]",[] changelog:id44,changelog,id44,0.51.1 (2020-10-31),Improvements to the new Binary data documentation page.,"[""Changelog""]",[] internals:internals-response-asgi-send,internals,internals-response-asgi-send,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)","[""Internals for plugins"", ""Response class""]",[] changelog:id18,changelog,id18,0.61 (2022-03-23),"In preparation for Datasette 1.0, this release includes two potentially backwards-incompatible changes. Hashed URL mode has been moved to a separate plugin, and the way Datasette generates URLs to databases and tables with special characters in their name such as / and . has changed. Datasette also now requires Python 3.7 or higher. URLs within Datasette now use a different encoding scheme for tables or databases that include ""special"" characters outside of the range of a-zA-Z0-9_- . This scheme is explained here: Tilde encoding . ( #1657 ) Removed hashed URL mode from Datasette. The new datasette-hashed-urls plugin can be used to achieve the same result, see datasette-hashed-urls for details. ( #1661 ) Databases can now have a custom path within the Datasette instance that is independent of the database name, using the db.route property. ( #1668 ) Datasette is now covered by a Code of Conduct . ( #1654 ) Python 3.6 is no longer supported. ( #1577 ) Tests now run against Python 3.11-dev. ( #1621 ) New datasette.ensure_permissions(actor, permissions) internal method for checking multiple permissions at once. ( #1675 ) New datasette.check_visibility(actor, action, resource=None) internal method for checking if a user can see a resource that would otherwise be invisible to unauthenticated users. ( #1678 ) Table and row HTML pages now include a element and return a Link: URL; rel=""alternate""; type=""application/json+datasette"" HTTP header pointing to the JSON version of those pages. ( #1533 ) Access-Control-Expose-Headers: Link is now added to the CORS headers, allowing remote JavaScript to access that header. Canned queries are now shown at the top of the database page, directly below the SQL editor. Previously they were shown at the bottom, below the list of tables. ( #1612 ) Datasette now has a default favicon. ( #1603 ) sqlite_stat tables are now hidden by default. ( #1587 ) SpatiaLite tables data_licenses , KNN and KNN2 are now hidden by default. ( #1601 ) SQL query tracing mechanism now works for queries executed in asyncio sub-tasks, such as those created by asyncio.gather() . ( #1576 ) datasette.tracer mechanism is now documented. Common Datasette symbols can now be imported directly from the top-level datasette package, see Import shortcuts . Those symbols are Response , Forbidden , NotFound , hookimpl , actor_matches_allow . ( #957 ) /-/versions page now returns additional details for libraries used by SpatiaLite. ( #1607 ) Documentation now links to the Datasette Tutorials . Datasette will now also look for SpatiaLite in /opt/homebrew - thanks, Dan Peterson. ( #1649 ) Fixed bug where custom pages did not work on Windows. Thanks, Robert Christie. ( #1545 ) Fixed error caused when a table had a column named n . ( #1228 )","[""Changelog""]","[{""href"": ""https://github.com/simonw/datasette/issues/1657"", ""label"": ""#1657""}, {""href"": ""https://github.com/simonw/datasette/issues/1661"", ""label"": ""#1661""}, {""href"": ""https://github.com/simonw/datasette/issues/1668"", ""label"": ""#1668""}, {""href"": ""https://github.com/simonw/datasette/blob/main/CODE_OF_CONDUCT.md"", ""label"": ""Code of Conduct""}, {""href"": ""https://github.com/simonw/datasette/issues/1654"", ""label"": ""#1654""}, {""href"": ""https://github.com/simonw/datasette/issues/1577"", ""label"": ""#1577""}, {""href"": ""https://github.com/simonw/datasette/issues/1621"", ""label"": ""#1621""}, {""href"": ""https://github.com/simonw/datasette/issues/1675"", ""label"": ""#1675""}, {""href"": ""https://github.com/simonw/datasette/issues/1678"", ""label"": ""#1678""}, {""href"": ""https://github.com/simonw/datasette/issues/1533"", ""label"": ""#1533""}, {""href"": ""https://github.com/simonw/datasette/issues/1612"", ""label"": ""#1612""}, {""href"": ""https://github.com/simonw/datasette/issues/1603"", ""label"": ""#1603""}, {""href"": ""https://github.com/simonw/datasette/issues/1587"", ""label"": ""#1587""}, {""href"": ""https://github.com/simonw/datasette/issues/1601"", ""label"": ""#1601""}, {""href"": ""https://github.com/simonw/datasette/issues/1576"", ""label"": ""#1576""}, {""href"": ""https://github.com/simonw/datasette/issues/957"", ""label"": ""#957""}, {""href"": ""https://github.com/simonw/datasette/issues/1607"", ""label"": ""#1607""}, {""href"": ""https://datasette.io/tutorials"", ""label"": ""Datasette Tutorials""}, {""href"": ""https://github.com/simonw/datasette/pull/1649"", ""label"": ""#1649""}, {""href"": ""https://github.com/simonw/datasette/issues/1545"", ""label"": ""#1545""}, {""href"": ""https://github.com/simonw/datasette/issues/1228"", ""label"": ""#1228""}]" settings:setting-truncate-cells-html,settings,setting-truncate-cells-html,truncate_cells_html,"In the HTML table view, truncate any strings that are longer than this value. The full value will still be available in CSV, JSON and on the individual row HTML page. Set this to 0 to disable truncation. datasette mydatabase.db --setting truncate_cells_html 0","[""Settings"", ""Settings""]",[] cli-reference:cli-help-install-help,cli-reference,cli-help-install-help,datasette install,"Install new Datasette plugins. This command works like pip install but ensures that your plugins will be installed into the same environment as Datasette. This command: datasette install datasette-cluster-map Would install the datasette-cluster-map plugin. [[[cog help([""install"", ""--help""]) ]]] Usage: datasette install [OPTIONS] [PACKAGES]... Install plugins and packages from PyPI into the same environment as Datasette Options: -U, --upgrade Upgrade packages to latest version -r, --requirement PATH Install from requirements file -e, --editable TEXT Install a project in editable mode from this path --help Show this message and exit. [[[end]]]","[""CLI reference""]","[{""href"": ""https://datasette.io/plugins/datasette-cluster-map"", ""label"": ""datasette-cluster-map""}]" internals:internals-database,internals,internals-database,Database class,"Instances of the Database class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas.","[""Internals for plugins""]",[] json_api:tablecreateview-example,json_api,tablecreateview-example,Creating a table from example data,"Instead of specifying columns directly you can instead pass a single example row or a list of rows . Datasette will create a table with a schema that matches those rows and insert them for you: POST //-/create Content-Type: application/json Authorization: Bearer dstok_ { ""table"": ""creatures"", ""rows"": [ { ""id"": 1, ""name"": ""Tarantula"" }, { ""id"": 2, ""name"": ""Kākāpō"" } ], ""pk"": ""id"" } Doing this requires both the create-table and insert-row permissions. The 201 response here will be similar to the columns form, but will also include the number of rows that were inserted as row_count : { ""ok"": true, ""database"": ""data"", ""table"": ""creatures"", ""table_url"": ""http://127.0.0.1:8001/data/creatures"", ""table_api_url"": ""http://127.0.0.1:8001/data/creatures.json"", ""schema"": ""CREATE TABLE [creatures] (\n [id] INTEGER PRIMARY KEY,\n [name] TEXT\n)"", ""row_count"": 2 } You can call the create endpoint multiple times for the same table provided you are specifying the table using the rows or row option. New rows will be inserted into the table each time. This means you can use this API if you are unsure if the relevant table has been created yet. If you pass a row to the create endpoint with a primary key that already exists you will get an error that looks like this: { ""ok"": false, ""errors"": [ ""UNIQUE constraint failed: creatures.id"" ] } You can avoid this error by passing the same ""ignore"": true or ""replace"": true options to the create endpoint as you can to the insert endpoint . To use the ""replace"": true option you will also need the update-row permission. Pass ""alter"": true to automatically add any missing columns to the existing table that are present in the rows you are submitting. This requires the alter-table permission.","[""JSON API"", ""The JSON write API""]",[] changelog:javascript-modules,changelog,javascript-modules,JavaScript modules,"JavaScript modules were introduced in ECMAScript 2015 and provide native browser support for the import and export keywords. To use modules, JavaScript needs to be included in