https://github.com/dogsheep/github-to-sqlite/actions/runs/4080058087/jobs/7032116511

Bumps the python-packages group with 1 update: black.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

:books: Documentation preview :books:: https://datasette--2206.org.readthedocs.build/en/2206/

I start with new python3 verison 3.12.0 Also have the error where connect DataBase

Traceback (most recent call last): File "/home/t/Development/python/FKPJ/ClinicSYS/run.py", line 1, in <module> import re, os, io, json, sqlite_utils, requests, pytz, logging File "/home/t/.local/lib/python3.12/site-packages/sqlite_utils/__init__.py", line 1, in <module> from .db import Database File "/home/t/.local/lib/python3.12/site-packages/sqlite_utils/db.py", line 277, in <module> class Database: File "/home/t/.local/lib/python3.12/site-packages/sqlite_utils/db.py", line 306, in Database filename_or_conn: Optional[Union[str, pathlib.Path, sqlite3.Connection]] = None, ^^^^^^^^^^^^^^^^^^ This bug come from sqlite-utils since's v3.33. Anyone get the same ?

As well now of the resolved plan just keep the sqlite-utils version in python3.12 with v3.32.1 [tested] but where are the sqlite3.Connection problem....

This won't happen on python version down to 3.11[tested] Just the python3.12.0, I have test this error are come from the sqlite3 connection The error say from sqlite_utils and with the sqlite3 Connection, what can I do.

Let fix together.

I'm starting this issue without a clear reproduction in case someone else has seen this behavior, and to use the issue as a notebook for research.

I'm using Datasette with the SWITRS data set, which is a California Highway Patrol collection of traffic incident data from the past decade or so. I receive data from them in CSV and want to work with it in Datasette, then export it to CSV for mapping in Felt.com.

Their data makes extensive use of codes for incident column data (1 for Monday and so on), some of it integer codes and some of it letter/text codes. The text codes are sometimes blank or -. During import, I'm creating lookup tables for foreign key references to make the Datasette UI presentation of the data easier to read.

If I import the data and set up the integer foreign keys, everything works fine, but if I set up the text foreign keys, CSV export starts to fail.

The foreign key configuration is as follows:

```

Some tables use integer ids, like sensible tables do. Let's import them first

since we favor them.

for TABLE in DAY_OF_WEEK CHP_SHIFT POPULATION SPECIAL_COND BEAT_TYPE COLLISION_SEVERITY do sqlite-utils create-table records.db $TABLE id integer name text --pk=id sqlite-utils insert records.db $TABLE lookup-tables/$TABLE.csv --csv sqlite-utils add-foreign-key records.db collisions $TABLE $TABLE id sqlite-utils create-index records.db collisions $TABLE done

Other tables use letter keys, like they were raised by WOLVES. Let's put them

at the end of the import queue.

for TABLE in WEATHER_1 WEATHER_2 LOCATION_TYPE RAMP_INTERSECTION SIDE_OF_HWY \ PRIMARY_COLL_FACTOR PCF_CODE_OF_VIOL PCF_VIOL_CATEGORY TYPE_OF_COLLISION MVIW \ PED_ACTION ROAD_SURFACE ROAD_COND_1 ROAD_COND_2 LIGHTING CONTROL_DEVICE \ STWD_VEHTYPE_AT_FAULT CHP_VEHTYPE_AT_FAULT PRIMARY_RAMP SECONDARY_RAMP do sqlite-utils create-table records.db $TABLE key text name text --pk=key sqlite-utils insert records.db $TABLE lookup-tables/$TABLE.csv --csv sqlite-utils add-foreign-key records.db collisions $TABLE $TABLE key sqlite-utils create-index records.db collisions $TABLE done ```

You can see the full code and import script here: https://github.com/radical-bike-lobby/switrs-db

If I run this code and then hit the CSV export link in the Datasette interface (the simple link or the "advanced" dialog), export fails after a small number of CSV rows are written. I am not seeing any detailed error messages but this appears in the logging output:

``` INFO: 127.0.0.1:57885 - "GET /records/collisions.csv?_facet=PRIMARY_RD&PRIMARY_RD=ASHBY+AV&_labels=on&_size=max HTTP/1.1" 200 OK Caught this error:

```

(No other output follows error: other than a blank line.)

I've stared at the rows directly after the error occurs and can't yet see what is causing the problem. I'm going to set up a development environment and see if I get any more detailed error output, and then stare more at some problematic lines to see if I can get a simple reproduction.

At the bottom of database pages, datasette gives users the opportunity to download the underlying sqlite database. It would be great if that could be served gzip compressed.

this is similar to #1213, but for me, i don't need datasette to compress html and json because my CDN layer does it for me, however, cloudflare at least, will not compress a mimetype of "application"

(see list of mimetype: https://developers.cloudflare.com/speed/optimization/content/brotli/content-compression/)

I have a schema that uses numbers for a column (actually it's a boolean 1 or 0 but SQLite doesn't have Boolean). I can't seem to get the facet to work or even filtering on this column.

My guess is that Datasette is "stringifying" the number and it's not matching? Example: https://debian-sqlelf.fly.dev/debian/elf_symbols?_sort_desc=name&_facet=exported&exported=0

There are several places where sqlite3.OperationalError is caught as part of an exception handler which catches multiple exceptions, but is then caught again immediately afterwards by a dedicated exception handler.

Because the exception will be caught by the first handler, the logic in the second handler is unreachable and will never be executed. If this is intended behavior, the second handler can be removed. If this is not intended, and the second handler should be the one that catches this exception, then sqlite3.OperationalError should be removed from the tuple of exceptions in the first handler.

This issue was found via a CodeQL query on the repository, and I've listed the occurrences found by the query below. There may be other instances of this issue in the code that were not surfaced by the query. I'd be happy to share the query if others would like to view or run it.

One example:

https://github.com/simonw/datasette/blob/452a587e236ef642cbc6ae345b58767ea8420cb5/datasette/views/database.py#L534-L537

Other instances:

https://github.com/simonw/datasette/blob/main/datasette/views/base.py#L266-L270 https://github.com/simonw/datasette/blob/main/datasette/views/base.py#L452-L456

I thought this would never happen, but now that I'm deep in the weeds of running SQLite in production for Datasette Cloud I'm starting to reconsider my policy of only supporting SQLite.

Some of the factors making me think PostgreSQL support could be worth the effort: - Serverless. I'm getting increasingly excited about writable-database use-cases for Datasette. If it could talk to PostgreSQL then users could easily deploy it on Heroku or other serverless providers that can talk to a managed RDS-style PostgreSQL. - Existing databases. Plenty of organizations have PostgreSQL databases. They can export to SQLite using db-to-sqlite but that's a pretty big barrier to getting started - being able to run datasette postgresql://connection-string and start trying it out would be a massively better experience. - Data size. I keep running into use-cases where I want to run Datasette against many GBs of data. SQLite can do this but PostgreSQL is much more optimized for large data, especially given the existence of tools like Citus. - Marketing. Convincing people to trust their data to SQLite is potentially a big barrier to adoption. Even if I've convinced myself it's trustworthy I still have to convince everyone else. - It might not be that hard? If this required a ground-up rewrite it wouldn't be worth the effort, but I have a hunch that it may not be too hard - most of the SQL in Datasette should work on both databases since it's almost all portable SELECT statements. If Datasette did DML this would be a lot harder, but it doesn't. - Plugins! This feels like a natural surface for a plugin - at which point people could add MySQL support and suchlike in the future.

The above reasons feel strong enough to justify a prototype.

See discussion in https://github.com/simonw/datasette/issues/2208

:books: Documentation preview :books:: https://datasette--2209.org.readthedocs.build/en/2209/

When a column named 'value' is included there are no suggested facets is shown as the query uses an alias of 'value'.

https://github.com/simonw/datasette/blob/452a587e236ef642cbc6ae345b58767ea8420cb5/datasette/facets.py#L168-L174

Currently the following is shown (from https://latest.datasette.io/fixtures/facetable)

When I add a column named 'value' only the JSON facets are processed.

I think that not using aliases could be a solution (except if someone wants to use a column named count(*) though this seems to be unlikely). I'll open a PR with that.

There is also a TODO with a similar question in the same file. I have not looked into that yet.

https://github.com/simonw/datasette/blob/452a587e236ef642cbc6ae345b58767ea8420cb5/datasette/facets.py#L512

No matter what I do, I'm getting this error:

$ datasette Traceback (most recent call last): File "/Users/honza/Library/Caches/pypoetry/virtualenvs/juniorguru-Lgaxwd2n-py3.11/bin/datasette", line 5, in <module> from datasette.cli import cli File "/Users/honza/Library/Caches/pypoetry/virtualenvs/juniorguru-Lgaxwd2n-py3.11/lib/python3.11/site-packages/datasette/cli.py", line 6, in <module> from click_default_group import DefaultGroup ModuleNotFoundError: No module named 'click_default_group'

I have datasette in my dependencies like this:

toml [tool.poetry.group.dev.dependencies] datasette = {version = "1.0a7", allow-prereleases = true}

I had the latest regular version (not pre-release) there originally, but the result was the same:

toml [tool.poetry.group.dev.dependencies] datasette = "0.64.5"

Full pyproject.toml is at https://github.com/honzajavorek/junior.guru/ Previously datasette worked for me, but I guess something had to upgrade and now I can't even launch it.

Context

We ran into this bug randomly, noticing that deleted ROWID would get reused after migrating the DB. Using transform to change any column in the table will also unexpectedly strip away the AUTOINCREMENT keyword from the primary key definition, even if it was not the transformation target.

Reproducible example

Original database

```sql $ sqlite3 test.db << EOF CREATE TABLE mytable ( col1 INTEGER PRIMARY KEY AUTOINCREMENT, col2 TEXT NOT NULL ) EOF

$ sqlite3 test.db ".schema mytable" CREATE TABLE mytable ( col1 INTEGER PRIMARY KEY AUTOINCREMENT, col2 TEXT NOT NULL ); ```

Modified database after sqlite-utils

```sql $ sqlite-utils transform test.db mytable --rename col2 renamedcol2

$ sqlite3 test.db "SELECT sql FROM sqlite_master WHERE name = 'mytable';" CREATE TABLE IF NOT EXISTS "mytable" ( [col1] INTEGER PRIMARY KEY, [renamedcol2] TEXT NOT NULL ); ```

https://github.com/simonw/datasette/blob/452a587e236ef642cbc6ae345b58767ea8420cb5/datasette/utils/asgi.py#L137-L139

In GET requests you can do ?foo=1&foo=2 - you can do the same in POST requests, but the dict() call here eliminates those duplicates.

You can't even try calling post_body() and implement your own custom parsing because of: - #2204

This code here:

https://github.com/simonw/datasette/blob/452a587e236ef642cbc6ae345b58767ea8420cb5/datasette/utils/asgi.py#L127-L135

It consumes the messages, which means if you try to call it a second time you won't be able to get at the body.

This is efficient - we don't end up with a request object property with potentially megabytes of content that we never look at again - but it's inconvenient for cases like middleware or functions where we don't know if the body has been consumed yet or not.

Potential solution: set request._body the first time it is called, and return that on subsequent calls.

Potential optimization: only do this for bodies that are shorter than a certain threshold - maybe 1MB - and raise an exception if you attempt to call post_body() multiple times against one of those larger bodies.

I'm a bit nervous about that option though, since it could result in errors that don't show up in testing but do show up in production.

Thanks again for such a powerful project.

For deploying to cloudrun from github actions, I'd like to create a service account with minimal permissions.

It would be great to document what those minimum permission that need to be set in the IAM.

Hi, I'm not sure if this is the right repo for this issue.

I'm using datasette with the parquet (to read a duckdb), and jellyfish plugins. Both work perfectly.

Now I need to create a simple plugin that uses the python rouge package and returns a similarity score (similarly to how the jellyfish plugin works). If I create a custom plugin, even the example hello_world one, copied directly from the tutorial, I get the following error: duckdb.duckdb.CatalogException: Catalog Error: Scalar Function with name hello_world does not exist!

Since the jellyfish plugin doesn't do anything more complex, I'm wondering if there is some other kind of issue with my setup.

https://github.com/simonw/sqlite-utils-plugins should be in the official documentation.

I can use the same pattern as https://llm.datasette.io/en/stable/plugins/directory.html

https://til.simonwillison.net/readthedocs/stable-docs

https://datasette.io/discord leads to https://discord.com/invite/ktd74dm5mw and returns the following:

this page

is using about 400 mb in firefox 97 on mac os x. if you download the html for the page, it's about 11mb and if you get the csv for the data its about 1mb.

it's using over a 1G on chrome 99.

i found this because, i was trying to figure out why editing the SQL was getting very slow.

evernote-to-sqlite enex evernote.db ./我的笔记.enex Importing from ENEX [#####-------------------------------] 14% Traceback (most recent call last): File "/usr/local/bin/evernote-to-sqlite", line 8, in <module> sys.exit(cli()) ^^^^^ File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1157, in __call__ return self.main(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) ^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1688, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/evernote_to_sqlite/cli.py", line 31, in enex save_note(db, note) File "/usr/local/lib/python3.11/site-packages/evernote_to_sqlite/utils.py", line 46, in save_note "created": convert_datetime(created), ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/evernote_to_sqlite/utils.py", line 111, in convert_datetime return datetime.datetime.strptime(s, "%Y%m%dT%H%M%SZ").isoformat() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/Cellar/python@3.11/3.11.5/Frameworks/Python.framework/Versions/3.11/lib/python3.11/_strptime.py", line 568, in _strptime_datetime tt, fraction, gmtoff_fraction = _strptime(data_string, format) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/Cellar/python@3.11/3.11.5/Frameworks/Python.framework/Versions/3.11/lib/python3.11/_strptime.py", line 349, in _strptime raise ValueError("time data %r does not match format %r" % ValueError: time data '2014-11-21T11:44:12.000Z' does not match format '%Y%m%dT%H%M%SZ'

enex is exported by evernote mac client

Exception: Datasette no longer accepts plugin configuration in --metadata. Move your "plugins" configuration blocks to a separate file - we suggest calling that datasette..json - and start Datasette with datasette -c datasette..json. See https://docs.datasette.io/en/latest/configuration.html for more details.

I think we should link directly to documentation that tells people how to perform this upgrade.

Originally posted by @simonw in https://github.com/simonw/datasette/issues/2190#issuecomment-1759947021

Using each of python -m datasette counties.db -m metadata.yml --load-extension=SpatiaLite

and

python -m datasette counties.db --load-extension="C:\Windows\System32\mod_spatialite.dll"

and

python -m datasette counties.db --load-extension=C:\Windows\System32\mod_spatialite.dll

I got the error:

``` File "C:\Users\m3n7es\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\datasette\database.py", line 209, in in_thread self.ds._prepare_connection(conn, self.name) File "C:\Users\m3n7es\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\datasette\app.py", line 596, in _prepare_connection conn.execute("SELECT load_extension(?, ?)", [path, entrypoint]) sqlite3.OperationalError: The specified module could not be found.

```

I finally tried modifying the code in app.py to read:

``` def _prepare_connection(self, conn, database): conn.row_factory = sqlite3.Row conn.text_factory = lambda x: str(x, "utf-8", "replace") if self.sqlite_extensions: conn.enable_load_extension(True) for extension in self.sqlite_extensions: # "extension" is either a string path to the extension # or a 2-item tuple that specifies which entrypoint to load. #if isinstance(extension, tuple): # path, entrypoint = extension # conn.execute("SELECT load_extension(?, ?)", [path, entrypoint]) #else: conn.execute("SELECT load_extension('C:\Windows\System32\mod_spatialite.dll')")

``` At which point the counties example worked.

Is there a correct way to install/use the extension on Windows? My method will cause issues if there's a second extension to be used.

On an unrelated note, my next step is to figure out how to write a query across the two loaded databases supplied from the command line: python -m datasette rm_toucans_23_10_07.db counties.db -m metadata.yml --load-extension=SpatiaLite

Opening db files in immutable mode sometimes leads to the file being mutated, which causes duplication in the docker image layers: see #1836, #1480

That this happens in "immutable" mode is surprising, because the sqlite docs say that setting this should open the database as read only.

https://www.sqlite.org/c3ref/open.html

immutable: The immutable parameter is a boolean query parameter that indicates that the database file is stored on read-only media. When immutable is set, SQLite assumes that the database file cannot be changed, even by a process with higher privilege, and so the database is opened read-only and all locking and change detection is disabled. Caution: Setting the immutable property on a database file that does in fact change can result in incorrect query results and/or SQLITE_CORRUPT errors. See also: SQLITE_IOCAP_IMMUTABLE.

Perhaps this is a bug in sqlite?

:books: Documentation preview :books:: https://datasette--1870.org.readthedocs.build/en/1870/

Currently using both insert-files and convert is needed in order to create sqlar files, it would be more convenient if it could be done with just one command.

```shell ~ ❯ cat test.py import os

class Example: def init(self, arg1, arg2): self.arg1 = arg1

~ ❯ sqlite-utils insert-files test.sqlar sqlar test.py -c name:name -c data:content -c mode:mode -c mtime:mtime -c sz:size --pk=name [####################################] 100%

~ ❯ sqlite-utils convert test.sqlar sqlar data "zlib.compress(value)" --import=zlib --where "name = 'test.py'" [####################################] 100%

~ ❯ cat test.py | sqlite-utils convert test.sqlar sqlar data "zlib.compress(sys.stdin.buffer.read())" --import=zlib --import=sys --where "name = 'test.py'" # Alternative way [####################################] 100%

~ ❯ sqlite3 test.sqlar "SELECT hex(data) FROM sqlar WHERE name = 'test.py';" | python3 -c "import sys, zlib; sys.stdout.buffer.write(zlib.decompress(bytes.fromhex(sys.stdin.read())))" import os

class Example: def init(self, arg1, arg2): self.arg1 = arg1

~ ❯ rm test.py

~ ❯ sqlar -l test.sqlar test.py

~ ❯ sqlar -x test.sqlar

~ ❯ cat test.py import os

class Example: def init(self, arg1, arg2): self.arg1 = arg1

```

Original title: Perhaps metadata should all live in a _metadata in-memory database

Inspired by #1150 - metadata should be exposed as an API, and for large Datasette instances that API may need to be paginated. So why not expose it through an in-memory database table?

One catch to this: plugins. #860 aims to add a plugin hook for metadata. But if the metadata comes from an in-memory table, how do the plugins interact with it?

The need to paginate over metadata does make a plugin hook that returns metadata for an individual table seem less wise, since we don't want to have to do 10,000 plugin hook invocations to show a list of all metadata.

If those plugins write directly to the in-memory table how can their contributions survive the server restarting?

Update hupper's entry point so that click commands are processed properly.

Fixes #2123

:books: Documentation preview :books:: https://datasette--2155.org.readthedocs.build/en/2155/

As seen in https://til.simonwillison.net/sqlite/import-csv - .mode csv and then .import school.csv schools is hugely faster than importing via sqlite-utils insert and doing the work in Python - but it can only be implemented by shelling out to the sqlite3 CLI tool, it's not functionality that is exposed to the Python sqlite3 module.

An option to use this would be useful - maybe something like this:

sqlite-utils insert blah.db blah blah.csv --fast

... which raises the challenge that datasette publish doesn't yet know what to do with a config file!

Originally posted by @simonw in https://github.com/simonw/datasette/issues/2194#issuecomment-1730259871

When running datasette serve with the --reload flag, the serve command is picked up as a file argument:

$ datasette serve --reload test_db Starting monitor for PID 13574. Error: Invalid value for '[FILES]...': Path 'serve' does not exist. Press ENTER or change a file to reload.

If a 'serve' file is created it launches properly (albeit with an empty database called serve):

$ touch serve; datasette serve --reload test_db Starting monitor for PID 13628. INFO: Started server process [13628] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://127.0.0.1:8001 (Press CTRL+C to quit)

Version (running from HEAD on main):

$ datasette --version datasette, version 1.0a2

This issue appears to have existed for awhile as https://github.com/simonw/datasette/issues/1380#issuecomment-953366110 mentions the error in a different context.

I'm happy to debug and land a patch if it's welcome.

There's a ton of tools/languages that compile to SQL, which may be nice in Datasette. Some examples:

• Logica https://logica.dev
• PRQL https://prql-lang.org
• Malloy, but not sure if it works with SQLite? https://github.com/malloydata/malloy

It would be cool if plugins could extend Datasette to use these languages, in both the code editor and API usage.

A few things I'd imagine a datasette-prql or datasette-logica plugin would do:

• prql= instead of sql=
• Code editor support (syntax highlighting, autocomplete)
• Hide/show SQL

Sometimes a plugin may want to add content to an existing default template - for example datasette-search-all adds a new search box at the top of index.html. I also want datasette-upload-csvs to add a CTA on the database.html page: https://github.com/simonw/datasette-upload-csvs/issues/18

Currently plugins can do this by providing a new version of the index.html template - but if multiple plugins try to do that only one of them will succeed.

It would be better if there were known areas of those templates which plugins could add additional content to, such that multiple plugins can use the same spot.

Hi, is there any way to use datasette for serving json only without displaying webpage? I've tried to search about this in documentation but didn't get any information

The cause of this bug: - https://github.com/simonw/datasette-export-notebook/issues/17

Is that datasette-export-notebook was consulting data["filtered_table_rows_count"] in the render output plugin function in order to show the total number of rows that would be exported.

That field is no longer available by default - the "count" field is only available if ?_extra=count was passed.

It would be useful if plugins like this could access the total count on demand, should they need to.

This made me think. That {"$env": "ENV_VAR"} hack was introduced back here:

• https://github.com/simonw/datasette/issues/538

The problem it was solving was that metadata was visible to everyone with access to the instance at /-/metadata but plugins clearly needed a way to set secret settings.

Now that this stuff is moving to config, we have some decisions to make:

1. Add /-/config to let people see the configuration of their instance, and keep the $env trick for secret settings.
2. Say all configuration aside from metadata is secret and make $env optional or ditch it entirely.
3. Allow plugins to announce which of their configuration options are secret so we can automatically redact them from /-/config

I've found /-/metadata extraordinarily useful as a user of Datasette - it really helps me understand exactly what's going on if I run into any problems with a plugin, if I can quickly check what the settings look like.

So I'm leaning towards option 1 or 3.

Originally posted by @simonw in https://github.com/simonw/datasette/pull/2183#discussion_r1325076924

Also refs: - #2093

Given this schema: sql CREATE TABLE departments ( campus_name TEXT NOT NULL, dept_code TEXT NOT NULL, dept_name TEXT, PRIMARY KEY (campus_name, dept_code) ); CREATE TABLE courses ( course_code TEXT PRIMARY KEY, course_name TEXT, campus_name TEXT NOT NULL, dept_code TEXT NOT NULL, FOREIGN KEY (campus_name, dept_code) REFERENCES departments(campus_name, dept_code) ); The output of db["courses"].foreign_keys right now is: [ForeignKey(table='courses', column='campus_name', other_table='departments', other_column='campus_name'), ForeignKey(table='courses', column='dept_code', other_table='departments', other_column='dept_code')] Which suggests two normal foreign keys, not one compound foreign key.

Very often I get tripped up when trying to configure my Datasette instances. For example: if I want to change the port my app listen too, do I do that with a CLI flag, a --setting flag, inside metadata.json, or an env var? If I want to up the time limit of SQL statements, is that under metadata.json or a setting? Where does my plugin configuration go?

Normally I need to look it up in Datasette docs, and I quickly find my answer, but the number of places where "config" goes it overwhelming.

• Flat CLI flags like --port, --host, --cors, etc.
• --setting, like default_page_size, sql_time_limit_ms etc
• Inside metadata.json, including plugin configuration

Typically my Datasette deploys are extremely long shell commands, with multiple --setting and other CLI flags.

Proposal: Consolidate all "config" into datasette.toml

I propose that we add a new datasette.toml that combines "settings", "metadata", and other common CLI flags like --port and --cors into a single file. It would be similar to "Cargo.toml" in Rust projects, "package.json" in Node projects, and "pyproject.toml" in Python, etc.

A sample of what it could look like:

```toml

"top level" configuration that are currently CLI flags on datasette serve

[config] port = 8020 host = "0.0.0.0" cors = true

replaces multiple --setting flags

[settings] base_url = "/app/datasette/" default_allow_sql = true sql_time_limit_ms = 3500

replaces metadata.json.

The contents of datasette-metadata.json could be defined in this file instead, but supporting separate files is nice (since those are easy to machine-generate)

[metadata] include="./datasette-metadata.json"

plugin-specific

[plugins] [plugins.datasette-auth-github] client_id = {env = "DATASETTE_AUTH_GITHUB_CLIENT_ID"} client_secret = {env = "GITHUB_CLIENT_SECRET"}

[plugins.datasette-cluster-map]

latitude_column = "lat" longitude_column = "lon" ```

Pros

• Instead of multiple files and CLI flags, everything could be in one tidy file
• Editing config in a separate file is easier than editing CLI flags, since you don't have to kill a process + edit a command every time
• New users will know "just edit my datasette.toml instead of needing to learn metadata + settings + CLI flags
• Better dev experience for multiple environment. For example, could have datasette -c datasette-dev.toml for local dev environments (enables SQL, debug plugins, long timeouts, etc.), and a datasette -c datasette-prod.toml for "production" (lower timeouts, less plugins, monitoring plugins, etc.)

Cons

• Yet another config-management system. Now Datasette users will need to know about metadata, settings, CLI flags, and datasette.toml. However with enough documentation + announcements + examples, I think we can get ahead of it.
• If toml is chosen, would need to add a toml parser for Python version <3.11
• Multiple sources of config require priority. For example: Would --setting default_allow_sql off override the value inside [settings]? What about --port?

Other Notes

Toml

I chose toml over json because toml supports comments. I chose toml over yaml because Python 3.11 has builtin support for it. I also find toml easier to work with since it doesn't have the odd "gotchas" that YAML has ("ex 3.10 resolving to 3.1, Norway NO resolving to false, etc.). It also mimics pyproject.toml which is nice. Happy to change my mind about this however

Plugin config will be difficult

Plugin config is currently in metadata.json in two places:

1. Top level, under "plugins.[plugin-name]". This fits well into datasette.toml as [plugins.plugin-name]
2. Table level, under "databases.[db-name].tables.[table-name].plugins.[plugin-name]. This doesn't fit that well into datasette.toml, unless it's nested under [metadata]?

Extensions, static, one-off plugins?

We could also include equivalents of --plugins-dir, --static, and --load-extension into datasette.toml, but I'd imagine there's a few security concerns there to think through.

Explicitly list with plugins to use?

I believe Datasette by default will load all install plugins on startup, but maybe datasette.toml can specify a list of plugins to use? For example, a dev version of datasette.toml can specify datasette-pretty-traces, but the prod version can leave it out

There's a long justification for why this might be needed here: - https://github.com/simonw/datasette-auth-tokens/issues/10#issuecomment-1701820001

Short version: it would be neat if it was possible to stash some data on the request object such that a later plugin/middleware-type-thing could use that to influence the final returned response - similar to the kinds of things you can do with Django middleware.

The asgi_wrapper() mechanism doesn't have access to the request or response objects - it gets scope and can mess around with receive and send, but those are pretty low-level primitives.

Since Datasette has well-defined request and response objects now it might be nice to have a middleware layer that can manipulate those directly.

In: - https://github.com/simonw/datasette.io/issues/152#issuecomment-1712323347

This turned out to be the fix:

bash dogsheep-beta index dogsheep-index.db templates/dogsheep-beta.yml sqlite-utils rebuild-fts dogsheep-index.db

WIP

This PR builds on #5 to continue implementing gmail import support.

Building on @UtahDave's work, these commits add a few performance and bug fixes:

• Decreased memory overhead for import by manually parsing mbox headers.
• Fixed error where some messages in the mbox would yield a row with NULL in all columns.

I will send more commits to fix any errors I encounter as I run the importer on my personal takeout data.

refs 2157, specifically this comment

Passing in --internal my_internal.db over and over again can get repetitive.

This PR adds a new configurable env variable DATASETTE_INTERNAL_DB_PATH. If it's defined, then it takes place as the path to the internal database. Users can still overwrite this behavior by passing in their own --internal internal.db flag.

In draft mode for now, needs tests and documentation.

Side note: Maybe we can have a sections in the docs that lists all the "configuration environment variables" that Datasette respects? I did a quick grep and found:

• DATASETTE_LOAD_PLUGINS
• DATASETTE_SECRETS

:books: Documentation preview :books:: https://datasette--2174.org.readthedocs.build/en/2174/

I unfortunately was not successful to deploy to fly.io. Please see the details above of the three scenarios that I took. I am also new to datasette.

Failed to deploy. Attaching logs: 1. Tried with an app created via flyctl apps create frosty-fog-8565 and the ran datasette publish fly covid.db --app frosty-fog-8565 ``` Deploying frosty-fog-8565 ==> Validating app configuration --> Validating app configuration done Services TCP 80/443 ⇢ 8080

Error error connecting to docker: An unknown error occured.

Traceback (most recent call last): File "c:\users\grott\anaconda3\lib\runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "c:\users\grott\anaconda3\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "C:\Users\grott\Anaconda3\Scripts\datasette.exe__main__.py", line 7, in <module> File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 829, in call return self.main(args, kwargs) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 782, in main rv = self.invoke(ctx) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1259, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1259, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1066, in invoke return ctx.invoke(self.callback, ctx.params) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 610, in invoke return callback(args, **kwargs) File "c:\users\grott\anaconda3\lib\site-packages\datasette_publish_fly__init__.py", line 156, in fly "--remote-only", File "c:\users\grott\anaconda3\lib\contextlib.py", line 119, in exit next(self.gen) File "c:\users\grott\anaconda3\lib\site-packages\datasette\utils__init__.py", line 451, in temporary_docker_directory tmp.cleanup() File "c:\users\grott\anaconda3\lib\tempfile.py", line 811, in cleanup _shutil.rmtree(self.name) File "c:\users\grott\anaconda3\lib\shutil.py", line 516, in rmtree return _rmtree_unsafe(path, onerror) File "c:\users\grott\anaconda3\lib\shutil.py", line 395, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "c:\users\grott\anaconda3\lib\shutil.py", line 404, in _rmtree_unsafe onerror(os.rmdir, path, sys.exc_info()) File "c:\users\grott\anaconda3\lib\shutil.py", line 402, in _rmtree_unsafe os.rmdir(path) PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\Users\grott\AppData\Local\Temp\tmpgcm8cz66\frosty-fog-8565' ```

1. Tried also with an app that gets autogenerate when running flyctl launch. This also generates the .toml file. Ran then datasette publish fly covid.db --app dark-feather-168 but different error now ```Deploying dark-feather-168 ==> Validating app configuration

Error not possible to validate configuration: server returned Post "https://api.fly.io/graphql": unexpected EOF

Traceback (most recent call last): File "c:\users\grott\anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"main", mod_spec) exec(code, run_globals) File "C:\Users\grott\Anaconda3\Scripts\datasette.exe__main__.py", line 7, in <module> File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 829, in call return self.main(args, kwargs) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 782, in main rv = self.invoke(ctx) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1259, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1259, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1066, in invoke return ctx.invoke(self.callback, ctx.params) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 610, in invoke return callback(args, **kwargs) File "c:\users\grott\anaconda3\lib\site-packages\datasette_publish_fly__init__.py", line 156, in fly "--remote-only", File "c:\users\grott\anaconda3\lib\contextlib.py", line 119, in exit next(self.gen) File "c:\users\grott\anaconda3\lib\site-packages\datasette\utils__init__.py", line 451, in temporary_docker_directory tmp.cleanup() File "c:\users\grott\anaconda3\lib\tempfile.py", line 811, in cleanup _shutil.rmtree(self.name) File "c:\users\grott\anaconda3\lib\shutil.py", line 516, in rmtree return _rmtree_unsafe(path, onerror) File "c:\users\grott\anaconda3\lib\shutil.py", line 395, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "c:\users\grott\anaconda3\lib\shutil.py", line 404, in _rmtree_unsafe onerror(os.rmdir, path, sys.exc_info()) File "c:\users\grott\anaconda3\lib\shutil.py", line 402, in _rmtree_unsafe os.rmdir(path) PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\Users\grott\AppData\Local\Temp\tmpnoyewcre\dark-feather-168' ```

These are also the contents of the generated .toml file in 2 scenario:

```

fly.toml file generated for dark-feather-168 on 2021-09-28T20:35:44+02:00

app = "dark-feather-168"

kill_signal = "SIGINT" kill_timeout = 5 processes = []

[env]

[experimental] allowed_public_ports = [] auto_rollback = true

[[services]] http_checks = [] internal_port = 8080 processes = ["app"] protocol = "tcp" script_checks = []

[services.concurrency] hard_limit = 25 soft_limit = 20 type = "connections"

[[services.ports]] handlers = ["http"] port = 80

[[services.ports]] handlers = ["tls", "http"] port = 443

[[services.tcp_checks]] grace_period = "1s" interval = "15s" restart_limit = 6 timeout = "2s" ```

1. But also trying datasette package covid.db to create a local DOCKERFILE to later try to push it via flyctl deploy fails as well.

```[+] Building 147.3s (11/11) FINISHED => [internal] load build definition from Dockerfile 0.2s => => transferring dockerfile: 396B 0.0s => [internal] load .dockerignore 0.1s => => transferring context: 2B 0.0s => [internal] load metadata for docker.io/library/python:3.8 4.7s => [auth] library/python:pull token for registry-1.docker.io 0.0s => [internal] load build context 0.1s => => transferring context: 82.37kB 0.0s => [1/5] FROM docker.io/library/python:3.8@sha256:530de807b46a11734e2587a784573c12c5034f2f14025f838589e6c0e3 108.3s => => resolve docker.io/library/python:3.8@sha256:530de807b46a11734e2587a784573c12c5034f2f14025f838589e6c0e3b5 0.0s => => sha256:56182bcdf4d4283aa1f46944b4ef7ac881e28b4d5526720a4e9ba03a4730846a 2.22kB / 2.22kB 0.0s => => sha256:955615a668ce169f8a1443fc6b6e6215f43fe0babfb4790712a2d3171f34d366 54.93MB / 54.93MB 21.6s => => sha256:911ea9f2bd51e53a455297e0631e18a72a86d7e2c8e1807176e80f991bde5d64 10.87MB / 10.87MB 15.5s => => sha256:530de807b46a11734e2587a784573c12c5034f2f14025f838589e6c0e3b5c5b6 1.86kB / 1.86kB 0.0s => => sha256:ff08f08727e50193dcf499afc30594c47e70cc96f6fcfd1a01240524624264d0 8.65kB / 8.65kB 0.0s => => sha256:2756ef5f69a5190f4308619e0f446d95f5515eef4a814dbad0bcebbbbc7b25a8 5.15MB / 5.15MB 6.4s => => sha256:27b0a22ee906271a6ce9ddd1754fdd7d3b59078e0b57b6cc054c7ed7ac301587 54.57MB / 54.57MB 37.7s => => sha256:8584d51a9262f9a3a436dea09ba40fa50f85802018f9bd299eee1bf538481077 196.45MB / 196.45MB 82.3s => => sha256:524774b7d3638702fe9ae0ea3fcfb81b027dfd75cc2fc14f0119e764b9543d58 6.29MB / 6.29MB 26.6s => => extracting sha256:955615a668ce169f8a1443fc6b6e6215f43fe0babfb4790712a2d3171f34d366 5.4s => => sha256:9460f6b75036e38367e2f27bb15e85777c5d6cd52ad168741c9566186415aa26 16.81MB / 16.81MB 40.5s => => extracting sha256:2756ef5f69a5190f4308619e0f446d95f5515eef4a814dbad0bcebbbbc7b25a8 0.6s => => extracting sha256:911ea9f2bd51e53a455297e0631e18a72a86d7e2c8e1807176e80f991bde5d64 0.6s => => sha256:9bc548096c181514aa1253966a330134d939496027f92f57ab376cd236eb280b 232B / 232B 40.1s => => extracting sha256:27b0a22ee906271a6ce9ddd1754fdd7d3b59078e0b57b6cc054c7ed7ac301587 5.8s => => sha256:1d87379b86b89fd3b8bb1621128f00c8f962756e6aaaed264ec38db733273543 2.35MB / 2.35MB 41.8s => => extracting sha256:8584d51a9262f9a3a436dea09ba40fa50f85802018f9bd299eee1bf538481077 18.8s => => extracting sha256:524774b7d3638702fe9ae0ea3fcfb81b027dfd75cc2fc14f0119e764b9543d58 1.2s => => extracting sha256:9460f6b75036e38367e2f27bb15e85777c5d6cd52ad168741c9566186415aa26 2.9s => => extracting sha256:9bc548096c181514aa1253966a330134d939496027f92f57ab376cd236eb280b 0.0s => => extracting sha256:1d87379b86b89fd3b8bb1621128f00c8f962756e6aaaed264ec38db733273543 0.8s => [2/5] COPY . /app 2.3s => [3/5] WORKDIR /app 0.2s => [4/5] RUN pip install -U datasette 26.9s => [5/5] RUN datasette inspect covid.db --inspect-file inspect-data.json 3.1s => exporting to image 1.2s => => exporting layers 1.2s => => writing image sha256:b5db0c205cd3454c21fbb00ecf6043f261540bcf91c2dfc36d418f1a23a75d7a 0.0s

Use 'docker scan' to run Snyk tests against images to find vulnerabilities and learn how to fix them Traceback (most recent call last): "main", mod_spec) File "c:\users\grott\anaconda3\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "C:\Users\grott\Anaconda3\Scripts\datasette.exe__main__.py", line 7, in <module> File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 829, in call return self.main(args, kwargs) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 782, in main rv = self.invoke(ctx) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1259, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 1066, in invoke return ctx.invoke(self.callback, ctx.params) File "c:\users\grott\anaconda3\lib\site-packages\click\core.py", line 610, in invoke return callback(args, **kwargs) File "c:\users\grott\anaconda3\lib\site-packages\datasette\cli.py", line 283, in package call(args) File "c:\users\grott\anaconda3\lib\contextlib.py", line 119, in exit next(self.gen) File "c:\users\grott\anaconda3\lib\site-packages\datasette\utils__init__.py", line 451, in temporary_docker_directory tmp.cleanup() File "c:\users\grott\anaconda3\lib\tempfile.py", line 811, in cleanup _shutil.rmtree(self.name) File "c:\users\grott\anaconda3\lib\shutil.py", line 516, in rmtree return _rmtree_unsafe(path, onerror) File "c:\users\grott\anaconda3\lib\shutil.py", line 395, in _rmtree_unsafe _rmtree_unsafe(fullname, onerror) File "c:\users\grott\anaconda3\lib\shutil.py", line 404, in _rmtree_unsafe onerror(os.rmdir, path, sys.exc_info()) File "c:\users\grott\anaconda3\lib\shutil.py", line 402, in _rmtree_unsafe os.rmdir(path) PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\Users\grott\AppData\Local\Temp\tmpkb27qid3\datasette'```

• build: use poetry for package management, asdf for python version
• build: cleanup poetry config, add keywords, ignore dist
• ci: migrate circleci to gh actions
• fix: dup method definition

This came up in discussion over: - https://github.com/simonw/datasette/pull/2174

1. The code uses two different ways of writing encoding formats, mac_roman and macroman. It is uncertain whether there are any typo errors.
2. When there are Chinese characters in the content, exporting it results in garbled code. Changing it to utf8 can fix the issue.

Currently the constructor accepts memory=True or memory_name=... and uses those to create a connection, but does not record what those values were:

https://github.com/simonw/sqlite-utils/blob/1260bdc7bfe31c36c272572c6389125f8de6ef71/sqlite_utils/db.py#L307-L349

This makes it hard to tell if a database object is to an in-memory or a file-based database, which is sometimes useful to know.

I used a custom SQL function in a migration script and then realized that it should be de-registered before the end of the script to avoid leaking into the calling code.

The return type isn't documented here: https://github.com/simonw/datasette/blob/4c3ef033110407f3b3dbce501659d523724985e0/docs/internals.rst#L327-L350

On inspecting the code I'm not 100% sure if it's possible for this. method to return None, or if it can only return True or False. Need to confirm that.

https://github.com/simonw/datasette/blob/4c3ef033110407f3b3dbce501659d523724985e0/datasette/app.py#L822C15-L853

The current _internal database is used by Datasette core to cache info about databases/tables/columns/foreign keys of databases in a Datasette instance. It's a temporary database created at startup, that can only be seen by the root user. See an example _internal DB here, after logging in as root.

The current _internal database has a few rough edges:

• It's part of datasette.databases, so many plugins have to specifically exclude _internal from their queries examples here
• It's only used by Datasette core and can't be used by plugins or 3rd parties
• It's created from scratch at startup and stored in memory. Why is fine, the performance is great, but persistent storage would be nice.

Additionally, it would be really nice if plugins could use this _internal database to store their own configuration, secrets, and settings. For example:

• datasette-auth-tokens creates a _datasette_auth_tokens table to store auth token metadata. This could be moved into the _internal database to avoid writing to the gues database
• datasette-socrata creates a socrata_imports table, which also can be in _internal
• datasette-upload-csvs creates a _csv_progress_ table, which can be in _internal
• datasette-write-ui wants to have the ability for users to toggle whether a table appears editable, which can be either in datasette.yaml or on-the-fly by storing config in _internal

In general, these are specific features that Datasette plugins would have access to if there was a central internal database they could read/write to:

• Dynamic configuration. Changing the datasette.yaml file works, but can be tedious to restart the server every time. Plugins can define their own configuration table in _internal, and could read/write to it to store configuration based on user actions (cell menu click, API access, etc.)
• Caching. If a plugin or Datasette Core needs to cache some expensive computation, they can store it inside _internal (possibly as a temporary table) instead of managing their own caching solution.
• Audit logs. If a plugin performs some sensitive operations, they can log usage info to _internal for others to audit later.
• Long running process status. Many plugins (datasette-upload-csvs, datasette-litestream, datasette-socrata) perform tasks that run for a really long time, and want to give continue status updates to the user. They can store this info inside_internal
• Safer authentication. Passwords and authentication plugins usually store credentials/hashed secrets in configuration files or environment variables, which can be difficult to handle. Now, they can store them in _internal

Proposal

• We remove _internal from datasette.databases property.
• We add new datasette.get_internal_db() method that returns the _internal database, for plugins to use
• We add a new --internal internal.db flag. If provided, then the _internal DB will be sourced from that file, and further updates will be persisted to that file (instead of an in-memory database)
• When creating internal.db, create a new _datasette_internal table to mark it a an "datasette internal database"
• In datasette serve, we check for the existence of the _datasette_internal table. If it exists, we assume the user provided that file in error and raise an error. This is to limit the chance that someone accidentally publishes their internal database to the internet. We could optionally add a --unsafe-allow-internal flag (or database plugin) that allows someone to do this if they really want to.

New features unlocked with this

These features don't really need a standardized _internal table per-say (plugins could currently configure their own long-time storage features if they really wanted to), but it would make it much simpler to create these kinds of features with a persistent application database.

• datasette-comments : A plugin for commenting on rows or specific values in a database. Comment contents + threads + email notification info can be stored in _internal
• Bookmarks: "Bookmarking" an SQL query could be stored in _internal, or a URL link shortener
• Webhooks: If a plugin wants to either consume a webhook or create a new one, they can store hashed credentials/API endpoints in _internal

I just had to write a one-off custom plugin to redirect niche-musems.com to www.niche-museums.com (https://github.com/simonw/museums/issues/21) - it would be great if this kind of thing could be handled by a configurable plugin.

https://github.com/simonw/museums/blob/6b1faf00c463b2228860d4d62d104b11935e01b1/plugins/redirect_www.py

I've been thinking about what it might look like to allow command-line arguments to be used to define any of the configuration options in datasette.yml, as alternative and more convenient syntax.

Here's what I've come up with: datasette \ -s settings.sql_time_limit_ms 1000 \ -s plugins.datasette-auth-tokens.manage_tokens true \ -s plugins.datasette-auth-tokens.manage_tokens_database tokens \ mydatabase.db tokens.db Which would be equivalent to datasette.yml containing this: yaml plugins: datasette-auth-tokens: manage_tokens: true manage_tokens_database: tokens settings: sql_time_limit_ms: 1000 More details in https://github.com/simonw/datasette/issues/2143#issuecomment-1690792514

This came up working on this feature: - https://github.com/simonw/llm/pull/186

I have a table with this schema: sql CREATE TABLE [collections] ( [id] INTEGER PRIMARY KEY, [name] TEXT, [model] TEXT ); CREATE UNIQUE INDEX [idx_collections_name] ON [collections] ([name]); So the primary key is an integer (because it's going to have a huge number of rows foreign key related to it, and I don't want to store a larger text value thousands of times), but there is a unique constraint on the name - that would be the primary key column if not for all of those foreign keys.

Problem is, fetching the collection by name is actually pretty inconvenient.

Fetch by numeric ID:

python try: table["collections"].get(1) except NotFoundError: # It doesn't exist Fetching by name: python def get_collection(db, collection): rows = db["collections"].rows_where("name = ?", [collection]) try: return next(rows) except StopIteration: raise NotFoundError("Collection not found: {}".format(collection)) It would be neat if, for columns where we know that we should always get 0 or one result, we could do this instead: python try: collection = table["collections"].get(name="entries") except NotFoundError: # It doesn't exist The existing .get() method doesn't have any non-positional arguments, so using **kwargs like that should work:

https://github.com/simonw/sqlite-utils/blob/1260bdc7bfe31c36c272572c6389125f8de6ef71/sqlite_utils/db.py#L1495

Closes #2095.

:books: Documentation preview :books:: https://datasette--2159.org.readthedocs.build/en/2159/

This adds a brand link to the top navbar if 'brand' key is populated in metadata.json. The link will be either '#' or use the contents of 'brand_url' in metadata.json for href.

I was able to get this done on my own site by replacing templates/_crumbs.html with a custom version, but I thought it would be nice to incorporate this in the tool directly.

:books: Documentation preview :books:: https://datasette--2158.org.readthedocs.build/en/2158/

I use datasette in an iframe inside another HTML file that contains other ways to represent my data (mostly leaflets maps built with R on summarized data), and the datasette iframe is a tab in that page.

This particular use prevents users to access the full URLs of their datasette views and queries, which is a shame because the way datasette handles URLs to make every view or query easy to share is awesome. I know how to get the URL from the context menu of my browser, but I don't think many visitors would do it or even notice that datasette uses permalinks for pretty much every action they do. Would it be possible to add a "Share link" button to the interface, either in datasette itself or in a plugin?

Metadata in Datasette is a really powerful feature, but is a bit difficult to work with. It was initially a way to add "metadata" about your "data" in Datasette instances, like descriptions for databases/tables/columns, titles, source URLs, licenses, etc. But it later became the go-to spot for other Datasette features that have nothing to do with metadata, like permissions/plugins/canned queries.

Specifically, I've found the following problems when working with Datasette metadata:

1. Metadata cannot be updated without re-starting the entire Datasette instance.
2. The metadata.json/metadata.yaml has become a kitchen sink of unrelated (imo) features like plugin config, authentication config, canned queries
3. The Python APIs for defining extra metadata are a bit awkward (the datasette.metadata() class, get_metadata() hook, etc.)

Possible solutions

Here's a few ideas of Datasette core changes we can make to address these problems.

Re-vamp the Datasette Python metadata APIs

The Datasette object has a single datasette.metadata() method that's a bit difficult to work with. There's also no Python API for inserted new metadata, so plugins have to rely on the get_metadata() hook.

The get_metadata() hook can also be improved - it doesn't work with async functions yet, so you're quite limited to what you can do.

(I'm a bit fuzzy on what to actually do here, but I imagine it'll be very small breaking changes to a few Python methods)

Add an optional datasette_metadata table

Datasette should detect and use metadata stored in a new special table called datasette_metadata. This would be a regular table that a user can edit on their own, and would serve as a "live updating" source of metadata, than can be changed while the Datasette instance is running.

Not too sure what the schema would look like, but I'd imagine:

sql CREATE TABLE datasette_metadata( level text, target any, key text, value any, primary key (level, target) )

Every row in this table would map to a single metadata "entry".

• level would be one of "datasette", "database", "table", "column", which is the "level" the entry describes. For example, level="table" means it is metadata about a specific table, level="database" for a specific database, or level="datasette" for the entire Datasette instance.
• target would "point" to the specific object the entry metadata is about, and would depend on what level is specific.
• level="database": target would be the string name of the database that the metadata entry is about. ex "fixtures"
• level="table": target would be a JSON array of two strings. The first element would be the database name, and the second would be the table name. ex ["fixtures", "students"]
• level="column": target would be a JSON array of 3 strings: The database name, table name, and column name. Ex ["fixtures", "students", "student_id"]
• key would be the type of metadata entry the row has, similar to the current "keys" that exist in metadata.json. Ex "about_url", "source", "description", etc
• value would be the text value of be metadata entry. The literal text value of a description, about_url, column_label, etc

A quick sample:

level | target | key | value -- | -- | -- | -- datasette | NULL | title | my datasette title... db | fixtures | source | <description of my database source> table | ["fixtures", "students"] | label_column | student_name column | ["fixtures", "students", "birthdate"] | description | <description of the fixtures.students.birthdate column>

This datasette_metadata would be configured with other tools, and hopefully not manually by end users. Datasette Core could also offer a UI for editing entries in datasette_metadata, to update descriptions/columns on the fly.

Re-vamp metadata.json and move non-metadata config to another place

The motivation behind this is that it's awkward that metadata.json contains config about things that are not strictly metadata, including:

• Plugin configuration
• Authentication/permissions (ex the allow key on datasettes/databases/tables
• Canned queries. might be controversial, but in my mind, canned queries are application-specific code and configuration, and don't describe the data that exists in SQLite databases.

I think we should move these outside of metadata.json and into a different file. The datasette.json idea in #2093 may be a good solution here: plugin/permissions/canned queries can be defined in datasette.json, while metadata.json/datasette_metadata will strictly be about documenting databases/tables/columns.

I'm interested in making a plugin that saves every query that gets run to a table in the database. (I know about datasette-query-history but thought it would be good to have a server-side option.)

As far as I can tell reading the docs, there isn't really a hook setup to allow this.

Maybe I could hack it with some of the hooks that are passed requests, but that doesn't seem good.

I'm a little surprised this isn't possible, so I thought I would open an issue and see if that's a deeply considered decision or just "haven't needed it yet." I'm potentially interested in implementing the hook if the latter.

still testing, hope it works!

:books: Documentation preview :books:: https://datasette--2151.org.readthedocs.build/en/2151/

I want to use isort to order imports. A few steps here:

• [x] Add a .isort.cfg file (see below)
• [x] Use isort -rc to reformat existing code
• [ ] Commit this change
• [x] Add a unit test that ensures future changes remain isort compatible

Stumbled across this while experimenting with datasette-write-ui. The error I got was a 500 on the /db page:

'NoneType' object has no attribute 'encode'

Tracked it down to this code, which assembles the URL for a row page:

https://github.com/simonw/datasette/blob/943df09dcca93c3b9861b8c96277a01320db8662/datasette/utils/init.py#L120-L134

That's because tilde_encode can't handle None: https://github.com/simonw/datasette/blob/943df09dcca93c3b9861b8c96277a01320db8662/datasette/utils/init.py#L1175-L1178

I got this error trying to drop a column from a table that was part of a SQL view:

error in view plugins: no such table: main.pypi_releases

Upon further investigation I found that this pattern seemed to fix it: python def transform_the_table(conn): # Run this in a transaction: with conn: # We have to read all the views first, because we need to drop and recreate them db = sqlite_utils.Database(conn) views = {v.name: v.schema for v in db.views if table.lower() in v.schema.lower()} for view in views.keys(): db[view].drop() db[table].transform( types=types, rename=rename, drop=drop, column_order=[p[0] for p in order_pairs], ) # Now recreate the views for name, schema in views.items(): db.create_view(name, schema) So grab a copy of any view that might reference this table, start a transaction, drop those views, run the transform, recreate the views again.

I wonder if this should become an option in sqlite-utils? Maybe a recreate_views=True argument for table.tranform(...)? Should it be opt-in or opt-out?

Originally posted by @simonw in https://github.com/simonw/datasette-edit-schema/issues/35#issuecomment-1683370548

When creating a new table, it would be good to have an option to set unique columns similar to how not_null is set.

```python from sqlite_utils import Database

columns = {"mRID": str, "name": str} db = Database("example.db") db["ExampleTable"].create(columns, pk="mRID", not_null=["mRID"], if_not_exists=True) db["ExampleTable"].create_index(["mRID"], unique=True, if_not_exists=True) ```

So something like this would add the UNIQUE flag to the table definition.

python db["ExampleTable"].create(columns, pk="mRID", not_null=["mRID"], unique=["mRID"], if_not_exists=True)

sql CREATE TABLE ExampleTable ( mRID TEXT PRIMARY KEY NOT NULL UNIQUE, name TEXT );

This would be really useful as it would allow us to tie in with colour schemes.

:books: Documentation preview :books:: https://datasette--2100.org.readthedocs.build/en/2100/

This idea first came up here: - https://github.com/simonw/datasette/issues/2112#issuecomment-1652751140

If datasette.render_template(...) takes an optional Context subclass as an alternative to a context dictionary, I could then use dataclasses to define the context made available to specific templates - which then gives me something I can use to help document what they are.

Also refs: - https://github.com/simonw/datasette/issues/1510

This URL here:

https://latest.datasette.io/fixtures/simple_primary_key/1.json?_extras=foreign_key_tables

json { "database": "fixtures", "table": "simple_primary_key", "rows": [ { "id": "1", "content": "hello" } ], "columns": [ "id", "content" ], "primary_keys": [ "id" ], "primary_key_values": [ "1" ], "units": {}, "foreign_key_tables": [ { "other_table": "foreign_key_references", "column": "id", "other_column": "foreign_key_with_blank_label", "count": 0, "link": "/fixtures/foreign_key_references?foreign_key_with_blank_label=1" }, { "other_table": "foreign_key_references", "column": "id", "other_column": "foreign_key_with_label", "count": 1, "link": "/fixtures/foreign_key_references?foreign_key_with_label=1" }, { "other_table": "complex_foreign_keys", "column": "id", "other_column": "f3", "count": 1, "link": "/fixtures/complex_foreign_keys?f3=1" }, { "other_table": "complex_foreign_keys", "column": "id", "other_column": "f2", "count": 0, "link": "/fixtures/complex_foreign_keys?f2=1" }, { "other_table": "complex_foreign_keys", "column": "id", "other_column": "f1", "count": 1, "link": "/fixtures/complex_foreign_keys?f1=1" } ], "query_ms": 4.226590999678592, "source": "tests/fixtures.py", "source_url": "https://github.com/simonw/datasette/blob/main/tests/fixtures.py", "license": "Apache License 2.0", "license_url": "https://github.com/simonw/datasette/blob/main/LICENSE", "ok": true, "truncated": false }

That ?_extras= should be ?_extra= - plus the row JSON should be redesigned to fit the new default JSON representation.

• 2109

I ended up duplicating it here: https://github.com/simonw/datasette/blob/7532feb424b1dce614351e21b2265c04f9669fe2/datasette/views/database.py#L164-L167

I think it should move to datasette.render_template() - and maybe have a renamed template variable too.

Refactored out the SQL for extracting aesthetic scores to use osxphotos -- adds compatbility for Big Sur via osxphotos which has been updated for new table names in Big Sur. Have not yet refactored the SQL for extracting labels which is still compatible with Big Sur.

https://latest.datasette.io/_memory.csv?sql=select+blah is a blank page right now:

bash curl -I 'https://latest.datasette.io/_memory.csv?sql=select+blah' HTTP/2 200 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 content-type: text/plain; charset=utf-8 x-databases: _memory, _internal, fixtures, fixtures2, extra_database, ephemeral date: Mon, 07 Aug 2023 23:12:15 GMT server: Google Frontend

Originally posted by @simonw in https://github.com/simonw/datasette/issues/2118#issuecomment-1668688947

What would it take to add a format hook? I'm still thinking about my GIS workflow, and being able to do sqlite-utils query ... --geojson would be nice. It's the one place my Datasette workflow is messy, having to do datasette . --get /path/to/query.geojson --setting max_rows_returned 10000 --load-extension spatialite. I know the current pattern is --csv, but maybe --format geojson is more future-proof.

https://discord.com/channels/823971286308356157/997738192360964156/1133076679011602432

I was using sqlite-utils to create a DB from a CSV and it turns out the CSV contains a NUL byte.

When the processing reaches the line that contains the NUL an exception is raised.

I'm wondering if there is something that can be done in sqlite-utils to say "skip lines with encoding errors" or some such. I think it isn't super straightforward though as the exception comes from inside the csv module that does all the parsing.

Concretely the file is the KernelVersions.csv from https://www.kaggle.com/datasets/kaggle/meta-kaggle

This is the command and output: $ sqlite-utils insert --csv kaggle.db kaggle KernelVersions.csv [------------------------------------] 0% [#####################---------------] 60% 00:04:24Traceback (most recent call last): File "/home/foobar/miniconda/envs/meta-kaggle/bin/sqlite-utils", line 10, in <module> sys.exit(cli()) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/click/core.py", line 1128, in __call__ return self.main(*args, **kwargs) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/click/core.py", line 1053, in main rv = self.invoke(ctx) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/click/core.py", line 1659, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/click/core.py", line 1395, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/click/core.py", line 754, in invoke return __callback(*args, **kwargs) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/sqlite_utils/cli.py", line 1223, in insert insert_upsert_implementation( File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/sqlite_utils/cli.py", line 1085, in insert_upsert_implementation db[table].insert_all( File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/sqlite_utils/db.py", line 3198, in insert_all chunk = list(chunk) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/sqlite_utils/db.py", line 3742, in fix_square_braces for record in records: File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/sqlite_utils/cli.py", line 1071, in <genexpr> docs = (decode_base64_values(doc) for doc in docs) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/sqlite_utils/cli.py", line 1068, in <genexpr> docs = (verify_is_dict(doc) for doc in docs) File "/home/foobar/miniconda/envs/meta-kaggle/lib/python3.10/site-packages/sqlite_utils/cli.py", line 1003, in <genexpr> docs = (dict(zip(headers, row)) for row in reader) _csv.Error: line contains NUL

This PR changes the display of facets to hopefully make them more readable.

Before | After ---|--- |

Gotta tell it where to look

Hi,

currently parameters in canned queries are just text fields. It would be cool to have one of the options below. Would you accept a PR doing something in this direction? (Possibly this could even work as a plugin.)

• adding facets, which would work like facets on tables or views, giving a list of selectable options (and leaving parameters as is)
• making it possible to provide a query which returns selectable values for a parameter, e.g. calendar_entries_current_instrument: sql: | select * from calendar_entries where DTEND_UNIX > UNIXEPOCH() and DTSTART_UNIX < UNIXEPOCH() + :days *24*60*60 and current = 1 and MACHINE = :instrument order by DTSTART_UNIX params: days: sql: "SELECT VALUE FROM generate_series(1, 30, 1)" # this obviously requires the corresponding sqlite extension instrument: sql: "SELECT DISTINCT MACHINE FROM calendar_entries"
• making it possible to provide a fixed list of parameters calendar_entries_current_instrument: sql: | select * from calendar_entries where DTEND_UNIX > UNIXEPOCH() and DTSTART_UNIX < UNIXEPOCH() + :days *24*60*60 and current = 1 and MACHINE = :instrument order by DTSTART_UNIX params: days: values: [1, 2, 3, 5, 10, 20, 30] instrument: values: [supermachine, crappymachine, boringmachine]

Currently looks like this: https://github.com/simonw/datasette/blob/08181823990a71ffa5a1b57b37259198eaa43e06/datasette/init.py#L1-L6

Adding __all__ = ["Permission", "Forbidden"...] would let me get rid of those # noqa comments.

According to the documentation, we can make a csv output using the CLI tool, but not the Python library. Could we have the latter?

here's a CTE query that should always fail with a syntax error:

sql with foo as (nonsense) select * from foo;

when we make this query against the default endpoint, we do indeed get a 400 status code the problem is returned to the user: https://global-power-plants.datasettes.com/global-power-plants?sql=with+foo+as+%28nonsense%29+select+*+from+foo%3B

but, if we use the csv endpoint, we get a 200 status code and no indication of a problem: https://global-power-plants.datasettes.com/global-power-plants.csv?sql=with+foo+as+%28nonsense%29+select+*+from+foo%3B

same with this bad sql

sql select a, from foo;

https://global-power-plants.datasettes.com/global-power-plants?sql=select%0D%0A++a%2C%0D%0Afrom%0D%0A++foo%3B

vs

https://global-power-plants.datasettes.com/global-power-plants.csv?sql=select%0D%0A++a%2C%0D%0Afrom%0D%0A++foo%3B

but, datasette catches this bad sql at both endpoints:

sql slect a from foo;

https://global-power-plants.datasettes.com/global-power-plants?sql=slect%0D%0A++a%0D%0Afrom%0D%0A++foo%3B https://global-power-plants.datasettes.com/global-power-plants.csv?sql=slect%0D%0A++a%0D%0Afrom%0D%0A++foo%3B

sqlite-utils should detect and have specially handling for column with a JSON column. For example:

sql CREATE TABLE "dogs" ( id INTEGER PRIMARY KEY, name TEXT, friends JSON );

Automatic Nesting

According to "Nested JSON Values", sqlite-utils will only expand JSON if the --json-cols flag is passed. It looks like it'll try to json.load all text column to test if its JSON, which can get expensive on non-json columns.

Instead, sqlite-utils should be default (ie without the --json-cols flags) do the maybe_json() operation on columns with a declared JSON type. So the above table would expand the "friends" column as expected, withoutthe --json-cols flag:

bash sqlite-utils dogs.db "select * from dogs" | python -mjson.tool

[ { "id": 1, "name": "Cleo", "friends": [ { "name": "Pancakes" }, { "name": "Bailey" } ] } ]

I'm sure there's other ways sqlite-utils can specially handle JSON columns, so keeping this open while I think of more

I've been using this project reliably over the past two years to periodically download my liked tweets, but unfortunately since 19th July I get:

[2023-07-19 21:00:04.937536] File "/home/pi/code/liked-tweets/lib/python3.7/site-packages/twitter_to_sqlite/utils.py", line 202, in fetch_timeline [2023-07-19 21:00:04.937606] raise Exception(str(tweets["errors"])) [2023-07-19 21:00:04.937678] Exception: [{'message': 'You currently have access to a subset of Twitter API v2 endpoints and limited v1.1 endpoints (e.g. media post, oauth) only. If you need access to this endpoint, you may need a different access level. You can learn more here: https://developer.twitter.com/en/portal/product', 'code': 453}]

It appears like Twitter has now shut down their v1 endpoints, which is rather gracious of them, considering they announced they'd be deprecated on 29th April.

Unfortunately retrieving likes using the v2 API is not part of their free plan. In fact, with the free plan one can only post and delete tweets and retrieve information about oneself.

So I'm afraid this is the end of this very nice project. It was very useful, thank you!

I use this nginx config: ``` location /datasette-llm { return 302 /datasette-llm/; }

location /datasette-llm/ {
  proxy_set_header Upgrade           $http_upgrade;
  proxy_set_header Connection        "Upgrade";
  proxy_http_version 1.1;
  proxy_set_header X-Real-IP         $remote_addr;
  proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
  proxy_set_header X-Forwarded-Proto https;
  proxy_set_header X-Forwarded-Host  $http_host;
  proxy_set_header Host              $host;
  proxy_max_temp_file_size           0;
  proxy_pass                         http://127.0.0.1:8001/datasette-llm/;
  proxy_redirect                     http:// https://;
  proxy_buffering off;
  proxy_request_buffering off;
  proxy_set_header Origin            '';
  client_max_body_size 0;
  auth_basic                         "datasette-llm";
  auth_basic_user_file               /etc/nginx/custom-userdb;
}

Then I start datasette with this command: datasette serve --setting base_url /datasette-llm/ $(llm logs path) ``` Everything else works right, except the links in "This data as json, CSV". They get an extra URL element "datasette-llm" like this:

https://192.168.1.3:5432/datasette-llm/datasette-llm/logs.json?sql=select+*+from+_llm_migrations

https://192.168.1.3:5432/datasette-llm/datasette-llm/logs.csv?sql=select+*+from+_llm_migrations&_size=max

When I remove that extra "datasette-llm" from the URL, those links work too.

Plugins can then take advantage of this pattern, for example: - https://github.com/simonw/datasette-auth-tokens/pull/8

Maybe put it on the <tr> but probably better to go on the td.type-pk.

https://discord.com/channels/823971286308356157/823971286941302908/1120705940728066080

May I add a request to the whole metadata / settings ? Allow to pass --settings path/to/settings.json instead of having to rely exclusively on directory mode to centralize settings (this would reflect the behavior of providing metadata)

Requested here: https://discord.com/channels/823971286308356157/823971286941302908/1129034187073134642

The former datasette-insert plugin had an option ?alter=1 to auto-add new columns. Does the JSON write API also have this?

I'm getting an error on my initial attempt to import data:

console $ healthkit-to-sqlite 20201119\ healthkit\ export.zip healthkit.db Importing from HealthKit [###################################-] 98% 00:00:01 Traceback (most recent call last): File "venv/bin/healthkit-to-sqlite", line 8, in <module> sys.exit(cli()) File "venv/lib/python3.9/site-packages/click/core.py", line 829, in __call__ return self.main(*args, **kwargs) File "venv/lib/python3.9/site-packages/click/core.py", line 782, in main rv = self.invoke(ctx) File "venv/lib/python3.9/site-packages/click/core.py", line 1066, in invoke return ctx.invoke(self.callback, **ctx.params) File "venv/lib/python3.9/site-packages/click/core.py", line 610, in invoke return callback(*args, **kwargs) File "venv/lib/python3.9/site-packages/healthkit_to_sqlite/cli.py", line 57, in cli convert_xml_to_sqlite(fp, db, progress_callback=bar.update, zipfile=zf) File "venv/lib/python3.9/site-packages/healthkit_to_sqlite/utils.py", line 34, in convert_xml_to_sqlite workout_to_db(el, db, zipfile) File "venv/lib/python3.9/site-packages/healthkit_to_sqlite/utils.py", line 57, in workout_to_db pk = db["workouts"].insert(record, alter=True, hash_id="id").last_pk File "venv/lib/python3.9/site-packages/sqlite_utils/db.py", line 1660, in insert return self.insert_all( File "venv/lib/python3.9/site-packages/sqlite_utils/db.py", line 1778, in insert_all self.insert_chunk( File "venv/lib/python3.9/site-packages/sqlite_utils/db.py", line 1588, in insert_chunk result = self.db.execute(query, params) File "venv/lib/python3.9/site-packages/sqlite_utils/db.py", line 213, in execute return self.conn.execute(sql, parameters) sqlite3.IntegrityError: UNIQUE constraint failed: workouts.id

Version 3.33

sqlite-utils query library.db 'select asin from audible' --fmt plain --no-headers | head -3 asin 0062804006 0062891421

G'day! I got confused while debugging, earlier today. That's on me, but it does strike me a little repetition in the metadata documentation might help those flicking around it rather than reading it from top to bottom. No worries if you think otherwise.

:books: Documentation preview :books:: https://datasette--2096.org.readthedocs.build/en/2096/

Using the CSS media query prefers-color-scheme we can provide a dark-mode version of Datasette

Aside: this makes me think it might be cool if sqlite-utils had a way of working with dataclasses rather than just dicts, and knew how to create a SQLite table to match a dataclass and maybe how to code-generate dataclasses for a specific table schema (dynamically or even using code-generation that can be written to disk, for better editor integrations).

Originally posted by @simonw in https://github.com/simonw/llm/issues/65#issuecomment-1616742529

When #2052 merges, I'd like to add support to add extensions/functions to the Datasette code editor.

I'd eventually like to build a JS plugin for sqlite-docs, to add things like:

• Inline documentation for tables/columns on hover
• Inline docs for custom functions that are loaded in
• More detailed autocomplete for tables/columns/functions

I did some hacking to see what this would look like, see here:

There can be a new hook that allows JS plugins to add new "extension" in the CodeMirror editorview here:

https://github.com/simonw/datasette/blob/8cd60fd1d899952f1153460469b3175465f33f80/datasette/static/cm-editor-6.0.1.js#L25

Will need some more planning. For example, the Codemirror bundle in Datasette has functions that we could re-export for plugins to use (so we don't load 2 version of "@codemirror/autocomplete", for example.

https://beta.ruff.rs/docs/

Documented context plus protective unit tests. Goal is that custom templates built for 1.x will not break without a 2.x release.

It's easy to misspell the name of a database or table and then be puzzled when the metadata settings silently fail.

To avoid this, let's sanity check the provided metadata.json on startup and quit with a useful error message if we find any obvious mistakes.

https://discord.com/channels/823971286308356157/823971286941302908/1120516587036889098

One thing that would be helpful would be message at launch indicating a metadata.json is getting picked up. I'm using directory mode and was editing the wrong file for awhile before I realize nothing I was doing was having any effect.

Simon did a excellent prototype of an interactive row selection in Datasette.

I hope this functionality can be turned into a Datasette plugin.

Django has this very nice filter for datetime fields -

It would be nice to have something similar to facet by a field that contains a timestamp in datasette too - Which doesn't seem to do anything with timestamps right now...

Hi! Let me firstly say that I am a big fan of your work -- I follow your tweets and blog posts with great interest 😄.

Now onto the matter at hand: I think it would be great if sqlite-utils included a merge or combine command, with the purpose of combining different SQLite databases into a single SQLite database. This way, the newly "merged" database would contain all differently named tables contained in the databases to be merged as-is, as well a concatenation of all tables of the same name.

This could look something like this:

bash sqlite-utils merge cats.db dogs.db > animals.db

I imagine this is rather straightforward if all databases involved in the merge contain differently named tables (i.e. no chance of conflicts), but things get slightly more complicated if two or more of the databases to be merged contain tables with the same name. Not only do you have to "do something" with the primary key(s), but these tables could also simply have different schemas (and therefore be incompatible for concatenation to begin with).

Anyhow, I would love your thoughts on this, and, if you are open to it, work together on the design and implementation!

Hi! this might be a question related to both SQLite & sqlite-utils, and you might be more experienced with them.

I have a large bunch of ids, and I'm wondering which is the best way to query them in terms of performance, and simplicity if possible.

The naive approach would be something like select * from table where rowid in (?, ?, ?...) but that wouldn't scale if ids are >1k.

Another approach might be creating a temp table, or in-memory db table, insert all ids in that table and then join with the target one.

I failed to attach an in-memory db both using sqlite-utils, and plain sql's execute(), so my closest approach is something like,

python def filter_existing_video_ids(video_ids): db = get_db() # contains a "videos" table db.execute("CREATE TEMPORARY TABLE IF NOT EXISTS tmp (video_id TEXT NOT NULL PRIMARY KEY)") db["tmp"].insert_all([{"video_id": video_id} for video_id in video_ids]) for row in db["tmp"].rows_where("video_id not in (select video_id from videos)"): yield row["video_id"] db["tmp"].drop()

That kinda worked, I couldn't find an option in sqlite-utils's create_table() to tell it's a temporary table. Also, tmp table is not dropped finally, neither using .drop() despite being created with the keyword TEMPORARY. I believe it should be automatically dropped after connection/session ends though I read.

When creating a dogsheep index from config.yml file on pocket.db (created using pocket-to-sqlite), I am getting this error

Traceback (most recent call last): File "/Users/khushmeeet/.pyenv/versions/3.11.2/bin/dogsheep-beta", line 8, in <module> sys.exit(cli()) ^^^^^ File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/click/core.py", line 1130, in __call__ return self.main(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) ^^^^^^^^^^^^^^^^ File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/click/core.py", line 1657, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/click/core.py", line 760, in invoke return __callback(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/dogsheep_beta/cli.py", line 36, in index run_indexer( File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/dogsheep_beta/utils.py", line 32, in run_indexer ensure_table_and_indexes(db, tokenize) File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/dogsheep_beta/utils.py", line 91, in ensure_table_and_indexes table.add_foreign_key(*fk) File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/sqlite_utils/db.py", line 2155, in add_foreign_key self.db.add_foreign_keys([(self.name, column, other_table, other_column)]) File "/Users/khushmeeet/.pyenv/versions/3.11.2/lib/python3.11/site-packages/sqlite_utils/db.py", line 1116, in add_foreign_keys cursor.execute( sqlite3.OperationalError: table sqlite_master may not be modified

Command I ran to get this error dogsheep-beta index pocket.db config.yml

Dogsheep version dogsheep-beta, version 0.10.2

Python version Python 3.11.2

I'm trying to use sqlite-utils to data generated incrementally. There are a few aspects of this that I don't currently know how to handle. I would like an option to apply writes incrementally, line-by-line as they are received. I would like an option to echo incremental progress. And, it would be nice to have

In particular, I'm using CoreLocationCLI -w -j to generate, newline-delimited JSON.

One variant of the command

stdbuf -oL CoreLocationCLI -w -j | pee 'sqlite-utils insert loc.db loc -' nl

pee, from moreutils, is like tee but spawns and pipes to the processes created by invoking each of its arguments, so, for gratuitous demonstration, pee 'sponge out.log' cat would behave like tee.

It looks like I can get what I want with: stdbuf -oL CoreLocationCLI -w -j | while read line; do <<<"$line" sqlite-utils insert loc.db loc -; echo "$line"; done | nl