issues

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.

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.

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

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

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.

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

I believe this is related to https://github.com/simonw/sqlite-utils/issues/98.

When pk is specified by table A's insert call, it throws an index error if a different table has written a row with a higher rowid than exists in the first table. Here's a basic example:

```py from sqlite_utils import Database

def test_pk_for_insert(fresh_db): user = {"id": "abc", "name": "david"}

fresh_db["users"].insert(user, pk="id")

fresh_db["comments"].insert_all(
    [
        {"id": "def", "text": "ok"},
        {"id": "ghi", "text": "great"},
    ],
)

fresh_db["users"].insert(
    user,
    ignore=True,
    # BUG: when specifying pk on the second insert call 
    # db.py goes into a block it doesn't expect and we get the error
    pk="id",
)

if name == "main": db = Database("bug.db") if db["users"].exists(): raise ValueError( "bug only shows on a new database - remove bug.db before running the script" ) test_pk_for_insert(db) ```

The error is:

py File "/Users/david/projects/reddit-to-sqlite/.venv/lib/python3.11/site-packages/sqlite_utils/db.py", line 2960, in insert_chunk row = list(self.rows_where("rowid = ?", [self.last_rowid]))[0] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^ IndexError: list index out of range

The issue is in this block:

https://github.com/simonw/sqlite-utils/blob/2747257a3334d55e890b40ec58fada57ae8cfbfd/sqlite_utils/db.py#L2954-L2958

relevant locals are:

• pk: 'id'
• result.lastrowid: 2

What's most interesting is the comment # self.last_rowid will be 0 if a "INSERT OR IGNORE" happened, which doesn't seem to be the case here.

Following: - #527

The only reason I didn't remove fix this mis-feature entirely is that it represents a backwards incompatible change. I'll make that change in 4.0.

Inspired by: - #534

Many thanks for the sqlite-utils suite of utilities. Has made my life much much easier. I used this to create a table and enable FTS. All works fine. The datasette utility detects FTS and shows a text box. Searching for a term using that interface works well.

However, when I start to use features by following https://www.sqlite.org/fts5.html section "3. Full-text Query Syntax" I seem to run into issues that I suspect is due to escape_fts wrapper function.

As an example, if i search for the term "^குகை"on the text box in datasette it produces 140 results. However, when i tweak the query produced by datasette to not use "escape_fts" it produces 5 results.

Similarly, when I try to restrict the search to a single column in FTS using a spec like {title : ^குகை} it returns no rows. The same thing pulls results when used without escape_fts. The text in the table is in Tamil language and the search term is a Tamil word.

... where posts_fts match escape_fts(:search) vs

... where posts_fts match (:search)

Any ideas why? How can I get the benefits of both escaping as well as utilizing different facets of providing / controlling search terms? Thanks.

It's pretty common that i need to trim leading or trailing white space from lots of columns in a database a part of an initial ETL.

I use the following recipe a lot, and it would be great to include this functionality into sqlite-utils

trimify.sql sql select 'select group_concat(''update [' || name || '] set ['' || name || ''] = trim(['' || name || ''])'', ''; '') || ''; '' as sql_to_run from pragma_table_info('''||name||''');' from sqlite_schema;

then something like:

bash sqlite3 example.db < scripts/trimify.sql > table_trim.sql && \ sqlite3 $example.db < table_trim.sql > trim.sql && \ sqlite3 $example.db < trim.sql

Got this idea from this example in https://jeqo.github.io/notes/2022-09-24-ingest-logs-sqlite/

bash sqlite-utils insert /tmp/kafka-logs.db logs server.log.2022-09-24-21 --text --convert " import re r = re.compile(r'^\[(?P<datetime>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2},\d{3})\] (?P<level>\w+) (?P<log>(.+(\n(?\!\[).+|)+))', re.MULTILINE) def convert(text): rows = [m.groupdict() for m in r.finditer(text)] for row in rows: row.update({'server': 'localhost'}) row.update({'component': 'broker'}) return rows " And the accompanying note:

The row.update allows to label rows as I’m planning to ingest logs from different hosts and potentially different components.

This made me think: it might be neat if you could inject additional variable values into that script with extra command-line options, to make this kind of reuse easier. Something like this:

bash sqlite-utils insert /tmp/kafka-logs.db logs server.log.2022-09-24-21 --text --convert " import re r = re.compile(r'^\[(?P<datetime>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2},\d{3})\] (?P<level>\w+) (?P<log>(.+(\n(?\!\[).+|)+))', re.MULTILINE) def convert(text): rows = [m.groupdict() for m in r.finditer(text)] for row in rows: row.update({'server': server}) row.update({'component': component}) return rows " --var server "localhost" --var component "broker"

The sqlite-utils tables command currently lists all tables.

If you have a huge table in there then running it with --counts can get expensive, because of the huge table.

Would be useful if it could accept an optional list of tables that it should execute against, as an alternative to the default of all of them.

This should be a backwards compatible change. Current design is: https://sqlite-utils.datasette.io/en/stable/cli-reference.html#tables

``` Usage: sqlite-utils tables [OPTIONS] PATH

List the tables in the database

Example:

  sqlite-utils tables trees.db

```

I recorded this one using https://github.com/nbedos/termtosvg - with pipx install termtosvg and then termtosvg - execute demo - exit to save.

json [ { "id": 1, "name": "Catimus" }, { "id": 2, "name": "Feliopia" } ]

https://www.sqlite.org/lang_savepoint.html

Savepoints are like regular transactions except they have names and can be nested.

Would there be any value in adding support to them to sqlite-utils, potentially as some kind of context manager? Something like this: python with db.savepoint("name"): # do stuff with db.savepoint("name2"): # do more stuff raise Release # Rolls back to before "name2" savepoint I've never used this feature so I'm not comfortable adding anything like this without a bunch of extra research.

I just got caught out by the fact that None values are not processed by the .convert() mechanism https://github.com/simonw/sqlite-utils/blob/0b7b80bd40fe86e4d66a04c9f607d94991c45c0b/sqlite_utils/db.py#L2504-L2510

I had run this code while working on #420 and I wasn't sure why it didn't work:

``` $ sqlite-utils add-column content.db articles score float $ sqlite-utils convert content.db articles score ' import random random.seed(10)

def convert(value): global random return random.random() ' `` The reason it didn't work is that the newly addedscorecolumn was full ofnull` values.

I fixed it by doing this instead:

$ sqlite-utils add-column content.db articles score float --not-null-default 1.0

But this indicates to me that the design of convert() here may be incorrect.

Say for example you want to populate a file_type column with the value gif. That could work like this:

sqlite-utils insert-files gifs.db images *.gif \ -c path -c md5 -c last_modified:mtime \ -c file_type:text:gif --pk=path So a column defined as a text column with a value that follows a second colon.

Same as: - https://github.com/simonw/datasette/issues/1621

I just started looking at Apache Arrow using pyarrow for import and export of tabular datasets, and it looks quite compelling. It might be worth looking at for sqlite-utils and/or datasette.

As a test, I took a random jsonl data dump of a dataset I have with floats, strings, and ints and converted it to arrow's parquet format using the naive pyarrow.parquet.write_file() command, which has automatic type inferrence. It compressed down to 7% of the original size. Conversion of a 26MB JSON file and serializing it to parquet was eyeblink instantaneous. Parquet files are portable and can be directly imported into pandas and other analytics software.

The only hangup is the automatic type inference of the naive reader. It's great for general laziness and for parsing JSON columns (it correctly interpreted a table of mine with a JSON array). However, I did get an exception for a string column where most entries looked integer-like but had a couple values that weren't -- the reader tried to coerce all of them for some reason, even though the JSON type is string. Since the writer optionally takes a schema, it shouldn't be too hard to grab the sqlite header types. With some additional hinting, you might get datetime columns and JSON, which are native Arrow types.

Somewhat tangentially, someone even wrote an sqlite vfs extension for Parquet: https://cldellow.com/2018/06/22/sqlite-parquet-vtable.html