issue_comments

Yes, Big Sur Photos database doesn't have ZGENERICASSET table. PR #31 will fix this.

Not sure where exactly to put the actual docs (presumably somewhere in docs/contributing.rst) but I've made a slight change to make it easier to run locally (copying the approach in excalidraw): https://github.com/simonw/datasette/compare/main...benpickles:prettier-docs

I'm seeing the problem on database page. Index page and table page runs quite fast.

• Tables have <10 columns (id, url, title, body_html, date, author, meta (for keeping unstructured json)). I've added index on date columns (using sqlite-utils) in addition to the index present on id columns.
• All tables have FTS enabled on text and varchar columns (title, body_html etc) to speed up searching.
• There are couple of tables related with foreign keys (think a thread in a forum and posts in that thread, related with thread_id)

I somewhat share your reluctance to add a package.json to seemingly every project out there but ultimately if they're project dependencies it's important they're managed within the codebase.

I was going to re-add the filter to only run Prettier when there have been changes in datasette/static but that would mean it wouldn't run when the package is updated. That plus the fact that the last run of the job took only 8 seconds is why I decided not to re-add the filter.

Actually, on more research, I found out this is handled by the trove-classifiers package now, so it's just a one-liner pr instead of fire-up-a-docker-container-and-do-some-migrations

FWIW, I had a look at watchdog for a datasette powered Jupyter notebook search tool: https://github.com/ouseful-testing/nbsearch/blob/main/nbsearch/nbwatchdog.py

Not a production thing, just an experiment trying to explore what might be possible...

The dict/set suggestion comes from pyupgrade --py36-plus, but then had to black the change.

The rest comes from PyCharm's Inspect code function. I reviewed all the suggestions and fixed a thing or two, such as leading/trailing spaces in the docstrings or turned around the chained conditions.

Then I tried to convert all os.path/glob/open to Path, but there were some local test issues, so I'll have to start over in smaller chunks if you want to have that too.

Fixes the issue mentioned here: https://github.com/dogsheep/dogsheep-photos/issues/15#issuecomment-748436115

@nickvazz @simonw I opened a PR that replaces the SQL for ZCOMPUTEDASSETATTRIBUTES to use osxphotos which now exposes all this data and has been updated for Big Sur. I did regression tests to confirm the extracted data is identical, with one exception which should not affect operation: the old code pulled data from ZCOMPUTEDASSETATTRIBUTES for missing photos while the main loop ignores missing photos and does not add them to apple_photos. The new code does not add rows to the apple_photos_scores table for missing photos.

@nickvazz ZGENERICASSET changed to ZASSET in Big Sur. Here's a list of other changes to the schema in Big Sur: https://github.com/RhetTbull/osxphotos/wiki/Changes-in-Photos-6---Big-Sur

I can't keep up with the renaming contexts, but I like having the ability to run datasette+ datasette-ripgrep against different configs:

datasette serve --metadata=./metadata.json

I have one for all of my code and one per client who has lots of code. So as long as I can point to datasette to something, it's easy to work with.

EDIT: I misunderstood the problem. This seems like a fix better suited for Safari. But I don't have any Apple device to test it.

body {
  min-height: 100vh;
  min-height: -webkit-fill-available;
}
html {
  height: -webkit-fill-available;
}

https://css-tricks.com/css-fix-for-100vh-in-mobile-webkit/

It's actually not that difficult to fix.
Well, this is actually a workaround to keep viewport in place.

I usually put a transition (forgot to do it here) that keeps page from resizing.

.container {
  min-height: 100vh;
  transition: height 10000s steps(0);
}

steps() function prevents excessive layout calculations, and lets the page snap back into place (10000s ~= 3h later) in a single step.
This fix also prevents page from jumping around when the keyboard pops up and down.

Somewhat related: https://github.com/simonw/datasette/issues/859
I fixed the issue with forking and disabling the counts for hidden tables.

I was confused by --config and I tried passing the json from datasette-ripgrep into config.json just as a wild guess.

A short term solution might be pointing out in plugins that their snippet json can go in metadata.json at least makes it easier to search for config options or to know where to start if someone is new.

(Even more annoying is that I see my editor leaked an extra delete space at the end of the line. I'm happy to rebuild this to be less annoying, but you probably don't want the changelog update either way)

Thanks; just a note that the datasette.urls.static(path) and datasette.urls.static_plugins(plugin_name, path) items both seem to be repeated and appear in the docs twice?

That workaround is probably fine. I was trying to work out whether there might be other situations where a pre-external package load might be useful but couldn't offhand bring any other examples to mind. The static plugins option also looks interesting.

No problem. I added a test. Let me know if it looks sufficient or if you want me to to tweak something!

If you don't mind, would you tag this PR as "hacktoberfest-accepted"? If you do mind, no problem and I'm sorry for asking :) My kiddos like the shirts.

Sorry, I was out of the loop this weekend. The missing sdists were in some the datasette-* plugins... i'll capture my findings more concretely in one spot when i have a chance...

I'm trying to run something behind a MyBinder proxy, but seem to have something set up incorrectly and not sure what the fix is?

I'm starting datasette with jupyter-server-proxy setup:

# __init__.py
def setup_nbsearch():

    return {
        "command": [
            "datasette",
            "serve",
            f"{_NBSEARCH_DB_PATH}",
            "-p",
            "{port}",
            "--config",
            "base_url:{base_url}nbsearch/"
        ],
        "absolute_url": True,
        # The following needs a the labextension installing.
        # eg in postBuild: jupyter labextension install jupyterlab-server-proxy
        "launcher_entry": {
            "enabled": True,
            "title": "nbsearch",
        },
    }

where the base_url gets automatically populated by the server-proxy. I define the loaders as:

# __init__.py
from datasette import hookimpl

@hookimpl
def extra_css_urls(database, table, columns, view_name, datasette):
    return [
        "/-/static-plugins/nbsearch/prism.css",
        "/-/static-plugins/nbsearch/nbsearch.css",
    ]

but these seem to also need a base_url prefix set somehow?

Currently, the generated HTML loads properly but internal links are incorrect; eg they take the form <link rel="stylesheet" href="/-/static-plugins/nbsearch/prism.css"> which resolves to eg https://notebooks.gesis.org/hub/-/static-plugins/nbsearch/prism.css rather than required URL of form https://notebooks.gesis.org/binder/jupyter/user/ouseful-testing-nbsearch-0fx1mx67/nbsearch/-/static-plugins/nbsearch/prism.css.

The main css is loaded correctly: <link rel="stylesheet" href="/binder/jupyter/user/ouseful-testing-nbsearch-0fx1mx67/nbsearch/-/static/app.css?404439">

From the docs, I note:

datasette.urls.instance()
Returns the URL to the Datasette instance root page. This is usually "/"

What about the proxy case? Eg if I am using jupyter-server-proxy on a MyBinder or local Jupyter notebook server site, https://example.com:PORT/weirdpath/datasette, what does datasette.urls.instance() refer to?

• https://example.com:PORT/weirdpath/datasette
• https://example.com:PORT/weirdpath/
• https://example.com:PORT/
• https://example.com
• something else?

Good luck on 1.0! It may also be worth lobbying for a Framework::Datasette::1.0 classifier. This would be a nice way to allow the ecosystem to self-document a bit more discoverably.

I was surprised to see the PR for Framework::Jupyter is a... database migration! Of course, there may be more workflow to it!

How does /-/static relate to current guidance docs around static regarding the --static option and metadata formulations such as "extra_js_urls": [ "/static/app.js"] (I've not managed to get this to work in a Jupyter server proxied set up; the datasette / jupyter server proxy repo may provide a useful test example, eg via MyBinder, for folk to crib from?)

🎉 Thanks so much @simonw ! 🎉

This closes issue #8

ref #46

Okay, I've rewritten this PR to preserve the batching behaviour but still fix #145, and rebased the branch to account for the db.execute() api change. It's not terribly sophisticated -- if it attempts to insert a batch which has too many variables, the exception is caught, the batch is split in two and each half is inserted separately, and then it carries on as before with the same batch_size. In the edge case where this gets triggered, subsequent batches will all be inserted in two groups too if they continue to have the same number of columns (which is presumably reasonably likely). Do you reckon this is acceptable when set against the awkwardness of recalculating the batch_size on the fly?

Just force-pushed to update d042f9c with more formatting changes to satisfy black==20.8b1 and pass the GitHub Actions "Test" workflow.

@simonw -- I've gone ahead updated the documentation to reflect the changes introduced in this PR. IMO it's ready to merge now.

In writing the documentation changes, I begin to wonder about the value and role of batch_size at all, tbh. May I assume it was originally intended to prevent using the entire row set to determine columns and column types, and that this was a performance consideration? If so, this PR entirely undermines its purpose. I've been passing in excess of 500,000 rows at a time to insert_all() with these changes and although I'm sure the performance difference is measurable it's not really noticeable; given #145, I don't know that any performance advantages outweigh the problems doing it this way removes. What do you think about just dropping the argument and defaulting to the maximum batch_size permissible given SQLITE_MAX_VARS? Are there other reasons one might want to restrict batch_size that I've overlooked? I could open a new issue to discuss/implement this.

Of course the documentation will need to change again too if/when something is done about #147.

Looks like black is up-to-date now, so this is no longer needed.

Note: had to adjust the test above because trying to exhaust a SQLITE_MAX_VARIABLE_NUMBER of 250000 in 99 records requires 2526 columns, and trips the "Rows can have a maximum of {} columns".format(SQLITE_MAX_VARS) check even before it trips the default SQLITE_MAX_COLUMN value (2000).

Thanks! And yeah, I had updating the docs on my list too :) Will try to get to it this afternoon (budgeting time is fraught with uncertainty at the moment!).

I tried changing the batch_size argument to the total number of records, but it seems only to effect the number of rows that are committed at a time, and has no influence on this problem.

So the reason for this is that the batch_size for import is limited (of necessity) here: https://github.com/simonw/sqlite-utils/blob/main/sqlite_utils/db.py#L1048

With regard to the issue of ignoring columns, however, I made a fork and hacked a temporary fix that looks like this:
https://github.com/simonwiles/sqlite-utils/commit/3901f43c6a712a1a3efc340b5b8d8fd0cbe8ee63

It doesn't seem to affect performance enormously (but I've not tested it thoroughly), and it now does what I need (and would expect, tbh), but it now fails the test here:
https://github.com/simonw/sqlite-utils/blob/main/tests/test_create.py#L710-L716

The existence of this test suggests that insert_all() is behaving as intended, of course. It seems odd to me that this would be a desirable default behaviour (let alone the only behaviour), and its not very prominently flagged-up, either.

@simonw is this something you'd be willing to look at a PR for? I assume you wouldn't want to change the default behaviour at this point, but perhaps an option could be provided, or at least a bit more of a warning in the docs. Are there oversights in the implementation that I've made?

Would be grateful for your thoughts! Thanks!

Bumping this, as the previous fix is passing the wrong type, and not actually addressing the issue...

The exclude argument needs an iterable of packages instead of a single string (but since str is iterable, it's currently excluding packages t, e, and s.)

Yep, I agree that makes more sense for backwards compat and more casual use cases. I think it should be possible for the Database/Queryable methods to DTRT based on seeing if it's within a context-manager-managed transaction.

Better transaction handling would be really great. Some of my thoughts on implementing better transaction discipline are in https://github.com/simonw/sqlite-utils/pull/118#issuecomment-655239728.

My preferences:

• Each CLI command should operate in a single transaction so that either the whole thing succeeds or the whole thing is rolled back. This avoids partially completed operations when an error occurs part way through processing. Partially completed operations are typically much harder to recovery from gracefully and may cause inconsistent data states.

• The Python API should be transaction-agnostic and rely on the caller to coordinate transactions. Only the caller knows how individual insert, create, update, etc operations/methods should be bundled conceptually into transactions. When the caller is the CLI, for example, that bundling would be at the CLI command-level. Other callers might want to break up operations into multiple transactions. Transactions are usually most useful when controlled at the application-level (like logging configuration) instead of the library level. The library needs to provide an API that's conducive to transaction use, though.

• The Python API should provide a context manager to provide consistent transactions handling with more useful defaults than Python's sqlite3 module. The latter issues implicit BEGIN statements by default for most DML (INSERT, UPDATE, DELETE, … but not SELECT, I believe), but not DDL (CREATE TABLE, DROP TABLE, CREATE VIEW, …). Notably, the sqlite3 module doesn't issue the implicit BEGIN until the first DML statement. It does not issue it when entering the with conn block, like other DBAPI2-compatible modules do. The with conn block for sqlite3 only arranges to commit or rollback an existing transaction when exiting. Including DDL and SELECTs in transactions is important for operation consistency, though. There are several existing bugs.python.org tickets about this and future changes are in the works, but sqlite-utils can provide its own API sooner. sqlite-utils's Database class could itself be a context manager (built on the sqlite3 connection context manager) which additionally issues an explicit BEGIN when entering. This would then let Python API callers do something like:

db = sqlite_utils.Database(path)

with db: # ← BEGIN issued here by Database.__enter__
    db.insert(…)
    db.create_view(…)
# ← COMMIT/ROLLBACK issue here by sqlite3.connection.__exit__

The only thing missing from this PR is updates to the documentation.

Ah, yes, thanks for this reminder! I've repushed with doc bits added.

I fixed my original oops by moving the DELETE FROM $table out of the chunking loop and repushed. I think this change can be considered in isolation from issues around transactions, which I discuss next.

I wanted to make the DELETE + INSERT happen all in the same transaction so it was robust, but that was more complicated than I expected. The transaction handling in the Database/Table classes isn't systematic, and this poses big hurdles to making Table.insert_all (or other operations) consistent and robust in the face of errors.

For example, I wanted to do this (whitespace ignored in diff, so indentation change not highlighted):

diff --git a/sqlite_utils/db.py b/sqlite_utils/db.py
index d6b9ecf..4107ceb 100644
--- a/sqlite_utils/db.py
+++ b/sqlite_utils/db.py
@@ -1028,6 +1028,11 @@ class Table(Queryable):
         batch_size = max(1, min(batch_size, SQLITE_MAX_VARS // num_columns))
         self.last_rowid = None
         self.last_pk = None
+        with self.db.conn:
+            # Explicit BEGIN is necessary because Python's sqlite3 doesn't
+            # issue implicit BEGINs for DDL, only DML.  We mix DDL and DML
+            # below and might execute DDL first, e.g. for table creation.
+            self.db.conn.execute("BEGIN")
             if truncate and self.exists():
                 self.db.conn.execute("DELETE FROM [{}];".format(self.name))
             for chunk in chunks(itertools.chain([first_record], records), batch_size):
@@ -1038,7 +1043,11 @@ class Table(Queryable):
                         # Use the first batch to derive the table names
                         column_types = suggest_column_types(chunk)
                         column_types.update(columns or {})
-                    self.create(
+                        # Not self.create() because that is wrapped in its own
+                        # transaction and Python's sqlite3 doesn't support
+                        # nested transactions.
+                        self.db.create_table(
+                            self.name,
                             column_types,
                             pk,
                             foreign_keys,
@@ -1139,7 +1148,6 @@ class Table(Queryable):
                     flat_values = list(itertools.chain(*values))
                     queries_and_params = [(sql, flat_values)]

-            with self.db.conn:
                 for query, params in queries_and_params:
                     try:
                         result = self.db.conn.execute(query, params)

but that fails in tests because other methods call insert/upsert/insert_all/upsert_all in the middle of their transactions, so the BEGIN statement throws an error (no nested transactions allowed).

Stepping back, it would be nice to make the transaction handling systematic and predictable. One way to do this is to make the sqlite_utils/db.py code generally not begin or commit any transactions, and require the caller to do that instead. This lets the caller mix and match the Python API calls into transactions as appropriate (which is impossible for the API methods themselves to fully determine). Then, make sqlite_utils/cli.py begin and commit a transaction in each @cli.command function, making each command robust and consistent in the face of errors. The big change here, and why I didn't just submit a patch, is that it dramatically changes the Python API to require callers to begin a transaction rather than just immediately calling methods.

There is also the caveat that for each transaction, an explicit BEGIN is also necessary so that DDL as well as DML (as well as SELECTs) are consistent and rolled back on error. There are several bugs.python.org discussions around this particular problem of DDL and some plans to make it better and consistent with DBAPI2, eventually. In the meantime, the sqlite-utils Database class could be a context manager which supports the incantations necessary to do proper transactions. This would still be a Python API change for callers but wouldn't expose them to the weirdness of the sqlite3's default transaction handling.

Ah, I see the problem. The truncate is inside a loop I didn't realize was there.

Hmm, while tests pass, this may not work as intended on larger datasets. Looking into it.

I was able to narrow this down to the fact that lifespan protocol is turned on.

I see the workaround you've used here: https://github.com/simonw/datasette-debug-asgi/commit/72d568d32a3159c763ce908c0b269736935c6987

If so, maybe it's time to update some of the asg_wrapper plugins.

FWIW, this error does NOT happen in datasette 0.45a4.

It only started on 0.45a5

Well tests need to be updated.

I need to get tests working on Windows.

Turns out we should include hidden tables in the result dict, or we're breaking tests. I've committed a refactor https://github.com/simonw/datasette/pull/883/commits/4f06e1bf6fbe4b73be770b87f610bf7c0e6e3ea7

I am calling the API from another origin, so injecting CSRF token into templates wouldn't work.

EDIT:

I'll try the new version, it sounds promising

Bearer tokens sound interesting. Where do tokens come from? An auth provider of my choosing? How do they get verified?

Does this mean custom routes get to expose endpoints accepting POST requests? I've tried earlier to add some POST endpoints, but requests were being rejected by Datasette due to CSRF

I've just realized Datasette tries to count hidden tables too. There are 5 visible tables, 25 hidden tables, which I haven't realize earlier to consider their effect. I've turned off counting for hidden tables to see if it has any effect.

What's the point of counting FTS tables?

I tried setting cache_size_kb=0 then cache_size_kb=100000, still getting this behavior. I even changed Database::table_counts and lowered time limit to 1

table_count = (
    await self.execute(
        "select count(*) from [{}]".format(table),
        custom_time_limit=1,
    )
).rows[0][0]
counts[table] = table_count

I feel like 10 seconds is a magic number, like a processing timeout and datasette gives up and returns the page.
Index page loads instantly, table page, query page, as well. But when I return to database page after some time, it loads in 10s.

EDIT:

It's always like 10 + 0.3s, like 10s wait and timeout then 300ms to render the page

The issue seems to appear sporadically, like when I return to database page after a while, during which some records have been added to the database.

I've just visited database, page first visit took ~10s, consecutive visits took 0.3s.

Hmm, not seeing the problem now.
I've removed the commented out sections in database.py and restarted the process. Database page now loads in <250ms.

I have couple of workers that check some pages regularly and scrape new content and save to the DB. Could it be that datasette tries to recount tables every time database size changes? Normally it keeps a count cache, but as DB gets updated so often (new content every 5 min or so) it's practically recounting every time I go to the database page?

EDIT:
It turns out it doesn't hold cache with mutable databases.

I'll update the issue with more findings and a better way to reproduce the problem if I encounter it again.

sqlite-generate many-cols.db --tables 2 --rows 200000 --columns 50

Looks like that will take 35 minutes to run (it's not a particularly fast tool).

Try chunking write operations into batches every 1000 records or so.

You said "200k+, 50+ rows in a couple of tables" - does that mean 50+ columns? I'll try with larger numbers of columns and see what difference that makes.

Ah that was a typo, I meant 50k.

I think I should mention that having FTS on all tables mean I have 5 visible, 25 hidden (FTS) tables displayed on database page.

I'm not sure if table counts are to blame. There shouldn't be a ~3 orders of magnitude difference.

user@klein /a/w/scrapyard (master)> set sql "select count(*) from table_1; select count(*) from table_2; select count(*) from table_3;"
user@klein /a/w/scrapyard (master)> time sqlite3 scrapyard.db "$sql"
187489
46492
2229

________________________________________________________
Executed in   25.57 millis    fish           external
   usr time    3.55 millis    0.00 micros    3.55 millis
   sys time   22.42 millis  1123.00 micros   21.30 millis

but not letting datasette count the tables definitely helps.

Oops, the same method is called from both index and database pages. But removing select count queries speed up the page load quite a bit.

Welp, I'm an idiot.

Turns out I had a sneaky comma , after sql key:

... (:name, :url),

which tells sqlite to expect another values(...) list.

Correcting the SQL solved the issue.

The server should reload in the config_dir mode.

Ref: #848

That would be perfect!

I've added support for the above exif data to v0.28.17 of osxphotos. PhotoInfo.exif_info will return an ExifInfo dataclass object with the following properties:

    flash_fired: bool
    iso: int
    metering_mode: int
    sample_rate: int
    track_format: int
    white_balance: int
    aperture: float
    bit_rate: float
    duration: float
    exposure_bias: float
    focal_length: float
    fps: float
    latitude: float
    longitude: float
    shutter_speed: float
    camera_make: str
    camera_model: str
    codec: str
    lens_model: str

It's not all the EXIF data available in most files but is the data Photos deems important to save. Of course, you can get all the exif_data

Note: this only works in Photos 5. As best as I can tell, EXIF data is not stored in the database for earlier versions.

I'm not familiar with ExifReader. I wrote my own wrapper around exiftool because I wanted a simple way to write EXIF data when exporting photos (e.g. writing out to PersonInImage and keywords to IPTC:Keywords) and the existing python packages like pyexiftool didn't do quite what I wanted. If all you're after is the camera and shot info, that's available in ZEXTENDEDATTRIBUTES table. I've got an open issue #11 to add this to osxphotos but it hasn't bubbled to the top of my backlog yet.

osxphotos will give you the location info: PhotoInfo.location returns a tuple of (lat, lon) though this info is in ZEXTENDEDATTRIBUTES too (though it might not be correct as I believe Photos creates this table at import and the user might have changed the location of a photo, e.g. if camera didn't have GPS).

CREATE TABLE ZEXTENDEDATTRIBUTES (
  Z_PK INTEGER PRIMARY KEY, Z_ENT INTEGER, 
  Z_OPT INTEGER, ZFLASHFIRED INTEGER, 
  ZISO INTEGER, ZMETERINGMODE INTEGER, 
  ZSAMPLERATE INTEGER, ZTRACKFORMAT INTEGER, 
  ZWHITEBALANCE INTEGER, ZASSET INTEGER, 
  ZAPERTURE FLOAT, ZBITRATE FLOAT, ZDURATION FLOAT, 
  ZEXPOSUREBIAS FLOAT, ZFOCALLENGTH FLOAT, 
  ZFPS FLOAT, ZLATITUDE FLOAT, ZLONGITUDE FLOAT, 
  ZSHUTTERSPEED FLOAT, ZCAMERAMAKE VARCHAR, 
  ZCAMERAMODEL VARCHAR, ZCODEC VARCHAR, 
  ZLENSMODEL VARCHAR
);

@simonw FYI, osxphotos includes a built in ExifTool class that uses exiftool to read and write exif data. It's not exposed yet in the docs because I really only use it right now in the osphotos command line interface to write tags when exporting. In v0.28.16 (just pushed) I added an ExifTool.as_dict() method which will give you a dict with all the exif tags in a file. For example:

import osxphotos
photos = osxphotos.PhotosDB().photos()
exiftool = osxphotos.exiftool.ExifTool(photos[0].path)
exifdata = exiftool.as_dict()
tags = exifdata["IPTC:Keywords"]

Not as elegant perhaps as a python only implementation because ExifTool has to make subprocess calls to an external tool but exiftool is by far the best tool available for reading and writing EXIF data and it does support HEIC.

As for implementation, ExifTool uses a singleton pattern so the first time you instantiate it, it spawns an IPC to exiftool but then keeps it open and uses the same process for any subsequent calls (even on different files).

Frustrates me when package authors create a "drop in" replacement with the same import name...this kind of thing has bitten me more than once! Would've been nicer I think for bpylist2 to do "import bpylist2 as bpylist"

Did removing old bpylist solve the original problem or do you still have a photo that throws circular reference?

@simonw does Photos show valid reverse geolocation info? Are you sure you're using bpylist2 and not bpylist? They're both unfortunately imported as "bpylist" so if you somehow got the wrong (original bpylist) version installed, it could be the issue.

Ugh....Yeah, I think easiest is to catch the exception and return no place as you suggest. This particular bit of code involves un-archiving a serialized NSKeyedArchiver which uses an object table and it is certainly possible to create a circular reference that way. Because this is happening in the decode, the circular reference must be in the original data. Does Photos show valid reverse geolocation info for the photo in question? If so, Photos may be doing something beyond a simple decode of the binary plist. For now, I'll push a patch to catch the exception.

FYI, I've got an issue to make osxphotos cross-platform but it's low on my priority list. About 90% of the functionality could be done cross-platform but right now the MacOS specific stuff is embedded throughout and would take some work. Though I try to minimize it, there's sprinklings of ObjC & Applescript throughout osxphotos.

I'm traveling w/o access to my Mac so can't help with any code right now. I suspected ZSCENEIDENTIFIER was a foreign key into one of these psi.sqlite tables. But looks like you're on to something connecting groups to assets. As for the UUID, I think there's two ints because each is 64-bits but UUIDs are 128-bits. Thus they need to be combined to get the 128 bit UUID. You might be able to use Apple's NSUUID, for example, by wrapping with pyObjC. Here's one example of using this in PyObjC's test suite. Interesting it's stored this way instead of a UUIDString as in Photos.sqlite. Perhaps it for faster indexing.

Superseded by #753.

With SQLITE_MAX_VARS = 999, or even 899, This hits the problem with the batch rows causing a overflow (works fine if SQLITE_MAX_VARS = 799).

p.s. I have tried a few list of dicts to sqlite modules and this was the easiest to use/understand

------------- file begins ------------------
import sqlite_utils as su

data = [
{'tickerId': 913324382, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'CONSTELLATION B', 'symbol': 'STZ B', 'disSymbol': 'STZ-B', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'status': 'D', 'close': '163.13', 'change': '6.46', 'changeRatio': '0.0412', 'marketValue': '31180699895.63', 'volume': '417', 'turnoverRate': '0.0000'},
{'tickerId': 913323791, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Molina Health', 'symbol': 'MOH', 'disSymbol': 'MOH', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '173.25', 'change': '9.28', 'changeRatio': '0.0566', 'pPrice': '173.25', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '10520341695.50', 'volume': '1281557', 'turnoverRate': '0.0202'},
{'tickerId': 913257501, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Seattle Genetics', 'symbol': 'SGEN', 'disSymbol': 'SGEN', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '145.64', 'change': '8.41', 'changeRatio': '0.0613', 'pPrice': '146.45', 'pChange': '0.8100', 'pChRatio': '0.0056', 'marketValue': '25117961347.60', 'volume': '2791411', 'turnoverRate': '0.0162'},
{'tickerId': 925381971, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Bandwidth', 'symbol': 'BAND', 'disSymbol': 'BAND', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '89.22', 'change': '7.66', 'changeRatio': '0.0939', 'pPrice': '89.00', 'pChange': '-0.2200', 'pChRatio': '-0.0025', 'marketValue': '2100025474.98', 'volume': '1508629', 'turnoverRate': '0.0641'},
{'tickerId': 913323935, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Magellan Health', 'symbol': 'MGLN', 'disSymbol': 'MGLN', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '68.00', 'change': '7.27', 'changeRatio': '0.1197', 'pPrice': '68.00', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '1697894040.00', 'volume': '448919', 'turnoverRate': '0.0180'},
{'tickerId': 913254854, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'On Assignment', 'symbol': 'ASGN', 'disSymbol': 'ASGN', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '53.04', 'change': '6.59', 'changeRatio': '0.1419', 'pPrice': '53.04', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '2811120000.00', 'volume': '1339771', 'turnoverRate': '0.0253'},
{'tickerId': 913255732, 'exchangeId': 95, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Arcturus', 'symbol': 'ARCT', 'disSymbol': 'ARCT', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NMS', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '40.86', 'change': '6.36', 'changeRatio': '0.1843', 'pPrice': '42.60', 'pChange': '1.740', 'pChRatio': '0.0426', 'marketValue': '812021444.46', 'volume': '1577508', 'turnoverRate': '0.0794'},
{'tickerId': 913256616, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'DexCom', 'symbol': 'DXCM', 'disSymbol': 'DXCM', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '341.52', 'change': '6.32', 'changeRatio': '0.0189', 'pPrice': '340.00', 'pChange': '-1.5200', 'pChRatio': '-0.0045', 'marketValue': '31522296000.00', 'volume': '1008849', 'turnoverRate': '0.0109'},
{'tickerId': 913255108, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Clorox', 'symbol': 'CLX', 'disSymbol': 'CLX', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '192.71', 'change': '6.27', 'changeRatio': '0.0336', 'pPrice': '192.95', 'pChange': '0.2400', 'pChRatio': '0.0012', 'marketValue': '24185773318.28', 'volume': '4996414', 'turnoverRate': '0.0398'},
{'tickerId': 925314627, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'FRANCO NEVADA', 'symbol': 'FNV', 'disSymbol': 'FNV', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '137.85', 'change': '5.64', 'changeRatio': '0.0427', 'pPrice': '138.50', 'pChange': '0.6500', 'pChRatio': '0.0047', 'marketValue': '26110405326.30', 'volume': '1047688', 'turnoverRate': '0.0055'},
{'tickerId': 913254955, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Aon Plc', 'symbol': 'AON', 'disSymbol': 'AON', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '178.21', 'change': '5.54', 'changeRatio': '0.0321', 'pPrice': '178.21', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '41181209117.22', 'volume': '2026234', 'turnoverRate': '0.0088'},
{'tickerId': 913324105, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Willis Towers', 'symbol': 'WLTW', 'disSymbol': 'WLTW', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '183.34', 'change': '5.05', 'changeRatio': '0.0283', 'pPrice': '183.34', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '23597461124.96', 'volume': '968943', 'turnoverRate': '0.0075'},
{'tickerId': 913254759, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'TELADOC HEALTH', 'symbol': 'TDOC', 'disSymbol': 'TDOC', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '169.43', 'change': '4.84', 'changeRatio': '0.0294', 'pPrice': '168.88', 'pChange': '-0.5500', 'pChRatio': '-0.0032', 'marketValue': '12614616858.38', 'volume': '2628946', 'turnoverRate': '0.0353'},
{'tickerId': 913255222, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Emergent Bio', 'symbol': 'EBS', 'disSymbol': 'EBS', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '78.70', 'change': '4.75', 'changeRatio': '0.0642', 'pPrice': '78.40', 'pChange': '-0.3000', 'pChRatio': '-0.0038', 'marketValue': '4113368277.10', 'volume': '783804', 'turnoverRate': '0.0150'},
{'tickerId': 913323443, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Pool', 'symbol': 'POOL', 'disSymbol': 'POOL', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '216.02', 'change': '4.36', 'changeRatio': '0.0206', 'pPrice': '216.02', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '8696077573.82', 'volume': '310837', 'turnoverRate': '0.0077'},
{'tickerId': 913257075, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Masimo', 'symbol': 'MASI', 'disSymbol': 'MASI', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '218.00', 'change': '4.09', 'changeRatio': '0.0191', 'pPrice': '217.00', 'pChange': '-1.0000', 'pChRatio': '-0.0046', 'marketValue': '11797070000.00', 'volume': '542131', 'turnoverRate': '0.0100'},
{'tickerId': 913253761, 'exchangeId': 10, 'type': 2, 'secType': [62], 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Pope Resources', 'symbol': 'POPE', 'disSymbol': 'POPE', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NAS', 'listStatus': 1, 'template': 'stock', 'status': 'D', 'close': '101.05', 'change': '3.95', 'changeRatio': '0.0407', 'pPrice': '99.90', 'pChange': '2.800', 'pChRatio': '0.0288', 'marketValue': '447370075.75', 'volume': '33138', 'turnoverRate': '0.0075'},
{'tickerId': 913323560, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Seneca Foods', 'symbol': 'SENEB', 'disSymbol': 'SENEB', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'status': 'D', 'close': '40.04', 'change': '3.84', 'changeRatio': '0.1061', 'marketValue': '347950039.71', 'volume': '501'},
{'tickerId': 913324274, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Resmed', 'symbol': 'RMD', 'disSymbol': 'RMD', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '159.07', 'change': '3.75', 'changeRatio': '0.0241', 'pPrice': '159.07', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '23004217759.29', 'volume': '1267075', 'turnoverRate': '0.0088'},
{'tickerId': 913323736, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Vertex Pharms', 'symbol': 'VRTX', 'disSymbol': 'VRTX', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '254.90', 'change': '3.70', 'changeRatio': '0.0147', 'pPrice': '255.00', 'pChange': '0.1000', 'pChRatio': '0.0004', 'marketValue': '66062980780.10', 'volume': '1939843', 'turnoverRate': '0.0075'},
{'tickerId': 913323767, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'MCCORMICK VTG', 'symbol': 'MKC V', 'disSymbol': 'MKC-V', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'status': 'D', 'close': '159.99', 'change': '3.42', 'changeRatio': '0.0218', 'marketValue': '21262671000.00', 'volume': '432', 'turnoverRate': '0.0000'},
{'tickerId': 950118595, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'ZOOM VIDEO', 'symbol': 'ZM', 'disSymbol': 'ZM', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '138.56', 'change': '3.39', 'changeRatio': '0.0251', 'pPrice': '138.99', 'pChange': '0.4300', 'pChRatio': '0.0031', 'marketValue': '38620532420.16', 'volume': '13786017', 'turnoverRate': '0.0495'},
{'tickerId': 916040738, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'WHEATON PRECIOUS', 'symbol': 'WPM', 'disSymbol': 'WPM', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '41.10', 'change': '3.34', 'changeRatio': '0.0885', 'pPrice': '41.09', 'pChange': '-0.0100', 'pChRatio': '-0.0002', 'marketValue': '18404536146.30', 'volume': '5019137', 'turnoverRate': '0.0112'},
{'tickerId': 913257174, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Royal Gold', 'symbol': 'RGLD', 'disSymbol': 'RGLD', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '125.86', 'change': '3.33', 'changeRatio': '0.0272', 'pPrice': '125.86', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '8253015011.08', 'volume': '853473', 'turnoverRate': '0.0130'},
{'tickerId': 913254394, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Fortune Brand', 'symbol': 'FBHS', 'disSymbol': 'FBHS', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '51.50', 'change': '3.30', 'changeRatio': '0.0685', 'pPrice': '51.50', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '7194870278.50', 'volume': '3004021', 'turnoverRate': '0.0214'},
{'tickerId': 913323312, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Liberty Global', 'symbol': 'LBTYK', 'disSymbol': 'LBTYK', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '21.49', 'change': '3.18', 'changeRatio': '0.1737', 'pPrice': '21.48', 'pChange': '-0.0100', 'pChRatio': '-0.0005', 'marketValue': '13594662302.41', 'volume': '19980228', 'turnoverRate': '0.0315'},
{'tickerId': 913323882, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Preformed Line', 'symbol': 'PLPC', 'disSymbol': 'PLPC', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'status': 'D', 'close': '52.82', 'change': '3.14', 'changeRatio': '0.0632', 'pPrice': '52.10', 'pChange': '-0.7200', 'pChRatio': '-0.0136', 'marketValue': '264979981.20', 'volume': '9305', 'turnoverRate': '0.0018'},
{'tickerId': 913323248, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Discovery', 'symbol': 'DISCB', 'disSymbol': 'DISCB', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'status': 'A', 'close': '57.95', 'change': '23.63', 'changeRatio': '0.6884', 'pPrice': '54.26', 'pChange': '-3.6900', 'pChRatio': '-0.0637', 'marketValue': '29362894177.95', 'volume': '218305', 'turnoverRate': '0.0004'},
{'tickerId': 913323930, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'MercadoLibre', 'symbol': 'MELI', 'disSymbol': 'MELI', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '605.52', 'change': '22.01', 'changeRatio': '0.0377', 'pPrice': '603.69', 'pChange': '-1.8300', 'pChRatio': '-0.0030', 'marketValue': '30226598045.28', 'volume': '699008', 'turnoverRate': '0.0140'},
{'tickerId': 913257170, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Liberty Global', 'symbol': 'LBTYA', 'disSymbol': 'LBTYA', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '22.28', 'change': '2.86', 'changeRatio': '0.1473', 'pPrice': '22.29', 'pChange': '0.0100', 'pChRatio': '0.0004', 'marketValue': '14094419548.52', 'volume': '10534672', 'turnoverRate': '0.0167'},
{'tickerId': 913303991, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Liberty Brodband', 'symbol': 'LBRDK', 'disSymbol': 'LBRDK', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '125.44', 'change': '2.76', 'changeRatio': '0.0225', 'pPrice': '125.44', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '22817900904.96', 'volume': '926177', 'turnoverRate': '0.0042'},
{'tickerId': 913257082, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Helen of Troy', 'symbol': 'HELE', 'disSymbol': 'HELE', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '167.04', 'change': '2.76', 'changeRatio': '0.0168', 'pPrice': '167.04', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '4216707982.08', 'volume': '341465', 'turnoverRate': '0.0135'},
{'tickerId': 913256458, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Forrester', 'symbol': 'FORR', 'disSymbol': 'FORR', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '33.88', 'change': '2.58', 'changeRatio': '0.0824', 'marketValue': '635419400.00', 'volume': '85115', 'turnoverRate': '0.0045'},
{'tickerId': 950158952, 'exchangeId': 95, 'type': 2, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'LYRA THERAPEUTICS, INC.', 'symbol': 'LYRA', 'disSymbol': 'LYRA', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NMS', 'listStatus': 1, 'template': 'ipo', 'status': 'A', 'close': '18.56', 'change': '2.56', 'changeRatio': '0.1600', 'pPrice': '18.96', 'pChange': '0.4000', 'pChRatio': '0.0216', 'marketValue': '229705575.68', 'volume': '1738472', 'turnoverRate': '0.1405'},
{'tickerId': 913257570, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Bio-Techne', 'symbol': 'TECH', 'disSymbol': 'TECH', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '227.54', 'change': '2.54', 'changeRatio': '0.0113', 'pPrice': '227.54', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '8726538309.18', 'volume': '497006', 'turnoverRate': '0.0130'},
{'tickerId': 913323246, 'exchangeId': 96, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Bel Fuse', 'symbol': 'BELFB', 'disSymbol': 'BELFB', 'disExchangeCode': 'NASDAQ', 'exchangeCode': 'NSQ', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '9.99', 'change': '2.53', 'changeRatio': '0.3391', 'pPrice': '9.75', 'pChange': '-0.2400', 'pChRatio': '-0.0240', 'marketValue': '122562454.86', 'volume': '177634', 'turnoverRate': '0.0145'},
{'tickerId': 916040647, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Agnico Eagle', 'symbol': 'AEM', 'disSymbol': 'AEM', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '61.20', 'change': '2.52', 'changeRatio': '0.0429', 'pPrice': '61.10', 'pChange': '-0.1000', 'pChRatio': '-0.0016', 'marketValue': '14739911553.60', 'volume': '2820765', 'turnoverRate': '0.0117'},
{'tickerId': 913303768, 'exchangeId': 12, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'CHASE CORP', 'symbol': 'CCF', 'disSymbol': 'CCF', 'disExchangeCode': 'AMEX', 'exchangeCode': 'ASE', 'listStatus': 1, 'template': 'stock', 'status': 'D', 'close': '96.71', 'change': '2.45', 'changeRatio': '0.0260', 'marketValue': '916799598.60', 'volume': '29229', 'turnoverRate': '0.0031'},
{'tickerId': 913324557, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'Allergan', 'symbol': 'AGN', 'disSymbol': 'AGN', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'A', 'close': '189.74', 'change': '2.40', 'changeRatio': '0.0128', 'pPrice': '189.76', 'pChange': '0.0200', 'pChRatio': '0.0001', 'marketValue': '62424842326.10', 'volume': '5787032', 'turnoverRate': '0.0176'},
{'tickerId': 913324566, 'exchangeId': 11, 'type': 2, 'secType': 61, 'regionId': 6, 'regionCode': 'US', 'currencyId': 247, 'name': 'West Pharm Svc', 'symbol': 'WST', 'disSymbol': 'WST', 'disExchangeCode': 'NYSE', 'exchangeCode': 'NYSE', 'listStatus': 1, 'template': 'stock', 'derivativeSupport': 1, 'status': 'D', 'close': '191.64', 'change': '2.38', 'changeRatio': '0.0126', 'pPrice': '191.64', 'pChange': '0.0000', 'pChRatio': '0.0000', 'marketValue': '14078267117.08', 'volume': '352460', 'turnoverRate': '0.0042'}
]

db = su.Database(f"overnight hold.db" )
db['active'].insert_all(data)

--------------- file ends ----------------------

I made a repo at https://github.com/code402/datasette-lambda to demonstrate the idea, and scratch my personal itch for this.

The demo relies on some central authority having already published a public, reusable Lambda layer with Datasette & its dependencies. I think that differs from the other publish plugins which seem to mainly publish Dockerfiles that the host will interpret to install deps from a requirements.txt file.

I chose that approach because uvloop appears to be a dependency with native code that needs to be compiled for the target runtime environment. In this case, that's Amazon Linux 2. I'm not 100% clear on whether that's still required, because:

• maybe uvloop is only needed for uvicorn, which the demo doesn't actually use since HTTP routing is handled by API Gateway
• it seems like uvloop may be an optional, drop-in optimization for asyncio in any case (but I may be misreading this; I'm very much a Python noob)

If it's the case that uvloop is truly optional, then I think the publish plugin could do the packaging on the user's machine, regardless of what flavour of operating system they're on. That'd be a bit slower for the user, but would provide the most long-term flexibility in terms of supporting plugins.

Hi Simon,

I'm thinking of attempting this. Can you clarify some questions I have?

1) I assume the goal is to have a CORS-friendly HTTPS endpoint that hosts the datasette service + user's db.

2) If that's the goal, I think Lambda alone is insufficient. Lambda provides the compute fabric, but not the HTTP routing. You'd also need to add Application Load Balancer or API Gateway to provide an HTTP endpoint that routes to the lambda function.

Do you have a preference between ALB or API GW? ALB has better economics at scale, but has a minimum monthly cost. API GW has worse per-request economics, but scales to zero when no requests are happening.

3) Does Datasette have any native components, or is it all pure python? If it has native bits, they'll likely need to be recompiled to work on Amazon Linux 2.

4) There are a few disparate services that need to be wired together to expose a Python service securely to the web. If I was doing this outside of the datasette publish system, I'd use an AWS CloudFormation template. Even within datasette, I think it still makes sense to use a CloudFormation template and just have the publish plugin invoke it (via the standard aws cli) with user-specified parameters. Does that sound reasonable to you?

Thanks for your help!

Fixed by @simonw; example here: https://github.com/simonw/jupyterserverproxy-datasette-demo

It was very helpful for me, using it for a 15M row table. Added a test, happy to amend though!

So could the polling support also allow you to call sqlite_utils to update a database with csv files? (Though I'm guessing you would only want to handle changed files? Do your scrapers check and cache csv datestamps/hashes?)

but this also doesn't have to land at all if it doesn't match your use case.

I think the existing code will be OK even if I strip the lines in the middle of a new line delimited string.

It's only used for the validation, SQLite handles the -- just fine and the whole SQL textarea still gets sent once it passes validation.

I can add your test case to my branch later this evening though.

I guess there is some extra flag for CliRunner.invoke to check exitcode and raise the exception, or that should be an extra assert added.

Thanks. That showed me that there was a click cli runner error, and setting export LANG=en_US.UTF-8 fixed it.

FWIW I did a dumb merge of the branch here: https://github.com/jsfenfen/datasette and it seemed to work in that I could run stuff at a subdirectory, but ended up abandoning it in favor of just posting a subdomain because getting the nginx configs right was making me crazy. I still would prefer posting at a subdirectory but the subdomain seems simpler at the moment.

Hi @simonw

Have you had a chance to look at this at all?

I'm going to have a chunk of time free next week so if there is additional work needed on this, that would be a particularly convenient time for me to revisit this.

Cheers

In passing, it looks like a start was made on a datasette Jupyter server extension in https://github.com/lucasdurand/jupyter-datasette although the build fails in MyBinder.

Would cookie cutter approaches also work for creating various flavours of customised templates?

I need to try to create a couple of sites for myself to get a feel for what sorts of thing are easily doable, and what cribbable cookie cutter items might be. I'm guessing https://simonwillison.net/2019/Nov/25/niche-museums/ is a good place to start from?

A bit of background: the reason heroku git:clone brings down an empty directory is because datasette publish heroku uses the builds API, rather than a git push, to release the app. I originally did this because it seemed like a lower bar than having a working git, but the downside is, as you found out, that tweaking the created app is hard.

So there's one option -- change datasette publish heroku to use git push instead of heroku builds:create.

@pkoppstein - what you suggested seems like it ought to work (you don't need maintenance mode, though). I'm not sure why it doesn't.

You could also look into using the slugs API to download the slug, change metadata.json, re-pack and re-upload the slug.

Ultimately though I think I think @simonw's idea of reading metadata.json from an external source might be better (#357). Reading from an alternate URL would be fine, or you could also just stuff the whole metadata.json into a Heroku config var, and write a plugin to read it from there.

Hope this helps a bit!

Hey @simonw is the url_prefix config option available in another branch, it looks like you've written some tests for it above? In 0.32 I get "url_prefix is not a valid option". I think this would be really helpful!

This would be really handy for proxying datasette in another domain's subdirectory I believe this will allow folks to run upstream authentication, but the links break if the url_prefix doesn't match.

I'd prefer not to host a proxied version of datasette on a subdomain (e.g. datasette.myurl.com b/c then I gotta worry about sharing authorization cookies with the subdomain, which I just assume not do, but...)

Edit: I see the wip-url-prefix branch, I may try with that https://github.com/simonw/datasette/commit/8da2db4b71096b19e7a9ef1929369b8483d448bf

Not sure – I'm always a bit weirded out when elements that I clicked disappear on me.

Submitted in #602!

The only negative impact that comes to mind is that now you have no way to get the read-only query to be formatted nicely, I think, so maybe a second PR adding the formatting functionality even to the read-only page would be good?

That looks right, and I completely agree with the intent.

Added tests.

Ah, thank you – I saw the need for unit tests but wasn't sure what the best way to add one would be.

How open are you to changing the config variable names (with appropriate deprecation, of course)? "about_url_text", "license_url_text" etc might be better suited to convey that these are just meant as basically URL titles.

Headless Chrome and Firefox via Selenium are a solid choice in my experience. You may be interested in how pretix and pretalx solve this problem: They use pytest to create those screenshots on release to make sure they are up to date. See this writeup and this repo.

I think this would be good, yeah – currently, databases are explicitly sorted by name in the IndexView, we could just remove that part (and use an OrderedDict for consistency, I suppose)?

@witeshadow The library version doesn't have helpers around CSV (at least not from what I can see in the code).

But here's a snippet that makes it easy to insert from CSV using the library.

import csv
from sqlite_utils import Database

# CSV Reader

csv_file = open("filename.csv")   # open the csv file.
reader = csv.reader(csv_file)  # Create a CSV reader
headers = next(reader)   # First line is the header
docs = (dict(zip(headers, row)) for row in reader)

# Now you can use the `sqlite_utils` library. 

db = Database("my_database.db")
db["table_name"].insert_all(docs)

This snippet is adapted from reading the CLI source code on how it implements the csv option.

I have merged the other PR (#56) into this one.

I have incorporated your suggestions. Cheers!

I have updated the other PR with the changes from this one and added tests. I have also changed the escaping from double quotes to brackets.

@simonw yes it works fine on Windows, but test suite doesn't run properly, for that I had to use WSL

I wanted to add a test for it too, but I've realized it's impossible to test a server process as we cannot get its exit code.

# tests/test_cli.py
def test_static_mounts_on_windows():
    if sys.platform != "win32":
        return
    runner = CliRunner()
    result = runner.invoke(
        cli, ["serve", "--static", r"s:C:\\"]
    )
    assert result.exit_code == 0