issue_comments

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 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 vs) 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'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!

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.

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!

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

I've also added another fix for using static mounts with absolute paths on Windows.

Chrome and Firefox both support headless screengrabs from command line, but I don't know how parameterised they can be?

Ah, apologies – I had found and read those issues, but I was under the impression that they refered only to the filtered row count, not the unfiltered total row count.

I'd rather not turn this into a systemd support thread, but you're trying to execute the package directory there. Your datasette executable is probably at /home/chris/Env/datasette/bin/datasette.

WorkingDirectory=/path/to/data

@russss, Which directory does this represent?

It's the working directory (cwd) of the spawned process. In this case if you set it to the directory your data is in, you can use relative paths to the db (and metadata/templates/etc) in the ExecStart command.

This example is useful to - I like how it has a Makefile that knows how to set up systemd: https://github.com/pikesley/Queube

I wasn't even aware it was possible to add a systemd service at an arbitrary path, but it seems a little messy to me.

Maybe worth noting that systemd does support per-user services which don't require root access. Cool but probably overkill for most people (especially when you're going to need root to listen on port 80 anyway, directly or via a reverse proxy).

On most modern Linux distros, systemd is the easiest answer.

Example systemd unit file (save to /etc/systemd/system/datasette.service):

[Unit]
Description=Datasette
After=network.target

[Service]
Type=simple
User=<username>
WorkingDirectory=/path/to/data
ExecStart=/path/to/datasette serve -h 0.0.0.0 ./my.db
Restart=on-failure

[Install]
WantedBy=multi-user.target

Activate it with:

$ sudo systemctl daemon-reload
$ sudo systemctl enable datasette
$ sudo systemctl start datasette

Logs are best viewed using journalctl -u datasette -f.

Putting this here in case anyone else encounters the same issue with nginx, I was able to resolve it by passing the header in the nginx proxy config (i.e., proxy_set_header Host $host).

Hey was this ever merged? Trying to run this behind nginx, and encountering this issue.

For a bit more context: this fixes a crash with unsupported operand type(s) for +: 'int' and 'NoneType' on the index page for me.

Also I have a slight preference against (ab)using __slots__ to enforce fields, although I have done it myself in the past. It would be possible to do this with __setattr__ instead, although that's an implementation detail and I'm not too fussed about it.

In this particular case I don't think there's an issue making all those required. However, I suspect we might have to allow optional values at some point - my preferred solution to russss/datasette-geo#2 would need one.

The second time I ran the command I got an error:

ERROR: (gcloud.beta.run.deploy) Deployment endpoint was not found. Perhaps the
provided region was invalid. Set the run/region property to a valid region and
retry. Ex: gcloud config set run/region us-central1

Yes, I was able to reproduce this; I used to get prompted for a run region interactively by the gcloud tool before, but maybe this is changing? (the documentation now assumes run/region is set).

Not sure which course of action is best: making datasette ensure that run/region is set beforehand or wait a bit until the gcloud CLI stabilizes?

Are you planning on removing inspect entirely?

I didn't spot this work before I started on datasette-geo, but ironically I think it has a use case which really needs the inspect functionality (or some replacement).

Datasette-geo uses it to store the bounding box of all the geographic features in the table. This is needed when rendering the map because it avoids having to send loads of tile requests for areas which are empty.

Even with relatively small datasets, calculating the bounding box seems to take around 5 seconds, so I don't think it's really feasible to do this on page load.

One possible fix would be to do this on startup, and then in a thread which watches the database for changes.

Can I pull those needs out of the Facet class somehow?

I was thinking that it might be handy for datasette to have a request object which wraps the Sanic Request. This could include the datasette-specific querystring decoding and the special_args parsing from TableView.data.

This would mean that we could expose the request object to plugin hooks without coupling them to Sanic.

Just for the record, this PR is now finished and ready to merge from my perspective.

I think the best approach to this is to pass through the view_name parameter I added in #441. It's then simple enough for me to add .geojson to the URL in JS - I don't need the pkey.

Also I just pushed a change to add registered output renderers to the templates:

I updated the hook to pass the datasette object through now.

You can see the working GeoJSON render function here - the hook function is here.

I also just realised that I should be passing the datasette object into the hook function...as I just found I need it. So hold off merging until I've fixed that.

This is the minimal example (I also included it in the docs):

from datasette import hookimpl

def render_test(args, data, view_name):
    return {
       'body': 'Hello World',
       'content_type': 'text/plain'
    }

@hookimpl
def register_output_renderer():
    return {
        'extension': 'test',
        'callback': render_test
    }

I'm working on the GeoJSON one now and it should be ready soon. (I forgot I was going to run into the same problem as before - that Spatialite's stupid binary format isn't WKB and I have no way of altering the query to change that - but I've just managed to write some code to rearrange the bytes from Spatialite blob-geometry into WKB...)

Actually no, I ended up not using the inspected column types in my plugin, and the binary column issue can be solved a lot more simply, so I'll close this.

This is pretty conflicty because I forgot how to use git fetch. If you're interested in merging this I'll rewrite it against an actual modern checkout...

Subsidiary note which I forgot in the commit message:

I've decided to give each view a short string name to aid in differentiating which view a hook is being called from. Since hooks are functions and not subclasses, and can get called from different places in the URL hierarchy, it's sometimes difficult to distinguish what data you're actually operating on. I think this will come in handy for other hooks as well.

Actually I think this is not the whole story because of the rowid issue. I'm going to think about this one a bit more.

I've just spotted that this implements #215.

Minor UI observation:

_where= renders a [remove] link whereas _facet= gets a cross to remove it.

Hmm... nope... I see an updated timestamp from ls -al on the db but no reload?

Does this also relate to https://github.com/simonw/datasette/issues/283 and the ability to ATTACH DATABASE?

This would be really interesting but several possibilities in use arise, I think?

For example:

• I put a new CSV file into the import dir and a new table is created therefrom
• I put a CSV file into the import dir that replaces a previous file / table of the same name as a pre-existing table (eg files that contain monthly data in year to date). The data may also patch previous months, so a full replace / DROP on the original table may well be in order.
• I put a CSV file into the import dir that updates a table of the same name as a pre-existing table (eg files that contain last month's data)

CSV files may also have messy names compared to the table you want. Or for an update CSV, may have the form MYTABLENAME-February2019.csv etc

Do you have any simple working examples of how to use --static? Inspection of default served files suggests locations such as http://example.com/-/static/app.css?0e06ee.

If datasette is being proxied to http://example.com/foo/datasette, what form should arguments to --static take so that static files are correctly referenced?

Use case is here: https://github.com/psychemedia/jupyterserverproxy-datasette-demo Trying to do a really simple datasette demo in MyBinder using jupyter-server-proxy.

Another route would be something like creating a datasette IPython magic for notebooks to take a dataframe and easily render it as a datasette. You'd need to run the app in the background rather than block execution in the notebook. Related to that, or to publishing a dataframe in notebook cell for use in other cells in a non-blocking way, there may be cribs in something like https://github.com/micahscopes/nbmultitask .

In terms of integration with pandas, I was pondering two different ways datasette/csvs_to_sqlite integration may work:

• like pandasql, to provide a SQL query layer either by a direct connection to the sqlite db or via datasette API;
• as an improvement of pandas.to_sql(), which is a bit ropey (e.g. pandas.to_sql_from_csvs(), routing the dataframe to sqlite via csvs_tosqlite rather than the dodgy mapping that pandas supports).

The pandas.publish_* idea could be quite interesting though... Would it be useful/fruitful to think about publish_ as a complement to pandas.to_?

I think you need to register a domain name you own separately in order to get a non-IP address address? https://www.digitalocean.com/docs/networking/dns/

That works for me. Was able to pull the public image and no errors on my canned query. (~although a small rendering bug. I'll create an issue and if I have time today, a PR to fix~ this turned out to be my error.)
Thanks for the quick response!

I'm using the docker image (0.23.2) and notice some differences/bugs between the docs and the published version with canned queries. (submitted a tiny doc fix also)

I was able to build the docker container locally using master and I'm using that for now.
Would it be possible to manually push 0.24 to DockerHub until the TravisCI stuff is fixed?

I would like to run this in our Kubernetes cluster but don't want to publish a version in our internal registry if I don't have to.
Thanks!

I had a quick look at this in relation to #343 and I feel like it might be worth modelling the inspected table metadata internally as an object rather than a dict. (We'd still have to serialise it back to JSON.)

There are a few places where we rely on the structure of this metadata dict for various reasons, including in templates (and potentially also in user templates). It would be nice to have a reasonably well defined API for accessing metadata internally so that it's clearer what we're breaking.

This probably depends on #294.

Looks like this was a red herring actually, and heroku had a blip when I was testing it...

@RusSs Different map projections can presumably be handled on the client side using a leaflet plugin to transform the geometry (eg kartena/Proj4Leaflet) although the leaflet side would need to detect or be informed of the original projection?

Well, as @simonw mentioned, GeoJSON only supports WGS84, and GeoJSON (and/or TopoJSON) is the standard we probably want to aim for. On-the-fly reprojection in spatialite is not an issue anyway, and in general I think you want to be serving stuff to web maps in WGS84 or Web Mercator.

@russs Different map projections can presumably be handled on the client side using a leaflet plugin to transform the geometry (eg kartena/Proj4Leaflet) although the leaflet side would need to detect or be informed of the original projection?

Another possibility would be to provide an easy way/guidance for users to create an FK'd table containing the WGS84 projection of a non-WGS84 geometry in the original/principle table? This could then as a proxy for serving GeoJSON to the leaflet map?

I don't think it's unreasonable to only support spatialite geometries in a coordinate reference system which is at least transformable to WGS84. It would be nice to support different CRSes in the database so conversion to spatialite from the source data is lossless.

I think the working CRS for datasette should be WGS84 though (leaflet requires it, for example) - it's just a case of calling ST_Transform(geom, 4326) on the column while we're loading the data.

I haven't had time to look further into this, but if doing this as a plugin results in useful hooks then I think we should do it that way. We could always require the plugin as a standard dependency.

I think this is going to result in quite a bit of refactoring anyway so it's a good time to add hooks regardless.

On the other hand, if we have to add lots of specialist hooks for it then maybe it's worth integrating into the core.

I'm not keen on anything that modifies the SQLite file itself on startup

Ah I didn't mean that - I meant altering the SELECT query to fetch the data so that it ran a spatialite function to transform that specific column.

I think that's less useful as a general-purpose plugin hook though, and it's not that hard to parse the WKB in Python (my default approach would be to use shapely, which is great, but geomet looks like an interesting pure-python alternative).

No objections;
It's good to go @simonw

On Wed, 23 May 2018, 14:51 Simon Willison, notifications@github.com wrote:

@r4vi https://github.com/r4vi any objections to me merging this?

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Running:

docker run -p 8001:8001 -v `pwd`:/mnt datasette \
    datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \
        --load-extension=/usr/local/lib/mod_spatialite.so

is now returning FTS5 enabled in the versions output:

{
    "datasette": {
        "version": "0.22"
    },
    "python": {
        "full": "3.6.5 (default, May  5 2018, 03:07:21) \n[GCC 6.3.0 20170516]",
        "version": "3.6.5"
    },
    "sqlite": {
        "extensions": {
            "json1": null,
            "spatialite": "4.4.0-RC0"
        },
        "fts_versions": [
            "FTS5",
            "FTS4",
            "FTS3"
        ],
        "version": "3.23.1"
    }
}

The old query didn't work because specifying (t TEXT) caused an error

I'm going to clean this up for consistency tomorrow morning so hold off
merging until then please

On Tue, May 22, 2018 at 6:34 PM, Simon Willison notifications@github.com
wrote:

Yeah let's try this without pysqlite3 and see if we still get the correct
version.

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Alright, that should work now -- let me know if you would prefer any different behaviour.

Sorry, just realised you rely on version being a module ...

I think I prefer the aesthetics of just "0.22" for the version string if it's a tagged release with no additional changes - does that work?

Yes! That's the default versioneer behaviour.

I'd like to continue to provide a tuple that can be imported from the version.py module as well, as seen here:

Should work now, it can be a two (for a tagged version), three or four items tuple.

In [2]: datasette.__version__
Out[2]: '0.12+292.ga70c2a8.dirty'

In [3]: datasette.__version_info__
Out[3]: ('0', '12+292', 'ga70c2a8', 'dirty')

>>> import sqlite3
>>> sqlite3.sqlite_version
'3.23.1'
>>> 

running the above in the container seems to show 3.23.1 too so maybe we don't need pysqlite3 at all?