issue_comments: 970853917

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html_url	issue_url	id	node_id	user	created_at	updated_at	author_association	body	reactions	issue	performed_via_github_app
https://github.com/simonw/datasette/issues/1513#issuecomment-970853917	https://api.github.com/repos/simonw/datasette/issues/1513	970853917	IC_kwDOBm6k_c453g4d	9599	2021-11-16T23:41:01Z	2021-11-16T23:41:01Z	OWNER	One very interesting difference between the two: on the single giant query page: ```json { "request_duration_ms": 376.4317020000476, "sum_trace_duration_ms": 370.0828700000329, "num_traces": 5 } ``` And on the page that uses separate queries: ```json { "request_duration_ms": 819.012272000009, "sum_trace_duration_ms": 201.52852100000018, "num_traces": 19 } ``` The separate pages page takes 819ms total to render the page, but spends 201ms across 19 SQL queries. The single big query takes 376ms total to render the page, spending 370ms in 5 queries <details><summary>Those 5 queries, if you're interested</summary> ```sql select database_name, schema_version from databases PRAGMA schema_version PRAGMA schema_version explain with cte as (\r\n select rowid, date, county, state, fips, cases, deaths\r\n from ny_times_us_counties\r\n),\r\ntruncated as (\r\n select null as _facet, null as facet_name, null as facet_count, rowid, date, county, state, fips, cases, deaths\r\n from cte order by date desc limit 4\r\n),\r\nstate_facet as (\r\n select 'state' as _facet, state as facet_name, count() as facet_count,\r\n null, null, null, null, null, null, null\r\n from cte group by facet_name order by facet_count desc limit 3\r\n),\r\nfips_facet as (\r\n select 'fips' as _facet, fips as facet_name, count() as facet_count,\r\n null, null, null, null, null, null, null\r\n from cte group by facet_name order by facet_count desc limit 3\r\n),\r\ncounty_facet as (\r\n select 'county' as _facet, county as facet_name, count() as facet_count,\r\n null, null, null, null, null, null, null\r\n from cte group by facet_name order by facet_count desc limit 3\r\n)\r\nselect from truncated\r\nunion all select * from state_facet\r\nunion all select * from fips_facet\r\nunion all select * from county_facet with cte as (\r\n select rowid, date, county, state, fips, cases, deaths\r\n from ny_times_us_counties\r\n),\r\ntruncated as (\r\n select null as _facet, null as facet_name, null as facet_count, rowid, date, county, state, fips, cases, deaths\r\n from cte order by date desc limit 4\r\n),\r\nstate_facet as (\r\n select 'state' as _facet, state as facet_name, count() as facet_count,\r\n null, null, null, null, null, null, null\r\n from cte group by facet_name order by facet_count desc limit 3\r\n),\r\nfips_facet as (\r\n select 'fips' as _facet, fips as facet_name, count() as facet_count,\r\n null, null, null, null, null, null, null\r\n from cte group by facet_name order by facet_count desc limit 3\r\n),\r\ncounty_facet as (\r\n select 'county' as _facet, county as facet_name, count() as facet_count,\r\n null, null, null, null, null, null, null\r\n from cte group by facet_name order by facet_count desc limit 3\r\n)\r\nselect from truncated\r\nunion all select * from state_facet\r\nunion all select * from fips_facet\r\nunion all select * from county_facet ``` </details> All of that additional non-SQL overhead must be stuff relating to Python and template rendering code running on the page. I'm really surprised at how much overhead that is! This is worth researching separately.	{"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0}	1055469073