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

Those 5 queries, if you're interested ```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 ```

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.

 
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