issue_comments

Conclusion: using a giant convoluted CTE and UNION ALL query to attempt to calculate facets at the same time as retrieving rows is a net LOSS for performance! Very surprised to see that.

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

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.

I tried adding cases > 10000 but the SQL query now takes too long - so moving this to my laptop.

cd /tmp wget https://covid-19.datasettes.com/covid.db datasette covid.db \ --setting facet_time_limit_ms 10000 \ --setting sql_time_limit_ms 10000 \ --setting trace_debug 1 http://127.0.0.1:8006/covid/ny_times_us_counties?_trace=1&_facet_size=3&_size=2&cases__gt=10000 shows in the traces:

json [ { "type": "sql", "start": 12.693033525, "end": 12.694056904, "duration_ms": 1.0233789999993803, "traceback": [ " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/base.py\", line 262, in get\n return await self.view_get(\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/base.py\", line 477, in view_get\n response_or_template_contexts = await self.data(\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/table.py\", line 705, in data\n results = await db.execute(sql, params, truncate=True, **extra_args)\n" ], "database": "covid", "sql": "select rowid, date, county, state, fips, cases, deaths from ny_times_us_counties where \"cases\" > :p0 order by rowid limit 3", "params": { "p0": 10000 } }, { "type": "sql", "start": 12.694285093, "end": 12.814936275, "duration_ms": 120.65118200000136, "traceback": [ " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/base.py\", line 262, in get\n return await self.view_get(\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/base.py\", line 477, in view_get\n response_or_template_contexts = await self.data(\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/table.py\", line 723, in data\n count_rows = list(await db.execute(count_sql, from_sql_params))\n" ], "database": "covid", "sql": "select count(*) from ny_times_us_counties where \"cases\" > :p0", "params": { "p0": 10000 } }, { "type": "sql", "start": 12.818812089, "end": 12.851172544, "duration_ms": 32.360455000000954, "traceback": [ " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/table.py\", line 856, in data\n suggested_facets.extend(await facet.suggest())\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/facets.py\", line 164, in suggest\n distinct_values = await self.ds.execute(\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/app.py\", line 634, in execute\n return await self.databases[db_name].execute(\n" ], "database": "covid", "sql": "select county, count(*) as n from (\n select rowid, date, county, state, fips, cases, deaths from ny_times_us_counties where \"cases\" > :p0 \n ) where county is not null\n group by county\n limit 4", "params": { "p0": 10000 } }, { "type": "sql", "start": 12.851418868, "end": 12.871268359, "duration_ms": 19.84949100000044, "traceback": [ " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/table.py\", line 856, in data\n suggested_facets.extend(await facet.suggest())\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/facets.py\", line 164, in suggest\n distinct_values = await self.ds.execute(\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/app.py\", line 634, in execute\n return await self.databases[db_name].execute(\n" ], "database": "covid", "sql": "select state, count(*) as n from (\n select rowid, date, county, state, fips, cases, deaths from ny_times_us_counties where \"cases\" > :p0 \n ) where state is not null\n group by state\n limit 4", "params": { "p0": 10000 } }, { "type": "sql", "start": 12.871497655, "end": 12.897715027, "duration_ms": 26.217371999999628, "traceback": [ " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/views/table.py\", line 856, in data\n suggested_facets.extend(await facet.suggest())\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/facets.py\", line 164, in suggest\n distinct_values = await self.ds.execute(\n", " File \"/usr/local/Cellar/datasette/0.58.1/libexec/lib/python3.9/site-packages/datasette/app.py\", line 634, in execute\n return await self.databases[db_name].execute(\n" ], "database": "covid", "sql": "select fips, count(*) as n from (\n select rowid, date, county, state, fips, cases, deaths from ny_times_us_counties where \"cases\" > :p0 \n ) where fips is not null\n group by fips\n limit 4", "params": { "p0": 10000 } } ] So that's: fetch rows: 1.0233789999993803 ms count: 120.65118200000136 ms facet county: 32.360455000000954 ms facet state: 19.84949100000044 ms facet fips: 26.217371999999628 ms = 200.1 ms total

Compared to: http://127.0.0.1:8006/covid?sql=with+cte+as+(%0D%0A++select+rowid%2C+date%2C+county%2C+state%2C+fips%2C+cases%2C+deaths%0D%0A++from+ny_times_us_counties%0D%0A)%2C%0D%0Atruncated+as+(%0D%0A++select+null+as+_facet%2C+null+as+facet_name%2C+null+as+facet_count%2C+rowid%2C+date%2C+county%2C+state%2C+fips%2C+cases%2C+deaths%0D%0A++from+cte+order+by+date+desc+limit+4%0D%0A)%2C%0D%0Astate_facet+as+(%0D%0A++select+%27state%27+as+_facet%2C+state+as+facet_name%2C+count(*)+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A)%2C%0D%0Afips_facet+as+(%0D%0A++select+%27fips%27+as+_facet%2C+fips+as+facet_name%2C+count(*)+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A)%2C%0D%0Acounty_facet+as+(%0D%0A++select+%27county%27+as+_facet%2C+county+as+facet_name%2C+count(*)+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A)%0D%0Aselect+*+from+truncated%0D%0Aunion+all+select+*+from+state_facet%0D%0Aunion+all+select+*+from+fips_facet%0D%0Aunion+all+select+*+from+county_facet&_trace=1

Which is 353ms total.

The separate queries ran faster! Really surprising result there.

One last experiment: I'm going to try running an expensive query in the CTE portion.

With trace.

https://covid-19.datasettes.com/covid/ny_times_us_counties?_trace=1&_facet_size=3&_size=2&_trace=1 shows the following:

fetch rows: 0.41762600005768036 ms facet state: 284.30423800000426 ms facet county: 273.2565999999679 ms facet fips: 197.80996999998024 ms = 755.78843400001ms total

It didn't run a count because that's the homepage and the count is cached. So I dropped the count from the query and ran it:

https://covid-19.datasettes.com/covid?sql=with+cte+as+(%0D%0A++select+rowid%2C+date%2C+county%2C+state%2C+fips%2C+cases%2C+deaths%0D%0A++from+ny_times_us_counties%0D%0A)%2C%0D%0Atruncated+as+(%0D%0A++select+null+as+_facet%2C+null+as+facet_name%2C+null+as+facet_count%2C+rowid%2C+date%2C+county%2C+state%2C+fips%2C+cases%2C+deaths%0D%0A++from+cte+order+by+date+desc+limit+4%0D%0A)%2C%0D%0Astate_facet+as+(%0D%0A++select+%27state%27+as+_facet%2C+state+as+facet_name%2C+count()+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A)%2C%0D%0Afips_facet+as+(%0D%0A++select+%27fips%27+as+_facet%2C+fips+as+facet_name%2C+count()+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A)%2C%0D%0Acounty_facet+as+(%0D%0A++select+%27county%27+as+_facet%2C+county+as+facet_name%2C+count()+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A)%0D%0Aselect++from+truncated%0D%0Aunion+all+select++from+state_facet%0D%0Aunion+all+select++from+fips_facet%0D%0Aunion+all+select+*+from+county_facet&_trace=1

Shows 649.4359889999259 ms for the query - compared to 755.78843400001ms for the separate. So it saved about 100ms.

Still not a huge difference though!

One disadvantage to this approach: if you have a SQL time limit of 1s and it takes 0.9s to return the rows but then 0.5s to calculate each of the requested facets the entire query will exceed the time limit.

Could work around this by catching that error and then re-running the query just for the rows, but that would result in the user having to wait longer for the results.

Could try to remember if that has happened using an in-memory Python data structure and skip the faceting optimization if it's caused problems in the past? That seems a bit gross.

Maybe this becomes an opt-in optimization you can request in your metadata.json setting for that table, which massively increases the time limit? That's a bit weird too - now there are two separate implementations of the faceting logic, which had better have a REALLY big pay-off to be worth maintaining.

What if we kept the query that returns the rows to be displayed on the page separate from the facets, but then executed all of the facets together using this method such that the cte only (presumably) has to be calculated once? That would still lead to multiple facets potentially exceeding the SQL time limit when single facets would not have.

Maybe a better optimization would be to move facets to happening via fetch() calls from the client, so the user gets to see their rows instantly and the facets then appear as and when they are available (though it would cause page jank).

https://covid-19.datasettes.com/covid is 805.2MB

https://covid-19.datasettes.com/covid/ny_times_us_counties?_trace=1&_facet_size=3&_size=2

Equivalent SQL:

https://covid-19.datasettes.com/covid?sql=with+cte+as+%28%0D%0A++select+rowid%2C+date%2C+county%2C+state%2C+fips%2C+cases%2C+deaths%0D%0A++from+ny_times_us_counties%0D%0A%29%2C%0D%0Atruncated+as+%28%0D%0A++select+null+as+_facet%2C+null+as+facet_name%2C+null+as+facet_count%2C+rowid%2C+date%2C+county%2C+state%2C+fips%2C+cases%2C+deaths%0D%0A++from+cte+order+by+date+desc+limit+4%0D%0A%29%2C%0D%0Astate_facet+as+%28%0D%0A++select+%27state%27+as+_facet%2C+state+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A%29%2C%0D%0Afips_facet+as+%28%0D%0A++select+%27fips%27+as+_facet%2C+fips+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A%29%2C%0D%0Acounty_facet+as+%28%0D%0A++select+%27county%27+as+_facet%2C+county+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A%29%2C%0D%0Atotal_count+as+%28%0D%0A++select+%27COUNT%27+as+_facet%2C+%27%27+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte%0D%0A%29%0D%0Aselect++from+truncated%0D%0Aunion+all+select++from+state_facet%0D%0Aunion+all+select++from+fips_facet%0D%0Aunion+all+select++from+county_facet%0D%0Aunion+all+select+*+from+total_count

sql with cte as ( select rowid, date, county, state, fips, cases, deaths from ny_times_us_counties ), truncated as ( select null as _facet, null as facet_name, null as facet_count, rowid, date, county, state, fips, cases, deaths from cte order by date desc limit 4 ), state_facet as ( select 'state' as _facet, state as facet_name, count(*) as facet_count, null, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), fips_facet as ( select 'fips' as _facet, fips as facet_name, count(*) as facet_count, null, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), county_facet as ( select 'county' as _facet, county as facet_name, count(*) as facet_count, null, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), total_count as ( select 'COUNT' as _facet, '' as facet_name, count(*) as facet_count, null, null, null, null, null, null, null from cte ) select * from truncated union all select * from state_facet union all select * from fips_facet union all select * from county_facet union all select * from total_count

_facet | facet_name | facet_count | rowid | date | county | state | fips | cases | deaths -- | -- | -- | -- | -- | -- | -- | -- | -- | --   |   |   | 1917344 | 2021-11-15 | Autauga | Alabama | 1001 | 10407 | 154   |   |   | 1917345 | 2021-11-15 | Baldwin | Alabama | 1003 | 37875 | 581   |   |   | 1917346 | 2021-11-15 | Barbour | Alabama | 1005 | 3648 | 79   |   |   | 1917347 | 2021-11-15 | Bibb | Alabama | 1007 | 4317 | 92 state | Texas | 148028 |   |   |   |   |   |   |   state | Georgia | 96249 |   |   |   |   |   |   |   state | Virginia | 79315 |   |   |   |   |   |   |   fips |   | 17580 |   |   |   |   |   |   |   fips | 53061 | 665 |   |   |   |   |   |   |   fips | 17031 | 662 |   |   |   |   |   |   |   county | Washington | 18666 |   |   |   |   |   |   |   county | Unknown | 15840 |   |   |   |   |   |   |   county | Jefferson | 15637 |   |   |   |   |   |   |   COUNT |   | 1920593 |   |   |   |   |   |   |  

(One thing I really like about this pattern is that it should work exactly the same when used to facet the results of arbitrary SQL queries as it does when faceting results from the table page.)

It's going to take another 15 minutes for the build to finish and deploy the version with _trace=1: https://github.com/simonw/covid-19-datasette/actions/runs/1469150112

Trace now enabled: https://global-power-plants.datasettes.com/global-power-plants/global-power-plants?_facet_size=3&_size=2&_nocount=1&_trace=1

Here are the relevant traces: json [ { "type": "sql", "start": 31.214430154, "end": 31.214817089, "duration_ms": 0.3869350000016425, "traceback": [ " File \"/usr/local/lib/python3.8/site-packages/datasette/views/base.py\", line 262, in get\n return await self.view_get(\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/views/base.py\", line 477, in view_get\n response_or_template_contexts = await self.data(\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/views/table.py\", line 705, in data\n results = await db.execute(sql, params, truncate=True, **extra_args)\n" ], "database": "global-power-plants", "sql": "select rowid, country, country_long, name, gppd_idnr, capacity_mw, latitude, longitude, primary_fuel, other_fuel1, other_fuel2, other_fuel3, commissioning_year, owner, source, url, geolocation_source, wepp_id, year_of_capacity_data, generation_gwh_2013, generation_gwh_2014, generation_gwh_2015, generation_gwh_2016, generation_gwh_2017, generation_data_source, estimated_generation_gwh from [global-power-plants] order by rowid limit 3", "params": {} }, { "type": "sql", "start": 31.215234586, "end": 31.220110342, "duration_ms": 4.875756000000564, "traceback": [ " File \"/usr/local/lib/python3.8/site-packages/datasette/views/table.py\", line 760, in data\n ) = await facet.facet_results()\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/facets.py\", line 212, in facet_results\n facet_rows_results = await self.ds.execute(\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/app.py\", line 634, in execute\n return await self.databases[db_name].execute(\n" ], "database": "global-power-plants", "sql": "select country_long as value, count(*) as count from (\n select rowid, country, country_long, name, gppd_idnr, capacity_mw, latitude, longitude, primary_fuel, other_fuel1, other_fuel2, other_fuel3, commissioning_year, owner, source, url, geolocation_source, wepp_id, year_of_capacity_data, generation_gwh_2013, generation_gwh_2014, generation_gwh_2015, generation_gwh_2016, generation_gwh_2017, generation_data_source, estimated_generation_gwh from [global-power-plants] \n )\n where country_long is not null\n group by country_long order by count desc, value limit 4", "params": [] }, { "type": "sql", "start": 31.221062485, "end": 31.228968364, "duration_ms": 7.905878999999061, "traceback": [ " File \"/usr/local/lib/python3.8/site-packages/datasette/views/table.py\", line 760, in data\n ) = await facet.facet_results()\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/facets.py\", line 212, in facet_results\n facet_rows_results = await self.ds.execute(\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/app.py\", line 634, in execute\n return await self.databases[db_name].execute(\n" ], "database": "global-power-plants", "sql": "select owner as value, count(*) as count from (\n select rowid, country, country_long, name, gppd_idnr, capacity_mw, latitude, longitude, primary_fuel, other_fuel1, other_fuel2, other_fuel3, commissioning_year, owner, source, url, geolocation_source, wepp_id, year_of_capacity_data, generation_gwh_2013, generation_gwh_2014, generation_gwh_2015, generation_gwh_2016, generation_gwh_2017, generation_data_source, estimated_generation_gwh from [global-power-plants] \n )\n where owner is not null\n group by owner order by count desc, value limit 4", "params": [] }, { "type": "sql", "start": 31.229809757, "end": 31.253902162, "duration_ms": 24.09240499999754, "traceback": [ " File \"/usr/local/lib/python3.8/site-packages/datasette/views/table.py\", line 760, in data\n ) = await facet.facet_results()\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/facets.py\", line 212, in facet_results\n facet_rows_results = await self.ds.execute(\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/app.py\", line 634, in execute\n return await self.databases[db_name].execute(\n" ], "database": "global-power-plants", "sql": "select primary_fuel as value, count(*) as count from (\n select rowid, country, country_long, name, gppd_idnr, capacity_mw, latitude, longitude, primary_fuel, other_fuel1, other_fuel2, other_fuel3, commissioning_year, owner, source, url, geolocation_source, wepp_id, year_of_capacity_data, generation_gwh_2013, generation_gwh_2014, generation_gwh_2015, generation_gwh_2016, generation_gwh_2017, generation_data_source, estimated_generation_gwh from [global-power-plants] \n )\n where primary_fuel is not null\n group by primary_fuel order by count desc, value limit 4", "params": [] }, { "type": "sql", "start": 31.255699745, "end": 31.256243889, "duration_ms": 0.544143999999136, "traceback": [ " File \"/usr/local/lib/python3.8/site-packages/datasette/facets.py\", line 145, in suggest\n row_count = await self.get_row_count()\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/facets.py\", line 132, in get_row_count\n await self.ds.execute(\n", " File \"/usr/local/lib/python3.8/site-packages/datasette/app.py\", line 634, in execute\n return await self.databases[db_name].execute(\n" ], "database": "global-power-plants", "sql": "select count(*) from (select rowid, country, country_long, name, gppd_idnr, capacity_mw, latitude, longitude, primary_fuel, other_fuel1, other_fuel2, other_fuel3, commissioning_year, owner, source, url, geolocation_source, wepp_id, year_of_capacity_data, generation_gwh_2013, generation_gwh_2014, generation_gwh_2015, generation_gwh_2016, generation_gwh_2017, generation_data_source, estimated_generation_gwh from [global-power-plants] )", "params": [] } ] fetch rows: 0.3869350000016425 ms facet country_long: 4.875756000000564 ms facet owner: 7.905878999999061 ms facet primary_fuel: 24.09240499999754 ms count: 0.544143999999136 ms Total = 37.8ms

I modified the query to include the total count as well: https://global-power-plants.datasettes.com/global-power-plants?sql=with+cte+as+%28%0D%0A++select+rowid%2C+country%2C+country_long%2C+name%2C+owner%2C+primary_fuel%0D%0A++from+%5Bglobal-power-plants%5D%0D%0A%29%2C%0D%0Atruncated+as+%28%0D%0A++select+null+as+_facet%2C+null+as+facet_name%2C+null+as+facet_count%2C+rowid%2C+country%2C+country_long%2C+name%2C+owner%2C+primary_fuel%0D%0A++from+cte+order+by+rowid+limit+4%0D%0A%29%2C%0D%0Acountry_long_facet+as+%28%0D%0A++select+%27country_long%27+as+_facet%2C+country_long+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A%29%2C%0D%0Aowner_facet+as+%28%0D%0A++select+%27owner%27+as+_facet%2C+owner+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A%29%2C%0D%0Aprimary_fuel_facet+as+%28%0D%0A++select+%27primary_fuel%27+as+_facet%2C+primary_fuel+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte+group+by+facet_name+order+by+facet_count+desc+limit+3%0D%0A%29%2C%0D%0Atotal_count+as+%28%0D%0A++select+%27COUNT%27+as+_facet%2C+%27%27+as+facet_name%2C+count%28%29+as+facet_count%2C%0D%0A++null%2C+null%2C+null%2C+null%2C+null%2C+null%0D%0A++from+cte%0D%0A%29%0D%0Aselect++from+truncated%0D%0Aunion+all+select++from+country_long_facet%0D%0Aunion+all+select++from+owner_facet%0D%0Aunion+all+select++from+primary_fuel_facet%0D%0Aunion+all+select+*+from+total_count&_trace=1

sql with cte as ( select rowid, country, country_long, name, owner, primary_fuel from [global-power-plants] ), truncated as ( select null as _facet, null as facet_name, null as facet_count, rowid, country, country_long, name, owner, primary_fuel from cte order by rowid limit 4 ), country_long_facet as ( select 'country_long' as _facet, country_long as facet_name, count(*) as facet_count, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), owner_facet as ( select 'owner' as _facet, owner as facet_name, count(*) as facet_count, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), primary_fuel_facet as ( select 'primary_fuel' as _facet, primary_fuel as facet_name, count(*) as facet_count, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), total_count as ( select 'COUNT' as _facet, '' as facet_name, count(*) as facet_count, null, null, null, null, null, null from cte ) select * from truncated union all select * from country_long_facet union all select * from owner_facet union all select * from primary_fuel_facet union all select * from total_count The trace says that query took 34.801436999998714 ms.

To my huge surprise, this convoluted optimization only shaves the sum query time down from 37.8ms to 34.8ms!

That entire database file is just 11.1 MB though. Maybe it would make a meaningful difference on something larger?

The query takes 42.794ms to run.

Here's the equivalent page using separate queries: https://global-power-plants.datasettes.com/global-power-plants/global-power-plants?_facet_size=3&_size=2&_nocount=1

Annoyingly I can't disable facet suggestions but keep facets.

I'm going to turn on tracing so I can see how long the separate queries took.

I came up with the following query which seems to work!

sql with cte as ( select rowid, country, country_long, name, owner, primary_fuel from [global-power-plants] ), truncated as ( select null as _facet, null as facet_name, null as facet_count, rowid, country, country_long, name, owner, primary_fuel from cte order by rowid limit 4 ), country_long_facet as ( select 'country_long' as _facet, country_long as facet_name, count(*) as facet_count, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), owner_facet as ( select 'owner' as _facet, owner as facet_name, count(*) as facet_count, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ), primary_fuel_facet as ( select 'primary_fuel' as _facet, primary_fuel as facet_name, count(*) as facet_count, null, null, null, null, null, null from cte group by facet_name order by facet_count desc limit 3 ) select * from truncated union all select * from country_long_facet union all select * from owner_facet union all select * from primary_fuel_facet (Limits should be 101, 31, 31, 31 but I reduced size to get a shorter example table).

Results look like this:

_facet | facet_name | facet_count | rowid | country | country_long | name | owner | primary_fuel -- | -- | -- | -- | -- | -- | -- | -- | --   |   |   | 1 | AFG | Afghanistan | Kajaki Hydroelectric Power Plant Afghanistan |   | Hydro   |   |   | 2 | AFG | Afghanistan | Kandahar DOG |   | Solar   |   |   | 3 | AFG | Afghanistan | Kandahar JOL |   | Solar   |   |   | 4 | AFG | Afghanistan | Mahipar Hydroelectric Power Plant Afghanistan |   | Hydro country_long | United States of America | 8688 |   |   |   |   |   |   country_long | China | 4235 |   |   |   |   |   |   country_long | United Kingdom | 2603 |   |   |   |   |   |   owner |   | 14112 |   |   |   |   |   |   owner | Lightsource Renewable Energy | 120 |   |   |   |   |   |   owner | Cypress Creek Renewables | 109 |   |   |   |   |   |   primary_fuel | Solar | 9662 |   |   |   |   |   |   primary_fuel | Hydro | 7155 |   |   |   |   |   |   primary_fuel | Wind | 5188 |   |   |   |   |   |  

This is a neat proof of concept.