issue_comments

Tests are failing and I think it's because the facets come back in different orders, need a tie-breaker. https://github.com/simonw/datasette/runs/4219325197?check_suite_focus=true

Also note that this demo data is using a SQL view to create the JSON arrays - the view is defined as such:

sql CREATE VIEW ads_with_targets as select ads.*, json_group_array(targets.name) as target_names from ads join ad_targets on ad_targets.ad_id = ads.id join targets on ad_targets.target_id = targets.id group by ad_targets.ad_id; So running JSON faceting on top of that view is a pretty big ask!

Actually with the cache warmed up it looks like the facet query is taking 150ms which is good enough.

I tried this and it seems to work correctly: python for source_and_config in self.get_configs(): config = source_and_config["config"] source = source_and_config["source"] column = config.get("column") or config["simple"] facet_sql = """ with inner as ({sql}), deduped_array_items as ( select distinct j.value, inner.* from json_each([inner].{col}) j join inner ) select value as value, count(*) as count from deduped_array_items group by value order by count(*) desc limit {limit} """.format( col=escape_sqlite(column), sql=self.sql, limit=facet_size + 1 ) The queries are very slow though - I had to bump up to 2s time limit even against only a view returning 3,499 rows.

This looks like it might work: sql with inner as ( select * from ads_with_targets where :p0 in ( select value from json_each([ads_with_targets].[target_names]) ) ), deduped_array_items as ( select distinct j.value, inner.* from json_each([inner].[target_names]) j join inner ) select value, count(*) from deduped_array_items group by value order by count(*) desc

It uses a CTE which were introduced in SQLite 3.8 - and AWS Lambda Python 3.9 still provides 3.7 - but I've checked and I can use pysqlite3-binary to work around that there so I'm OK relying on CTEs for this.