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.

Given this query: https://json-view-facet-bug-demo-j7hipcg4aq-uc.a.run.app/russian-ads?sql=select%0D%0A++j.value+as+value%2C%0D%0A++count%28*%29+as+count%0D%0Afrom%0D%0A++%28%0D%0A++++select%0D%0A++++++id%2C%0D%0A++++++file%2C%0D%0A++++++clicks%2C%0D%0A++++++impressions%2C%0D%0A++++++text%2C%0D%0A++++++url%2C%0D%0A++++++spend_amount%2C%0D%0A++++++spend_currency%2C%0D%0A++++++created%2C%0D%0A++++++ended%2C%0D%0A++++++target_names%0D%0A++++from%0D%0A++++++ads_with_targets%0D%0A++++where%0D%0A++++++%3Ap0+in+%28%0D%0A++++++++select%0D%0A++++++++++value%0D%0A++++++++from%0D%0A++++++++++json_each%28%5Bads_with_targets%5D.%5Btarget_names%5D%29%0D%0A++++++%29%0D%0A++%29%0D%0A++join+json_each%28target_names%29+j%0D%0Agroup+by%0D%0A++j.value%0D%0Aorder+by%0D%0A++count+desc%2C%0D%0A++value%0D%0Alimit%0D%0A++31&p0=people_who_match%3Ainterests%3AAfrican-American+culture

sql select j.value as value, count(*) as count from ( select id, file, clicks, impressions, text, url, spend_amount, spend_currency, created, ended, target_names from ads_with_targets where :p0 in ( select value from json_each([ads_with_targets].[target_names]) ) ) join json_each(target_names) j group by j.value order by count desc, value limit 31 How can I return a count of the number of documents containing each tag, but not the number of total tags that match including duplicates?

It looks like the problem here is that some of the tags occur more than once in the documents:

So they get counted more than once, hence the 182 count for something that couldn't possibly return more than 172 documents.

I think this code is wrong in the ArrayFacet class: https://github.com/simonw/datasette/blob/502c02fa6dde6a8bb840af6c4c8cf858aa1db687/datasette/facets.py#L357-L364

Applied that fix to the arraycontains filter but I'm still getting bad results for the faceting:

Should never get 182 results on a page that faceting against only 172 items.

I think this SQL recipe may work instead: sql select * from ads_with_targets where 'people_who_match:interests:African-American Civil Rights Movement (1954—68)' in ( select value from json_each(target_names) ) and 'interests:Martin Luther King III' in ( select value from json_each(target_names) ) https://json-view-facet-bug-demo-j7hipcg4aq-uc.a.run.app/russian-ads?sql=select%0D%0A++*%0D%0Afrom%0D%0A++ads_with_targets%0D%0Awhere%0D%0A++%27people_who_match%3Ainterests%3AAfrican-American+Civil+Rights+Movement+%281954%E2%80%9468%29%27+in+%28%0D%0A++++select%0D%0A++++++value%0D%0A++++from%0D%0A++++++json_each%28target_names%29%0D%0A++%29%0D%0A++and+%27interests%3AMartin+Luther+King+III%27+in+%28%0D%0A++++select%0D%0A++++++value%0D%0A++++from%0D%0A++++++json_each%28target_names%29%0D%0A++%29&interests=&African=&Martin=

It may be that some facet implementations (ArrayFacet in this case) need a way to detect if they are supported by the thing they are running against (must be a rowid table in this case) and avoid suggesting themselves if they are not compatible. This may require a change to the information we make available to the suggest() method (information passed to the Facet class constructor).