How to improve count of text query for Django with Postgres

If you drop the SearchRank and just filter using the query it will use the GIN index and perform much, much faster:

query = SearchQuery(termo,config='portuguese')
entries = Article.objects.filter(search_vector=query)

You can add .explain() to end to take a look at the query and see if the index is used:

print(entries.explain(analyze=True))

You should see the query using Bitmap Heap Scan and the Execution Time should be much faster.

Bitmap Heap Scan on your_table
...
Planning Time: 0.176 ms Execution Time: 0.453 ms

When you annotate like you are above, you are annotating every Article object - so postgres decides to perform a Seq Scan (or Parallel Seq Scan) which it decides is more efficient. More info here

Try adding .explain(verbose=True) or .explain(analyze=True) to your initial SearchRank method to compare.

query = SearchQuery(termo,config='portuguese')
search_rank = SearchRank(F('search_vector'), query)
entries = Article.objects.annotate(rank=search_rank).filter(search_vector=query).order_by('-rank')

print(entries.explain(analyze=True))

I'm facing this issue myself, with a table with 990k entries that takes ~10 seconds. If you can filter the query before the annotation using any other fields - it will push the query planner back toward using the Index.

If the SELECT returns more than approximately 5-10% of all rows in the table, a sequential scan is much faster than an index scan.

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