NB: I am using db (not ndb) here. I know ndb has a count_async() but I am hoping for a solution that does not involve migrating over to ndb.
Occasionally I need an accurate count of the number of entities that match a query. With db this is simply:
q = some Query with filters
num_entities = q.count(limit=None)
It costs a small db operation per entity but it gets me the info I need. The problem is that I often need to do a few of these in the same request and it would be nice to do them asynchronously but I don’t see support for that in the db library.
I was thinking I could use run(keys_only=True, batch_size=1000) as it runs the query asynchronously and returns an iterator. I could first call run() on each query and then later count the results from each iterator. It costs the same as count() however run() has proven to be slower in testing (perhaps because it actually returns results) and in fact it seems that batch_size is limited at 300 regardless of how high I set it which requires more RPCs to do a count of thousands of entities than the count() method does.
My test code for run() looks like this:
queries = list of Queries with filters
iters = []
for q in queries:
iters.append( q.run(keys_only=True, batch_size=1000) )
for iter in iters:
count_entities_from(iter)
No, there’s no equivalent in db. The whole point of ndb is that it adds these sort of capabilities which were missing in db.