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Mogrify performance on arrays
Reported by Psycopg website | August 28th, 2013 @ 02:18 PM
Submitted by: ChrisB
If I run cursor.mogrify on a 64000 double array it is taking 350ms. If I just do a 'ARRAY[' + ','.join(map(str, array)) + ']' and put it into a string format myself it only takes 55ms. Although the documentation tells you not to do this, it is bringing the insert time of my app down massively.
Comments and changes to this ticket
- State changed from invalid to open
- Tag cleared.
- Assigned user set to Federico Di Gregorio
Daniele, wait. He was talking about a simple benchmark and I can see where the bottleneck is. The mogrification of arrays is slow, because the arguments are checked one by one: we can probably make this faster by caching the adapter when the list is know to contain only objects of the same type. I'll investigate this.
I think the speedup you have in mind is not easy to implement; OTOH it can be generalized. The adaptation codepath is not straightforward as it depends on several choices: is the object
__conform__, is there a
prepare()to be called etc. We can cache a mapping class -> choices made and reuse it instead of exploring the adaptation tree for each object: this would speed up not only array adaptation but any operation such as
execute(SQL, ["s1", "s2", "s3"]): we wouldn't have to check how to adapt the string object 3 times but only once.
The cache should be short-lived as changing the adapters map should invalidate it and because the number of classes could be unbound (if they are created dynamically, e.g. namedtuples). But creating a new cache for every
executemany()would be already a nice speedup and there should be no side effect (as there should be no program relying on a change of the adapters map during a concurrent
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Psycopg is the most used PostgreSQL adapter for the Python programming language. At the core it fully implements the Python DB API 2.0 specifications. Several extensions allow access to many of the features offered by PostgreSQL.