These numbers are somewhat misleading, and the phrasing may confuse people.
> The fastest python solution using an ORM is a tenth as fast as the Java solutions
This may be true if your bottleneck is the ORM in the first place (this is relatively easily verifiable by running a profiler on your python program, comparing the results against (e.g.) EXPLAIN ANALYZE output, etc.)
I run a service with a decently-sized database (over 100G); so far all the bottlenecks were at the "query execution in the DB" level. I use lower-level SQLAlchemy primitives to form the queries, and check the resulting raw SQL against the raw SQL queries that I had designed for the DB before. If you know what you're doing, SQLAlchemy will know, too.
Use cases and situations vary. But the way you phrase those particular benchmarking results is somewhat misleading.
> The fastest python solution using an ORM is a tenth as fast as the Java solutions
This may be true if your bottleneck is the ORM in the first place (this is relatively easily verifiable by running a profiler on your python program, comparing the results against (e.g.) EXPLAIN ANALYZE output, etc.)
I run a service with a decently-sized database (over 100G); so far all the bottlenecks were at the "query execution in the DB" level. I use lower-level SQLAlchemy primitives to form the queries, and check the resulting raw SQL against the raw SQL queries that I had designed for the DB before. If you know what you're doing, SQLAlchemy will know, too.
Use cases and situations vary. But the way you phrase those particular benchmarking results is somewhat misleading.