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Qualification criteria is:

• Needs to support minimum 2 databases, e.g. sqlite + something-else
• Runs on Python3.7
• Actively developed
• Has ability to generate initial DDL off specified models
• Handle one-to-many relationships

These benchmarks are not meant to be used as a direct comparison.They suffer from co-operative back-off, and is a lot simpler than common real-world scenarios.

1. Insert: Single (single entry at a time)
2. Insert: Batch (many batched in a transaction)
3. Insert: Bulk (using bulk insert operations)
4. Filter: Large (a large result set)
5. Filter: Small (a limit 20 with random offset)
6. Get
7. Filter: dict
8. Filter: tuple
9. Update: Whole (update the whole object)
10. Update: Partial (update only a single field of the whole object)
11. Delete

model Journal:    id: autonumber primary key    timestamp: datetime → now()    level: small int(enum) → 10/20/30/40/50 (indexed)    text: varchar(255) → A selection of text (indexed)

model Journal:    id: autonumber primary key    timestamp: datetime → now()    level: small int(enum) → 10/20/30/40/50 (indexed)    text: varchar(255) → A selection of text (indexed)    parent: FK to parent BigTree    child: reverse-FB to parent BigTree    knows: M2M to BigTree

model BigTree:    id: uuid primary key    created_at: datetime → initial-now()    updated_at: datetime → always-now()    level: small int(enum) → 10/20/30/40/50 (indexed)    text: varchar(255) → A selection of text (indexed)    # Repeated 2 times with defaults, another 2 times as optional:    col_float: double    col_smallint: small integer    col_int: integer    col_bigint: big integer    col_char: char(255)    col_text: text    col_decimal: decimal(12,8)    col_json: json

Django:

https://www.djangoproject.com/

Pros:

• Provides all the essential features
• Simple, clean, API
• Great test framework
• Excellent documentation
• Migrations done right™

Cons:

• Brings whole Django along with it

peewee:

https://github.com/coleifer/peewee

Pony ORM:

https://github.com/ponyorm/pony

Pros:

• Fast
• Does cacheing automatically

Cons:

• Does not support bulk insert.

SQLAlchemy ORM:

http://www.sqlalchemy.org/

Pros:

• The "de facto" ORM in the python world
• Supports just about every feature and edge case
• Documentation re DB quirks is excellent

Cons:

• Complicated, layers upon layers of leaky abstractions
• You have to manage transactions manually
• You have to write a script to get DDL SQL
• Documentation expects you to be intimate with SQLAlchemy
• Migrations are add ons

SQLObject:

https://github.com/sqlobject/sqlobject

• Does not support 16-bit integer forlevel, used 32-bit instead.
• Does not support bulk insert.

Tortoise ORM:

https://github.com/tortoise/tortoise-orm

• Currently the onlyasync ORM as part of this suite.
• Disclaimer: I'm an active contributor to this project

Results for SQLite, using theSHM in-memory filesystem on Linux, to try and make the tests more CPU limited, but still do FS round-trips. Also more consistent than an SSD.

Py39:

PyPy7.3-Py3.6: (Outdated)

PostgreSQL 14.2 on my iMac.

MySQL 8.0.28 on my iMac.

PyPy7.3-Py3.6: (Outdated)

• Pony ORM is heavily optimised for performance.
• Django & SQLAlchemy is surprisingly similar in performance.
• Tortoise ORM is competitive.
• Get is surprisingly slow for everyone.
• Pony ORM, SQLAlchemy & SQLObject does merge operations for updates, so is technically always partial updates.
• Tortoise ORM performance using theasyncpg PostgreSQL driver is really good, winning overall.
• Tortoise ORM performance using theaiomysql MySQL driver is mediocre, the driver itself is taking the majority of CPU time. PyPy runs this driver a lot faster, which indicates that the slow paths are likely just in Python itself.

• peewee andPony ORM gets a noticeable performance improvement
• SQLAlchemy ORM andDjango performs similarily
• Tortoise ORM has slow Reads and fast Create, Update & Delete operation
• SQLObject fails

• peewee andTortoise ORM gets a noticeable performance improvement
• Pony ORM is marginally faster
• SQLAlchemy ORM andDjango is marginally slower
• SQLObject fails

Note that these benchmarks have since changed, so state is not exactly the same as above.This should only be used as a "guideline" of the improvement in performance since we started with the performance optimization process.

• base.executor._field_to_db() could be replaced with a pre-computed dict lookup
• Queryset.resolve_filters is doing lots of unnecessary stuff, especially for .get() method
• Get operation is slow (likely slow SQL generation, could be resolved with parametrized query cacheing)

Since pypika is immutable, and our Queryset object is as well, we need tests to guarantee our immutability.Then we can aggresively cache querysets.

Also, we can make more queries use parameterised queries, cache SQL generation, and cache prepared queries.

It seems in cases where we can cache the PyPika result (and use prepared statements), PyPy performance increase is even larger than CPython.

1. aiosqlitepolling misalignment(sqlite specific)

   (20-40% speedup for retrieval,10× — 15× speedup for insertion):omnilib/aiosqlite#12

2. pypikaimproved copy implementation(generic)

   (53% speedup for insertion):kayak/pypika#160

3. tortoise.models.__init__restructure(generic)

   (25-30% speedup for retrieval)tortoise/tortoise-orm#51

4. tortoise.models.__init__restructure(generic)

   (9-11% speedup for retrieval)tortoise/tortoise-orm#52

5. aiosqlitemacros(sqlite specific)

   (1-5% speedup for retrieval, 10-40% speedup for insertion)omnilib/aiosqlite#13

6. Simple prepared insert statements(generic)

   (35-250% speedup for insertion)omnilib/aiosqlite#13tortoise/tortoise-orm#54

7. pre-generate initial pypika query object per model(generic)

   (25-50% speedup for small fetch operations)tortoise/tortoise-orm#54

8. pre-generate filter map, and standard select for all values per model(generic)

   (15-30% speedup for small fetch operations)tortoise/tortoise-orm#64

9. More optimal queryset cloning(generic)

   (6-15% speedup for small fetch operations)tortoise/tortoise-orm#64

10. pypikaimproved copy implementation(generic)

    (10-15% speedup for small fetch operations)kayak/pypika#205

11. Optimised inserts/updates & Bulk create(generic)

    (5-40% speedup for small insert operations)

    (350-600% speedup for bulk insert over small insert operations)tortoise/tortoise-orm#142

12. De-lazied some metadata objects & More efficient queryset manipulation(generic)

    (15-25% speedup for large fetch operations)

    (5-30% speedup for small fetches)tortoise/tortoise-orm#158

13. Parametrized delete/update(generic)

    (260-280% speedup for delete operations)

    (300-600% speedup for update operations)tortoise/tortoise-orm#177

14. Lazy Relation properties(generic)

    (15~140% speedup for all on Test 2 (Small & Relational))tortoise/tortoise-orm#187

15. Know about default converters & native DB types(generic)

    (20-25% speedup for Fetch operations)tortoise/tortoise-orm#190

16. Connection Pooling(MySQL & PostgreSQL)

    (30-50% speedup overall)tortoise/tortoise-orm#229

17. Many small tweaks(generic)

    (5-30% depending on driver)tortoise/tortoise-orm#241