asyncmy

asyncmy — The fastest asyncio MySQL/MariaDB driver

asyncmy is the fastest asyncio MySQL/MariaDB driver for Python. It keeps the familiar aiomysql API while rewriting the entire protocol core in Cython — down to pointer-level packet parsing. See our benchmarks for Python-driver and Go/Rust comparisons, including their measured settings and results.

Features

• 🚀 Fast Python queries — large-result fetching takes about half mysqlclient’s time; pooled queries deliver 3.6× aiomysql’s throughput in the local benchmark
• 🔌 Drop-in aiomysql replacement — same API, same cursors (DictCursor, SSCursor), same pool semantics
• 🧬 Server-side prepared statements (binary protocol) via conn.prepare() or cached regular Cursor calls — numeric and temporal values decode directly from their binary representation
• ⚡ C-speed protocol core — rows are parsed in bulk from the receive buffer in a single C loop, values decode straight from wire bytes via the CPython C-API
• 🏊 Built-in connection pool — asyncmy.create_pool(), with reusable connections and cached prepared statements
• 📡 MySQL replication protocol over asyncio (BinLogStream)
• ✅ CI-tested on MySQL and MariaDB (workflow)

Install

Requirements: Python ≥ 3.9

pip install asyncmy

Windows

asyncmy uses Cython extensions; on Windows you need Microsoft C++ Build Tools to build them.

1. Download Microsoft C++ Build Tools.

2. Open CMD as Administrator (recommended) and cd to the folder where the installer was downloaded.

3. Rename the installer (e.g. vs_buildtools__XXXXXXXXX.XXXXXXXXXX.exe) to vs_buildtools.exe for convenience.

4. Run (ensure ~5–6GB free disk space):

   vs_buildtools.exe --norestart --passive --downloadThenInstall --includeRecommended --add Microsoft.VisualStudio.Workload.NativeDesktop --add Microsoft.VisualStudio.Workload.VCTools --add Microsoft.VisualStudio.Workload.MSBuildTools
   

5. Wait for installation to complete, then restart your computer.

6. Install asyncmy:

   pip install asyncmy
   

You can uninstall the Build Tools afterward if desired.

Usage

connect

Use asyncmy.connect() for a single connection. For many concurrent connections, use a connection pool.

import asyncio
import os

from asyncmy import connect
from asyncmy.cursors import DictCursor


async def main():
    conn = await connect(
        user=os.getenv("DB_USER"),
        password=os.getenv("DB_PASSWORD", ""),
    )
    async with conn.cursor(cursor=DictCursor) as cursor:
        await cursor.execute("CREATE DATABASE IF NOT EXISTS test")
        await cursor.execute("""
            CREATE TABLE IF NOT EXISTS test.`asyncmy` (
                `id`       int PRIMARY KEY AUTO_INCREMENT,
                `decimal`  decimal(10, 2),
                `date`     date,
                `datetime` datetime,
                `float`    float,
                `string`   varchar(200),
                `tinyint`  tinyint
            ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_general_ci
        """.strip())
    await conn.ensure_closed()


if __name__ == "__main__":
    asyncio.run(main())

Prepared statements (binary protocol)

For repeated queries, server-side prepared statements skip client-side escaping entirely and read results in MySQL’s binary protocol — numeric and temporal columns decode natively with no text parsing. Placeholders use native ? syntax.

stmt = await conn.prepare("SELECT id, name FROM users WHERE id = ?")
result = await stmt.execute((42,))
print(result.rows)           # tuple of row tuples
print(result.affected_rows)  # for INSERT/UPDATE/DELETE
await stmt.close()

# or as a context manager
async with await conn.prepare("SELECT ? + ?") as stmt:
    result = await stmt.execute((1, 2))

Transparent mode: pass stmt_cache_size=N to connect()/create_pool() and regular cursor.execute("... %s ...", args) calls automatically run as cached server-side prepared statements — no code changes needed (ORMs benefit too). Queries the server can’t prepare fall back to the text protocol silently.

pool = await asyncmy.create_pool(
    host="localhost", user="root", password="", stmt_cache_size=128,
)

Note: with the binary protocol, FLOAT columns return the exact stored value rather than the text protocol’s decimal-rounded rendering, which is why this is opt-in.

Pool

For multiple connections, use a connection pool. Pass the same kwargs as connect() (e.g. host, user, password).

import asyncio
import asyncmy


async def main():
    pool = await asyncmy.create_pool(host="localhost", user="root", password="")
    async with pool.acquire() as conn:
        async with conn.cursor() as cursor:
            await cursor.execute("SELECT 1")
            ret = await cursor.fetchone()
            assert ret == (1,)
    pool.close()
    await pool.wait_closed()


if __name__ == "__main__":
    asyncio.run(main())

Type checking

asyncmy ships py.typed and stubs for its compiled modules, so mypy and Pylance resolve the API without extra configuration:

conn = await asyncmy.connect(host="localhost", user="root")   # -> Connection
async with conn.cursor() as cur:
    rows = await cur.fetchall()                               # -> list[Any]

Contributors: make stubs regenerates the stubs after changing a .pyx signature. make check runs stubtest, which compares every stub against the compiled module and fails on drift.

Rotating credentials

Some credentials expire while a pooled connection outlives them — AWS RDS IAM auth tokens last 15 minutes, for instance. Pass password_creator instead of password and it is consulted before every connection attempt, including the ones the pool makes on its own when it recycles or reconnects:

import boto3

client = boto3.client("rds")


def rds_auth_token():
    return client.generate_db_auth_token(
        DBHostname="mydb.cluster.amazonaws.com",
        Port=3306,
        DBUsername="dbuser",
        Region="us-east-1",
    )


pool = await asyncmy.create_pool(
    host="mydb.cluster.amazonaws.com",
    user="dbuser",
    password_creator=rds_auth_token,
)

The callable may be a plain function or return an awaitable, and must return str or bytes. If both password and password_creator are given, the creator wins.

Statement logging

echo=True logs every statement and its duration to the asyncmy logger at INFO level. Nothing appears until logging is configured — logging.basicConfig(level=logging.INFO) at minimum, since the root logger defaults to WARNING.

For anything beyond that, pass query_callback. It is called as callback(cursor, query, elapsed_ms) after every successful statement, with the duration as a float in milliseconds:

import logging

logger = logging.getLogger("myapp.sql")


def log_slow_queries(cursor, query, elapsed_ms):
    if elapsed_ms > 100:
        logger.warning("[%sms] %s", elapsed_ms, query)


conn = await asyncmy.connect(host="localhost", user="root", query_callback=log_slow_queries)

executemany and callproc report once for the whole call rather than once per row. The callback is independent of echo: set both and you get the log line and the callback.

Replication

asyncmy supports the MySQL replication protocol (like python-mysql-replication) over asyncio.

import asyncio

from asyncmy import connect
from asyncmy.replication import BinLogStream


async def main():
    conn = await connect()
    ctl_conn = await connect()

    stream = BinLogStream(
        conn,
        ctl_conn,
        server_id=1,
        master_log_file="binlog.000172",
        master_log_position=2235312,
        resume_stream=True,
        blocking=True,
    )
    async for event in stream:
        print(event)
    await conn.ensure_closed()
    await ctl_conn.ensure_closed()


if __name__ == "__main__":
    asyncio.run(main())

Benchmark

Measured on 2026-10-22 against local MySQL over TCP. The Python-driver suite and cross-language suite use different workloads and sampling methods; each comparison below keeps those settings explicit. All times are in milliseconds, lower is better.

Python driver comparison

One warmup followed by best of three runs, using standard asyncio and autocommit=True. The dataset contains 100,000 rows; the filtered large-result query returns 33,334 of them.

Workload	asyncmy	mysqlclient	PyMySQL	aiomysql
Large result set, 33,334 mixed-type rows	29	60	112	122
50 concurrent queries, one connection each	6	—	—	8
2,000 pooled queries, 5–20 connections	56	—	—	202
Batch insert, 10,000 rows	123	145	138	123

asyncmy leads on result fetching, concurrent queries and pooled queries. Large result fetching takes about half mysqlclient’s time; pooled queries deliver about 3.6× aiomysql’s throughput. Batch insertion is effectively tied with aiomysql in this run. Times are rounded to one millisecond; — means the suite does not measure that combination.

The asyncmy column uses the text protocol. Its separate binary large-result case took 30 ms with stmt_cache_size=16. This schema’s text and binary results are close; the cross-language suite below also measures integer-heavy and mixed-type scans separately.

Versions: asyncmy 0.2.17 (working tree), mysqlclient 2.3.0, PyMySQL 1.2.3, aiomysql 0.3.2. See the Python benchmark guide for workloads and commands, and the recorded run for the console-reported timings and settings.

Cross-language comparison

Same table, queries and protocol mode, comparing asyncmy with go-sql-driver/mysql and mysql_async. Times are the median of 504 samples per runtime/case, collected over 72 rounds with balanced client order. Every measured sample is retained.

Workload	asyncmy asyncio	asyncmy uvloop	Go	Rust
50k rows × 8 integer columns, text	12.94	13.95	19.12	18.01
50k rows × 8 integer columns, binary	9.97	9.74	12.64	9.60
50k rows × 10 mixed columns, text	36.71	41.86	33.73	33.41
50k rows × 10 mixed columns, binary	23.56	23.55	25.49	20.43
Pooled 20×200 point queries, text	51.24	45.03	34.87	33.49
Pooled 20×200 point queries, binary	34.68	31.11	32.06	34.12

For pooled binary point queries, the ordinary Cursor API with uvloop, stmt_cache_size=32 and autocommit=True leads both native drivers:

Compared with	Mean per-round throughput advantage	95% confidence interval
Go	+2.79%	+1.65% to +3.88%
Rust	+9.37%	+8.64% to +10.22%

These percentages average per-round throughput ratios; they are calculated separately from the aggregate median times above. Individual rounds overlap: asyncmy wins 50/72 round medians against Go and 71/72 against Rust. Its average advantage is positive in 11/12 balanced six-round blocks against Go and all 12 against Rust. The confidence intervals resample those complete blocks.

Both native drivers lead on text point queries, and Rust leads on mixed scans. The results show where asyncmy leads and where further optimization remains.

Environment and measurement details

Component	Version / setting
Hardware / OS	Apple M4 Max / macOS 27.0.1 arm64
MySQL	26.10.0, TCP 127.0.0.1:3306
CPython / Cython	3.15.1 / 3.3.0
uvloop	0.23.0
Go / go-sql-driver/mysql	1.27.3 / 1.10.1
Rust / mysql_async / Tokio	1.99.1 / 0.37.1 / 1.54.1

The cross-language measurement uses:

• Balanced sampling: all six client-order permutations repeated twelve times, with one warmup and seven measured samples per case in each round. Clients run sequentially. Python uses one event-loop thread, Rust uses Tokio’s current-thread runtime, and Go sets GOMAXPROCS(1); helper threads can exist.
• Pooled point queries: ten prewarmed connections, 20 workers × 200 queries, autocommit enabled. Each query includes acquiring a connection, executing, fetching and releasing it. Binary statements are cached per connection; asyncmy uses the regular Cursor API with stmt_cache_size=32.
• Complete scan results: all 50,000 rows are fetched, materialized and retained. Binary scans use an explicitly prepared statement, with preparation outside timing. Temporal values include a duration for TIME in every language. Python constructs Decimal values; Go/Rust retain DECIMAL strings, so mixed materialization differs. Checksums are validated after timing; Python runs cyclic garbage collection before each measured sample.

The measured point-query settings are opt-in: asyncmy defaults to autocommit=False and stmt_cache_size=0. Use transaction settings that match your application. With uvloop installed, replace asyncio.run(main()) with uvloop.run(main()) after importing uvloop.

What makes asyncmy fast

• Bulk packet parsing: complete row packets in the receive buffer are parsed in one C loop, reducing packet objects and event-loop round-trips.
• Direct value decoding: integers, floats, strings and temporal values decode from wire bytes through Cython and the CPython C-API, avoiding intermediate strings and general-purpose parsing where possible.
• Cached binary results: prepared statements reuse per-connection state; complete responses can populate Cursor rows directly after metadata checks.
• Cheaper pool operations: healthy checkout avoids coroutine allocation, and coalesced notifications reduce waiter scheduling work.
• Small-query fast paths: scalar parameters avoid general packet-building overhead, while strings without special characters skip escape translation.

See the full cross-language report for exact queries, source hashes, compatibility checks, confidence-interval calculation and reproduction commands. The raw cross-language results retain all samples and separate earlier toolchains and optimization stages. The benchmark overview covers both suites.

Acknowledgments

asyncmy builds on these projects:

• PyMySQL — pure Python MySQL client
• aiomysql — asyncio MySQL driver
• python-mysql-replication — MySQL replication protocol (pure Python, on top of PyMySQL)

License

Apache-2.0