asynch

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Introduction

asynch is an asynchronous ClickHouse Python driver with native TCP interface support, complying with PEP 249.

Fast: the protocol hot path (streams, column codecs, connection, cursors) is compiled with Cython — it matches or beats clickhouse-driver, the synchronous C-extension driver, while staying fully asynchronous (see Performance)
asyncio-native: async/await everywhere, with a built-in connection pool and streaming result sets
PEP 249 API: Connection, Cursor/DictCursor, familiar execute/fetch* semantics
Typed: ships .pyi stubs for the compiled modules (PEP 561)

Installation

> pip install asynch

Binary wheels are published for Linux (x86_64/arm64), Windows and macOS (Intel/ARM) on Python 3.11–3.14 — no compiler needed. On platforms without a wheel, the sdist compiles from source and requires a C toolchain.

If you want to install clickhouse-cityhash to enable transport compression

> pip install asynch[compression]

Usage

Basically, a connection to a ClickHouse server can be established in two ways:

with a DSN string, e.g., clickhouse://[user:password]@host:port/database;

from asynch import Connection

# connecting with a DSN string
async def connect_database():
    async with Connection(
        dsn = "clickhouse://ch_user:P%4055w0rD@127.0.0.1:9000/chdb",
    ) as conn:
        pass

with separately given connection/DSN parameters: user (optional), password (optional), host, port, database.

from asynch import Connection

# connecting with DSN parameters
async def connect_database():
    async with Connection(
        user = "ch_user",
        password = "P@55w0rD",
        host = "127.0.0.1",
        port = 9000,
        database = "chdb",
    ) as conn:
        pass

If a DSN string is given, it takes priority over any specified connection parameter.

Create a database and a table by executing SQL statements via an instance of the Cursor class (here its child DictCursor class) acquired from an instance of the Connection class.

async def create_table(conn: Connection):
    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.asynch
            (
                `id`       Int32,
                `decimal`  Decimal(10, 2),
                `date`     Date,
                `datetime` DateTime,
                `float`    Float32,
                `uuid`     UUID,
                `string`   String,
                `ipv4`     IPv4,
                `ipv6`     IPv6
            )
            ENGINE = MergeTree
            ORDER BY id
            """
        )

Fetching one row from an executed SQL statement:

async def fetchone(conn: Connection):
    # by default, an instance of the `Cursor` class
    async with conn.cursor() as cursor:
        await cursor.execute("SELECT 1")
        ret = await cursor.fetchone()
        assert ret == (1,)

Fetching all the rows from an executed SQL statement:

async def fetchall():
    async with conn.cursor() as cursor:
        await cursor.execute("SELECT 1")
        ret = await cursor.fetchall()
        assert ret == [(1,)]

Executing an SQL statement with parameters:

async def execute(conn: Connection):
    async with conn.cursor() as cursor:
        await cursor.execute(
            """
            SELECT
                EXISTS(
                    SELECT 1
                    FROM table_a
                    WHERE profile_id = %(profile_id)s
                ) AS has_a,
                EXISTS(
                    SELECT 1
                    FROM table_b
                    WHERE profile_id = %(profile_id)s
                ) AS has_b
            """,
            {"profile_id": profile_id}
        )
        ret = await cursor.fetchone()
        assert ret == (True,)

Using an instance of the DictCursor class to get results as a sequence of dictionaries representing the rows of an executed SQL query:

async def dict_cursor():
    async with conn.cursor(cursor=DictCursor) as cursor:
        await cursor.execute("SELECT 1")
        ret = await cursor.fetchall()
        assert ret == [{"1": 1}]

Inserting data with dicts via a DictCursor instance:

from asynch.cursors import DictCursor

async def insert_dict():
    async with conn.cursor(cursor=DictCursor) as cursor:
        ret = await cursor.execute(
            """INSERT INTO test.asynch(id,decimal,date,datetime,float,uuid,string,ipv4,ipv6) VALUES""",
            [
                {
                    "id": 1,
                    "decimal": 1,
                    "date": "2020-08-08",
                    "datetime": "2020-08-08 00:00:00",
                    "float": 1,
                    "uuid": "59e182c4-545d-4f30-8b32-cefea2d0d5ba",
                    "string": "1",
                    "ipv4": "0.0.0.0",
                    "ipv6": "::",
                }
            ],
        )
        assert ret == 1

Inserting data with tuples:

async def insert_tuple():
    async with conn.cursor(cursor=DictCursor) as cursor:
        ret = await cursor.execute(
            """INSERT INTO test.asynch(id,decimal,date,datetime,float,uuid,string,ipv4,ipv6) VALUES""",
            [
                (
                    1,
                    1,
                    "2020-08-08",
                    "2020-08-08 00:00:00",
                    1,
                    "59e182c4-545d-4f30-8b32-cefea2d0d5ba",
                    "1",
                    "0.0.0.0",
                    "::",
                )
            ],
        )
        assert ret == 1

Streaming results

For result sets that should not be materialized in memory at once, enable streaming and iterate the cursor: rows are fetched block by block from the server.

async def stream_rows(conn: Connection):
    async with conn.cursor() as cursor:
        cursor.set_stream_results(stream_results=True, max_row_buffer=65536)
        await cursor.execute("SELECT number FROM system.numbers LIMIT 1000000")
        async for row in cursor:
            process(row)

JSON columns

JSON columns (ClickHouse 24.8+) read as nested dicts and accept dicts or JSON text on insert.

async def use_json(conn: Connection):
    async with conn.cursor() as cursor:
        await cursor.execute(
            "CREATE TABLE test.events (id UInt32, doc JSON) ENGINE = MergeTree ORDER BY id"
        )
        await cursor.execute(
            "INSERT INTO test.events (id, doc) VALUES",
            [
                (1, {"user": {"name": "ada"}, "tags": ["a", "b"]}),
                (2, '{"user": {"name": "bob"}}'),   # JSON text works too
            ],
        )

        await cursor.execute("SELECT doc FROM test.events ORDER BY id")
        assert await cursor.fetchone() == ({"user": {"name": "ada"}, "tags": ["a", "b"]},)

Cancelling a query

A long-running query can be stopped from another task; the connection is left usable.

async def cancel_slow_query(conn: Connection):
    async with conn.cursor() as cursor:
        task = asyncio.create_task(cursor.execute("SELECT count() FROM numbers(20000000000)"))
        await asyncio.sleep(1)
        await conn.cancel()   # or cursor.cancel()
        await task            # returns with whatever the server had sent

Connection Pool

from asynch import Pool

async def use_pool():
    # init a Pool and fill it with the `minsize` opened connections
    async with Pool(dsn="clickhouse://127.0.0.1:9000", minsize=1, maxsize=10) as pool:
        # acquire a connection from the pool
        async with pool.connection() as conn:
            async with conn.cursor() as cursor:
                await cursor.execute("SELECT 1")
                ret = await cursor.fetchone()
                assert ret == (1,)

Or, you may open/close the pool manually:

async def use_pool():
    pool = Pool(dsn="clickhouse://127.0.0.1:9000", minsize=1, maxsize=10)
    await pool.startup()

    # some logic

    await pool.shutdown()

By default the pool keeps every connection it opens. Pass idle_timeout to have it release connections that have been idle for too long, down to minsize:

Pool(dsn="clickhouse://127.0.0.1:9000", minsize=2, maxsize=20, idle_timeout=60)

Query statistics

Connection.last_query reports what the server said about the most recent query:

async def show_stats(conn: Connection):
    async with conn.cursor() as cursor:
        await cursor.execute("SELECT number FROM system.numbers LIMIT 100000")
        await cursor.fetchall()

    stats = conn.last_query
    print(stats.elapsed, stats.progress.rows, stats.progress.bytes)

Performance

Since v0.4.0 the protocol hot path (streams, column codecs, connection, cursors) is compiled with Cython, putting asynch on par with clickhouse-driver (the synchronous C-extension driver) for most column types — while staying fully asynchronous.

Sample results (Apple Silicon, ClickHouse 26.7, best of 3; run make benchmark to reproduce on your own hardware):

Scenario	asynch	clickhouse-driver	asynch vs driver
Export 500k rows from a wide events table (8 mixed columns)	438 ms	728 ms	+66%
100 concurrent queries (pool of 10)	2103 queries/s	1310 queries/s	+61%
Filtered slice (~1% of rows)	4.2 ms	4.0 ms	on par (server-bound)
GROUP BY aggregation over 500k rows	5.6 ms	5.6 ms	on par (server-bound)
Batch insert, 200k rows	1.7 s	1.6 s	on par (server-bound)

Small queries and inserts are dominated by server work, where both drivers sit at the wire limit; the asynchronous advantage shows once results get large or queries run concurrently.

Column-type micro-benchmarks (500k-row SELECTs), for the decode paths behind the numbers above:

Case	asynch	clickhouse-driver	asynch vs driver
Int64	24.6M rows/s	24.7M rows/s	on par
Float64	23.2M rows/s	20.1M rows/s	+15%
String	21.7M rows/s	15.2M rows/s	+43%
FixedString	19.9M rows/s	15.6M rows/s	+28%
Nullable(Int64)	15.7M rows/s	13.4M rows/s	+17%
Date	17.8M rows/s	15.3M rows/s	+16%
DateTime	13.8M rows/s	2.3M rows/s	+500%
DateTime64(3)	9.8M rows/s	2.2M rows/s	+345%
UUID	3.7M rows/s	2.4M rows/s	+56%
Decimal(10, 2)	4.9M rows/s	3.4M rows/s	+44%
LowCardinality(String)	17.7M rows/s	17.3M rows/s	+2%
Array(Int64)	5.3M rows/s	4.6M rows/s	+15%
Map(String, Int64)	3.8M rows/s	3.1M rows/s	+23%
Tuple(Int64, String)	12.2M rows/s	10.8M rows/s	+13%

The benchmark suite lives in benchmark/:

# SELECT / INSERT / concurrency / pool scenarios, rich-table report
> make benchmark
# or a single scenario
> python -m benchmark.select

BENCHMARK_ROWS / BENCHMARK_INSERT_ROWS environment variables scale the workload; CLICKHOUSE_* variables point it at a non-default server.

Development

asynch is managed with uv; building it from source needs a C compiler and Cython (wheels from PyPI do not).

# install all dependency groups and build the extensions in place
> make deps

# lint + typecheck + stubtest
> make check

# run the test suite (needs a local ClickHouse on port 9000)
> docker run -d -p 9000:9000 -e CLICKHOUSE_SKIP_USER_SETUP=1 clickhouse/clickhouse-server
> make test

# regenerate the .pyi stubs after changing a .pyx module
> make stubs

clickhouse-driver, ClickHouse Python Driver with native interface support.

License

This project is licensed under the Apache-2.0 License.

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Algorithm	Hash digest
SHA256	`2c0ff7b4ad24331735a9551d0a769ff909860d8ae451c88b5bd42eb71fd5583c`
MD5	`d9187fa30a77edf8bcf9b105b67df59a`
BLAKE2b-256	`3e4b1ea6f5982cd2990b4fb78d89bb0ce3f45902898ae98cf467218ceb1bc45e`

Algorithm	Hash digest
SHA256	`8e25818a62c1807a2c851a6e3fd075f5f080f9fdfb72b7b02b06066450676f50`
MD5	`afbe3d25c023a377ded0010f685db041`
BLAKE2b-256	`34db55368b8c8b6b089a8aa6bf3dd01831d83c02a937af36d1ba0ec032a2a60d`

Algorithm	Hash digest
SHA256	`793623b563e392c8fefd249458ee1b02b15ffd572e7ed9dadeaa07572fefd346`
MD5	`db2512208fc17977652021739b16c285`
BLAKE2b-256	`7c6ce2458dbf01284ffd5c123fd2aa93068026f9fdc8bb49c88815d023603fc6`

Algorithm	Hash digest
SHA256	`ea54b2fda68fdd3342fc43e2eb43e19eefd04c534c3b96e62d85a46005b226b6`
MD5	`a5d95c1efe9213a84e320555ef151863`
BLAKE2b-256	`f4d38208cdaff3f21e7c3a5a5125072242e06372e95f13faaa5641e591b062b6`

Algorithm	Hash digest
SHA256	`9b44b57634ee97c95d8edef7cda152c24a71b1d99dba0f9e7bbca8a957362dd4`
MD5	`ce06197d83e44ec535ae51a1b7c73074`
BLAKE2b-256	`28c6df89a815ca7b78d6dd32ff37fc6aab4884fa8aab681784cfeca789dff134`

Algorithm	Hash digest
SHA256	`1a94540713fc5cbc56cc05310c8a7e675fd63bb44c535031dcde2d028f05292b`
MD5	`e224bce7b08d094194bcb81b631916c3`
BLAKE2b-256	`c9d46234acb29c09815d9eef302ea0908ee9c9605ea652ddfaef370adeca6ab8`

Algorithm	Hash digest
SHA256	`b77d48dcfeb55795041ec82961e69609e592d8966f6f1ab252b70c29ddec4c37`
MD5	`e7bf516759ab4396db9460a8cf9e883b`
BLAKE2b-256	`8bdc0c9d6a5e8f1976b5857629ab15bd449a0a7b46fd5bc64be6e0ab95b23ec3`

Algorithm	Hash digest
SHA256	`f5ff65d66a06ad3edd8271aa086d4b8d5753f1c35a14d809bb0314281ae7c221`
MD5	`7fd5425df5115399e1efaaa1487c53e8`
BLAKE2b-256	`ebc4b232e733c63d2ecef48465fb16568ff35b1cd0ae2a54f6a672b95c016df9`

Algorithm	Hash digest
SHA256	`eadcd74ef5fbc8b07e92707dc470e5fc988ff73a669409645345bfdde42e3c03`
MD5	`4d0cd06cc8c06f1b2023f24c03a9e619`
BLAKE2b-256	`ae1bc687c7702075be7d8e2fd29876adf15b1b724b994bd5f4317c9a4261aec9`

asynch 0.4.0

asynch

Introduction

Installation

Usage

Streaming results

JSON columns

Cancelling a query

Connection Pool

Query statistics

Performance

Development

ThanksTo

License

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

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File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

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File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

Algorithm	Hash digest
SHA256	`6d31b86ad06ca6a5bf53cbecc4e5190264f171478420943d37f268e815a9bda7`
MD5	`2ccd309cbf617634eacc476f6b5c1f62`
BLAKE2b-256	`58c0f16bebcc791f15e0abda167b1755ed1e5fa2036343f75a915efdb71fbe73`

Algorithm	Hash digest
SHA256	`54fe65c672c17427bf7a5685b481454e0a7095e68987fbe2db0a4492f09297f4`
MD5	`2681c227c8802350eb11fcbc7bde0282`
BLAKE2b-256	`fdb3dfca310707c2979571365df6b8818abacd07ce0bbe0b3a20df1d75380b00`

Algorithm	Hash digest
SHA256	`11e15a78b43d7ed50d4f0a22e77414d653f945da2716aef694024ef3aaaf95f1`
MD5	`c6d17c6ab1244477b4135044ae1f1a63`
BLAKE2b-256	`a865fcabfca476b0deeaaec60bf23ba5adc1fb58a9847cfc1328891f03790c93`

Algorithm	Hash digest
SHA256	`dd1e0f070132a823b7e6b86bb9bc02998d072ef0281240b462f13e71647ac680`
MD5	`394ad01fb999a5dfc39b18c93f13d781`
BLAKE2b-256	`1e8e0b951feaf2e14f6b9ff98fbd2c13b7ac94b3e1fa3603e174985c080f0afe`

Algorithm	Hash digest
SHA256	`45d4a74f9a1eb419ce04cf913bcbc1e33dc1daa55ab67cf7d37c6dfd76cbfead`
MD5	`aa2a354c278e64d0f0a74220f2fc5b47`
BLAKE2b-256	`41e6744eb84c3bd732641bc5b671963ddbfb1893f9ab011aa52641a8497e15da`

Algorithm	Hash digest
SHA256	`baa4a9b3de33145d45391add66933fa1a5d5e87a79ac80f0c139d6d1d34853b9`
MD5	`89010f00e3f4fde997d237978a944e6e`
BLAKE2b-256	`e25eb7ca48df96f43fda5da5e073bbb6f95fecbef0c4d4058065757d4484518d`

Algorithm	Hash digest
SHA256	`a960d839c6c779171722b6afc76392c8ac5cb7877384c599a4ffecbb67451f2d`
MD5	`bfd60fcef1a7635edc232788f5b5649d`
BLAKE2b-256	`aa6c43a02e8e216c7666c960e422cfaf41c755b63592f68e1f0f1449289efbbb`

Algorithm	Hash digest
SHA256	`88dc5813efe9e403d4d9d78b509cc0d5f29bba7d034e591d0ced3b3ac0457662`
MD5	`22cad8906a2ce2d9c79558adde0ab73e`
BLAKE2b-256	`bbd2994727acc5bf61f00fe0fbd2f107e7b591f6af91f341cc1ce391fcf0c028`

Algorithm	Hash digest
SHA256	`a38134736ba164cc5591263e5fa164b0bb42bc335e792400fcee7636e847cb20`
MD5	`6dade1a73d744f717313bbaced67558b`
BLAKE2b-256	`ce48b213175129a53593a1c89e642a2235742bac29020eb89fc483f31a5285bc`

Algorithm	Hash digest
SHA256	`9b42dffa9c3a6c8f84e99fbdbee8fc43444e5ec748bebd401bc9f295f79da74f`
MD5	`b328c0ee7014686eab106ab4b002bc6d`
BLAKE2b-256	`e65f3a7403257591848b0f6823fab62f25df90da0963d73a0b77824859a51e87`