aiochclient 2.3.0

Async http clickhouse client for python 3.6+

aiochclient

An async http(s) ClickHouse client for python 3.6+ supporting type conversion in both directions, streaming, lazy decoding on select queries, and a fully typed interface.

Table of Contents

• Installation
• Quick Start
• Documentation
• Type Conversion
• Connection Pool Settings
• Notes on Speed

Installation

You can use it with either aiohttp or httpx http connectors.

To use with aiohttp install it with command:

> pip install aiochclient[aiohttp]

Or aiochclient[aiohttp-speedups] to install with extra speedups.

To use with httpx install it with command:

> pip install aiochclient[httpx]

Or aiochclient[httpx-speedups] to install with extra speedups.

Installing with [*-speedups] adds the following:

• cChardet for aiohttp speedup
• aiodns for aiohttp speedup
• ciso8601 for ultra-fast datetime parsing while decoding data from ClickHouse for aiohttp and httpx.

Additionally the installation process attempts to use Cython for a speed boost (roughly 30% faster).

Quick Start

Connecting to ClickHouse

aiochclient needs aiohttp.ClientSession or httpx.AsyncClient to connect to ClickHouse:

from aiochclient import ChClient
from aiohttp import ClientSession


async def main():
    async with ClientSession() as s:
        client = ChClient(s)
        assert await client.is_alive()  # returns True if connection is Ok

Querying the database

await client.execute(
    "CREATE TABLE t (a UInt8, b Tuple(Date, Nullable(Float32))) ENGINE = Memory"
)

For INSERT queries you can pass values as *args. Values should be iterables:

await client.execute(
    "INSERT INTO t VALUES",
    (1, (dt.date(2018, 9, 7), None)),
    (2, (dt.date(2018, 9, 8), 3.14)),
)

For fetching all rows at once use the fetch method:

all_rows = await client.fetch("SELECT * FROM t")

For fetching first row from result use the fetchrow method:

row = await client.fetchrow("SELECT * FROM t WHERE a=1")

assert row[0] == 1
assert row["b"] == (dt.date(2018, 9, 7), None)

You can also use fetchval method, which returns first value of the first row from query result:

val = await client.fetchval("SELECT b FROM t WHERE a=2")

assert val == (dt.date(2018, 9, 8), 3.14)

With async iteration on the query results stream you can fetch multiple rows without loading them all into memory at once:

async for row in client.iterate(
    "SELECT number, number*2 FROM system.numbers LIMIT 10000"
):
    assert row[0] * 2 == row[1]

Use fetch/fetchrow/fetchval/iterate for SELECT queries and execute or any of last for INSERT and all another queries.

Working with query results

All fetch queries return rows as lightweight, memory efficient objects. Before v1.0.0 rows were only returned as tuples. All rows have a full mapping interface, where you can get fields by names or indexes:

row = await client.fetchrow("SELECT a, b FROM t WHERE a=1")

assert row["a"] == 1
assert row[0] == 1
assert row[:] == (1, (dt.date(2018, 9, 8), 3.14))
assert list(row.keys()) == ["a", "b"]
assert list(row.values()) == [1, (dt.date(2018, 9, 8), 3.14)]

Documentation

To check out the api docs, visit the readthedocs site..

Type Conversion

aiochclient automatically converts types from ClickHouse to python types and vice-versa.

ClickHouse type	Python type
UInt8	int
UInt16	int
UInt32	int
UInt64	int
Int8	int
Int16	int
Int32	int
Int64	int
Float32	float
Float64	float
String	str
FixedString	str
Enum8	str
Enum16	str
Date	datetime.date
DateTime	datetime.datetime
DateTime64	datetime.datetime
Decimal	decimal.Decimal
Decimal32	decimal.Decimal
Decimal64	decimal.Decimal
Decimal128	decimal.Decimal
IPv4	ipaddress.IPv4Address
IPv6	ipaddress.IPv6Address
UUID	uuid.UUID
Nothing	None
Tuple(T1, T2, ...)	Tuple[T1, T2, ...]
Array(T)	List[T]
Nullable(T)	None or T
LowCardinality(T)	T
Map(T1, T2)	Dict[T1, T2]

Connection Pool Settings

aiochclient uses the aiohttp.TCPConnector to determine pool size. By default, the pool limit is 100 open connections.

Notes on Speed

It's highly recommended using uvloop and installing aiochclient with speedups for the sake of speed. Some recent benchmarks on our machines without parallelization:

• 180k-220k rows/sec on SELECT
• 50k-80k rows/sec on INSERT

Note: these benchmarks are system dependent