aiochclient

Async http(s) ClickHouse client for python 3.6+ with types converting in both directions, streaming support, lazy decoding on select queries and fully typed interface

Contents

• Install
• Quick start
• Types converting
• Connection pool
• Speed

Install

> pip install aiochclient

Or to install with extras requirements for speedup:

> pip install aiochclient[speedups]

It will additionally install cChardet and aiodns for aiohttp speedup and ciso8601 for ultra fast datetime parsing while decoding data from ClickHouse.

Also while installing it will try to build Cython extensions for speed boost (about 30%).

Quick start

Connecting to ClickHouse

aiochclient needs aiohttp.ClientSession for connecting:

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

Making queries

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 fetch method:

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

For fetching first row from result use 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 query results steam 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 (from v1.0.0, before it - just tuples) with full mapping interface, where you can get fields by names or by 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)]

Types converting

aiochclient automatically converts values to needed type both from ClickHouse response and for client INSERT queries.

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
Decimal	decimal.Decimal
Decimal32	decimal.Decimal
Decimal64	decimal.Decimal
Decimal128	decimal.Decimal
Tuple(T1, T2, ...)	Tuple[T1, T2, ...]
Array(T)	List[T]
UUID	uuid.UUID
Nullable(T)	None or T
Nothing	None
LowCardinality(T)	T

Connection pool

If you want to change connection pool size, you can use aiohttp.TCPConnector. Note that by default pool limit is 100 connections.

Speed

Using of uvloop and installing with aiochclient[speedups] is highly recommended for sake of speed.

As for the last version of aiochclient its speed using one task (without gather or parallel clients and so on) is about 180k-220k rows/sec on SELECT and about 50k-80k rows/sec on INSERT queries depending on its environment and ClickHouse settings.

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