simplech 0.14.0

Simple ClickHouse client that simplify you interration with DBMS by using dicts as payload.

Simple ClickHouse lib

Very simple ClickHouse client that simplify you interration with DBMS by using dicts as payload. It contains two versions: synchronous for reguar usage and asynchronous for use with asyncio. Sync version internally uses low-level python http client. Both are use high-performance json serializer/parser ujson.

Installation

Install using pip from pypi repository

pip install -U simplech

Or latest version from git

pip install -U git+https://github.com/madiedinro/simple-clickhouse.git

Connection params

Has a two versins: async AsyncClickHouse and sync ClickHouse.

• host: [default: 127.0.0.1] Хост с clickhouse
• port: [default: 8123] Порт подключения
• db: [default: default] Название базы данных
• user: [default: default] Имя пользователя
• password: [default: ""] Пароль
• session: [default: False] Использовать сессию. Идентификатор сессии генерируется автоматически
• session_id: [default: ""] Идентификатор сессии взамен автоматически сгенериованного
• dsn: [default: ""] Использовать DSN для подключения (пример: http://default@127.0.0.1:8123/stats)
• debug: [default: False] Включение логов в режим отладки
• flush_every: [default: 5] Every X seconds data will be flushed to db
• buffer_limit: [default: 1000] Буффер записи на таблицу. При достижении будет произведена запись в БД
• loop: [default: None] При необходимости указать конкретный loop (для асинхронной версии)

Переменные окружения CH_DSN, CLICKHOUSE_DSN, при наличии которых, их значение будет использовано в качестве DSN.

Приоритет DSN: 1. аргумент конструктора dsn, 2. CH_DSN 3. CLICKHOUSE_DSN

Async version

Selecting without decoding

>>> from simplech import AsyncClickHouse
>>> ch = AsyncClickHouse(host='localhost', user='default')
>>> print(await ch.select('SHOW DATABASES'))

default
system

Selecting as dict's steam

Получить записи по отдельности, в виде dict. К запросу автоматически будет добавлено FORMAT JSONEachRow.

>>> async for obj in ch.objects_stream('SELECT * from events'):
>>>     print(obj)

{'browser_if': [0, 2],
 'browser_sr_asp': 4000,
 'browser_sr_avail_h': 740,
 'browser_sr_avail_w': 360,
 'browser_sr_oAngle': 0
 #...
}
#...

Disabling decoding for streaming data

>>> from simplech import bytes_decoder
>>> async for obj in ch.objects_stream('SELECT * from events', decoder=none_decoder):
>>>     print(obj)

b'{"browser_if": [0, 2],"browser_sr_asp": 4000,"browser_sr_avail_h": 740,"browser_sr_avail_w": 360,"browser_sr_oAngle": 0}'
#...

Чтобы получить результат в виде строки воспользуйтесь bytes_decoder

Executing sql statements

Для для записи данных, управления БД и других операция (не select) слудует использовать метод run

>>> await ch.run('CREATE TABLE my_table (name String, num UInt64) ENGINE=Log ')

''

Если все хорошо, сервер возвращает пустую строку ''.

Можно использовать для записи данных в произвольном формате.

>>> await ch.run('INSERT INTO my_table (name, num) VALUES("myname", 7)')

''

Batch writing

В simplech запись объекта производится при помощи метода push, но непосредственно запись будет произведена при достижении лимита буффера, устанавливаемого параметром конструктора buffer_limit.

with ch.batch('tablename') as b:
    for deal in deals:
        b.push(deal)

On exit context all data will be flushed.

Old manual conrolled mechanic.

for i in range(1, 1500):
    ch.push('my_table', {'name': 'hux', 'num': i})
ch.flush('my_table')

await ch.select('SELECT count() FROM my_table')

1499

Доступен метод flush_all(), он производит запись всех буфферов.

>>> ch.push('my_table', {'name': 'hux', 'num': 1})
>>> ch.push('other_table', my_other_obj)
>>> ch.flush_all()

Some Simpe Magick

Schema detection

Detect using present data

ch = ClickHouse()
td = ch.discovery(deals, 'deals')
td.date('date').idx('account_id', 'date').metrics('sale')

ch.merge_tree()

result

CREATE TABLE IF NOT EXISTS `deals` (
  `id`  UInt64,
  `uid`  UInt64,
  `cid`  String,
  `sale`  UInt64,
  `date`  Date,
  `date_time`  DateTime,
  `account_id`  UInt64
) ENGINE MergeTree() PARTITION BY toYYYYMM(`date`) ORDER BY (`account_id`, `date`) SETTINGS index_granularity=8192

ch.discover(data, 'table_name')

-> TableDiscovery instanse

Manual set datatypes

TableDiscovery.int(*args) set columnts to int

returns self

Set date columns

TableDiscovery.date(*args)

Set date column

returns self

Set str columns

TableDiscovery.str(*args)

Set strinmg column

returns self

Columns configuration

Set primary key columns

.idx(*args)

returns self

Set metrics

.metrics(*args)

returns self

other marked as dimensions

Set dimensions

.dimensions(*args)

other marked as metrics

Print table create statement / execute query

td.merge_tree(Execute=True|False)

Chaining

td.date('date').metrics('sale').idx('account_id', 'date')

Discovery TODO

• Support all ClickHouse types, especially Arrays
• Discovery by Table structure

Difference handling. Be careful currently it Proof of concept

Sync version

ch = ClickHouse()

upd = [{'name': 'lalala', 'value': 1}, {'name': 'bababa', 'value': 2}, {'name': 'nanana', 'value': 3}]
td = ch.discover('test1', upd).metrics('value')

d1 = '2019-01-10'
d2 = '2019-01-13'

new_recs = []
with td.difference(d1, d2, upd) as delta:
    for row in delta:
        print(row)
        td.push(row)

All pushed records will be flushed on exit context

Async version

ch = AsyncClickHouse()

# new data
upd = [{'name': 'lalala', 'value': 1}, {'name': 'bababa', 'value': 2}, {'name': 'nanana', 'value': 3}]
td = ch.discover('test1', upd).metrics('value')

d1 = '2019-01-10'
d2 = '2019-01-13'

async with td.difference(d1, d2, upd) as delta:
    async for row in delta:
        td.push(row)

# Stop flush timer
ch.close()

Difference TODO

• Focus on CollapsingMergeTree

Code generationm

Next times after use table auto discovery you shoud use fixed layout. To to this easy try TableDiscovery.pycode()

code = td.pycode()
print(code)

will be returned

td = ch.discover('deals', columns={'id': 'Int64', 'uid': 'Int64', 'cid': 'String', 'sale': 'Int64', 'date': 'Date', 'date_time': 'DateTime', 'account_id': 'Int64'}).metrics(*['sale']).dimensions(*['date_time', 'account_id', 'cid', 'uid', 'id', 'date']).date(*['date']).idx(*['account_id', 'date'])

Синхронная версия

Выполнение запроса и чтение всего результата сразу

>>> from simplech import ClickHouse
>>> ch = ClickHouse(host='localhost', user='default')
>>> print(ch.select('SHOW DATABASES'))

Получение записей потоком

>>> for obj in ch.objects_stream('SELECT * from events'):
>>>     print(obj)

Выполнение SQL операций

>>> ch.run('CREATE TABLE my_table (name String, num UInt64) ENGINE=Log ')

Запись данных

>>> for i in range(1, 1500):
>>> 	ch.push('my_table', {'name': 'hux', 'num': i})
>>> ch.flush('my_table')

или

>>> ch.flush_all()

License

The MIT License (MIT)

Copyright (c) 2018 Dmitry Rodin

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.