https://github.com/Infinidat/infi.clickhouse_orm

A Python library for working with the ClickHouse database (https://clickhouse.yandex/)
https://github.com/Infinidat/infi.clickhouse_orm

Last synced: 3 months ago
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A Python library for working with the ClickHouse database (https://clickhouse.yandex/)

• Host: GitHub
• URL: https://github.com/Infinidat/infi.clickhouse_orm
• Owner: Infinidat
• License: bsd-3-clause
• Created: 2016-06-26T15:13:52.000Z (over 8 years ago)
• Default Branch: develop
• Last Pushed: 2024-06-03T12:23:17.000Z (5 months ago)
• Last Synced: 2024-07-19T12:18:09.037Z (4 months ago)
• Language: Python
• Size: 686 KB
• Stars: 410
• Watchers: 33
• Forks: 135
• Open Issues: 61
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: docs/contributing.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-clickhouse - Infinidat/infi.clickhouse_orm - A Python library for working with the ClickHouse database. (Language bindings / Python)

README

        
Introduction

============

This project is simple ORM for working with the [ClickHouse database](https://clickhouse.yandex/).

It allows you to define model classes whose instances can be written to the database and read from it.

Let's jump right in with a simple example of monitoring CPU usage. First we need to define the model class,

connect to the database and create a table for the model:

```python

from infi.clickhouse_orm import Database, Model, DateTimeField, UInt16Field, Float32Field, Memory, F

class CPUStats(Model):

    timestamp = DateTimeField()

    cpu_id = UInt16Field()

    cpu_percent = Float32Field()

    engine = Memory()

db = Database('demo')

db.create_table(CPUStats)

```

Now we can collect usage statistics per CPU, and write them to the database:

```python

import psutil, time, datetime

psutil.cpu_percent(percpu=True) # first sample should be discarded

while True:

    time.sleep(1)

    stats = psutil.cpu_percent(percpu=True)

    timestamp = datetime.datetime.now()

    db.insert([

        CPUStats(timestamp=timestamp, cpu_id=cpu_id, cpu_percent=cpu_percent)

        for cpu_id, cpu_percent in enumerate(stats)

    ])

```

Querying the table is easy, using either the query builder or raw SQL:

```python

# Calculate what percentage of the time CPU 1 was over 95% busy

queryset = CPUStats.objects_in(db)

total = queryset.filter(CPUStats.cpu_id == 1).count()

busy = queryset.filter(CPUStats.cpu_id == 1, CPUStats.cpu_percent > 95).count()

print('CPU 1 was busy {:.2f}% of the time'.format(busy * 100.0 / total))

# Calculate the average usage per CPU

for row in queryset.aggregate(CPUStats.cpu_id, average=F.avg(CPUStats.cpu_percent)):

    print('CPU {row.cpu_id}: {row.average:.2f}%'.format(row=row))

```

This and other examples can be found in the `examples` folder.

To learn more please visit the [documentation](docs/toc.md).