Infinidat/infi.clickhouse_orm
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README.md
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@ -4,70 +4,19 @@ This repository expects to use more type hints, and will drop support for Python
Supports both synchronous and asynchronous ways to interact with the clickhouse server. Means you can use asyncio to perform asynchronous queries, although the asynchronous mode is not well tested. Supports both synchronous and asynchronous ways to interact with the clickhouse server. Means you can use asyncio to perform asynchronous queries, although the asynchronous mode is not well tested.
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Introduction Introduction
============ ============
This project is simple ORM for working with the [ClickHouse database](https://clickhouse.yandex/). 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. It allows you to define model classes whose instances can be written to the database and read from it.
First you have to install like this:
```
pip install ch-orm
```
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 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. This and other examples can be found in the `examples` folder.
To learn more please visit the [documentation](docs/toc.md). To learn more please visit the [documentation](docs/toc.md).

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pyproject.toml
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@ -24,7 +24,7 @@ dependencies = [
"iso8601 >= 0.1.12", "iso8601 >= 0.1.12",
"setuptools" "setuptools"
] ]
version = "0.1.1" version = "0.2.0"
[tool.setuptools.packages.find] [tool.setuptools.packages.find]
where = ["src"] where = ["src"]