| Name | numpy-flight JSON |
| Version |
0.0.14
JSON |
| download |
| home_page | None |
| Summary | A little client/server for applications driven by numpy |
| upload_time | 2025-07-10 07:28:41 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# [Numpy with Apache Arrow Flight](https://tschm.github.io/numpy-flight/book)
[](https://badge.fury.io/py/numpy-flight)
[](LICENSE.txt)
[](https://github.com/tschm/numpy-flight/actions/workflows/ci.yml)
[](https://coveralls.io/github/tschm/numpy-flight?branch=main)
[](https://www.codefactor.io/repository/github/tschm/numpy-flight)
[](https://github.com/tschm/package)
[](https://github.com/renovatebot/renovate)
## A Problem
We provide
- An abstract base class for an Apache flight server
- A client class to communicate with such servers
The client provides a simple interface for sending NumPy arrays,
performing computations, and retrieving results, all while handling
the serialization and deserialization automatically in the background
using Apache Arrow.
To create a server we expect the user to overload a function performing
the calcutation based on a dictionary of numpy arrays.
## Features
- Seamless conversion between NumPy arrays and Arrow Tables
- Simple interface for data transfer operations
- Type-safe operations with proper error handling
## Installation
You can install this client via
```bash
pip install numpy-flight
```
## Usage
### Basic Setup
We introduce the Baseclass 'Server':
```python
>>> from flight import Server
>>> class TestServer(Server):
... def f(self, matrices):
... self.logger.info(f"{matrices.keys()}")
... # Simple implementation for testing - just return the input
... return {key : 2*value for key, value in matrices.items()}
```
All complexity is hidden in the class 'Server' which is itself a child
of the pyarrrow's FlightServerBase class. It is enough to implement
the method 'f' which is expecting a dictionary of numpy arrays. It will
also return a dictionary of numpy arrays.
The server can be started locally with
```python
>>> server = TestServer.start(host="127.0.0.1", port=5555)
```
While the server is running we can use a Python client for computations
```python
>>> import numpy as np
>>> from flight import Client
>>> with Client(location="grpc://127.0.0.1:5555") as client:
... output = client.compute(command="compute", data={"input": np.array([1,2,3])})
>>> print(output["input"])
[2 4 6]
```
Clients for other languages are thinkable.
We shut the server down with
```python
server.shutdown()
```
Raw data
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