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flink-rest-client-1.0.8
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مشاهده بیشترتوضیحات
Easy to use Flink REST API client implementation
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | flink-rest-client-1.0.8 |
| نام | flink-rest-client |
| نسخه کتابخانه | 1.0.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Attila Nagy |
| ایمیل نویسنده | anagy@frego.dev |
| آدرس صفحه اصلی | https://github.com/frego-dev/flink-rest-client |
| آدرس اینترنتی | https://pypi.org/project/flink-rest-client/ |
| مجوز | MIT |
# Flink REST Client
[![PyPI Version][pypi-image]][pypi-url]
[![Build Status][build-image]][build-url]
[![Code Coverage][coverage-image]][coverage-url]
[pypi-image]: https://img.shields.io/pypi/v/flink_rest_client
[pypi-url]: https://pypi.org/project/flink_rest_client/
[build-image]: https://github.com/frego-dev/flink-rest-client/actions/workflows/build.yml/badge.svg
[build-url]: https://github.com/frego-dev/flink-rest-client/actions/workflows/build.yml
[coverage-image]: https://codecov.io/gh/frego-dev/flink-rest-client/branch/master/graph/badge.svg?token=OHRAGS8A40
[coverage-url]: https://codecov.io/gh/frego-dev/flink-rest-client
## What is it?
The Flink REST Client provides an easy-to-use python API for Flink REST API.
The client implements all available REST API endpoints that are documented on the [official Flink site](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/ops/rest_api/).
Using this client, you can easily query your Flink cluster status, or you can upload and run arbitrary Flink jobs wrapped in a Java archive file.
## Installation
The source code is currently hosted on GitHub at: https://github.com/frego-dev/flink-rest-client
The installer for the latest released version is available at the [Python Package Index (PyPI)](https://pypi.org/project/flink-rest-client).
```sh
# via PyPI
pip install flink_rest_client
```
## Documentation
The official documentation is hosted on: [official documentation](https://frego-dev.github.io/flink-rest-client/)
## License
[MIT](https://github.com/frego-dev/flink-rest-client/blob/master/LICENSE)
## API structure
In the client implementation, the API end points are categorized based on their functionality:
- cluster level: API endpoints for managing the whole cluster.
- jobmanager: API endpoints for managing the job manager(s).
- taskmanager: API endpoints for managing the taskmanagers.
- jars: API endpoints for managing the uploaded jars.
- jobs: API endpoints for managing the submitted jobs.
You find the REST API endpoint -> python method mapping in the documentation: [link to mapping]
## Usage examples
### How to create a new FlinkRestClient instance
The simplest way to create a new FlinkRestClient instance is using its static factory method:
```python
from flink_rest_client import FlinkRestClient
rest_client = FlinkRestClient.get(host="localhost", port=8082)
```
To check that the client can connect to Flink Jobmanager's webserver the overview method can be used, which returns an
overview over the Flink cluster.
```python
from flink_rest_client import FlinkRestClient
rest_client = FlinkRestClient.get(host="localhost", port=8082)
result = rest_client.overview()
print(result)
```
**Output:**
```json
{
"taskmanagers": 1,
"slots-total": 4,
"slots-available": 4,
"jobs-running": 0,
"jobs-finished": 0,
"jobs-cancelled": 0,
"jobs-failed": 0,
"flink-version": "1.12.4",
"flink-commit": "5364a6b"
}
```
### How to upload and run a Flink job
```python
from flink_rest_client import FlinkRestClient
rest_client = FlinkRestClient.get(host="localhost", port=8082)
# Path to the jar file on your file system
path_to_my_jar = "/path/to/StateMachineExample.jar"
# The upload_and_run method returns with the unique identifier of the already started Flink job
job_id = rest_client.jars.upload_and_run(path_to_jar=path_to_my_jar)
# Using the job_id, you can query the current status of the submitted job.
job = rest_client.jobs.get(job_id=job_id)
print(job)
```
**Output:**
```json
{
"jid": "d8a3c7f257231678c1ca4b97d2316c45",
"name": "State machine job",
"isStoppable": false,
"state": "RUNNING",
"start-time": 1625758267958,
"end-time": -1,
"duration": 206345,
"now": 1625758474303,
"timestamps": {
"FAILING": 0,
"FINISHED": 0,
"INITIALIZING": 1625758267958,
"RESTARTING": 0,
"CREATED": 1625758268002,
"FAILED": 0,
"SUSPENDED": 0,
"CANCELLING": 0,
"CANCELED": 0,
"RECONCILING": 0,
"RUNNING": 1625758268038
},
"vertices": [
...
],
"status-counts": {
"CREATED": 0,
"CANCELING": 0,
"FAILED": 0,
"CANCELED": 0,
"FINISHED": 0,
"SCHEDULED": 0,
"RUNNING": 2,
"DEPLOYING": 0,
"RECONCILING": 0
},
"plan": {
"jid": "d8a3c7f257231678c1ca4b97d2316c45",
"name": "State machine job",
"nodes": [
...
]
}
}
```
Sometimes you need to pass arguments/parameters to successfully start your Flink job.
For example, you have the following Java Main class:
```java
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
public class MyFlinkJob {
private static final String PARAM_THRESHOLD = "my.flink.job.threshold";
public static void main(String[] args) {
// Reading configuration
ParameterTool argsParams = ParameterTool.fromArgs(args);
int threshold = argsParams.getInt(PARAM_THRESHOLD);
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// ...
}
}
```
The required argument can be passed via upload_and_run method's `arguments` parameter:
```python
from flink_rest_client import FlinkRestClient
rest_client = FlinkRestClient.get(host="localhost", port=8082)
# Path to the jar file on your file system
path_to_my_jar = "/path/to/StateMachineExample.jar"
# Put the arguments in a dictionary
job_id = rest_client.jars.upload_and_run(path_to_jar=path_to_my_jar, arguments={
"my.flink.job.threshold": 55
})
```
نیازمندی
| مقدار | نام |
|---|---|
| - | requests |
| - | importlib-resources |
نحوه نصب
نصب پکیج whl flink-rest-client-1.0.8:
pip install flink-rest-client-1.0.8.whl
نصب پکیج tar.gz flink-rest-client-1.0.8:
pip install flink-rest-client-1.0.8.tar.gz