pyconductor-grpc 0.2.0

A GRPC Library for Netflix Conductor

pyconductor-grpc

Python GRPC client for Netflix's Conductor

Installation

pip install pyconductor_grpc

Usage

Example task definition JSON:

[
  {
    "name": "sleep",
    "retryCount": 3,
    "timeoutSeconds": 20,
    "pollTimeoutSeconds": 36,
    "inputKeys": ["duration", "rem"],
    "outputKeys": ["rem"],
    "timeoutPolicy": "TIME_OUT_WF",
    "retryLogic": "FIXED",
    "retryDelaySeconds": 6,
    "responseTimeoutSeconds": 19,
    "concurrentExecLimit": null,
    "rateLimitFrequencyInSeconds": null,
    "rateLimitPerFrequency": null,
    "ownerEmail": "[e-mail]"
  }
]

Example workflow definition JSON:

{
  "name": "my_workflow",
  "description": "My Workflow",
  "version": 3,
  "tasks": [
    {
      "name": "sleep",
      "taskReferenceName": "heavy-sleeper",
      "type": "SIMPLE",
      "inputParameters": {
        "duration": 10.0,
        "rem": true
      }
    }
  ],
  "outputParameters": {
    "rem": "${heavySleeper.output.rem}"
  },
  "ownerEmail": "[e-mail]"
}

Upload the definitions:

import requests
base_url = 'http://localhost/api'
headers = {'content-type': 'application/json'}
payload = open("tasks.json")
r = requests.post(f'{base_url}/metadata/taskdefs', data=payload, headers=headers)
print(r.status_code, r.text)
payload = open("workflow.json")
r = requests.post(f'{base_url}/metadata/workflow', data=payload, headers=headers)
print(r.status_code, r.text)

TaskWorker usage:

import time
from pyconductor_grpc.grpc_client import TaskService, TaskWorker


# Define task client
task_service = TaskService()
"""
Specify a connection URI or a custom channel object.
If a URI is specified, a channel will automatically be created.
If a channel object is specified, the URI will be ignored.
Default is to connect to localhost:8090

:param connection_uri: URI of Conductor API
:param channel: gRPC channel
"""


# Define work function. Input data dict keys come pre-expanded as function parameters.
def sleep_work_function(duration: int, rem: bool) -> dict:
    time.sleep(duration)
    output_data = {'rem': rem}
    return output_data


# Work. This worker will spawn up to 10 work threads
# Each task is passed to the work function individually.
with TaskWorker(
    task_service=task_service,
    task_type='sleep',
    work_function=sleep_work_function,
    work_concurrency=10,
) as worker:
    worker.work()

BatchTaskWorker usage:

import time
import typing
from pyconductor_grpc.grpc_client import BatchTaskWorker, ConductorTask, ConductorTaskResult, TaskService


# Define task client
task_service = TaskService()


# Define work function. Inputs are all tasks in a batch. Outputs are all task results.
def sleep_work_function(tasks: typing.Sequence[ConductorTask]) -> typing.Sequence[ConductorTaskResult]:
    # intentionally sleeping many times in series just to demonstrate batch worker
    task_results = []
    for task in tasks:
        # each task dict obeys JSON equivalent of schema conductor.proto.Task (same keys but in camel case)
        input_data = task['inputData']
        time.sleep(input_data['duration'])
        output_data = {'rem': input_data['rem']}  # this must be a dictionary
        task_result = ConductorTaskResult(
            taskId=task['taskId'],
            workflowInstanceId=task['workflowInstanceId'],
            outputData=output_data,
            status='COMPLETED',
        )
        # task_result = ConductorTaskResult(
        #     taskId=task['taskId'],
        #     workflowInstanceId=task['workflowInstanceId'],
        #     status='FAILED',
        #     reasonForIncompletion='this is why it failed',
        # )
        task_results.append(task_result)
    return task_results


# Work. This worker will work on up to 10 batches in parallel, with each batch containing up to 1000 tasks.
# Each batch of 1000 will be passed to the work function.
# Within each batch, the worker will make up to 20 concurrent requests to the Conductor API.
with BatchTaskWorker(
    batch_size=1000,
    batch_threads=20,
    task_service=task_service,
    task_type='sleep',
    work_function=sleep_work_function,
    work_concurrency=10,
) as worker:
    worker.work()

Kick off a workflow instance and monitor for a done state:

import time
from pyconductor_grpc.grpc_client import WorkflowService


workflow_service = WorkflowService()  # same inputs as TaskService
workflow_id = workflow_service.start_workflow('annotation_workflow', input={'variant': {'start': '1', 'end': '3', 'chrom': '5'}})
status = None
while status is None:
    time.sleep(1)
    workflow_output = workflow_client.get_workflow_status(workflow_id)
    status = workflow_ouput.get('status')

successful = status == 'COMPLETED'
output = workflow_output['output']

Code generation

To rebuild the proto files and autogenerated python grpc sdk

scripts/generate-code.sh

Notes

The generated Python message types verify field types when objects are constructed from them. For example:

>>> from model.task_result_pb2 import TaskResult
>>> TaskResult(task_id=1)
TypeError: Cannot set conductor.proto.TaskResult.task_id to 0: 0 has type <class 'int'>, but expected one of: (<class 'bytes'>, <class 'str'>) for field TaskResult.task_id
>>> TaskResult(task_id='1')
>>>

Publishing

To build and upload:

pip install --upgrade build twine
python -m build
twine upload dist/*

or

pipx install build
pipx install twine
pyproject-build
twine upload dist/*