SDK Reference
Python SDK
Logging

Logging

Hatchet comes with a built-in logging view where you can push debug logs from your workflows. This is useful for debugging and monitoring your workflows. You can use either Python's built-in logging package, or the context.log method for more control over the logs that are sent.

Using the built-in logging package

You can pass a custom logger to the Hatchet class when initializing it. For example:

import logging
 
from hatchet_sdk import ClientConfig, Hatchet
 
logging.basicConfig(level=logging.INFO)
 
root_logger = logging.getLogger()
 
hatchet = Hatchet(
    debug=True,
    config=ClientConfig(
        logger=root_logger,
    ),
)
 
@hatchet.workflow()
class LoggingWorkflow:
    @hatchet.step()
    def step1(self, context: Context):
        for i in range(12):
            root_logger.info("executed step1 - {}".format(i))
            time.sleep(1)
        return {"status": "success"}

It's recommended that you pass the root logger to the Hatchet class, as this will ensure that all logs are captured by the Hatchet logger. If you have workflows defined in multiple files, they should be children of the root logger. For example, with the following file structure:

  • client.py
  • worker.py
    • my-workflow.py

    • You should pass the root logger to the Hatchet class in client.py:

    client.py
    import logging
 
from hatchet_sdk import ClientConfig, Hatchet
 
logging.basicConfig(level=logging.INFO)
 
root_logger = logging.getLogger()
 
hatchet = Hatchet(
    debug=True,
    config=ClientConfig(
        logger=root_logger,
    ),
)

    And then in workflows/my-workflow.py, you should create a child logger:

    workflows/my-workflow.py
    import logging
from client import hatchet
 
logger = logging.getLogger(__name__)
 
@hatchet.workflow()
class LoggingWorkflow:
    @hatchet.step()
    def step1(self, context: Context):
        for i in range(12):
            logger.info("executed step1 - {}".format(i))
            time.sleep(1)
        return {"status": "success"}

    Using the context.log method

    You can also use the context.log method to log messages from your workflows. This method is available on the Context object that is passed to each step in your workflow. For example:

    @hatchet.workflow(on_events=["user:create"],schedule_timeout="10m")
class LoggingWorkflow:
    @hatchet.step()
    def logger(self, context : Context):
 
        for i in range(1000):
            context.log(f"Logging message {i}")
 
        return {
            "step1": "completed",
        }

    Each step is currently limited to 1000 log lines.

    Concurrency and FairnessREST API