Understanding and Implementing Schemas in Python

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Understanding and Implementing Schemas in Python Introduction In the world of programming, particularly in the context of data management and validation, schemas play a vital role. A schema is essentially a blueprint or a predefined structure that defines the expected format, data types, and constraints for a given data entity. In this blog, we will delve into the concept of schemas in Python, exploring what they are, why they are important, and how you can implement them in your projects. What is a Schema? A schema serves as a contract between different components of a system, ensuring that data is consistent, valid, and well-structured. It defines the rules for how data should be organized, what fields it should contain, and what types of values those fields can hold. In essence, a schema acts as a set of rules that data must adhere to in order to be considered valid. Why Are Schemas Important? Data Validation: Schemas provide a way to validate incoming data. When data is received o
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How to integrate Azure Application Insights with logging in a Python

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How to integrate Azure Application Insights with logging in a Python

 

To integrate Azure Application Insights with logging in a Python application, you can use the applicationinsights package. Here's an example of how to configure logging with Application Insights in Python:

  1. Install the applicationinsights package:
pip install applicationinsights

  1. Import the necessary modules:
import logging from applicationinsights import TelemetryClient from applicationinsights.logging import LoggingHandler

  1. Configure the Application Insights instrumentation key:
INSTRUMENTATION_KEY = 'your-instrumentation-key'

  1. Create a TelemetryClient instance:
tc = TelemetryClient(INSTRUMENTATION_KEY)

  1. Set up a LoggingHandler to capture and send logs to Application Insights:
logging_handler = LoggingHandler(tc)

  1. Configure the root logger to use the LoggingHandler:
logging.getLogger().addHandler(logging_handler)

  1. Log events using the Python logging module:
# Log an informational message logging.info('This is an informational message') # Log a warning message logging.warning('This is a warning message') # Log an error message logging.error('This is an error message')

With the above setup, any logs generated by your application will be sent to Application Insights. You can view these logs, along with other telemetry data, in the Azure portal.

Make sure to replace 'your-instrumentation-key' in Step 3 with your actual Application Insights instrumentation key, which you can obtain from the Azure portal.

Note: The applicationinsights package also provides additional functionalities for tracking custom events, metrics, and exceptions. You can explore the package's documentation for more advanced usage scenarios.

Remember to handle any exceptions or errors that may occur during the logging process to ensure the smooth functioning of your application.


Happy Learning!! Happy Coding!!

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