Serverless Logging: Challenges, Methods, and Best Practices (2023)

Serverless Logging: Challenges, Methods, and Best Practices

What is serverless logging?

Serverless computing is a cloud computing execution model where the cloud provider dynamically manages the allocation of machine resources. It allows developers to build and run apps and services without having to think about infrastructure.

Serverless logging is essential for debugging, monitoring, and maintaining serverless applications and works very differently from traditional applications. There are several ways to record serverless function activity:

• Use a registration library:Many programming languages ​​have libraries that can be used to write log entries to a file, database, or other destination. For example, Python has the built-in logging module and Node.js has the popular Winston library.
• Use a cloud provider's registration service:Cloud providers that offer serverless computing platforms, such as AWS, Azure, and Google Cloud, often have their own logging services that can be used to store and manage log records. For example, AWS integrates Lambda with Amazon CloudWatch Logs, Azure Functions with Azure Log Analytics, and Google Cloud Functions with Stackdriver Logging.
• Use a third-party registration service:There are also many third-party logging services that can be used to store and manage serverless feature log entries, including those built for serverless environments such as Lumigo.

This is part of a series of articles aboutserverless monitoring.

In this article:

The difference between traditional logging and serverless logging

Registering serverless roles is similar in many ways to registering traditional applications, but there are also some differences:

• Weight:One of the main differences is that serverless functions can be scaled up and down very quickly in response to changes in demand, which means that the logging system must be able to handle a potentially large number of log entries.
• Clutch:Serverless functions are usually called in response to a specific trigger, such as a user action or a change in data, which means it can be important to include context about the trigger in log entries.
• Pay per use:Serverless features are typically billed on a pay-as-you-go basis, which means it can be important to consider registration costs when designing a serverless application.

Serverless Registration Challenges

There are several key challenges when registering serverless applications:

• Weight:Serverless applications can scale up and down very quickly in response to changes in demand, which means that the logging system must be able to handle a potentially large number of log entries.
• Clutch:Serverless functions are usually called in response to a specific trigger, such as a user action or a change in data, which means it can be important to include context about the trigger in log entries.
• Short term features:Serverless functions are generally volatile, meaning they are dynamically created and destroyed as needed. This can make it difficult to save log data and maintain consistent log context across multiple calls to a function.
• Stateless functions:Serverless functions are generally stateless, meaning they don't maintain state between calls. This can make it difficult to track the progress of a long-running task or process.

What should you log if you use Serverless?

In serverless applications, there are several types of information you may want to log:

• Event invitations and messages:It can be useful to record information about calls to your serverless functions, including the time of the call, the function that was called, and the event that caused the call. You may also want to capture input data sent to the function, such as the payload of an HTTP request or data changed in a database.
• Reply charge:It can be useful to log the output of your serverless functions, such as the payload of an HTTP response or data written to a database. This can help you track the results of your functions and identify any issues that need to be addressed.
• Performance Levels:It can be important to monitor the performance of your serverless functions, including execution time and resources consumed. You may want to record information about the execution of your functions, such as execution time, memory usage, and number of requests per second.
• Request authorization:If your serverless functions require authentication, you may want to record information about authentication requests, including the time of the request, the user or service making the request, and the outcome of the request (for example, whether the request succeeded or failed). This can help you track the security of your features and identify any issues that need to be addressed.

Serverless registration methods

synchronous register

Synchronous logging is a logging method where log entries are generated and written to a destination in real time while the software or system is running. This is in contrast to asynchronous logging, where log entries are generated and queued for later writing.

Here's an example of using synchronous logging with Python's built-in logging module:

import logging# Set up a logger logger = logging.getLogger(__name__)def my_function(event, context): # Write a log entry logger.info("Processing") # Do some work result = do_some_work(event) # Write another log entry logger.info("Request processed successfully") def do(_2): b0_work(_2): b0_work(_2): sum=a* bif __name__ == "__main __" : my_function( None, None)

In this example, the logger writes two log entries: one when the function starts processing the request and one when the function finishes processing the request. Log entries are written to the default log destination, usually a file or the console.

Using synchronous logging has the advantage of recording what is happening in the software or system in real time, which can be useful for debugging, monitoring, and performance analysis. However, it can also lead to performance overhead, as log entries must be written in real time, which can slow down the software or system.

To minimize this overhead, it is important to design the logging system as efficiently as possible and minimize the number of log entries written using logging levels, filtering, or sampling techniques.

Asynchronous registration

Asynchronous logging is a method of logging where log entries are generated and queued for later writing instead of being written in real time while the software or system is running. This can help minimize logging performance costs since log entries are not written immediately and do not block software or system execution.

The following example uses Pythonjk-asyhncio-logginglibrary. You can install it using this command:

pip installer jk-asyncio-logging

Here's the code:

import asyncioimport loggingimport jk_asyncio_logging# Set a logger logger = logging.getLogger(__name__)# Create a function that calls an async method to run tasksasync def my_function(event, context): # Write a log entry logger.info("Processing request") # Do some work result =(event_info logger else. ("Request process" asynchronous with success") return result# Create an async function running something random takensync def do_some_work_async(event):for i in range(100):for j in range(200):i*j# Print the logwait information log.info("This is a test for INFO.") return =synloganticioate the 1_# InstLologga object.gger.create(logMsgFormatter= jk_asyncio_logging.COLOR_LOG _MESSAGE_ FORMATTER)loop = asyn cio.get_event_loop()loop.run_un to_complete(my_function(None, None))

In this example, the logger writes two log entries: one when the function starts processing the request and one when the function finishes processing the request. Log entries are not written immediately, but are queued to be written later.

Using asynchronous logging can help minimize logging's performance overhead since log entries are not written immediately and do not block software or system execution. However, it can also make it difficult to track the progress of a task or process in real time, as log entries are not written immediately.

4 best practices for serverless logging

Here are some best practices for logging into serverless applications:

• Registry value structure:It can be helpful to structure your log values ​​in a consistent and meaningful way using a structured log format such as JSON. This can make it easier to analyze your log entries and extract specific values ​​or fields.
• To use log levels:It can be useful to use log levels to control the scope of your log entries and only log entries of certain levels (such as "error" or "warning") in production environments. This can help reduce the number of login items and focus on the most important or critical messages.
• Apply correlation IDs to logs:It can be useful to use correlation IDs to associate log entries related to the same request or process, especially if your serverless functions are called asynchronously or in parallel. This can make it easier to track the progress of a task or process and identify any problems.
• Examples of logs to reduce noise:If you generate a large number of log entries, it can be useful to use sampling to reduce the number of log entries and focus on a representative sample. This can help reduce noise in your logs and make it easier to spot patterns and trends.

Serverless monitoring with Lumigo

Lumigo is a distributed trace platform built specifically for debugging microservices in production. Developers building serverless applications with AWS Lambda and other serverless services use Lumigo to monitor, track, and debug their serverless applications. Deployed unchanged and automated with a single click, Lumigo unifies all interactions between micro and managed services into end-to-end stack traces, providing full visibility in serverless environments. By using Lumigo to monitor and debug their applications, developers get:

• End-to-end virtual stack traces on every micro and managed service that make up a serverless application, in context
• API visibility that makes all data sent between services available and accessible, allowing root cause analysis to be performed without digging through the logs
• Distributed tracking implemented without code and automated with a single click
• Unified platform to explore and query microservices, gain real-time insight into applications, and optimize performance

Learn more about lighting