Logging vs Monitoring: Best Practices for Integration

Logging and monitoring are both valuable components to maintaining optimal application performance. Using a combination of logging tools and real-time monitoring systems helps improve observability and reduces the time spent sifting through log files to determine the root cause of performance problems.

Logging is used as both a verb and a noun, referring either to the practice of logging errors and changes or to the application logs that are collected. The purpose of logging is to create an ongoing record of application events. Log files can be used to review any event within a system, including failures and state transformations. Consequently, log messages can provide valuable information to help pinpoint the cause of performance problems.  Log data can help DevOps teams troubleshoot issues by identifying which changes resulted in error reporting, but is only as valuable as the information it contains. 

Log management also serves other purposes, such as creating written records for audit and compliance purposes, identifying trends over time, and securing sensitive information. Logging performs a valuable role in applications of all sizes, but should be implemented thoughtfully. Avoid storing, transferring, or evaluating extraneous information by prioritizing actionable items. Logging too much data can create a drain on resources, both in terms of cost and time. A good logging strategy generally provides two types of data: structured data for machines and data that alerts system administrators to a potential problem.

Monitoring is an umbrella term that can include many facets of system evaluation, but in this context, we're referring to application performance monitoring (APM). APM is the process of instrumenting an application to collect, aggregate and analyze metrics to better evaluate the use of the system by gauging availability, response time, memory usage, bandwidth, and CPU time consumption. 

Monitoring systems rely on metrics to alert IT teams to operating anomalies across applications and cloud services. Ideally, teams would implement instrumentation and monitoring on all systems.

Logging and monitoring are two different processes that work together to provide a range of data points that help track the health and performance of your infrastructure. APM uses application metrics to measure availability and manage performance. Logging creates a record of log events generated from applications, devices, or web servers that serves as a detailed record of occurrences within a system.  

Using a combination of log management to collect, organize, and review data and monitoring tools to track metrics offers a comprehensive view of your system's availability along with detailed insight into any issues which could potentially affect the user experience. APM tells you how applications are behaving and log data from applications, network infrastructure, and web servers provides greater insight that will tell you why the application is performing as it is. An effective logging strategy enhances application performance monitoring. 

In a metaphorical sense, monitoring metrics is like the security alarm that alerts you to a possible intrusion; log files act as the security camera footage that will provide clues to tell you what happened and how.

There will be some use cases where you will only need one or another, but having both offers you a greater ability to fully understand your system and its vulnerabilities.

Ultimately, your goal is to maintain healthy applications and user experience. By integrating logging and monitoring to accomplish this goal, your developers and operations teams will be able to plan for and troubleshoot application issues faster.

Create an effective strategy to optimize the integration of your logging and monitoring solutions by using the following best practices: 

Enable both methods to work together

If your ultimate goal is to optimize the benefits of analyzing log data and application metrics, facilitate that process by configuring your system to send log data directly to your monitoring tool. Storing log messages on the disk or sending them solely to a logging tool creates more of a drain on resources as well as a potential workflow bottleneck. 

Make sure that your monitoring tool supports your application's programming language to ensure compatibility and ease of use. 

Log the right data

Log data needs to tell a succinct but complete story. Data should be selective, descriptive, and provide the correct context to assist with troubleshooting. Helpful log data generally includes actionable items, and includes information such as a timestamp, user IDs, session IDs, and resource-usage metrics. Collecting a full range of applicable data enhances the information obtained from your monitoring tool.

Use structured log data 

Streamline your data by making it easier to search, index, and store be ensuring that it is structured. Structured data provides a more complete view as to what happened, and can provide your monitoring tool with unique identifiers such as which customer ID experienced the error. Providing customer ID information obtained via logging enables your monitoring tool to see how that specific user was affected, and what other issues they may be experiencing as a result. 

Take full advantage of log data 

Logging offers more than simple troubleshooting and debugging. Identify application and system trends by applying statistical analysis to system events. Log data contains important information about your applications and underlying infrastructure, including all of your databases. Use the historical information provided by log data to determine averages that will make it easier to definitively identify anomalies, or to group event types in a way that allows for accurate comparisons. This data can also be beneficial for collecting, aggregating and viewing this data according to your enterprises’ needs.

Having statistical data sets for review allows for a more accurate analysis and an improved opportunity to make informed business decisions.