Log analysis tools help improve DevOps by reducing the time spent diagnosing and managing applications and infrastructure and providing information which helps guide development decisions.
How Does Log Analysis Work?
Log files are produced in real-time by applications, operating systems, networks, and other components of a technology stack. They consist of a collection of log messages which are arranged in order of occurrence and saved for storage and analysis on a hard drive, in files, or with a log management system.
Log analysis can involve a large amount of data, depending on the scope of technology included in the evaluation. Smaller programs may be able to analyze logs manually, however, larger enterprises may require the use of machine learning or dedicated software programs to organize or discard information based on relevancy. Using a program that can identify and eliminate routine messages can help log analysts work more efficiently when trying to determine the root cause of a problem.
A review of log entries allows analysts to search for patterns or inconsistencies that may indicate the potential for problems on a web server or websites, such as system-related issues or security risks.
Log Analysis Use Cases
Log monitoring is the process of collecting the information and alerting when a potential issue is involved. Log analysis is the evaluation of that information to mitigate issues or improve existing processes.
The benefits of investing in log analysis are primarily related to avoiding risks which could have a negative impact on the health of your business. Log analysis can help ensure compliance with security policies and industry regulations, and can ultimately provide better user experience by facilitating the troubleshooting of technical issues and highlighting areas in need of performance improvements.
Having a trail of log messages that indicates what occurred along with information related to the occurrence allows system administrators to rapidly detect security threats, outages, or failed processes and mitigate issues with greater speed and accuracy.
Log analytics also clarify patterns that relate to performance. Reviewing logs from data sources helps determine trends, allows for greater understanding of user behavior, and improves search functionality of application issues.
How To Do Log Analysis
Due to the large amounts of data from various sources, log analysis can require a complex strategy for maximum efficiency and productivity. The three core components of effective log analysis involve cleansing, structuring, and analyzing the information contained within data sets.
When working with large and varied data sets, it's important that the data stored is usable and accurate. Data can become corrupted if:
the data's storage disk crashes
applications are improperly or abnormally terminated
the system has been infected with a virus
there are issues related to the input/output configuration
Data cleansing is a process that involves the detection and replacement or removal of inaccurate, incomplete, or irrelevant information.
Since log data is collected from a variety of sources, data sets often use different naming conventions for similar informational elements.
The ability to correlate the data from different sources is a crucial aspect of log analysis. Using normalization to assign the same terminology to similar aspects can help reduce confusion and error during analysis.
Once the data is collected, cleaned, and organized, it is ready to be reviewed and evaluated. Depending on processes in place, intended use, and the size of the data sets, there are various methods of analysis. Best practices include:
Pattern recognition: Filtering messages based on a detected pattern can help you recognize data patterns that may facilitate the identification of anomalies.
Classification: Labeling log elements with keyword tags organizes them into different categories that can make it easier to filter and adjust your display of data.
Correlation analysis: Collecting information from a range of sources such as servers, network devices, and operating systems is ineffective without a way to compare and contrast that data when investigating a single system-wide event. Correlation analysis sorts relevant messages from all components that relate to a certain event.
Artificial ignorance: Routine log messages can increase the density of data in a way that makes it more difficult to sift through when trying to identify the root cause of a problem. Artificial ignorance is a machine learning process that learns to ignore routine updates unless they failed to occur, which indicates an anomaly worth investigation.
Log Analysis vs APM
Since both APM and log analysis are methods to assess the availability and performance of an application, many system administrators believe one or the other is sufficient for monitoring. However, due to distinct differences between the two techniques, a combination of both allows for the most comprehensive understanding of your system.
Log analysis involves collecting, evaluating, and managing the data reported by various components. It is the practice of managing all of the log data produced by your applications and infrastructure.
APM helps monitor and manage the performance of your application. Consequently, log management facilitates APM by providing in-depth data that gives greater insight into issues with availability and user experience relating to applications and infrastructure.
Log data serves as a valuable source of metrics for APM solutions by allowing you to trace information to specific processes or components. Log analysis and APM are both essential monitoring tools with their own distinct purposes, but their effectiveness multiplies when they are used in conjunction with one another.
Due to the value of log analytics, when it comes time to perform APM, you will have complete and accurate data for tracking the availability and performance of your applications and infrastructure. Without effective log management, you cannot perform APM reliably.