Effective ways to Avoid Application Performance Overhead with Log data

Effective ways to Avoid Application Performance Overhead with Log data

It becomes critical for every forward-thinking organization to adopt effective ways to avoid performance overhead when managing their logs.

Log data remains an untapped or poorly managed resource for many organizations. In fact, logs are enormously valuable as they unlock the potential to understand customers’ behaviors, analyze usage and security data, and solve performance issues. However, these core benefits are defeated if every log must have a known value, and the cost of ingesting, storing, and querying log data can be far more expensive than the value created.

While several log management solutions deliver innovative features like machine learning ML and AIOps for easier monitoring, they miss the pivotal point that all these will increase application performance overhead. Since we agree that not all log information is actionable and that logging everything will increase the total cost of ownership, especially when licensing models are measured in volume. Log management should be visualized as an affordable commodity service that minimizes the cost and complexities of utilizing log data.

It becomes critical for every forward-thinking organization to adopt effective ways to avoid performance overhead when managing their logs.

Here are some strategies organizations can adopt to avoid performance overhead with log data;

• Sort logs according to source groups
• Define log severity levels
• Define log processing pipeline

Sort logs according to source groups

Log data is generated from various sources during events occurring in a device, application, or service installed throughout the network. Depending on the kind of network, services or application managed, some system administrators will find some log data more interesting than others.  

A clearly defined source group will help the system administrator navigate the problem quickly when investigating events. For instance, if there is a security downtime in network infrastructure, the admin can quickly navigate to the security log group to fix the issue with certainty.

Log groups are primarily divided into three as often seen in computer networks;

• Computer security logs: Contains info about possible attacks, intrusions, viruses, or authentication actions against a device.
• Application logs: Contains info about operating system-related events.
• Operating system logs: Contains info about the application, database servers, and infrastructure data.

These log source groups can filter and divide the log data during searches and analysis to remove irrelevant information. This can help network administrators save time since the number of log messages required to be searched and analyzed is decreased.

Define Logging severity levels

It is imperative to define log severity levels because it describes the asperity of events within the logs. Severity levels make it easy to filter out the verbosity of log information. The severity of log data can give valuable insight into the device’s operations and current state of affairs. Therefore, system admins can respond uniquely to log severity by enabling levels explaining the severity and other ranges relating to normal or irrelevant logs. Technical admins can get prompt notification of severe log events, and on the other hand, if the log event isn’t extreme, notification can come in periodically. For instance, application logging frameworks like slfj4 or log4j can deliver logging solutions that segregate several logs according to their log severity levels.

Different standards for measuring log severity depend strongly on the operating system, application, and devices that generated the log data. Some log severity levels are represented with numbers ranging from 0-8, while others are described with acronyms such as FATAL, WARN, NORM, DEBUG, etc.

Number standard

Acronyms

Define log processing pipeline

Another important strategy to follow is to define your company’s log processing pipelines. This pipeline shows the processing the logs are put through during its period. Having a complete understanding of the log processing pipeline gives system admins the right perspective on storing and analyzing log data for maximum efficiency.

There are four significant steps the log passes through, include;

Processing: the logs pass through several processes like parsing, filtering, and event aggregation. During this phase, the system can further process the log data appearance changes to a more uniform template.

Storage: After the logs have been processed, they are then stored to be available in the future for events like log rotation, log compression, log archiving, and integrity checking.

Analysis: logs are reviewed with remarkable correlation and relationship finding tools to gain valuable insights between past and present events or the peculiarity of current events.

Disposal: for instance, logs are stored for a specified period due to PSI and other relevant compliance. However, once that phase elapses, the technical admins aligned with the company’s framework for centralized log management can decide whether to keep or dispose of the logs.

How to avoid application performance overhead with log data

Any innovation comes with its advantages and disadvantages. Organizations that wish to make efficient use of their logs must implement effective ways to avoid application performance overhead when managing their logs. Given the outstanding benefits of avoiding application performance overhead related to the reduced total cost of ownership (TCU), simplicity, and scalability, we will see more companies ready to master the art of log monitoring and avoid application performance overhead in the process.