Log Monitoring Tools

Valerio Barbera
monitoring tool categories - logs

In the next lessons I will introduce an overview of various monitoring tool categories, defining their specific use cases. Starting with Log Monitoring Tools.

So, based on the technical problem that worries you the most, you can immediately understand which category or categories of tools can give you the most value. 

Just do a search on Google and you will find many products to try for each category. You will be able to direct the choice, based on your real needs.

View the full video: https://www.youtube.com/watch?v=u3pvNyZnauQ

Log Monitoring Tools Use Cases

Advanced log management is usually the first thing a developer looks for when he starts approaching the problem of having a more structured monitoring system.

This instinct to take the first steps towards Logs is probably given by the fact that keeping application logs under control has been one of the dominant habits of developers and systems engineers since the dawn of software development.

Today, however, applications are becoming more and more complex, but also given the wide offer of faster and more effective tools in supporting developers, logs have become more of a tool for lower-level analysis.

The use of logs has mainly benefited applications in the field of cyber security, or in other similar contexts. Log management is a necessity given by regulatory obligations. Such as software in the financial field or healthcare. ISO 27001 certification also requires log management and archiving.

Log Monitoring Tools Weaknesses

On the other hand Logs are not a “data format” suitable for transporting complex information, such as metrics and statistical measurements.

This makes it rather difficult to extract relevant information from the logs to monitor the behavior of the application as a whole: such as resources consumption, stability, and performance.

Sometimes certain tools support the ability to build intermediate processing pipelines before incoming logs are stored in data stores. 

These intermediate transformations obviously have costs that can quickly become too high. I mean days or weeks that developers have to spend to write these pipelines and maintain them over time instead of working on new developments, and the computing resources needed to execute these processes at scale.

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When an error occurr after a delivery cycle Inspector not only alerts you with a notification, but also creates a pull request on your GitHub repository to automatically fix the error.

Now you are able to release bug fixes after a few minutes the error occurred without human intervention in between. Learn more on the documentation.

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