Log monitoring is necessary for cloud-native structures.

Log monitoring is necessary for cloud-native structures.

For a longtime, log handling was relatively simple. Logs are simple and predictable in terms of volume, type, and structure.

All of this clarity, however, has been thrown out the window in recent years. The move to cloud-native platforms, such as loosely connected services, services architectures, and techniques like microservices and Kubernetes, has rendered previous log management solutions obsolete. In a cloud-native world, managing logs properly necessitates fundamental changes in how logs are collected, analysed, and more.

What Is the Difference Between Cloud Native Logging and Other Logging Methods?

Log administration in a cloud-native environment may appear to be similar to traditional logging at first inspection. The core elements of the log management process — gathering, collection, assessment, and rotation — still apply to cloud-native systems and applications.

When you try to monitor a cloud-native system, however, it immediately becomes evident that adequately managing logs is considerably more challenging.

Additional Logs

To begin with, there are just more logs to deal with.

Most programs were structures that ran on single servers before the cloud-native era. Typically, each application simply produced one log if it even created its own log at all; sometimes, applications logged data to Syslog instead. Each server also created a small number of logs, with Server and auth being the most common. As a result, you just had a few logs to deal with when managing logs for the full environment.

In cloud-native settings, on the other hand, you’re more likely to deal with microservices architectures, in which a dozen or more separate services are operating, each delivering a different piece of the functionality needed to put together the full application. Every microservice has the potential to

Logs Come in a Variety of Shapes and Sizes

There are now more logs in general, but there are also more categories of logs. Instead of simply server and software updates, you also have records for your virtualized environment, Kubernetes or Docker logs, authentication logs, logs for both Desktop and Mobile (because it’s becoming more typical to utilize both software packages in the same shop), and more.

This variability adds to the problem, not only since there are more forms of log data to maintain, or because these different sorts of logs are frequently formatted differently. As a result, utilizing regular matching or other forms of general queries to parse all records at once is more difficult.

Logs Come in a Variety of Shapes and Sizes

There are now more logs in general, but there are also more categories of logs. Instead of simply server and software updates, you also have records for your virtualized environment, Kubernetes or Docker logs, authentication logs, logs for both Desktop and Mobile (because it’s becoming more typical to utilize both software packages in the same shop), and more.

This variability adds to the problem, not only since there are more forms of log data to maintain, or because these different sorts of logs are frequently formatted differently. As a result, utilizing regular matching or other forms of general queries to parse all records at once is more difficult.

Log acquisition and compilation should be unified.

Having to handle the logs with each wire is impossible with so many different sorts of log formats and designs to support and remember.

Instead, use a unified, centralized log management system that captures data from all aspects of your ecosystem and organizes it in one place.

Adopt a Log Management System that is Versatile.

Without needing to alter the environment, your data cleansing tools and technologies should be able to handle any sort of environment.

If you already have one Cloud server that exposes log data one way and another that exposes log data another way, you should be able to gather and analyze logs from both groups without needing to change how either cluster handles logs. Similarly, if you have one application operating on one public cloud and another on a separate cloud, you shouldn’t have to change either cloud’s default logging behavior to monitor its logs from such a centralized spot.

Logs are collected in real-time.

Collecting log data in real-time and aggregating it in a separate location is one technique to ensure that logs from environments without persistent storage do not vanish. As a result, log data is saved in a persistent log manager as soon as it is created and is accessible even if the container is shut down.

This method is better for collecting log data from inside containers just at set intervals, which puts you at risk of losing some logs if the containers shut down sooner than intended.

About Enteros

​Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.