Cloud-native architectures require log monitoring.

Log monitoring for cloud-native architectures

For current online applications, log monitoring for cloud-native architectures necessitates a slightly different strategy than traditional web apps. It’s partly owing to the application landscape’s complexity, which includes getting data from microservices, leveraging Kubernetes and other container technologies, and integrating open source components in many situations. Because of this, you’ll need to reconsider your approach to aggregating, analyzing, and storing your application logs.

Interpreting logs is a beautiful approach to checking on the health of your application, especially if you want to learn more about services that are only active for a short period. On the other hand, new tools and technologies offer you unprecedented amounts of data, making it more challenging to filter out the noise. We’ll look at some of the problems with log monitoring for cloud-native architectures in this article and four stages to assist you in developing an effective plan for your apps.

Top takeaways

• Using open standards, having a central log management solution, and avoiding collecting personal information in logs are some of the best practices for log monitoring in cloud-native environments.
• In cloud-native architectures, taking the wrong approach to log management can limit your capacity to respond to issues quickly or lead to vendor lock-in.

​​

What are the challenges with log monitoring for cloud-native architectures?

Log monitoring used to be simpler because most application logs followed a similar structure and format. It was straightforward to transform and aggregate this data, allowing teams to collect and analyze various logs into a unified perspective of the environment’s performance. It is no longer the case in a cloud-native world.

The above are among the significant problems that teams confront today:

• Teams could easily find up dealing with hundreds of thousands of individual logs due to the number of microservices, containers, layers of infrastructure, and orchestration in cloud-native design. 
• Logs are commonly kept in containerized environments’ internal file systems, which may only exist while executing the app. To examine performance and troubleshoot issues later, teams must collect log data onto persistent storage. 
• Cloud-native applications create a wide range of data from the app and server, but they also rely on cloud services, orchestrators, and APIs to run. These components produce valuable data that you must acquire from various instances, nodes, gateways, hosts, or proxies. 
• If you exclusively utilize a single vendor’s logging tools, you risk becoming tied to that environment and its proprietary log management solution. Using multiple logging tools for different service providers in a multi-cloud setup might make monitoring performance, debugging issues, and comprehending dependencies challenging. 

You can overcome these obstacles with an intelligent model if you start with the appropriate attitude.

An intelligent model for log monitoring in cloud-native architectures

The following are some excellent practices to incorporate into your log-monitoring strategy.

1. Set up a log management system.
​
Because of the variety of log data created in your environment, using a log management solution that combines all logs into a single collection is the best option. Log management from a centralized system allows you to automatically connect all of your records into a digestible amount of data for future analysis. Observability solutions simplify collecting and storing log data, allowing you to visualize and analyze data from your application, infrastructure, and end-users.

2. Adopt open application log standards.
​
Open standards like OpenTelemetry let you avoid vendor lock-in and use vendor-neutral APIs to optimize your log monitoring procedures. OpenTelemetry is a collection of tools, SDKs, and APIs that enable you to instrument code, produce, collect, and export log data, traces, and metrics. It merges two earlier standards (OpenTracing and OpenCensus) into a single collection of tools, SDKs, and APIs.
Adopting open standards for your application telemetry will help ease your log monitoring process, thanks to extensive language support and interoperability with major frameworks. OpenTelemetry is now in beta in several languages, is open source, and is backed by several industry heavyweights.

3. Make use of the most up-to-date tracing and logging technologies.
​
Consider leveraging emerging technologies like eBPF for data collection once you’ve implemented a centralized log management solution from an observability platform. Look for tools with no-code interfaces for visualizing your data and bespoke log parsers for quickly transforming and shaping log data into useable formats.
You can do the following with increased log production, collecting, and viewing capabilities:

• Trace each service request throughout the environment to troubleshoot your application’s performance. 
• Capacity planning, load balancing, and application security can all be improved. 
• To figure out what’s going on during each request, compare transaction data with operational data. 
• To find patterns in your data, ingest data, and grow log monitoring. 

4. Only keep track of the information you require.
​
Finally, logs must have the required metadata to give sufficient context when examining performance. Creating records is simple with a log management solution, but the information will be useless if it isn’t instantly helpful. Log information should assist you in understanding what is going on in the program or making quick decisions.
Remember to use anonymous identifiers for all private information to keep sensitive data out of your logs. Use this Log Management Best Practices guide to help you plan your strategy and avoid typical cloud-native log monitoring problems.

Gain full-stack observability with New Relic One log management

You can find bottlenecks, swiftly resolve issues, and enhance your performance for every commit using AIOps capabilities, a centralized logging system, and distributed tracing.

About Enteros

IT organizations routinely spend days and weeks troubleshooting production database performance issues across multitudes of critical business systems. Fast and reliable resolution of database performance problems by Enteros enables businesses to generate and save millions of direct revenue, minimize waste of employees’ productivity, reduce the number of licenses, servers, and cloud resources and maximize the productivity of the application, database, and IT operations teams.