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In the past, distributed systems were typically monolithic, meaning that they were composed of a single, large piece of software.
Modern distributed systems are typically composed of many smaller, microservices-based components.These components may be distributed across multiple cloud providers and may be constantly changing.
The problem is the increasing use of containers to deploy and manage distributed systems. Containers are isolated environments that run on top of a shared operating system.
Observability data can be used to identify performance bottlenecks across the entire system, including in containers and serverless applications.
Observability data can be used to troubleshoot code in individual microservices, even if they are distributed across multiple cloud providers. This can help to reduce the time it takes to fix bugs.
Observability data can be used to understand how the system is behaving under load, how new changes are impacting the system, and how users are interacting with the system.
A cloud-native observability platform can collect and analyze telemetry data from all of the different components of a distributed system, including containers and serverless applications.
Distributed tracing allows you to track the flow of requests through a distributed system, even if the requests are processed by multiple microservices.
Metrics and logs are essential for observability. Metrics provide quantitative data about the system’s performance, while logs provide qualitative data about the system’s behavior.
It is important to monitor your system’s performance under load to identify any potential bottlenecks. You can use load testing tools to simulate load on your system.
Alerts and dashboards can help you to stay on top of your system’s health and to be notified of any problems quickly.
