How Observability Can Help You Achieve a More Resilient Infrastructure

Richa Chawla
Published On :
April 19, 2023

As organizations move towards more complex and distributed systems, ensuring that the infrastructure is resilient has become necessary. Resilience refers to the ability of a system to absorb and recover from failures or disruptions. Achieving resilience in complex systems can be challenging, but observability can be a helping hand.

Observability is a concept that refers to the ability to understand what is happening inside a system based on its external outputs. In other words, observability is the ability to infer the internal state of a system based on the events and data it produces. Observability is particularly important in complex systems because they can exhibit emergent behaviour that is difficult to predict.

Here are some ways that observability can help you achieve a more resilient infrastructure:

#1 Detecting Failures Early

One of the most important aspects of resilience is the ability to detect failures early. The longer it takes to detect a failure, the longer it will take to recover from it. Observability can help you detect failures early by providing visibility into the internal state of your system. By monitoring key metrics and events, you can detect when things are not working as expected and take corrective action before the situation gets worse.

For example, if you have a distributed system that relies on multiple services, you can use observability tools to monitor the health of each service and detect when one of them fails. You can set up alerts that notify you when a service is not responding and use this information to quickly diagnose and fix the problem.

#2 Understanding System Behaviour

Observability can also help you understand the behaviour of your system under normal and abnormal conditions. By monitoring key metrics and events, you can gain insight into how your system is performing and how it is responding to different types of loads and stress. This information can be used to optimize the performance of your system and identify potential problems before they occur.

If you have a web application that experiences a sudden surge in traffic, observability tools can help you understand how the system is responding to the increased load. You can use this information to optimize your infrastructure and make sure that it can handle similar surges in the future.

#3 Improving Incident Response

When a failure occurs, it is important to have a well-defined incident response plan in place. Observability can help you improve your incident response by providing real-time visibility into the state of your system. By monitoring key metrics and events, you can quickly identify the root cause of the problem and take corrective action.

#4 Enabling Continuous Improvement

Observability can also help you continuously improve your infrastructure over time. By monitoring key metrics and events, you can identify areas for improvement and make changes to optimize performance and reduce the likelihood of failures. This can help you achieve a more resilient infrastructure that can adapt to changing requirements and handle unexpected events.

For example, if you have a database that is experiencing slow response times, you can use observability tools to identify the bottleneck and make changes to improve performance. You can also use this information to optimize your infrastructure for future growth and scalability.

In conclusion, observability is a powerful tool for achieving a more resilient infrastructure. By providing real-time visibility into the internal state of your system, observability can help you detect failures early, understand system behaviour, improve incident response, and enable continuous improvement. As organizations continue to adopt more complex and distributed systems, observability will become an essential requirement for achieving resilience and maintaining business continuity.

What is trace retention

Tracing is an indispensable tool for application performance management (APM) providing insights into how a certain transaction or a request performed – the services involved, the relationships between the services and the duration of each service. This is especially useful in a multi-cloud, distributed microservices environment with complex interdependent services. These data points in conjunction with logs and metrics from the entire stack provide crucial insights into the overall application performance and help debug applications and deliver a consistent end-user experience.
Amongst all observability ingest data, trace data is typically stored for an hour or two. This is because trace data by itself is humongous. For just one transaction, there will be multiple services or APIs involved and imagine an organization running thousands of business transactions an hour which translates to hundreds of millions of API calls an hour. Storing traces for all these transactions would need Tera Bytes of storage and extremely powerful compute engines for index, visualization, and search.

Why is it required

To strike a balance between storage/compute costs and troubleshooting ease, most organizations choose to retain only a couple of hours of trace data. What if we need historical traces? Today, modern APM tools like SnappyFlow have the advantage of intelligently and selectively retaining certain traces beyond this limit of a couple of hours. This is enabled for important API calls and certain calls which are deemed anomalous by the tool. In most troubleshooting scenarios, we do not need all the trace data. For example, a SaaS-based payment solutions provider would want to monitor more important APIs/services related to payments rather than say customer support services.

Intelligent trace retention with SnappyFlow

SnappyFlow by default retains traces for
SnappyFlow by default retains traces for
HTTP requests with durations > 90th percentile (anomalous incidents)
In addition to these rules, users can specify additional rules to filter out services, transaction types, request methods, response codes and transaction duration. These rules are run every 30 minutes and all traces that satisfy these conditions are retained for future use.
With the built-in trace history retention and custom filters enabled, SREs and DevOps practitioners can look further to understand historical API performance, troubleshoot effectively and provide end-users with a consistent and delightful user experience.
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