Mastering Observability: 6 Best Tips for Scaling Up Your Business

Richa Chawla
Published On :
August 17, 2023

Observability is a crucial aspect of any modern business. It allows you to monitor and analyse your systems to ensure they're performing optimally, while also providing deep insights into how they work and how they can be improved.

However, as your business grows, so does the complexity of your systems, and hence mastering observability becomes increasingly challenging. In this blog, we'll explore some best tips for scaling your observability to meet the needs of your tech stack and a growing business.

Tip #1 Start with a Solid Foundation

The first step to scaling observability is to establish a solid foundation. This means defining what you want to monitor and how you plan to do it.

Start by identifying the key performance indicators (KPIs) that are crucial to both your business and technical stack. Undoubtedly, business KPIs shape and guide technical KPIs as well in observability. Aligned with business objectives, metrics like SLAs, SLOs etc. impact performance optimization, resource allocation, risk mitigation, and user satisfaction.

Thus, establishing effective observability directly contributes to achieving business goals by enabling proactive issue detection, efficient problem resolution, and continuous optimization of systems and processes.

Tip #2 Use Distributed Tracing

Distributed tracing is a technique that allows you to trace the path of a request as it travels through your system. This can help you identify bottlenecks and performance issues that might not be immediately obvious when looking at individual components.

When implementing distributed tracing, it's important to choose a tool that can handle the scale and complexity of your system. You'll also need to ensure that your applications are instrumented to provide the necessary trace data. Tracing requests as they move through your system allows you to identify potential issues and areas for improvement, such as slow database queries or inefficient API calls.

Tip #3 Implement Log Aggregation

Log aggregation is pivotal in ensuring system reliability, troubleshooting, and performance optimization. By aggregating logs from diverse sources such as servers, applications, databases, and network devices, organizations can create a unified repository of valuable data. This repository in turn offers a comprehensive view of system behaviour, making it easier to identify and address issues efficiently.

And this is not it, log aggregation also aids in detecting anomalies, errors, and potential security breaches, enabling swift response and mitigation.

Tip #4 Use Metrics to Track Performance

Metrics are a key component of observability and can help you track the performance of your system over time. Businesses can also use these metrics to trigger alerts when performance KPIs fall outside of acceptable ranges.

Metrics can be a good way to track performance but choosing the correct metrics that point you in the right direction is also important. You'll also need to ensure that your metrics are instrumented correctly and that you have a system in place to handle large volumes of metric data.

Tip #5 Foster an Observability Culture

Observability isn't just the responsibility of the operations team - it's a team effort that involves developers, testers, and other stakeholders as well. By involving everyone in the observability process, you can ensure that everyone is working towards a common goal and that issues are identified and addressed as quickly as possible.

To make observability a team effort, you'll need to establish processes and tools that encourage collaboration and communication. A tool like SnappyFlow supports access to multiple users at minimal cost to no cost. Additionally, it enables role-based access at specific need-based hierarchical levels within the organization.

Tip #6 Choose the Right Tool

Choosing the right observability tool requires you to take a strategic approach. It depends on factors like- What are your specific needs and objectives? What are your data sources? How scalable do you want your business to be? What are the integrations you’re looking for with your existing systems?

Filter the tools based on your priorities like tools offering customizable dashboards and meaningful visualizations, Compatibility with your tech stack and cloud environment etc. Evaluate vendor support, documentation, and user community. Seek tools aligned with your business goals, capable of identifying anomalies and optimizing performance. Ultimately, the key lies in selecting a tool that empowers efficient issue resolution, proactive monitoring, and data-driven decision-making for enhanced observability.

SnappyFlow is one such unified tool that helps you solve your observability problems. Interested to explore how? Read more.

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.
Get in touch
Or fill the form below, we will get back!
Is SnappyFlow right for you ?
Sign up for a 14-day trial
Subscribe to our newsletter