Observability Vs Monitoring: Understanding the Key Differences

Richa Chawla
Published On :
May 15, 2023

Observability and monitoring are two terms that are often used interchangeably but are not same in literal sense. While both practices involve gathering data and analysing it to gain insights into how a system is performing, they differ in their goals and approaches.

In this blog post, we will explore the key differences between observability and monitoring, and why it is important to understand these differences.

What is Monitoring?

Monitoring is the practice of collecting and analysing data to determine whether a system is functioning as expected. This involves monitoring metrics such as response time, CPU usage, and memory usage, tracking KPIs (key performance indicators) and setting up alerts to notify teams of any issues.

Monitoring is often focused on answering questions such as:

- Is the system up or down?

- Is the system experiencing high CPU or memory usage?

- Are there any errors or exceptions occurring in the system?

By monitoring these metrics, teams can quickly get an idea about issues that may be happening. But when it comes to identifying and deeply analysing the impact of the issues on the system – Only observability can give you the answer.

What is Observability?

Observability is a more holistic approach to understanding the behaviour of a system. It involves gathering data not just on the performance of the system, but also on its internal state and the interactions between its various components.

Observability is often focused on answering questions such as:

- Why is the system behaving in a certain way?

- What is the root cause of an issue?

- How can we optimize the system to improve performance?

With observability, one can get into a deeper analysis of what is happening in the system and why? With SnappyFlow, it is made even easier with SFAgent that collects data on system’s internal state and interactions to analyse better. This data is then aggregated and analysed to gain insights into how the system is functioning.

Key Differences between Observability and Monitoring

The key differences between observability and monitoring can be summarized as follows:

1. Focus: Monitoring is focused on the current state that impact the performance or availability of a system, while observability is focused on analysing and addressing issues, gaining a deeper understanding of how a system behaves and why.

2. Scope: Monitoring typically focuses on a specific set of metrics or KPIs, while observability involves more than collecting data on a wide range of internal and external factors that may impact the system and understanding why the impact is there.

3. Approach: Monitoring is often reactive, meaning that alerts are triggered when specific thresholds are crossed. Observability, on the other hand, is proactive and involves actively seeking out insights and identifying potential issues before they occur.

4. Instrumentation: Monitoring typically involves only collecting the data and representing it in a better way, while observability collects and analyses data on internal components and their interactions with one another.

Why is it Important to Understand the Differences?

Monitoring is often reactive in nature, where predefined thresholds or alerts trigger actions when specific metrics exceed predefined limits. It helps respond to known issues. In contrast, observability takes a more proactive approach by providing deep insights into the system's behaviour and allowing for root cause analysis. It enables the detection of unknown issues and anomalies and facilitates proactive measures to mitigate problems.

As systems grow increasingly complex with the rise of microservices, cloud-native architectures, and distributed systems, understanding the differences between monitoring and observability becomes even more significant. Traditional monitoring approaches may struggle to capture and make sense of the intricate interactions and dependencies within modern systems. Observability, with its focus on high-fidelity data and contextual insights, helps navigate complex systems more effectively, providing a comprehensive understanding of system health, performance, and behaviour.

By recognizing these distinctions, businesses can adopt appropriate strategies and tools to gain meaningful insights into their systems. With increasing necessity in complex business infrastructures, observability is a must addition to the list as it can provide a comprehensive and proactive view of system operations, empowering teams to deliver robust and reliable services.

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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