Is Observability Just Application Monitoring with a New Name?
Observability and Application Performance Monitoring (APM) are two important practices for understanding and optimizing performance. These approaches are similar to one another, so it’s not uncommon for people to use these terms interchangeably. However, APM is just one of the steps utilized in certain types of observability practices and technologies.
What is APM?
Just to set the stage, APM is a tool used to root out the source of performance issues in an application. One might assume this is a basic form of performance monitoring. However, APM actually goes far above ordinary performance monitoring .
APM is typically used to assess the user experience in modern applications. This includes tracking the application’s load and different key performance indicators. Such monitoring allows developers to get insight into the user experience. It also indicates how the user experience evolves as the application’s burden increases.
While APM is more commonly used to develop insight into an application’s user experience, it can also be used in other ways. For example, APM is one tool that assists with root cause analysis. Determining the root cause of a fundamental error, or a P1, can be tricky, but it is vital to do in order to stop the issue from recurring. APM compares real-time metrics alongside recorded telemetry data to determine where the problem is occurring.
There are many such APM tools out there such as AppDynamics (now owned by Cisco), Dynatrace, DataDog, etc. These technologies provide the details around the fact that ‘something’ occurred, and help direct IT professionals in the direction of the root cause.
So What is Observability?
Observability is another method that allows teams to assess or monitor the general health of complex systems. It sounds similar to APM, but is much more complex due to the nature of cloud environments.
Observability generally relies on three data sources called “pillars of observability” or the three “dimensions of observability”. These are:
Observability metrics cover a wide range of KPIs to offer better insight into system performance. When monitoring a website, these metrics will include peak load, response time, and the number of requests served. Similarly, when monitoring a server, some metrics you will look at are memory usage, CPU capacity, latency, and error rates.
When put together these KPIs quantify performance and offer actionable insights to improve the system. Metrics can also provide alerts and inform teams about issues in real-time. Metrics do provide a lot of information, but it is difficult to diagnose an event using metrics alone. This is because of the challenges associated with grouping metrics using tags and filters.
Logs were one of the first tools used to monitor systems since the invention of the server. Logs offer historical records for all types of systems, including distributed ones. They provide timestamps in the form of plain text or binary. It is also possible to use structured logs which combine plain text with metadata. Such logs make the querying process easier.
Logs are especially useful because they help answer important “who”, “what”, “when”, “where”, and “how” type questions related to activities. In decentralized architecture, logs are sent to a centralized location where they correlate problems. This saves users the trouble of accessing logs for each server or microservice.
Traces are a relatively new concept that were created to track a series of distributed events and the things that happen between them. Distributed traces keep a record of the user journey as they use an application or interact. They then aggregate the data from decentralized servers. A typical trace will show you backend systems, user requests, and process requests end-to-end.
Distributed traces are especially useful when requests are sent through numerous containerized microservices. Traces offer a great solution because they are generated automatically, standardized, and are generally easy to use.
Traces also present information visually in the form of bars. These have different lengths for each step, which make it easy to identify what you’re looking for.
How APM and Observability Relate to One Another
APM and Observability are the foundations of DevOps. APM is generally what makes Observability possible. For example, when a DevOps team monitors an application, they will typically view different metrics together to create a picture of the application’s performance and health. This metric data is important because it can indicate when an application or system has an issue. However, it remains difficult for the team to find the problem’s root cause without using Observability.
APM vs Observability
You might be wondering if all the APM vendors have just adopted Observability and have simply begun using it as the new name for APM for the cloud? This isn’t exactly true.
The primary difference between observability and monitoring is based on whether the data retrieved from the system is predetermined or not. APM typically collects and analyzes the predetermined data retrieved from individual applications while observability compiles data produced by all systems.
In addition, monitoring tools generally utilize dashboards that indicate metrics related to performance and usage. IT teams can consult this dashboard to learn about abnormalities or performance issues they anticipate. However, this approach can’t be used for abnormalities and performance issues they can’t anticipate.
For this reason, APM tools aren’t very good at monitoring complex cloud environments for performance and security issues that lead to a system outage. Such security issues are often unpredictable and multi-faceted, which makes them difficult to anticipate.
Observability software, on the other hand, uses metrics, logs, and tracers obtained from the entire IT infrastructure system. These immediately notify IT teams regarding potential issues. Such software can then help them debug the system to try to identify and manage the problem.
To put it simply, APM simply shows data, while Observability infrastructure measures each of the inputs and outputs collectively across numerous microservices, applications, servers, programs, and databases. Observability enables teams to develop a better understanding of the relationship between systems and offers actionable insights into the system’s health.
The Bottom line - Even though many, if not all of the APM vendors led the charge to Observability, they had to evolve their solutions to meet the needs of the cloud to do so.
The bigger questions - did they do enough?
Answering the Why Question
Although both APM and Observability “monitor”, performing a root cause analysis using either approach will still leave you struggling to answer the question, “what changed?” - the first question everyone will ask when an issue occurs. A change can go undetected for months until it triggers a symptom in these systems. Even with all the advancements of Observability, metrics, logs and traces still only detail symptoms of an issue or a problem. Neither APM nor Observability tools alone can detect the actual granular changes and tell you “why” the issue occured in the first place. This is critical information needed to diagnose the true root cause of the problem.
On the other hand, Four-Dimensional Observability has been defined to include a fourth pillar in addition to metrics, logs and traces - that of Change. Change tracks the configuration over time and knows the state of your systems, infrastructure, applications, network, code, etc.at any given moment - last week, yesterday or a minute ago, vs now, in near-real time - to tell you exactly “why” an issue occured. To answer “what changed?” is to know “why” an issue occurred. To know this answer is to understand your true root cause to prevent reoccurence.
To understand more about embracing changes as the secret ingredient in improved observability, customer experience, and business innovation, please read the latest from former Gartner Analyst, Charley Rich.
Observability in Modern Systems
Observability has become increasingly important in the modern digital era where applications use microservices-based structures and run across distributed infrastructure. Such applications are akin to machinery with hundreds of moving parts, so the chance of issues arising is always present. This large number of parts also makes it more challenging to identify where exactly the issue’s source is. For this reason, IT teams must implement modern development techniques with application and infrastructure observability in mind.
The increased reliance on observability allows DevOps and CloudOps personnel to tackle the complexities that arise due to the fragmentation present in distributed systems. However, to do this, the team must apply processes that promote better performance monitoring, improved log management, distributed tracing, and configuration and change intelligence. When put together, all these will help personnel respond when the system’s stability is threatened by issues within the application or infrastructure.
Why Choose Evolven?
As you can see, APM and Observability both play a vital role in detecting issues and maintaining application and infrastructure health. Whether you are using APM for your on-premises environment or considering Observability for your cloud environment, Evolven provides the unified view of the detailed end-to-end configuration state of your enterprise, no matter where you are in your digital transformation. Using AI-based analytics, Evolven detects and prioritizes risks triggered by actual, granular changes in configuration, application, infrastructure, and data so that you can prevent and rapidly resolve stability, compliance, and security issues.
If you are interested in learning more about how we can help you detect risky changes carried out in your environment, please contact our experts at Evolven.
Both APM and observability help teams manage an application or system’s health.