MELT Explained: Metrics, Events, Logs & Traces

With 71% of companies believing that their observability data is growing at an alarming rate, observability is becoming an essential aspect of managing and maintaining high-performing software systems. This is where understanding the concept of MELT becomes important.

The MELT (Metrics, Events, Logs, and Traces) framework offers a comprehensive approach to observability, delivering valuable insights into system health, performance, and behavior.

This allows teams to swiftly detect, diagnose, and resolve issues — while optimizing overall system performance!

In this blog post, we'll have a closer look at MELT and each of its four distinct telemetry data types, how it can be implemented, and some common questions about MELT.

(Dig into the key differences between telemetry, observability, and monitoring.)

An introduction to MELT: metrics, events, logs, and traces

The MELT framework brings together four fundamental telemetry data types:

  1. Metrics
  2. Events
  3. Logs
  4. Traces

Each data type provides a unique perspective on the system’s behavior, allowing teams to understand application performance and system health better.Unifying these data types creates a more comprehensive picture of software systems, enabling rapid identification and resolution of issues.

Let's have a deeper look at each of them.


Metrics are numerical measurements that offer a high-level view of a system’s performance. They enable mathematical modeling and forecasting, which can be represented in a specific data structure. Examples of metrics that can help understand system behavior include:

  • CPU % used
  • Error rate

Utilizing metrics has several advantages, such as facilitating extended data retention and simplified querying. This makes them great for constructing dashboards that display past trends across multiple services.


Events in MELT are discrete occurrences with precise temporal and numerical values, enabling us to track crucial events and detect potential problems related to a user request. Put simply — these events are something that has happened in a system at a point in time.

Since events are highly time-sensitive, they typically come with timestamps.

Events also help provide context for the metric data, above. We can use events to identify our application’s most critical points, giving us better visibility into user behaviors that may affect performance or security. Examples of events include:

  • User login attempts
  • Alert notifications
  • HTTP requests/responses


Logs provide a descriptive record of the system’s behavior at a given time, serving as an essential tool for debugging. By parsing log data, one can gain insight into application performance that is not accessible via APIs or application databases.

A simple explanation would be that logs are a record of all activities that occur within your system.

Logs can take various shapes, such as plain text or JSON objects, allowing for a range of querying techniques. This makes logs one of the most useful data points for investigating security threats and performance issues.

To make better use of logs, aggregating them to a centralized platform is essential. This helps in quickly finding and fixing errors, as well as in monitoring application performance.

(For more on making the most of logs, dive into log management.)


A trace refers to the entire path of a request or workflow as it progresses from one component of the system to another, capturing the end-to-end request flow through a distributed system.

Therefore, it is a collection of operations representing a unique transaction handled by an application and its constituent services. A span represents a single operation within a trace. A span is an integral part of a distributed system and acts as the basic element in distributed tracing.

Traces offer insight into the directionality and relationships between two data points, providing insights into service interactions and the effects of asynchrony. By analyzing trace data, we can better understand the performance and behavior of a distributed system.

Some examples of traces include:

  • A SQL query execution
  • A function call during a user authentication request

Instrumentation for tracing can be difficult, as each component of a request must be modified to transmit tracing data. Furthermore, many applications are based on open-source frameworks or libraries that may require additional instrumentation.

Implementing MELT in distributed systems

Distributed systems play a crucial role in modern applications, especially since they:

  • Handle a large amount of data. 
  • Provide high availability and fault tolerance.

Implementing MELT in distributed systems is essential for ensuring effective observability and optimizing performance. This involves:

Collecting telemetry data

Telemetry data refers to the automatic collection and transmission of data from remote or inaccessible sources to a centralized location for monitoring and analysis. Metrics, events, logs, and traces each provide crucial insights into the application’s performance, latency, throughput, and resource utilization.

Telemetry data can be sourced from:

  • Application logs
  • System logs
  • Network traffic
  • Third-party services
  • APIs

This data can then be leveraged to observe system performance and recognize potential problems. It can also detect irregularities and probe the origin of issues.

(Read about OpenTelemetry, an open-source observability framework that helps you collect telemetry data from a variety of cloud sources.)

Managing aggregated data

Managing aggregated data requires proper organization, storage, and analysis of collected data to derive meaningful insights.

Data aggregation is the process of collecting and summarizing raw data from multiple sources into a single location for statistical analysis, thereby helping to summarize data from different, disparate, and multiple sources.

To effectively organize and store aggregated data, it is necessary to implement a system that can accommodate large amounts of data while providing efficient access. This can be accomplished by utilizing a database system, such as a relational database or a NoSQL database.

To analyze aggregated data, one must utilize statistical methods and tools to identify patterns and trends in the data. This can be achieved through:

  • Data mining techniques, such as clustering, classification, and regression
  • Leveraging aggregated data to identify customer trends, optimize marketing initiatives, and enhance customer service
  • Employing aggregated data to identify potential areas for improvement in a business, such as recognizing areas of waste or inefficiency

Aggregating data is especially useful for logs, which make up a large portion of collected telemetry data and are a crucial part of observability. Logs can be aggregated with other data sources to provide holistic feedback on application performance and user behavior.

These aggregated logs are also used for the implementation of Security Information and Event Management (SIEM) solutions, which detect and respond to potential security threats.

Leveraging tools and techniques

Leveraging tools and techniques can also help with the implementation of MELT. Here are some examples:

  • Application performance monitoring (APM): APM is an all-encompassing tool used to monitor, detect, and diagnose performance issues in distributed systems. It provides visibility into the entire system by collecting data from across the application stack and mapping out data flows between components.
  • AIOps analytics: Tools that utilize artificial intelligence and machine learning to optimize system performance and recognize potential issues.
  • Automated root cause analysis: The root cause of an issue is automatically identified by AI, assisting in swiftly identifying and addressing potential problems and optimizing system performance.

This is further supported by a report by IBM, where it was found that organizations using AI and automation had a 74-day shorter breach lifecycle.

Final thoughts

Implementing MELT in distributed systems is essential for achieving effective observability and optimizing performance. It enables organizations to gain valuable insights by combining information collected from metrics, events, logs, and traces.

By leveraging the power of MELT, organizations can proactively address issues, optimize performance, and ultimately deliver an exceptional customer experience.

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Austin Chia
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Austin Chia

Austin Chia is the Founder of AnyInstructor.com, where he writes about tech, analytics, and software. With his years of experience in data, he seeks to help others learn more about data science and analytics through content. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.