The software development industry is evolving rapidly, from system architectures to user expectations. High availability, reliability, and visibility are prerequisites for any company to remain competitive in their respective markets. Relying on legacy systems that only aggregate measurements does not provide the type of visibility needed to quickly identify and correct anomalies. New methods to efficiently find and solve problems are required for limiting downtime and mitigating the risk of negative business impact. Poor user experiences, for example, can result in harm to a company’s reputation or loss of prospects — and even loss of existing customers.
Observability is now an essential component of any architecture to effectively manage a system, determine whether it works properly, and decide what needs to be fixed, modified, or improved on any level. The terms “monitoring” and “observability” are often used interchangeably; however, they have distinct meanings and different goals in their applications to a business’ use case:
- A monitoring platform obtains a state from a system based on a predefined set of measurements to detect a known set of problems.
- Observability aims to measure the understanding of a system’s state based on multiple outputs, meaning it is a capability — like reliability, scalability, or security — that must be designed and implemented during the initial system build, coding, and testing.
Like DevSecOps, observability is the responsibility of everyone. It requires appropriate implementation, user integration, and collaboration among organizational teams to facilitate adoption of the platform.
Pillars of Observability
As explained in the previous section, observability complements monitoring based on three pillars: logs, metrics, and traces. Monitoring indicates when the status of any application, system, cloud service, etc. is incorrect while observability indicates why. Monitoring is a subset and a key action for transforming observability from a reactive to proactive approach.
Metrics
Metrics are numerical measurements collected by traditional monitoring systems to represent the system state. Coupled with tags, metrics can be easily grouped, searched, and graphically represented to understand and predict a system’s behavior over time. The measures, by design, have many advantages: This type of data is suitable for storage, processing, compression, and retrieval, making it easier to compact, store, and query data with dashboards that reflect historical trends.
Metrics remain the entry point to any monitoring platform based on the collection of CPU, memory, disk, network measures, etc. And as such, they no longer belong solely to operators — a metric can be created by anyone. For example, a developer may choose to expose an application-specific set of measures such as the number of treatments performed, the time required to complete them, and the status of those treatments. Their objective is to link these data to different levels (system and application) to define an application profile in pursuance of identifying the necessary architecture for the distributed system itself. This results in improved performance, increased reliability, and security system wide.
Metrics used by development teams to identify improvement points in the source code can also be used by operators to determine the architecture needed to support user demand and by the executive team to control and improve the adoption and use of the application by customers.
Logs
Logs are immutable timestamped records of events that happened over time. Logs provide another point of view, in addition to metrics, of what occurred in the application at any given moment. There are three log file formats:
- Plaintext – Most common format with no specific structure
- Structured – Most recent format used with a specific structure (e.g., JSON files)
- Binary – Format used by multiple applications to improve the performance of data management (e.g., MySQL binlog replication, system journal log, Avro)
Logs provide more granular visibility of the actions performed by an application. These are extremely important to development and operations teams, as they often include elements necessary for debugging and optimizing application performance. The complexity of distributed systems often creates various interconnected failures that can be difficult to identify, so diligent log management is essential to guaranteeing optimal service continuity.
Traces
A trace can be seen as a graphical representation of event logs. Distributed tracing tracks and observes service requests flowing through distributed systems by collecting data as the requests go from one service to another. Put simply, traces represent the lifecycle of a request across a distributed system. They are used by multiple teams — for instance:
- Developers can measure and optimize least performant calls in the code.
- SREs can identify potential security breaches.
- DBAs can detect long-running queries that slow down the user experience.
By analyzing traces, it is possible to measure the overall health of the system, point out bottlenecks, discover and resolve problems faster, and prioritize high-value areas for optimization and improvement. While metrics, logs, and traces serve their own purpose, they all work together to help you better understand the performance and behavior of your distributed systems.
