What is observability?
Observability is a process that examines the properties of a system. A system is observable if its current state can be determined in a finite time period using the system’s outputs. The meaning of “observability” has changed as development models have evolved.
Before cloud computing and DevOps, on-premises data centers were exclusively used and developers relied on the sequential development model called “waterfall.” In those days, control system engineers relied on monitoring a system’s external outputs to observe how well deployed software was working. Each team or person was assigned a role and only worked within the scope of that role. A developer built an application, a tester tested it, and an operator worked out how to run, manage, and monitor it. As the world adapts to the Agile methodologies for software development, these roles are converging into one team with one delivery cycle managed by DevOps.
In modern cloud environments, developers are closer to operations and need a more expansive observability process because records of every activity are generated continuously. Methodologies like GitOps and DevOps provide developers with tools to collaborate and accelerate software delivery while adhering to security rules. GitOps provides context to deployments and makes them more observable.
The objective of observability is to understand what is happening in a system by instrumenting it to collect data categorized into event logs, metrics, and traces.
- Event Logs. An event log is a record of an event that occurred on a system. Event logs are auto-generated and timestamped, then written into a file that cannot be modified.
- Metrics. A metric is a measured value derived from system performance. Metrics carry information about application service level indicators (SLIs) such as latency, availability, error rate, throughput, or response time.
- Trace. A trace is a log of events that happened in a system, recorded in chronological order. It is formatted or presented as a list of event logs taken from different systems involved in fulfilling the request. Many distributed tracing tools (Jaeger, Zipkin, AWS X-Ray, etc.) and standards (W3C Trace Context and the OpenTelemetry project, for example) have emerged, allowing sophisticated organizations to create custom solutions.
Observability tools enable you to collect and analyze data from applications and infrastructure. They provide alerts on availability and performance issues so you can troubleshoot and resolve them quickly to improve MTTR and the end-user experience. That said, organizations may be using a proliferation of observability tools. Data is often not federated between those tools, which can make implementing an observability strategy quite challenging.
Benefits of observability
- Reliable Infrastructure. Observability helps monitor user behavior, network, system availability, capacity, and other metrics to ensure the system performs as it should.
- Security & Compliance. The observability of a system is of the utmost importance to organizations with regulatory or compliance requirements to secure sensitive data. As the application architecture keeps getting more complex, observability is essential to avoid legal risks due to noncompliance.
- Unified/Connected Context. Data needs to be connected to understand the relationships between system components and how they tie to your business. Such connections give your data context and meaning.
Observability ≠ Monitoring
Observability is sometimes considered a synonym for monitoring, but monitoring is, in fact, a subset of observability. Monitoring is the process of collecting data about the system and letting your team respond whenever an error or issue occurs. Observability is end-to-end visibility into the entire landscape and provides a framework for gathering actionable information on when the issue occurred and why it occurred.
Why observability is essential for DevOps teams
Modern cloud application environments are complex, running across hundreds or even thousands of compute instances in multiple systems with independent operations. With the growth of microservices adoption, there are many individual and isolated system components, making tracing the source of failure challenging and time-consuming.
As more companies adopt Agile practices, the frequency of deployments enables DevOps teams to accelerate software delivery. Frequent deployments in any system mean introducing more risk into the system. With a focus on continuous delivery and continuous integration (CI/CD), DevOps teams rely on feedback to effectively debug and diagnose systems. Observability provides that feedback.
Automation is a vital component in DevOps to enable teams to take action with shared data, connect the right people with the correct processes, enhance performance across the entire organization, and tie it to specific business outcomes. Observability is a process of expertly giving proper contexts to all kinds of data that the application environment produces so that it is easier to audit the solution on a regular basis. It’s based on exploring properties and patterns not defined in advance. Automation keeps observability data flowing.
Observability enables DevOps teams to understand what’s happening across multiple environments and technologies to identify and resolve critical issues. It keeps systems efficient and reliable and customers happy.
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