Observability - definition & overview

A system is observable if its current state can be determined in a finite time period using only the outputs of the system. For such a system, all of the behaviors and activities of the system can be evaluated based on the outputs of the system. Conversely, a system whose output sensors provide insufficient data or information to allow the operator to determine the behavior of the system would be considered unobservable.

In software

An observability software platform is a tool that aggregates data into logs, metrics and traces. Observability platforms then process that data into events and KPIs that can be leveraged by information teams to measure system performance.

In DevOps

Observability is a solution that allows DevOps teams to proactively measure and address system bugs, events, errors, and more. Observability is important for DevOps teams as it reduces the time to resolve issues that pertain to systems. Observability makes it easier to identify issues and trace them back to the root cause.

In software engineering

Observability is a solution used by software engineers to monitor, understand, and maintain the health of software systems. It addresses when and why errors occur to ensure that they do not persist.

For SREs

Site reliability engineers (SREs) are responsible for managing multiple, and growing systems. Their responsibilities and use of observability are similar to other software engineers but with a greater emphasis on system health, performance, uptime, and other issues related to the customer experience.

The benefits of observability are:

• Enabling SRE, DevOps, and software engineering teams to quickly identify the root cause of issues
• Generating a better user experience by reducing the number of bugs and issues
• Establishing and verifying uptime and performance of systems
• Efficiency for software delivery at scale
• Reducing costs by optimizing systems being used and performance

The key to achieving true observability of IT infrastructure and cloud computing environments is not the event logs themselves—rather, it is the capability of monitoring and analyzing those events, along with KPIs and other data, that drives observability and yields actionable insights. IT organizations can implement observability platform software tools that streamline the aggregation and analysis of event logs.
A cloud computing environment generates data in three formats that can be aggregated and analyzed to enhance network observability: event logs, metrics, and traces.

An event log is a record of a system event. It is automatically computer-generated and timestamped, then written into a file that cannot be modified. Event logs provide a complete and accurate record of discrete events, including additional metadata about the system state when the event occurred. Log files may be written in plaintext or structured in a specified format.

A metric is a numerical representation of data measured over time. Unlike an event log, which records a specific event, a metric is a measured value derived from system performance. Metrics frequently carry information about application service level indicators (SLIs), like how much memory or processing power is used or the latency.

A trace is the documented record of a series of causally related events on a network. The events do not have to take place within a single application, but they do have to be part of the same request flow. A trace can be formatted or presented as a list of event logs taken from different systems involved in fulfilling the request.

IT infrastructure produces logs, metrics, and traces that tell a story about activity on the network. These three data formats deliver two types of information that observability platforms need to derive insights into network security and performance: events and KPIs. The ability to capture and isolate network events and compute KPIs from logs, metrics, and traces is the key to achieving business goals with enhanced observability.

Log files are the main source of data about events. The entire purpose of log files is to help developers debug their software by providing visibility into the events that the software is producing.

Log files, metrics, and traces all contribute to KPI computation:

• Log files can be used to compute KPIs. For example, a failed login is an event, but a high number of failed logins from an external IP address is a Key Risk Indicator (KRI) that could indicate a brute-force attempt to gain access to your application.
• Metrics can include measurements of how much memory or processing power an application is using. These metrics can act as KPIs, indicating when application performance is poor or when a DDoS attack could be underway.
• Traces provide insight into request flows and transaction times in the system. They can be used to inform KPI measurements like request processing time or time per transaction.

A software observability platform aggregates data in three main formats (logs, metrics, and traces), processes it into events and KPI measurements, and uses that data to drive actionable insights into system security and performance.

The observability of a cloud computing environment is not a goal on its own - it should be seen as a necessary step toward achieving key business objectives. The goal of developing observability is to enable security analysts, IT operators and managers to better understand and address problems in the system that could negatively impact the business. There are three key objectives associated with developing the observability of cloud computing networks:

Reliability

Reliably is one of the first goals of observability. If we want to build an IT infrastructure that functions in a reliable way and according to the needs of the customer, we need to measure its performance. With an observability platform software tool, we can monitor user behavior, network speed, system availability, capacity, and other metrics to ensure the system is performing as it should.

Security and compliance

The observability of cloud computing environments is of the utmost importance to organizations with regulatory or compliance requirements to secure sensitive data against improper exposure. With full visibility into the cloud computing environment through event logs, organizations can detect potential intrusions, security threats, and attempted brute force or DDoS attacks before the attacker can complete the attack and steal data.

Revenue growth

Businesses can drive revenue growth with network observability. The ability to analyze events on the network can yield valuable information about user behaviors and how they may be affected by underlying variables like application format, availability, speed, and others. This data can be analyzed to develop actionable insights on how to optimize the network and applications to generate more revenue from customers and attract new ones.

Observability of cloud computing platforms depends on your ability to capture logs, metrics, and traces, process them into a useful format, and parse the data to discover useful insights.

Sumo Logic's cloud-native platform is an all-in-one solution for the observability of cloud computing environments. With Sumo Logic, your IT organization can aggregate log files, metrics and traces, evaluate network performance against the most critical KPIs and gain the insights and network visibility needed to meet your business objectives for system reliability, security and customer satisfaction.

What role does telemetry data play in enhancing observability?

Telemetry data plays a crucial role in enhancing observability by providing real-time insights into the performance and behavior of systems. It enables monitoring of various metrics such as response times, error rates and resource utilization, which helps in detecting issues, optimizing performance and ensuring reliability. By collecting telemetry data from different sources within a system, organizations can gain comprehensive visibility into how their applications and infrastructure are functioning, leading to improved observability and actionable insights for better decision-making

What are some common challenges in implementing observability in a system?

• Dealing with huge data volume generated by various components

• Ensuring data reliability and quality for accurate insights

• Integrating different tools for monitoring and observability across the stack

• Managing security concerns in a cloud-based observability solution

• Troubleshooting a performance issue effectively with actionable insights

• Handling distributed system complexity for comprehensive visibility

• Balancing the need for real-time monitoring with minimal impact on system performance

• Scaling observability practices to match the growth of the system and data team

• Incorporating best practices for incident management and response

• Aligning observability efforts with user experience and business goals