OBSERVABILITY ENGINEERING POINT OF VIEW

Introduction

Observability is the ability to understand the internal state of a system by analysing its outputs. It is a concept that has gained significant importance in modern software development and operations.

The primary goal of observability is to provide a clear understanding of how a system is performing and how it can be improved. By monitoring and analysing the outputs of a system, developers and system administrators can detect and diagnose issues and improve the overall performance of the system.

Three pillars of observability

The three pillars of observability are Logs, Metrics, and Traces. Each of these pillars represents a different type of data that can be collected and analysed to gain insight into a system's performance.

Logs: Logs are records of events that occur within a system, such as application errors, user actions, and system events. Log data can be used to troubleshoot issues, identify patterns, and gain insight into how a system is being used. For example, log data might show that a particular application is experiencing frequent errors, which can help developers diagnose and fix the issue.

Metrics: Metrics are quantitative measurements of a system's performance, such as CPU usage, memory usage, or network traffic. Metric data can be used to monitor system health, identify trends, and detect anomalies. For example, metric data might show that a particular server is experiencing high CPU usage, which can help administrators take action to prevent performance issues.

Traces: Traces are records of the transactions that occur within a system, such as user requests or database queries. Trace data can be used to identify bottlenecks, analyse performance, and optimize system behaviour. For example, trace data might show that a particular database query is taking longer than expected, which can help developers optimize the query to improve performance.

Best Practices for Implementing Observability

Define Business Objectives: The first step in implementing observability is to define the business objectives that the organization wants to achieve. This includes identifying key performance indicators (KPIs) that will be used to measure system performance and user satisfaction.

Choose a Toolset: Once the business objectives are defined, the next step is to choose the observability toolset that will be used to collect, store, and analyse data.

Instrumentation: The next step is to instrument the system with monitoring agents and tools to collect data from various sources. This includes adding code instrumentation to application code, installing monitoring agents on servers, and configuring data collection tools to receive data from various sources.

Data Collection and Aggregation: The data collected from various sources needs to be aggregated in a central location, such as a data lake or a data warehouse. This involves configuring data collection tools to send data to a central location and configuring data pipelines to extract, transform, and load data into the data store.

Analysis and Visualization: Once the data is collected and stored, it needs to be analyzed and visualized to gain insights into system performance. This involves setting up dashboards, reports, and alerts to monitor KPIs and detect issues in real-time.

Continuous Improvement: Observability is an ongoing process that requires continuous improvement. This involves reviewing KPIs and system performance regularly, identifying areas for improvement, and making changes to the system to optimize performance.

Collaboration: Finally, observability requires collaboration between different teams, including development, operations, and business teams. This involves sharing data, insights, and best practices to optimize system performance and achieve business objectives.

Observability – Tools

Metrics: Prometheus is one of the leading tools for collecting and analysing metric data. It provides a time-series database, a powerful query language, and integrations with many popular software systems. Another popular tool for metrics is Grafana, which provides a visualization platform for time-series data and integrates with many data sources, including Prometheus.

Logs: Elasticsearch is a popular tool for collecting, storing, and analysing log data. It provides a scalable and distributed search engine that can handle large volumes of log data. Logstash and Kibana are often used together with Elasticsearch to provide a comprehensive log analysis solution. Splunk is another popular tool for log analysis that provides advanced search capabilities, visualization tools, and machine learning algorithms for log data analysis.

Traces: Jaeger is a popular open-source tool for tracing distributed systems. It provides a scalable and distributed tracing system that can be used to analyse transactions across multiple services. Zipkin is another popular tracing tool that provides similar capabilities, along with integrations with many popular software systems.

Visualization: Grafana is one of the leading visualization platforms for observability data. It provides a wide range of visualization options, including graphs, dashboards, and alerts. Other popular visualization tools include Kibana, which provides visualization capabilities for log data, and Tableau, which provides a powerful data visualization platform that can be used for a wide range of data sources.

Key Benefits of Observability

Improved Reliability: Observability helps to ensure that systems are reliable by providing real-time insight into system performance. This allows teams to quickly detect and diagnose issues, and make informed decisions about how to improve the system's reliability.

Faster Problem Resolution: With observability, teams can quickly identify and diagnose issues, reducing the time it takes to resolve problems. This can lead to improved uptime, better user experiences, and increased customer satisfaction.

Better Collaboration: Observability tools allow DevOps teams to collaborate more effectively by providing a common view of system performance. With Observability DevOps teams can quickly identify the root cause of the issues and take corrective actions.

Enhanced Performance: By monitoring and analysing system performance, teams can identify opportunities for optimization and improvement. This can lead to better overall performance, faster response times, and increased scalability.

Improved Security: Observability tools can help teams to identify and respond to security threats more quickly. By monitoring system logs and events, teams can quickly detect suspicious activity and take action to prevent security breaches.

Why Observability is Important for SRE?

Site Reliability Engineering is providing details about availability and resilience, in order to reach to that level, we need to be able to detect and fix issues quickly. With observability in place, we can detect problems before they cause outages. We can identify the issues quickly and efficiently, which will give more time for us to resolve the issue before it reaches customers.

In addition, observability will provide detailed visibility into the system, so we can understand how the system is performing. With this information we can prevent the outages before even it is happening.

To maximize the reliability and performance of systems is the primary objective of SREs. The ability to not just identify the critical problems using available monitoring tools but also to understand the problem and possible solutions through observability is critical for modern SRE teams.

Summary

Observability is critical for organizations that rely on cloud native architectures to deliver digital services. With the evolution of cloud, microservices, containers, serverless computing, it is no longer sufficient to rely solely on monitoring systems alone, Observability provides insights into system behaviour, enabling teams to identify and resolve issues quickly and improve the overall performance.

To implement Observability effectively Organization needs to adapt a data driver approach which combined Metrics, Logs, Traces. They should use modern Observability tools that are designed to handle the scale and complexity of cloud native environments. Additionally, they should establish a culture of Collaboration between development and Operation teams, with a shared understanding of the importance of Observability in delivering high quality digital services.

We recommend the organizations to take a maturity-based approach to implement Observability starting with basic capabilities like infrastructure monitoring and problem alerting and then mature to more advanced capabilities such as distributed tracing and AI/ML based automated anomaly detection.