Observability is the ability to measure the internal states of a system derived from knowledge of its outputs. In simple terms, it is how well the product team and management can understand the system’s unknown from its outputs.
Observability is one of the fundamental software engineering tools/processes required to build highquality distributed software applications. Instrumentation, analytics, and automation are basic Software engineering techniques used to collect, analyze, visualize, detect, and manage Quality. Although present implementations of observability solutions use events, logs, metrics, and traces as building blocks to gain this knowledge, it is worth noting that the future may see new avenues to innovate.
Use Case Examples of Observability:-
- Performance: Root-cause analysis of unknown application performance issues.
- Security: Identify any abnormal activity or network breaches in software services.
- Data: Provide meaningful business insights from unstructured or noisy data.
- Customer experience: Provide insight into customer and end-user behavior and usability problems.
Challenges facing the industry to continuously adapt, sustain, and optimize software engineering stem from gaps in Quality and coverage, impediments, and delays/expenses due to complexity and duplication. The software engineering industry needs integrity, assimilation, consolidation, and automation across its ecosystem for this situation to resolve.
Capabilities
Observability must evolve to play a central role in solving software engineering challenges related to accuracy, coverage, velocity, and cost. It must be a comprehensive, complete set of capabilities, fully extensible to collaborate with the overall software engineering ecosystem, and trustworthy to the
customer/user.
| Comprehensive | Must provide complete coverage of instrumentation with e2e correlation without any gaps. Must be self-monitoring. Must provide unified observability to serve all use cases of Quality. Performance must work as a first-class citizen. Must possess autonomic capability for automated machine learning (ML), dynamic instrumentation throttling, anomaly detection, automated issue resolution, and defect management. It must be fully configurable and customizable for maximum flexibility. |
| Full Coverage | A solution that is all-inclusive readily instrumented, and offers correlated analytics for every component in a system architecture that includes hardware, networks, operating systems, software applications, interfaces, data, people, and processes. |
| Self-Monitoring | The observability solution must monitor itself and be observable. |
| Unified | For holistic observability, data must be collected, prepared, processed, and analyzed in an all-in-one namespace. |
| Use Cases of Quality | Performance management, data quality, and customer experience are examples relevant to this topic. |
| Performance | The application must be observable near real-time; observability overhead on its performance and infrastructure resource utilization must be under defined limits. The solution must be responsive, scalable, and efficient in resource utilization. |
| Autonomic | Observability has self-managing characteristics and adapts to unpredictable changes while masking intrinsic complexity from operators and users. |
| Automated ML | Self-managed machine learning development model that is automated to update itself, as in robotics continuously. |
| Dynamic Throttling | Optimization of instrumentation at runtime to
1) protect the observed application, 2) maximize observability, and 3) minimize the cost of observability infrastructure, in that order of priority. |
| Anomaly Detection | Must Identify observations that deviate significantly from most data and do not conform to a well-defined notion of normal behavior. |
| Auto-resolution issue | Identify the pattern(s) of the problem in observability data, group re-occurring issues, and close the issues once the patterns become absent. |
Extensible: The solution must be an open design to be integrable and integrative with various other software engineering techniques such as modeling, analytics, automation, and testing.
| Integrable | Designed with open interfaces to be integrated with other software engineering techniques to support a broad ecosystem. |
| Integrative | The solution must be designed to readily combine observability with other software engineering techniques to make the overall software engineering powerful for exceptional application quality. |
Trustworthy: Security and privacy, reliability, credibility, and business integrity must be inherent qualities of an observability solution. The customer must have these fundamental capabilities guaranteed despite systemic failures, human errors, environmental disruptions, and purposeful attacks.
| Security & Privacy | The solution must meet the customer’s expectation of attack-resilient systems and protect the confidentiality, integrity, and availability of the system and its data. |
| Reliability | The solution must behave consistently over any period. |
| Credibility | The solution must always behave predictably with absolute correctness under all conditions. |
| Business Integrity | The solution provider must act responsively and responsibly. |
An observability solution must be comprehensive, extensible, and trustworthy to meet the industry’s complex needs and future directions. Such a well-rounded, wide-reaching, and credible standing of software engineering will yield the accuracy, coverage, velocity, and cost benefits necessary to resolve the industrial need of software application space.
