Introduction
In today’s digital age, businesses rely heavily on complex software systems, making it critical to ensure optimal functionality. In the past, companies typically used monitoring tools to identify and fix issues after they happened. But with the increasing complexity of these systems especially in cloud-native era with Kubernetes and Docker and the demand for higher performance, software applications now require observability beyond just monitoring.
Monitoring involves continuously observing system parameters, metrics, and alerts to identify when predefined thresholds have been exceeded. This is generally a reactive process, with monitoring tools notifying relevant parties when issues arise, such as application crashes or server failures. While monitoring tools are essential, they may not always provide sufficient context or explanation for why an issue has occurred.
Observability is a new approach that represents a significant paradigm shift. It emphasizes the understanding of the system’s state by observing its outputs without the need for any additional inputs. In the other hand, observability measures how well the internal states of a system can be inferred from the knowledge of its external outputs.
Why its importance
The concept of observability in software applications involves designing systems that can provide self-explanatory information. This entails gathering and analyzing extensive telemetry data such as logs, metrics, and traces from software applications, enabling engineers to comprehend the system’s internal workings without requiring extra input.
Understanding system behavior under different circumstances (whether conventional or erroneous) is crucial for enhancing reliability, scalability, and performance. This is where observability comes into play. It facilitates proactive troubleshooting and better comprehension of system behavior, resulting in enhanced software quality and customer satisfaction.
NashTech Approach for Observability
At NashTech, we’ve had numerous projects that required increased efficiency in operation and supervision. To address this, our technical team proactively developed toolkits that accelerate the application of observability. These toolkits are particularly helpful for projects built according to the microservices model. Depending on specific characteristics, the accelerator comes in two types:
- If you’re working on projects that utilize platforms like Azure or AWS, the accelerator toolkit can help you make the most of the monitoring and observability features already built into these cloud platforms. This is because they are optimized for integration and management in operation. Of course, customers also have the option to use third-party solutions like Data Dog, New Relic, Lightstep, Dynatrace. The available commercial toolkits have many integrated features that can help reduce implementation time, making it a great advantage for businesses.
- NashTech offers a range of accelerators to help customers save costs and customize observability features as per customer-specific requirements. These accelerators are based on open-source platforms such as Grafana’s LGTM Stack, and ELK Stack (Elastic Logstash and Kibana).
Regardless of the options chosen, the approach to achieving observability in NashTech will remain consistent by following steps:
- Instrumentation: This involves adding code to generate telemetry data (logs, metrics, traces) from your software. These data are invaluable for understanding your systems’ internal states and behaviors.
- Analysis and Visualization: This phase involves collecting, storing, and analyzing the generated data. Advanced analytics and visualization tools can help parse this data to identify patterns, correlations, and trends.
- Automation: Automated tools can help identify anomalies, proactively alert about potential issues, and sometimes even self-heal the system.
- Culture Shift: Perhaps most importantly, moving towards observability requires a culture shift. Teams need to adopt a learning-oriented mindset, focused on understanding their systems deeply and continuously improving based on insights gathered.
The Future: Observability as a Standard Practice
As software applications increase in complexity and criticality, observability will become standard practice. Instead of just collecting data when something goes wrong, continuous observation will provide valuable insights that can drive proactive issue resolution and innovation.
In conclusion, observability isn’t a replacement for monitoring; instead, it is an extension that allows for deeper understanding and improved operation of software systems. By embracing observability, organizations can ensure they are well-equipped to handle the increasingly complex landscape of software applications, delivering superior experiences to the customers and users.
