As digital transformations are at scale, there is a sheer increase in the cloudification and architecture modernization journeys across business domains. As the cloud-native applications, microservices and distributed hybrid deployments are becoming the de facto, the technical complexity to build & deliver a resilient and reliable IT system has increased multi-fold in the recent years.
On the other hand, the customer expectations on ‘always-available’ application have grown exponentially, especially post Covid, with ZERO tolerance for IT outages or brownouts. This shift demands the need for adoption of fault-tolerant enterprise architecture and supported technology stack to build and operate applications that are ‘always-on’.
Although the new-age distributed architectures provide increased scalability and flexibility to release application features rapidly, the ability to perform root cause analysis to isolate the faults and fix the issues have become extremely difficult.
A recent survey by Mckinsey reported that an hour of server downtime costs $ 300,000 to $1 million. Today, businesses are keen in adopting best practices early during development life cycle and production operations to increase the system availability. In LogicMonitor’s survey on ‘IT Outage Impact Study’, more than 80% respondents cited ’Performance & Availability are the top issues that makes IT decision makers awake at night and approximately 50% of outages are avoidable’. Early & continuous observability is the secret sauce for delivering & sustaining a fault-tolerant and high available system.
What is Observability
Observability is a property and key characteristic of a modern IT system to expose details on the internal state of the system by generating external data such as metrics, logs, events & traces. These details could be monitored & correlated against each other to derive meaningful insights. Hence, monitoring forms a subset and key action for observability. That’s why observability has become crucial in cloud-native applications where thousands of microservices are deployed and identifying the potential root causes in case of failures is complex.
Introduction to Observability Tool
An Observability tool provides the ability to collect real-time data, monitor, correlate, analyse and visualize the hotspots to enhance the end-to-end visibility of the entire IT landscape. By using telemetry, a full stack observability solution collects and collates various types of data sources (metrics, events, logs & traces) across the architecture components of an IT system and provides an in-depth visibility into application and infrastructure health. This helps in accelerating troubleshooting and root cause analysis.
OpenTelemetry is an open-source standard that has become the de facto mechanism for an observability tool for implementing the data collection and transfer. It provides a unified set of instrumentation libraries and specification for telemetry of different groups of data including metrics, events, logs & traces.
The key features of a full stack observability solution include application health monitoring, infrastructure resource monitoring, digital experience monitoring that comprises of real user monitoring and synthetic monitoring, log analytics and SLA/SLO management SRE dashboards. The observability solution also provides intelligent prediction of anomalies and out of the box integration with widely used enterprise applications for incident management, service management & socialization platforms.
Importance of Observability Solution for delivering Resilient application
Resilience Engineering is an engineering discipline that focuses on building strategies to design and develop highly resilient applications, to test, and for continuous observability to increase the system availability.
Chaos Engineering forms a subset of Resilience Engineering that helps to test and validate the system’s resilience characteristics by subjecting the system to real time failures. It is the art of breaking the system by injecting failures into the system to develop confidence on the resilience mechanisms built into the system. There are various tools available in the market like Chaos Monkey, Gremlin, Litmus, Chaos Mesh, Pumba, to carryout multitude of network and infrastructure attacks on the system. Having a robust observability tool becomes a pre-requisite to carry out any node level or pod level attacks, as it is essential to evaluate the impact of the failure attack on the application and infrastructure. Effective impact analysis isn’t possible without the power of observability tool.
Observability tool forms a vital toolkit for Performance Engineering team to perform early Performance engineering and Chaos engineering as part of the CI/CD pipeline. This helps in adopting ‘Fail-Fast’ delivery culture by facilitating early feedbacks to development team and comply with Non-Functional Requirements (NFRs) of the system.
Validation of IT preparedness to handle unexpected turbulences and validation of resilience mechanisms like Disaster Recovery (DR) and self-healing automation could be made possible only through the power of observability solution. The correlation of telemetry data collected across architectural components helps in analyzing and identifying the impact of the failure and perform problem diagnosis to improve the system resiliency.
Importance of Observability Solution for delivering Reliable application
Reliability refers to the probability that the system will meet certain performance standards & yields correct output for a specific time.
Continuous monitoring of the Service Level Objectives (SLO), Service Level Indicators (SLIs) and Error budget is essential to control the velocity of the releases against the system reliability. A robust observability solution helps with SLO/SLI monitoring dashboards, system availability (uptime) and error budget utilization details and provides the ability to drill down and pinpoint the issues. Site Reliability Engineers(SREs) primarily use Observability tool for monitoring of the four golden signals – Latency, Traffic, Errors, Saturation and much more.
Threshold based alerting capability helps production support engineers to detect anomalies quickly thereby reducing the Mean Time to Detect (MTTD) to almost near zero. By facilitating correlation insights & predictive insights through AI/ML, it enables faster problem diagnosis & fixing of issues thereby reducing the Mean Time to Resolve (MTTR). Hence, observability solution becomes crucial to meet the high availability targets and deliver a reliable IT system to enhance customer experience.
Benefits of Observability
An Observability tool provides variety of benefits to IT teams. Some of the key benefits includes,
- Helps in proactive health monitoring of application & infrastructure.
- Increases the ability to gain in-depth visibility on application issues quickly
- Enables faster resolution of problems thereby reducing Mean Time to Resolve (MTTR)
- Offers early feedback and faster quality delivery of product releases
- Breaks down silos and enable collaborative environment to increase system availability.
- Helps in continuous monitoring and troubleshooting of failures before affecting end users thereby enhancing end user experience.
Know Our Writer
Ramya Ramalinga Moorthy
Industrialization Head - Reliability & Resilience Engineering
A seasoned & dynamic QA Professional with 18+ years of experience specialized in Performance Resilience & Reliability Engineering. She has expertise in providing technical consulting to clients across business domains for analysing and assuring the web system for its performance scalability high availability resiliency reliability capacity & security. She is an EC-Council certified Penetration Tester (with CEH & ECSA certifications) and DevOps Institute certified SRE consultant. She is a conference speaker & well known writer. She has authored several technical papers articles & e-books in several journals. She is a CMG US's William Mullen award winner (rewarded for her technical excellence and engaging presentation style) during the year 2017 for her paper on using machine learning models for performance anomaly detection & capacity forecasting. She is a Amazon best selling author of two inspirational self-development books.
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