What are the best ways to implement observability in a Cloud environment?

Observability in a cloud environment relies on three key pillars—logs, metrics, and traces. Logs provide detailed records of events and system behavior, metrics offer quantitative data on performance, and traces give insights into the flow of requests across distributed systems. Combining these pillars enables a comprehensive view, empowering teams to diagnose and troubleshoot issues effectively in complex cloud infrastructures.

At @elevondata we have been implementing cloud observability using a holistic strategy encompassing robust logging, intelligent analytics, and proactive alerting for our clients. We prioritize comprehensive monitoring, metrics, and distributed tracing for nuanced performance insights. We use our MOAT (Monitoring and Analysis of Threats) Framework for anomaly detection using big data approach & machine learning to predict and mitigate issues before business or user impact. Dashboards are used for stakeholder visibility and automate responses to enhance operational efficiency. Key is to address business pain points & align with modernization goals by fostering agility, scalability, and resilience in dynamic cloud environments.

Observability in a cloud environment is crucial for understanding, monitoring, and managing complex systems effectively. The three pillars of observability are often described as: 1. Logs Logs provide a detailed and chronological record of activities, helping in diagnosing issues, troubleshooting, and understanding the sequence of events. 2. Metrics Metrics offer a high-level view of system behavior, performance trends, and resource utilization. 3. Traces Traces help in understanding the end-to-end flow of requests, identifying performance bottlenecks, and diagnosing issues in distributed systems Integrating logs, metrics, and traces allows teams to have a holistic understanding of their systems, facilitating proactive issue resolution

Begin by establishing comprehensive logging, capturing relevant events and system activities. Implement metrics to quantify performance and resource utilization, aiding in real-time analysis. Integrate tracing to map the journey of requests through the system, uncovering bottlenecks. Leverage tools like AWS CloudWatch or Prometheus for metrics, Elasticsearch for logs, and OpenTelemetry for traces. Establish standardized formats for consistency. Ensure seamless integration and accessibility of these pillars, enabling cross-referencing for holistic insights into system behavior.

Metrics: How well a system performs. Observe reliability, security, performance and costs. Check CPU metrics for scaling out customer-facing apps. Logs: Records of events and errors. Observe what happened and who changed something. Monitor web app health, VM shutdowns, sandbox cleanups. Traces: Requests through a system. Observe bottlenecks or dependencies. Check customer experience, data errors, network connections.

It enhances system reliability by enabling proactive issue detection, reduces mean time to resolution (MTTR) by facilitating quicker problem identification, and aids in optimizing performance by providing actionable insights. Additionally, observability promotes a deeper understanding of system behavior, fostering continuous improvement and innovation within the cloud environment.

What’s distinguishes Observability from monitoring is the action. Observability in cloud-based systems refers to the ability to gain insights into the inner workings of applications and infrastructure. It involves collecting, analyzing, and acting upon telemetry data to understand system behavior. Actionable part is the crux. The benefits of observability align with improving quality and reliability by quickly resolving issues, enhancing user experience through optimal performance, minimizing costs and risks via resource optimization, and fostering innovation and agility through continuous delivery and feedback loops. In essence, observability e genders proactive problem-solving, resource efficiency, and overall system robustness.

The greatness of the cloud might be a boon or a curse. Observability illuminates your cloud operations like a reliable spotlight. It enables you to see around corners, identify irregularities before they become catastrophes, and use resources as efficiently as a ninja. Imagine being able to see the unseen, leading to faster troubleshooting, better performance, and happier customers! Accept observability as your superpower to take control of the cloud.

Observability provides a comprehensive view of the behavior and state of cloud-based systems and applications, enabling improved quality, enhanced user experience, reduced costs and risks, and facilitated innovation and agility.

Observability is a key concept for cloud-based applications. It refers to the ability to monitor and measure the internal state and behavior of your app. By observing your app, you can gain valuable insights into its health, performance, and usage. You can identify and troubleshoot issues before they affect your customers. You can also optimize your app for faster data transfer, lower latency, and reduced costs. For example, you can use observability tools to detect and resolve bottlenecks, scale up or down your resources, and choose the best cloud regions for your customers. Ultimately, observability helps you deliver a joyful experience to your customers with a well-performing and cost-efficient app.

Despite its benefits, observability comes with challenges. The sheer volume of data generated can be overwhelming, making it essential to establish effective data filtering and aggregation processes. Ensuring compatibility across diverse technologies and maintaining a balance between collecting enough data for insights and avoiding data overload are ongoing challenges in the observability landscape.

When it comes to observability, I'd like to talk about one of the most-used stacks currently used - EFK (Elasticsearch, Fluentd, and Kibana). Fluentd acts as a data aggregator and it is installed as a Daemonset, which forwards logs to Elasticsearch (which has a PVC), and Kibana is used as a dashboard. One thing to take into account is the country's data protection laws, which can be a hassle to deal with. When Elasticsearch first starts, Certificates and keys for TLS are generated for the transport and HTTP layers, and a user with a password is created for ES, while an enrollment token is created for Kibana. Older logs can be pushed to an S3 bucket with Glacier to save space, whilst sensitive logging data is masked from the backend.

- Filtering relevant data from noise. Monitor what you need, not what you can, and use clear metrics and indicators. - Assigning roles and responsibilities for app performance. Use the RACI matrix to identify who is responsible, accountable, consulted, and informed. - Complying with legal and ethical standards. Respect the privacy and rights of your users, and protect their personal data. - Balancing between centralized and decentralized monitoring. Choose the approach that fits your organizational culture and goals.

In my view, achieving a "single pane of glass" for observability in the cloud requires augmenting native tools. Instrument applications with OpenTelemetry for custom metrics and distributed tracing. Centralise logs using tools like Elasticsearch and implement monitoring as code. Leverage a service mesh for inter-service insights, implement synthetic monitoring, and create unified dashboards with tools like Grafana. Set up custom alerting based on application metrics and logs, integrating with incident response tools. Explore third-party observability platforms for cross-cloud insights. Continuously adapt and improve observability practices to align with evolving application architectures and business needs.

The ability to consume the mountains of telemetry data and leveraging them actionable insights is one of the top challenges. Complexity arises from the maintenance, security of generated data points and analysis of the same. This can be overcome by focusing on the purpose and priorities at the out start by having a vision and buy in from stakeholders.

Observability is a crucial practice for ensuring system reliability and responsiveness in today's dynamic digital landscape. To implement observability effectively, organizations should: * Align observability goals with business objectives and user needs. * Standardize metrics, logs, and traces for consistency and compatibility. * Leverage native observability features and services from cloud providers. * Automate and orchestrate observability processes for efficiency. * Continuously improve observability practices based on feedback and data. By following these best practices, organizations can build resilient and responsive systems that deliver a seamless user experience.

Observability for applications in cloud environments is important. Not just for monitoring, logging, SRE, etc. but also in identifying opportunities of improvements to make applications robust, reliable and sustainable. Observability is not just a practice that should only be observed by a team working with DevOps or Platform Engineers, it should be a mindset to be adopted and included in any team, any cloud development project.

I was part of a team building a machine learning application on Azure. We quickly realized that monitoring and observability would be key to running this system reliably in production. Through trial and error and talking to other teams, we learned some best practices. The most important thing is to design for observability from the very beginning. Trying to tack it on later is extremely difficult. We also made sure to send trace data so we could follow requests end-to-end through our microservices. We took advantage of Azure's native tools as much as possible. We did use Grafana and Prometheus for some custom metrics dashboards. Finally, we made heavy use of automation. We also automated aggregate metric collection and trace.

Observability is not a one-time project, but a continuous practice that requires collaboration and alignment across your organization. Leverage cloud-native tools and platforms to simplify and automate the observability process. For example, you can use Azure Monitor, AWS CloudWatch, or Google Cloud Operations to collect, visualize, and alert on your cloud data.Implement feedback loops and continuous improvement cycles to learn from your observability data and improve your system quality.

As mentioned in the previous note, starting from the start with a strong plan, vision and alignment with business objectives and along with stakeholders is the key to preclude getting overwhelmed from the aforementioned challenges. Standardizing specific KPI’s which will help in closing in on the business goals. Tools such as Prometheus, Grafana and the native toolsets provided by the public cloud provider can help in driving efficiencies by offloading the manageability aspect to the cloud provider.

My team was researching the latest observability trends to prepare for future growth of our machine learning system on Azure. I see observability becoming deeply integrated with cloud technologies over time. Most has already begun exploring predictive analytics with services. I also expect observability to merge with adjacent cloud disciplines like compliance and security. With flexible cloud runtimes, there is increased risk, making holistic governance critical. Unified control planes for observability, compliance and security will enable "trust but verify" cloud models. The future breakthrough I foresee is democratized observability using AI. Making it accessible to non-experts could massively accelerate cloud adoption.

AIOps will revolutionize observability by automating the collection, analysis, and correlation of vast amounts of data. This will lead to faster identification and resolution of issues, improved service uptime, and enhanced user experience. As AIOps matures and becomes more widely adopted, it will democratize observability, making it accessible to a wider range of IT professionals. This will enable more informed and data-driven decision-making across all levels of the organization.

Observability is an evolving concept that needs to adapt to new technologies and be integrated with other cloud-native principles, advanced tools and platforms, and democratized for broader adoption. By following these best practices, cloud-based systems and applications can achieve improved quality, performance, security, and user experience.

1. Evolution 2. Integration 3. Democratization These are the top three factors that can be envisioned to shape the future evolution of Observability. As application become more sophisticated, the continuous change and growth in applications will be key drivers in evolving the criteria for Observability. Integration with emerging technologies and systems will play a pivotal role in propelling Observability towards maturity and standardization. Lastly, w.r.t democratization, Observability is poised to become more accessible and inclusive. The democratization of Observability means that its benefits will be distributed widely across teams, enabling not just experts but also developers and stakeholders to leverage its insights.

The future of observability in cloud environments is characterized by advancements in automation, artificial intelligence, and machine learning. Intelligent observability solutions will leverage AI/ML algorithms to analyze vast amounts of data and identify patterns, anomalies, and potential issues in real-time. Enhanced automation capabilities will enable self-healing systems that can automatically mitigate issues and optimize performance without human intervention. Moreover, observability will evolve to encompass not only technical metrics but also business KPIs, providing holistic insights into the impact of system performance on organizational objectives.

Cloud-Specific Strategies: Leverage native observability features: Utilize built-in monitoring and logging capabilities offered by your cloud provider. These can be a great starting point and integrate seamlessly with other cloud services. Embrace distributed tracing: Implement distributed tracing tools to track requests across microservices and distributed systems, simplifying complex problem diagnosis. Deploy serverless observability: Use specialized tools and configurations for serverless functions to gain insights into their performance and resource utilization.

Whilst everyone speaks about observability, I'd like to draw some attention to traceability and dashboards. If you're using Istio, most tools like Grafana + Prometheus are out-of-the-box (The latter talks to the kubemetrics server and serves as a data source), which lets you see the status of the requests, polling rates as well as vCPU/vGPU, and memory usage over time. It also lets you set up alarms and has community JSON dashboards that can be imported. When it comes to traceability, Kiali + Zipkin is the way to go - the plus point is that it lets you observe how traffic moves between different microservices, as well as the former for versioning and the percentage of requests with their 2xx, 4xx or 5xx codes which helps in debugging.

Usually, people use the term observability to group two different concepts: monitoring and observability. In monitoring, we are looking at the system itself. For example, in the cloud-native, the system could be a Kubernetes cluster. Monitoring will not tell us what’s happening inside our application. It will give indicators such as low memory availability, but it won’t help us pinpoint the cause of an issue. In observability, we can dive directly into our application and watch its various processes and functions. We can see how the services execute and how they interact. Then, we can disclose several problems or discover optimizations we can make our application perform better. Remember: we monitor systems and observe applications.

Further integration of observability with DevOps practices, fostering a culture of collaboration and shared responsibility for system reliability. This may involve tighter integration with CI/CD pipelines and automation tools. Addressing the observability needs of edge computing environments. As edge computing becomes more prevalent, monitoring tools will need to extend their capabilities to effectively capture and analyze data from distributed edge devices. Integration of observability tools with security practices to provide comprehensive security monitoring.

There is a need for education on what observability means. There is this notion that having extended monitoring capabilities would take you towards observability. Similarly, another debate worth having is splitting observability across infrastructure and applications. This debate depends on how you leverage cloud (IaaS vs PaaS vs SaaS) and depending on the deployment model, this might change the way you tackle observability. An additional layer would be when you are in a hybrid multi cloud mode wherein you need to deal with traditional setups besides modern cloud architectures. This will require investments towards common frameworks and tools for observability. So building a common philosophy for observability in your organization is key.