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What is Cloud Native Observability?
Control theory defines "observability" as the ability to determine the internal states and, accordingly, the behavior of a system only by examining its inputs and outputs.
The ability to monitor the status of cloud-native systems is known as cloud-native observability. Telemetry data is collected from various locations and analyzed to generate meaningful insights. Cloud computing teams need high observability in the cloud environment's system logs and data flows. It is possible to know the performance status of your connected systems using cloud-native observability. Observability can also be defined as the ability to determine a complex system's status based on the system's output. Observing servers, applications, data processes, and hardware processes in computing is possible by logging, monitoring, and tracing them.
An approach to developing, building, and shipping applications using modern Cloud computing. Taken From Article, Cloud Native Applications Design and Architect
What is Microservices Architecture?
It is an architectural style for developing applications based on microservices (often abbreviated to microservices). As a result of microservices, large applications can be separated into smaller, independent portions, each with its responsibilities. An application that uses microservices can compose its response from many internal microservices to serve a single user request. In a microservices architecture, containers are an excellent example since they let you focus on developing the services without worrying about dependencies. Containers are often used to build cloud-native microservices.
Why do we need Cloud Observability?
Observability in the cloud doesn't just replicate logging, tracking, and monitoring before the cloud. The following are three "why your cloud infrastructure needs cloud native observability":
- DevOps: DevOps can be made more efficient through cloud-native observability. Automation of observability makes it possible for you to solve errors holistically by finding and correcting them holistically. By detecting and resolving issues before they occur between projects and containers, it is possible to avoid future problems.
- AI and Automation Detection: You can identify issues that might otherwise go unnoticed by your software engineers using artificial intelligence (AI) and automation. You can prevent problems before they arise by integrating AI into your cloud-native logging and monitoring tools.
- Real-time and Constant Data Availability: The digital panopticon works - as long as all the data is appropriately handled. Cloud-native observability uses a holistic approach to ensure everything is noticed. The machinations inside hardware and software can be recalled and analyzed, allowing you to determine the factors contributing to the results.
In addition to helping in understanding future incidents, cloud-native observability can help proactively resolve them. This is something traditional observability tools couldn't do.
By monitoring and analyzing data about the various components of a system, developers can identify and resolve problems more quickly. Taken From Article, Data Observability Tools and its Use Cases
A Paradigm Shift in Cloud Native Observability
Due to increased complexity, traditional observability tools cannot monitor cloud environment logs, containers, and data. Software systems can only be monitored by knowing how they are performing internally. Tools for cloud monitoring have undergone a significant paradigm shift over the past few years.AI, and machine learning has enhanced cloud-native observability by developing new technologies Artificial Intelligence for IT Operations (AIOps) has been proven to increase cloud-native observability. A recent global survey found that businesses using AIOps-based analytics platforms will increase ten times by 2024. Observability is deeply ingrained in the minds of more than 90% of IT professionals. Analytics platforms based on AIOps can help businesses form effective marketing strategies and decisions.
What are the various Components of Cloud Native Observability?
The various Components of Cloud Native Observability are described below:
Section 1: Application Performance Monitoring (APM)
APM is a critical component of cloud-native observability, as it allows you to track the performance of individual microservices and the interactions between them. This includes response times, error rates, and resource utilization. By using APM tools, you can identify performance bottlenecks and issues and take proactive measures to improve the overall performance of your application. Some standard APM tools include New Relic, AppDynamics, and Dynatrace.
Section 2: Log Management and Analysis
Logs provide a wealth of information about the behavior of an application. APM tools often use them to provide a complete view of performance and reliability. Log management and analysis tools allow you to collect, store, and analyze log data, helping you to identify trends and patterns that can indicate performance issues or other problems.
Some standard log management and analysis tools include Splunk, Elastic Stack, and Logz.io.
Section 3: Distributed Tracing
Distributed tracing allows you to track the flow of requests through a microservices-based application, helping to identify bottlenecks and performance issues. By analyzing the trace data, you can see how requests are routed through the various microservices that make up your application and identify any areas causing delays or other issues. Some standard distributed tracing tools include Zipkin, Jaeger, and AppOptics.
Section 4: Infrastructure Monitoring
Monitoring the underlying infrastructure is an essential aspect of cloud-native observability, as it allows you to ensure that the resources your applications depend on are available and performing optimally. Infrastructure monitoring tools provide visibility into the performance and availability of servers, networks, and storage, helping you to identify and resolve issues before they impact your applications.
Some joint infrastructure monitoring tools include Datadog, New Relic, and Nagios.
Section 5: Alerting and Incident Response
Automated alerting and incident response tools are essential for maintaining the availability and reliability of your applications. These tools can help to identify and respond to problems in real time, minimizing downtime and improving application availability.
Some common alerting and incident response tools include PagerDuty, VictorOps, and Alerta.
An approach to speed up building new applications, optimizing existing ones, and connecting all of them. Taken From Article, Cloud Native Applications for Enterprises
What are the best Cloud Native Tools for Observability?
The best Cloud Native Tools for Observability are described below:
Observability-related CNCF projects like Prometheus are trendy, so it does not need to be introduced, as most engineers already know about them. Several large companies use Prometheus to power their monitoring and alerting systems, including Amadocs, Soundcloud, Ericsson, etc.Using an HTTP pull model, Prometheus can discover services and collect data. Having organized metrics into time-series key-value pairs, it stores them in a database. Customizing these metrics according to the application and setting them up to trigger alerts is possible.
Developing distributed systems has been much more challenging due to the shift toward distributed networks, observability, and debugging. A project called Jaeger aims to resolve this dilemma. Its primary purpose is to monitor and troubleshoot transactions in complex distributed systems. As per the documentation, Jaeger has the following features:
- Distributed context propagation
- Distributed transaction monitoring
- Root cause analysis
- Service dependency analysis
- Performance/latency optimization
Fluentd is a logging system designed to be decoupled from the backend system. A Unified Logging Layer can resolve the incompatibility problem by unifying logging formats and routines. Many sources of events can be tracked by Fluentd, including web applications, mobile apps, and NGINX logs. Besides centralized logs, Fluentd can also port them to external systems and databases, such as Elasticsearch, MongoDB, Apache Hadoop, etc. Over 500 plugins are available for Fluentd to enable this. Fluentd can help send alerts in response to certain logs or enable scalable asynchronous logging.
The Thanos option is for those seeking more from Prometheus. Prometheus deployments can be enhanced with this metric system with unlimited storage capacity. An organization that uses multiple Prometheus servers and clusters might find using Thanos to get a global view of metrics helpful. The extensibility of Thanos allows you to store data on any storage device of your choice, which theoretically allows you to store limitless amounts of data. Using Thanos' ability to handle large amounts of data, it incorporates downsampling to speed up data queries.
New features and issues can be created more quickly with cloud-native solutions. Observability tools are essential in cloud-native solutions because issues can be obfuscated more than in traditional software designs. Developers need to incorporate observability tools from the beginning of their development process. Cloud-native observability tools can still be augmented with initial integration to ensure compatibility with cloud providers. Different teams can work on different microservices features built on a cloud-native architecture. When an issue is detected, teams should consider implementing observability tools to notify the appropriate person or team. Ideally, team members should have access to tools to assess the system's health.
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