#kubernets

Introduction In the world of Kubernetes, managing and exposing services can sometimes be a challenging task, especially when dealing with deprecated commands and network configurations. Recently, I encountered an issue while following instructions from the book Kubernetes in Action. The command kubectl run kubia --image=luksa/kubia --port=8080 --generator=run/v1 did not work due to its…

Opentelemetry vs Prometheus

Prometheus monitors, stores, and visualises metrics but does not keep logs or support traces for root cause analysis. The application cases of Prometheus are more limited than OpenTelemetry.

Programming language-agnostic integrations allow OpenTelemetry to track more complicated metrics than Prometheus. Automated instrumentation models make OTel more scalable and extensible than the Prometheus. OpenTelemetry requires a back-end infrastructure and no storage solution, unlike Prometheus.

Quick summary Prometheus calculates cumulative measurements as a total, whereas OpenTelemetry uses deltas. Prometheus stores short-term data and metrics, whereas OTel may be used with a storage solution. OpenTelemetry uses a consolidated API to send or pull metrics, logs, and traces and transform them into a single language, unlike Prometheus. Prometheus pulls data from hosts to collect and store time-series metrics. OTel can translate measurements and is language agonistic, providing developers additional options. Data and metrics are aggregated by Prometheus using PromQL. Web-visualized metrics and customisable alarms are provided by Prometheus. Integration with visualisation tools is required for OpenTelemetry. OTel represents metric values as integers instead of floating-point numbers, which are more precise and understandable. Prometheus cannot use integer metrics. Your organization’s demands will determine which option is best. OpenTelemetry may be better for complex contexts with dispersed systems, data holistic comprehension, and flexibility. This also applies to log and trace monitoring.

Prometheus may be suited for monitoring specific systems or processes using alerting, storage, and visualisation models.

Prometheus and OpenTelemetry Application performance monitoring and optimisation are crucial for software developers and companies. Enterprises have more data to collect and analyse as they deploy more applications. Without the correct tools for monitoring, optimising, storing, and contextualising data, it’s useless.

Monitoring and observability solutions can improve application health by discovering issues before they happen, highlighting bottlenecks, dispersing network traffic, and more. These capabilities reduce application downtime, improve performance, and enhance user experience.

App monitoring tools OpenTelemetry and the Prometheus are open-source Cloud Native Computing Foundation (CNCF) initiatives. An organization’s goals and application specifications determine which data and functions need which solutions. Before using OpenTelemetry or Prometheus, you should know their main distinctions and what they offer.

Java Opentelemetry OTel exports these three forms of telemetry data to Prometheus and other back ends. This lets developers chose their analysis tools and avoids vendor or back-end lock-in. OpenTelemetry integrates with many platforms, including Prometheus, to increase observability. Its flexibility increases because OTel supports Java, Python, JavaScript, and Go. Developers and IT staff may monitor performance from any browser or location.

Its ability to gather and export data across multiple applications and standardise the collecting procedure make OpenTelemetry strong. OTel enhances distributed system and the microservice observability.

For application monitoring, OpenTelemetry and Prometheus integrate and operate well together. The DevOps and IT teams can use OpenTelemetry and Prometheus to collect and transform information for performance insights.

Opentelemetry-demo OpenTelemetry (OTel) helps generate, collect, export, and manage telemetry data including logs, metrics, and traces in one place. OTel was founded by OpenCensus and OpenTracing to standardise data gathering through APIs, SDKs, frameworks, and integrations. OTel lets you build monitoring outputs into your code to ease data processing and export data to the right back end.

Telemetry data helps determine system health and performance. Optimised observability speeds up troubleshooting, improves system reliability, reduces latency, and reduces application downtime.

Opentelemetry architecture APIs OpenTelemetry APIs uniformly translate programming languages. This lets APIs capture telemetry data. These APIs help standardise OpenTelemetry measurements.

SDKs Software development tools. Frameworks, code libraries, and debuggers are software development building elements. OTel SDKs implement OpenTelemetry APIs and provide telemetry data generation and collection tools.

OpenTelemetry collector The OpenTelemetry collector accepts, processes, and exports telemetry data. Set OTel collectors to filter specified data types to the back end.

Instrumentation library OTel offers cross-platform instrumentation. The instrumentation libraries let OTel integrate with any programming language.

Opentelemetry collector contrib Telemetry data including metrics, logs, and traces can be collected without modifying code or metadata using the OpenTelemetry protocol (OTLP).

Metrics A high-level overview of system performance and health is provided via metrics. Developers, IT, and business management teams decide what metrics to track to fulfil business goals for application performance. A team may measure network traffic, latency, and CPU storage. You may also track application performance trends with metrics.

Logs Logs record programme or application events. DevOps teams can monitor component properties with logs. Historical data might demonstrate performance, thresholds exceeded, and errors. Logs track application ecosystem health.

Traces Traces provide a broader picture of application performance than logs and aid optimisation. They track a request through the application stack and are more focused than logs. Traces let developers pinpoint when mistakes or bottlenecks occur, how long they remain, and how they effect the user journey. This data improves microservice management and application performance.

What’s Prometheus? Application metrics are collected and organised using Prometheus, a monitoring and alerting toolkit. SoundCloud created the Prometheus server before making it open-source.

End-to-end time-series data monitoring is possible using Prometheus. Time-series metrics capture regular data, such as monthly sales or daily application traffic. Visibility into this data reveals patterns, trends, and business planning projections. Prometheus collects application metrics for dedicated functions that DevOps teams want to monitor after integration with a host.

Using PromQL, Prometheus metrics offer data points with the metric name, label, timestamp, and value. For better visualisation, PromQL lets developers and IT departments aggregate data metrics into histograms, graphs, and dashboards. Enterprise databases and exporters are accessible to Prometheus. Application exporters pull metrics from apps and endpoints.

Prometheus tracks four metrics Counters Counters measure increasing numerical values. Counters count completed tasks, faults, and processes or microservices.

Gauges Gauges measure numerical data that fluctuate due to external variables. They can monitor CPU, memory, temperature, and queue size.

Histograms Events like request duration and answer size are measured via histograms. They split the range of these measurements into buckets and count how many fall into each bucket.

Summaries Summaries assess request durations and response size like histograms, but they also count and total all observed values.

Prometheus’ data-driven dashboards and graphs are also useful.

Benefits of Prometheus Prometheus provides real-time application monitoring for accurate insights and fast troubleshooting. It also permits function-specific thresholds. When certain thresholds are hit or exceeded, warnings might speed up problem resolution. Prometheus stores and provides analytics teams with massive amounts of metrics data. It stores data for instant examination, not long-term storage. Prometheus typically stores data for two to fifteen days.

Prometheus works perfectly with Kubernetes, an open-source container orchestration technology for scheduling, managing, and scaling containerised workloads. Kubernetes lets companies create hybrid and multicloud systems with many services and microservices. These complicated systems gain full-stack observability and oversight with Prometheus and Kubernetes.

Grafana Opentelemetry Grafana, a powerful visualisation tool, works with Prometheus to create dashboards, charts, graphs, and alerts. Grafana can visualise metrics with Prometheus. The compatibility between these platforms makes complex data easier to share between teams.

Integration of OpenTelemetry with Prometheus No need to choose OpenTelemetry and Prometheus are compatible. Prometheus data models support OpenTelemetry metrics and OTel SDKs may gather them. Together, these systems provide the best of both worlds and enhanced monitoring. As an example:

When combined, OTel and Prometheus monitor complex systems and deliver real-time application insights. OTel’s tracing and monitoring technologies work with Prometheus’ alerting. Prometheus handles big data. This capability and OTel’s ability to combine metrics, traces, and logs into one interface improve system and application scalability. PromQL can generate visualisation models using OpenTelemetry data. To provide additional monitoring tools, OpenTelemetry and Prometheus interface with IBM Instana and Turbonomic. Instana’s connection map, upstream/downstream service connection, and full-stack visibility let OTel monitor all services. They give the same wonderful experience with OTel data as with all other data sources, providing you the context you need to swiftly detect and address application problems. Turbonomic automates real-time data-driven resourcing choices using Prometheus’ data monitoring capabilities. These optimised integrations boost application ecosystem health and performance.

"Building Super Careers: CKA Certification, Your Secret Weapon!"

Learn the skills you need to manage Kubernetes clusters effectively.

Visit : https://amritahyd.org/

What is Jenkins X?

Jenkins X is an open-source solution that provides automatic seamless integration and continuous distribution (CI / CD) and automated testing tools for cloud-native applications in Cubernet. It supports all major cloud platforms such as AWS, Google Cloud, IBM Cloud, Microsoft Azure, Red Hat OpenShift, and Pivotal. Jenkins X is a Jenkins sub-project (more on this later) and employs automation, DevOps best practices and tooling to accelerate development and improve overall CI / CD.  

Features of Jenkins X

Automated CI /CD:

Jenkins X offers a sleek jx command-line tool, which allows Jenkins X to be installed inside an existing or new Kubernetes cluster, import projects, and bootstrap new applications. Additionally, Jenkins X creates pipelines for the project automatically.

Environment Promotion via GitOps:

Jenkins X allows for the creation of different virtual environments for development, staging, and production, etc. using the Kubernetes Namespaces. Every environment gets its specific configuration, list of versioned applications and configurations stored in the Git repository. You can automatically promote new versions of applications between these environments if you follow GitOps practices. Moreover, you can also promote code from one environment to another manually and change or configure new environments as needed.

Extensions:

It is quite possible to create extensions to Jenkins X. An extension is nothing but a code that runs at specific times in the CI/CD process. You can also provide code through an extension that runs when the extension is installed, uninstalled, as well as before and after each pipeline.

Serverless Jenkins:

Instead of running the Jenkins web application, which continually consumes a lot of CPU and memory resources, you can run Jenkins only when you need it. During the past year, the Jenkins community has created a version of Jenkins that can run classic Jenkins pipelines via the command line with the configuration defined by code instead of the usual HTML forms.

Preview Environments:

Though the preview environment can be created manually, Jenkins X automatically creates Preview Environments for each pull request. This provides a chance to see the effect of changes before merging them. Also, Jenkins X adds a comment to the Pull Request with a link for the preview for team members.

How Jenkins X works?

The developer commits and pushes the change to the project’s Git repository.

JX is notified and runs the project’s pipeline in a Docker image. This includes the project’s language and supporting frameworks.

The project pipeline builds, tests, and pushes the project’s Helm chart to Chart Museum and its Docker image to the registry.

The project pipeline creates a PR with changes needed to add the project to the staging environment.

Jenkins X automatically merges the PR to Master.

Jenkins X is notified and runs the staging pipeline.

Introduction to Kubernetes Terraform :

The Kubernetes terraform is characterized as, the terraform is an open-source instrument that permits us to furnish fundamental design with intelligible usefulness when the terraform can handle the essential construction for both public and private cloud administrations in which it assists with interconnecting between various assets.

Working of Kubernetes Terraform :

presently terraform likewise support the mechanization work process in which we may naturally create the new framework for sending the new branch, we can likewise consequently get the arrangement to for refreshing the arrangement with its new form with no manual assistance which is straightforward.

Kubernetes Use Case of Terraform :

It has main three use cases of terraforming:

1. Public Cloud Provisioning : Public cloud provisioning is the primary assignment of the terraform as it can give one of the principal cloud suppliers, the terraform permits us to utilize the public cloud using what is known as a supplier, the supplier in terraforming can think.

2. Multi-Cloud Deployment :This is another utilization instance of the terraform in which it can execute multi-cloud organizations, one of the fundamental engaging qualities with regards to how to terraform is functions simultaneously, it has multi-cloud sending highlight as we can say that it can work for a considerable length of time arrangement at one time but it works essentially with every cloud supplier. It can ready to form assets into the different cloud suppliers which are advantageous because the designer can ready to involve a similar code for and follow with chain and there is no compelling reason to familiarize themselves with different devices and innovations.