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Dev
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Understanding and Inspecting Indexes in MySQL: A Comprehensive Guide
- Indexes in MySQL help improve performance by reducing the data volume scanned for queries.
- Inspecting and understanding indexes is crucial for database optimization and maintenance.
- MySQL supports primary, unique, full-text, spatial, composite, and prefix indexes.
- SHOW INDEX commands allow inspecting defined keys and their attributes in MySQL.
- Understanding index structures, cardinality, and composite designs is essential for efficient querying.
- Removing redundant indexes and optimizing composite designs can improve query performance.
- Evaluation of existing indexes before adding new ones is important for query efficiency.
- The information_schema.STATISTICS table provides a centralized view of all index metadata in a schema.
- Automation tools like Releem can help track query performance and suggest index improvements.
- Continuous monitoring and adjustment of indexes are essential for maintaining database performance.
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Medium
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Introducing DataSynclabs: A New Era for Decentralized AI Has Begun
- DataSynclabs is launching a decentralized AI ecosystem inspired by swarm intelligence, where every contributor plays a part in building, training, and refining AI models.
- Their mission is to break the centralized mold of AI by rethinking data processing, model governance, and scalability with transparency, equity, and resilience.
- The ecosystem consists of Queen AI (Core Protocol), Worker Bees (Contributors), and Hive Infrastructure (Platform) working together in a three-layer structure.
- $HVT (Hive Token) is the ecosystem's currency used for paying for compute jobs, staking for upgrades, rewarding contributors, and holders can decide on dataset approvals and more.
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Dev
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399
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How to Provide Secure Access to Your Kubernetes Clusters Using an Overlay Network
- Overlay networks provide a solution for securing access to Kubernetes clusters without compromising protection.
- Fine-grained access helps reduce the attack surface and simplifies access for non-developers in staging and testing environments.
- Overlay networks like NetBird operate on top of existing network infrastructure, allowing secure peer-to-peer communication.
- NetBird facilitates specifying access based on machines, ports, geolocations, and operating systems.
- Deploying NetBird involves setting up the Kubernetes operator, creating configurations, and establishing secure connections to services.
- Configuring NetBird involves installing agents on machines, creating access policies, configuring nameservers, and testing connections.
- Using an overlay network like NetBird helps ensure secure access to internal services running in Kubernetes clusters without exposing them publicly.
- Managing access to Kubernetes clusters and services for different teams becomes easier with NetBird's group-based access control.
- NetBird and its Kubernetes operator are open-source, allowing users to explore and contribute to the project.
- Implementing an overlay network like NetBird is a recommended security best practice for clusters not requiring public access.
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Kubernetes
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Gateway API v1.3.0: Advancements in Request Mirroring, CORS, Gateway Merging, and Retry Budgets
- Gateway API v1.3.0 introduces enhancements like percentage-based request mirroring, CORS filtering, Gateway merging, and retry budgets.
- The release includes percentage-based request mirroring allowing users to mirror a subset of requests, enabling testing without impacting all client responses.
- New experimental features in this release include CORS filtering for cross-origin resource sharing and XListenerSets for merging Listeners.
- XListenerSets allow shared listeners across Gateways and enable delegation of listener configuration, such as TLS, to applications in different namespaces.
- Retry budgets (XBackendTrafficPolicy) in v1.3.0 allow configuring limits on client-side retries for a destination Service over a specified interval.
- To try out these features, install the Experimental channel Gateway API YAMLs and follow the Getting Started Guide.
- Conformant implementations to Gateway API v1.3 experimental features include Airlock Microgateway, Cilium, Envoy Gateway, and Istio.
- Users running Kubernetes 1.26 or later can leverage Gateway API v1.3 without needing to upgrade Kubernetes itself.
- The community is encouraged to get involved, explore use-cases, try existing Gateway controllers, and participate in shaping the future of Gateway API.
- Gateway API v1.3.0 signifies progress in Kubernetes routing APIs and offers opportunities for community engagement and feature exploration.
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Hashicorp
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Terraform migrate 1.1 adds VCS workspace support and enhanced GitOps
- Terraform migrate 1.1 adds VCS workspace support and enhanced GitOps for easier migration processes.
- Infrastructure teams can now leverage automated Git-based migration flows, aligning with GitOps practices.
- Specific updates in Terraform migrate 1.1 include VCS workspace support, Git control via provider-level git attributes, and stability/performance improvements.
- This release enhances ease of use, adaptability to existing repositories, and overall stability for managing infrastructure with Terraform.
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Dev
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Load testing vs performance testing
- Load testing, performance testing, and stress testing are often confused but have distinct purposes in testing applications under realistic conditions.
- Performance testing encompasses load, stress, spike, and soak testing to evaluate how systems behave in various scenarios.
- Load testing, a subset of performance testing, focuses on simulating expected user volume and traffic patterns.
- Performance testing evaluates speed, scalability, and stability across different conditions, integrating throughout the software development lifecycle.
- Load testing validates behavior under normal and peak usage conditions, answering critical questions about system capacity and response times.
- Successful teams integrate performance testing throughout the development process, catching issues early and ensuring reliability under various conditions.
- Performance testing scenarios should mirror actual user behavior and system architecture, combining business-critical user journeys with technical validation.
- Gatling, a tool supporting multiple protocols, simplifies load testing with a no-code test builder and can be integrated into CI/CD pipelines.
- Global scale capabilities, reliable results, cost management features, and reduced operational overhead make Gatling ideal for modern DevOps teams.
- Gatling offers a seamless transition from the no-code approach to script-based testing in Java, JavaScript, Kotlin, or Scala for automated performance testing.
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Dev
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152
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🚀 Deploying Artifacts to Multiple S3 Buckets Using AWS CodePipeline
- Setting up an AWS CodePipeline for deploying artifacts to multiple S3 buckets is useful for scenarios like deploying static websites across different environments or regions.
- Prerequisites include an AWS account with necessary permissions, two or more S3 buckets configured for static website hosting, and a source artifact stored in an S3 bucket.
- The step-by-step setup involves creating a pipeline, adding a source stage with details of source bucket and object key, and adding deploy stages for each target bucket with actions like extracting files before deployment.
- Benefits of this setup include automation that streamlines deployment, consistency in deployment artifacts across target buckets, and scalability to extend to more buckets or regions.
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Microsoft
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318
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GitHub Secret Protection and GitHub Code Security for Azure DevOps
- GitHub has launched standalone security products, GitHub Secret Protection and GitHub Code Security for Azure DevOps, following changes to GitHub Advanced Security.
- GitHub Secret Protection for Azure DevOps is available for $19 per active committer per month, offering push protection, secret scanning alerts, and security overview.
- GitHub Code Security for Azure DevOps is available for $30 per active committer per month, offering dependency alerts, CodeQL scanning, security findings for third-party tools, and security overview.
- Customers can start using Secret Protection and Code Security plans as soon as it's available in their organization, with no disruption to the current Advanced Security experience for existing customers.
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Dev
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Top 8 GitOps Tools You Should Know
- GitOps is a methodology that uses Git repositories as the single source of truth for software development and infrastructure management.
- GitOps tools automate software delivery using declarative configuration, IaC, and CI/CD, following GitOps principles.
- Top GitOps tools include Spacelift, Argo CD, Flux CD, Codefresh, GitLab, Terraform, OpenTofu, and Werf.
- Spacelift automates IaC workflows, supports various technologies, and includes drift detection capabilities.
- Argo CD offers continuous delivery for Kubernetes, syncing manifests from Git repositories to clusters.
- Flux CD provides fully automated state reconciliation with integration options for cloud providers.
- Codefresh is an enterprise-scale SaaS GitOps solution with monitoring dashboards and debugging support.
- Terraform, known for IaC, extends GitOps to infrastructure configuration allowing automated changes based on repository content.
- OpenTofu, an open-source alternative to Terraform, enables syncing of infrastructure state with repository content.
- Werf is a CI/CD system automating application delivery to Kubernetes clusters based on Git repositories.
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Dev
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How to Become a DevOps Engineer
- DevOps Engineer bridges the gap between development and operations to ensure smooth and continuous software delivery.
- Skills needed to become a DevOps Engineer include programming languages, automation tools, containerization, cloud platforms, CI/CD pipelines, configuration management, IaC, monitoring, and soft skills.
- DevOps Engineers focus on infrastructure, automation, and deployment, while developers focus on building applications and implementing features.
- To prepare for a DevOps career, hands-on experience with tools like Docker, Kubernetes, Terraform, Ansible, monitoring tools, and creating CI/CD pipelines are essential.
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Dev
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237
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How to Deploy a Full Stack Application to Koyeb Using Docker Compose, Terraform, and GitHub Actions
- Deploying a full-stack application to a cloud platform can be made manageable and reliable by leveraging containerization, Infrastructure as Code (IaC), and automated CI/CD pipelines.
- This guide covers using Docker Compose, Terraform, and GitHub Actions to deploy a full-stack application on Koyeb for enhanced consistency and efficiency.
- Prerequisites include having a Koyeb account, Koyeb API token, a GitHub repository with application code and Docker Compose files, and basic familiarity with Terraform and GitHub Actions.
- Terraform is used to provision and manage the deployment infrastructure on Koyeb using the koyeb_app and koyeb_service resources defined to automate deployment from a GitHub repository.
- Setting up the Koyeb API token in GitHub Secrets securely allows authentication for Terraform to interact with your Koyeb account during deployment.
- The GitHub Actions workflow file automates the deployment process by checking out code, setting up Terraform, and deploying the application using the provided Terraform scripts.
- After pushing the code to trigger deployment, monitoring the deployment progress in GitHub Actions helps track the workflow run and ensures successful deployment on Koyeb.
- By following this guide, you can achieve a seamless deployment of your full-stack application on Koyeb using Docker Compose, Terraform, and GitHub Actions.
- This approach enhances deployment consistency and efficiency while providing a reliable, repeatable way to manage the deployment environment.
- For more practical DevOps walkthroughs, you can explore additional content by EphraimX and follow for future updates in this domain.
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Dev
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296
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Load balancing multiple Rathole tunnels with Traefik HTTP and TCP routers
- The article discusses how to load balance multiple Rathole tunnels and home servers using Traefik HTTP and TCP routers.
- It addresses the limitation of needing one VPS per home server by introducing load balancing for traffic distribution.
- The setup requires a working Rathole tunnel setup, multiple home servers, and Traefik HTTP and TCP routers.
- The primary challenge arises from the inability to bind more than one port to ports 80 and 443 simultaneously.
- To resolve this, a tool for load balancing traffic between tunnels, such as Traefik, is introduced.
- The article explains in detail the implementation of Traefik HTTP and TCP routers for load balancing multiple tunnels.
- Traefik is used to route HTTP traffic and handle encrypted HTTPS traffic in passthrough mode.
- The configuration involves defining HTTP routers with HostRegexp() matcher and TCP routers with HostSNIRegexp() matcher.
- Rathole server configurations for 2 HTTP/HTTPS tunnel pairs per home server are detailed in the article.
- The setup aims to enable users to expose multiple home servers in a cost-effective and practical manner.
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Dev
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300
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From Prototype to Production - Local Kubernetes: Our AI Resume Matcher's Journey
- The article discusses the transition of an AI Resume Matcher from a prototype to production using Local Kubernetes.
- Key learnings include containerizing the Java Spring Boot application with Docker, setting up a local Kubernetes environment, crafting Kubernetes deployment manifests, configuring Google Cloud authentication for Vertex AI, and exposing the application through a Kubernetes Service.
- Local Kubernetes offers advantages such as mimicking production environments, handling growth, building cloud-ready skills, and ensuring standardized deployments.
- Prerequisites include having the AI Resume Matcher project code, Docker Desktop for local Kubernetes, and a Google Cloud account with Vertex AI enabled.
- The process involves containerizing the application, launching Kubernetes with Docker Desktop, orchestrating with Kubernetes using manifest files, setting up Google Cloud authentication securely, and deploying the application.
- For Google Cloud authentication, a Service Account and JSON key file are utilized to create a Kubernetes Secret for secure authentication within the Kubernetes pod.
- Testing the deployed application involves checking pod status, verifying deployment, inspecting application logs, and sending a test request to ensure proper functioning.
- By following the steps outlined, developers can deploy the AI Resume Matcher on a local Kubernetes cluster, gaining practical experience with DevOps practices and cloud-native technologies.
- The journey from a Docker Compose setup to a Kubernetes cluster deployment enhances the application's robustness and scalability, paving the way for production readiness.
- Overall, the article provides a comprehensive guide to transitioning an AI application from a basic prototype to a fully orchestrated production environment while showcasing key DevOps principles.
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Dev
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372
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Code, Charts, and Checklists: Dev Workflows That Ship Faster
- Shipping code fast involves building a workflow that removes bottlenecks, increases visibility, and keeps developers focused.
- Key elements for faster development include clean code practices, visual progress tracking through charts, and dependable checklists.
- Issues in dev workflows include overloaded sprint boards, missed deadlines, rushed code reviews, and team burnout.
- Adopting a Minimum Viable Product (MVP) approach reduces wasted effort, shortens feedback loops, and keeps development cycles focused.
- Charts like burndown charts provide real-time visibility to track progress, spot scope creep, and promote team accountability.
- Checklists automate repetitive tasks, minimize errors, speed up onboarding, and free up mental energy for problem-solving.
- Integrating code practices, charts, and checklists into a cohesive workflow using smart project management platforms enhances planning, execution, tracking, and alignment with product goals.
- By combining lean coding, visual charts, and practical checklists, developers can create effective workflows that scale with the team.
- Strategies for efficient developer workflows include starting with an MVP mindset, utilizing burndown charts, and implementing templates and checklists.
- These proven strategies help developer teams ship on time and manage the development lifecycle effectively.
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Dev
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336
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Why Developers and Operations People Fight (And How to Stop Being Caught in the Middle)
- DevOps is a collaboration between developers and operations to ensure successful outcomes in tech projects.
- Developers focus on building features like login buttons, algorithms, and chat features, while operations ensure functionality and reliability.
- Tension arises due to differing priorities: developers aim for speed and innovation, while operations prioritize stability and security.
- High dev-ops collaboration leads to faster deployments, quicker incident recovery, and lower change failure rates.
- Effective solutions involve promoting communication, setting shared goals, building common understandings, and learning both development and operations.
- Tools like Docker, CI/CD, and monitoring tools help bridge the gap between developers and operations.
- Successful engineers understand both development and operations, stay curious, and ask critical questions about system impacts.
- Companies thrive when all team members work together towards common goals and understanding.
- Beginners are advised to learn Docker basics, understand monitoring, practice deployments, and grasp incident response.
- Continuous growth and progress are vital in tech, showcasing improvement through actions rather than just announcements.
- Aiming for consistency and teamwork, understanding different perspectives, and aiming towards growth are essential in navigating dev-ops dynamics.
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