# Docker Image Published articles for Docker Image. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## ClickHouse on Docker Hardened Images DevFeed: [ClickHouse on Docker Hardened Images]() Original publisher: [Read original article]() Author: Karolina Ruiz Rogelj; Melvyn Peignon Published: 2026-07-10T15:28:34Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Docker Hardened Images](), [Docker](), [Security](), [Vulnerabilities](), [Docker Image](), [vulnerability](), [Ubuntu](), [apt](), [Perl](), [Amazon Web Services]() Tags: [apt](), [clickhouse](), [deployment](), [developers](), [docker](), [docker-hardened-images](), [docker-hub](), [docker-image](), [images](), [security](), [ubuntu](), [vulnerability]() ### AI overview ClickHouse is now available as a Docker Hardened Image: a minimal, security-hardened build that preserves ClickHouse behavior while shipping only the components needed to run the database and pass enterprise vulnerability scans. ### Source excerpt ClickHouse is now available as a Docker Hardened Image: a minimal, security-hardened build that passes enterprise vulnerability scans by shipping only what the database needs to run, with no change to how ClickHouse behaves. ## Essential Tips for Gradle on Ephemeral CI Environments - Part 3 DevFeed: [Essential Tips for Gradle on Ephemeral CI Environments - Part 3]() Original publisher: [Read original article]() Author: Louis Jacomet Published: 2026-07-09T04:00:00Z Content type: tutorial Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [benchmarking](), [Cache](), [Docker](), [Spring Boot]() Tags: [benchmarking](), [build](), [cache](), [docker](), [docker-image](), [gradle](), [performance](), [spring-boot]() ### AI overview This tutorial explains how to optimize Gradle's Execution phase in ephemeral CI environments, focusing on the Gradle Build Cache and task-output reuse. It describes benchmarking a small Spring Boot project in a Docker environment and cautions that the measurements are approximate and specific to Gradle 9.5.1 and the stated test setup. ### Source excerpt In Part 1 and Part 2, we optimized Gradle's Startup and Configuration phases on ephemeral CI. In this final part, we reach the Execution phase: the point where Gradle checks whether task outputs can be reused and, when they cannot, runs the selected tasks. This is where the Gradle Build Cache becomes the main lever. Benchmarking Performance in Ephemeral CI The methodology is unchanged from Part 1 and Part 2: each scenario builds a fresh Docker image of the Spring Boot Project with --no-cache, base images are pre-pulled, and every figure is the average of ten runs against one consistent baseline. Individual runs vary by a second or two, so read small differences as approximate rather than exact. These numbers are not intended as universal Gradle performance claims. They show the relative impact of execution-phase optimizations for one small Spring Boot project, one Gradle version, one JDK image, and one local Docker environment. Larger builds, especially multi-module builds with expensive compilation, testing, code generation, or packaging tasks, will generally see bigger gains from task-output reuse than this sample project does. NOTE: All measurements in this post are pinned to Gradle 9.5.1. If you repeat the experiment with a newer Gradle version, rebuild the prime image and update any version-specific cache paths accordingly. The baseline (no optimizations) averaged 32.3s over ten runs, in line with the ~31.5s baselines from Parts 1 and 2. Optimizations for the Execution Phase During the Execution phase, Gradle runs the task graph that was selected and ordered during configuration. For each task, Gradle decides whether work can be avoided before it executes task actions: Input and output snapshotting: Gradle fingerprints the task's declared inputs, outputs, and relevant implementation details. Reuse checks: Gradle determines whether the task is already up to date locally, or whether matching outputs can be loaded from the Build Cache. Task action execution: Gradl ## Scaling out Distroless adoption With AI DevFeed: [Scaling out Distroless adoption With AI]() Original publisher: [Read original article]() Author: Jia Yee Chong Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [Security](), [migration](), [Dockerfile](), [Vulnerabilities](), [make](), [ci](), [configuration](), [Docker](), [Docker Image](), [Testcontainers](), [MySQL]() Tags: [artificial-intelligence](), [ci](), [containers](), [devsecops](), [docker](), [docker-image](), [engineering](), [migration](), [mysql](), [security](), [testcontainers](), [testing](), [vulnerabilities]() ### AI overview Grab describes migrating services from heavy base images to Distroless images to reduce security risks and eliminate unnecessary binaries and associated CVEs. The article explains the runtime-failure risks of this migration and presents medium tests using Docker images, Testcontainers, and network proxies to verify configuration and runtime dependencies in CI. ### Source excerpt Distroless adoption at Grab Grab is migrating from heavy base images to Distroless images to reduce security risks. By limiting each container to the application and its runtime dependencies, we shed non-essential binaries and associated Common Vulnerabilities and Exposures (CVEs). This migration is more than a compliance mandate; it is a strategic security decision to build a more resilient environment. Why Distroless requires rigorous testing Distroless adoption risk: Runtime failure Shifting to Distroless images introduces a critical technical risk: Runtime Failure. A service might build perfectly in Continuous Integration (CI), but fail at the deployment stage due to: Missing shared objects: Binaries might require specific libraries (.so files) present in Ubuntu but absent in Distroless. Implicit links: Third-party tools might expect specific system utilities or directory structures. Testing is required to ensure two things: The service spins up with the correct configuration. All runtime dependencies remain intact. Scaling this verification across thousands of services manually? That would take years, unless we found a way to automate the trust. The testing methodology As we perform changes to the Dockerfile definition of our services, it is important for us to include the corresponding test strategy to ensure that the changes that we make do not introduce a regression to our running services. Assessing the change introduced to our services, the lowest possible testing boundary would be that of what we define as medium tests in Grab. Medium tests in Grab At Grab, we categorize our test suites into 3 main sizes: small, medium and large. Small tests refer to functional tests whereby mocks are introduced via dependency injection. Large tests refer to end-to-end tests that run on actual services in our staging environment where nothing is mocked. Figure 1: Architecture diagram of a medium test environment Medium tests sit between the small and large tests. External ## Running MongoDB in Docker - A Complete Guide with Examples DevFeed: [Running MongoDB in Docker - A Complete Guide with Examples]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-05-29T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Docker](), [Docker Compose](), [Docker Container](), [Deployment](), [Docker Image](), [Authentication](), [Command-line interface]() Tags: [authentication](), [command-line](), [deployment](), [docker](), [docker-container](), [docker-image](), [examples](), [guide](), [linux](), [macos](), [mongodb](), [rancher](), [tutorial](), [windows]() ### AI overview A practical tutorial on deploying MongoDB with Docker for local or self-hosted use. It covers prerequisites, downloading official MongoDB Community Edition images, running MongoDB in an isolated container, port mapping, authentication configuration, and safer handling of sensitive information. ### Source excerpt So you're looking to self-host MongoDB or start dabbling with it in a local setting? There are a few options to get started if you don't want to jump directly into MongoDB Atlas, one of those options ... The post Running MongoDB in Docker - A Complete Guide with Examples appeared first on DataCamp. ## Running the Latest vLLM on the NVIDIA DGX Spark DevFeed: [Running the Latest vLLM on the NVIDIA DGX Spark]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-05-21T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [vllm](), [DGX Spark](), [Docker](), [Dockerfile](), [GitHub Actions](), [NCCL](), [CUDA](), [PyTorch](), [GitHub](), [NGC](), [GPU](), [Ubuntu]() Tags: [ai](), [cuda](), [dgx-spark](), [docker](), [docker-image](), [github](), [github-actions](), [gpu](), [homelab](), [nccl](), [ngc](), [nvidia-dgx](), [pytorch](), [self-hosted](), [ubuntu](), [vllm]() ### AI overview This tutorial presents a reproducible Docker image pipeline for running current vLLM releases on the NVIDIA DGX Spark's GB10 ARM64 platform. It explains why NVIDIA's NGC images can lag upstream and describes a GitHub Actions build on a self-hosted Spark runner with pinned inputs and generated lockfiles. ### Source excerpt When I built my local AI cluster on a pair of ASUS Ascent GX10s, the hard part was not serving a model. The hard part was getting a working vLLM image with current components. NVIDIA's official image was already over a month behind by the time I needed it, and waiting on their release schedule was not an option. If you saw that post, you know the GX10 is an ARM64 machine built around NVIDIA's ... ## From HTTP MCP to stdio: Lessons Connecting an AI Agent to a Desktop IDE DevFeed: [From HTTP MCP to stdio: Lessons Connecting an AI Agent to a Desktop IDE]() Original publisher: [Read original article]() Author: Eugene Petrenko Published: 2026-05-20T00:00:00Z Content type: article Language: en Sources: [Eugene Petrenko]() Topics: [Model Context Protocol](), [IntelliJ IDEA](), [AI Agent](), [Command-line interface](), [Cross-origin resource sharing (CORS)]() Tags: [agentic-coding](), [ai-agent](), [ai-agents](), [ai-coding](), [architecture](), [claude](), [cli](), [codex](), [devrig](), [docker-image](), [eugene-petrenko](), [integration-test](), [intellij](), [jonnyzzz](), [kotlin](), [ktor](), [mcp](), [mcp-steroid](), [report](), [tag-41199d53f463]() ### AI overview This article explains why an HTTP MCP server embedded in a desktop IntelliJ application proved unreliable on real workstations. It describes moving to stdio with a CLI coordinator called devrig, and emphasizes integration tests for the MCP layer, including issues involving dynamic ports, startup ordering, multiple IDEs, and mismatched transport support. ### Source excerpt The obvious way to connect an AI Agent to a running IntelliJ is an HTTP MCP server inside the IDE. I built it, shipped it, and used it daily -- and it kept breaking on real workstations: dynamic ports, start-order lottery, multiple IDEs, and the agent-up/IDE-down dead end. Here is why I moved the whole thing to stdio and a CLI coordinator called devrig, and what the journey taught me. ## Apache Airflow 3.2.0: Data-Aware Workflows at Scale DevFeed: [Apache Airflow 3.2.0: Data-Aware Workflows at Scale]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-04-07T00:00:00Z Content type: release Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [releases](), [data](), [Orchestration](), [Release notes](), [SDK](), [Amazon S3](), [BigQuery]() Tags: [airflow](), [apache](), [apache-airflow](), [bigquery](), [data](), [docker-image](), [orchestration](), [pypi](), [release](), [release-notes](), [s3](), [sdk](), [workflows]() ### AI overview Apache Airflow 3.2.0 introduces asset partitioning for more granular data-aware scheduling, along with multi-team deployments, synchronous deadline alert callbacks, and continued Task SDK separation. Partition-aware workflows trigger downstream DAGs only when the relevant partition is updated. ### Source excerpt We're proud to announce the release of Apache Airflow 3.2.0! Airflow 3.1 puts humans at the center of automated workflows. 3.2 brings that same precision to data: Asset partitioning for granular pipeline orchestration, multi-team deployments for enterprise scale, synchronous deadline alert callbacks, and continued progress toward full Task SDK separation. Details: πŸ“¦ PyPI: https://pypi.org/project/apache-airflow/3.2.0/ πŸ“š Docs: https://airflow.apache.org/docs/apache-airflow/3.2.0/ πŸ›  Release Notes: https://airflow.apache.org/docs/apache-airflow/3.2.0/release_notes.html 🐳 Docker Image: docker pull apache/airflow:3.2.0 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.2.0 πŸ—‚ Asset Partitioning (AIP-76): Only the Right Work Gets Triggered Asset partitioning has been one of the most requested additions to data-aware scheduling. If you work with date-partitioned S3 paths, Hive table partitions, BigQuery partitions, or really any partitioned data store, you've dealt with this: An upstream task updates one partition, and every downstream Dag fires regardless of which slice actually changed. It's wasteful, and for large deployments it creates real operational noise. Asset partitioning in 3.2 makes this granular. Downstream Dags trigger only when the specific partition they care about gets updated. It's the biggest change to data-aware scheduling since Assets were introduced, and it turns partition-driven orchestration into something Airflow handles natively rather than something you work around. Key Capabilities Partition-driven scheduling: Dags trigger on specific partition updates, not every asset change CronPartitionTimetable: Schedule Dags against partitions using cron expressions. Also available in the Task SDK Backfill for partitioned Dags: Backfill historical partitions without re-triggering everything downstream (#61464) Multi-asset partitions: A single Dag can listen for partitions across multiple assets, which matters when your downstream work depe ## Deploying Temporal Workers to Amazon ECS DevFeed: [Deploying Temporal Workers to Amazon ECS]() Original publisher: [Read original article]() Author: Ebenezer Ankrah Published: 2026-04-02T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Amazon Elastic Container Service](), [Deployment](), [AWS IAM](), [Dockerfile](), [container](), [health checks](), [Amazon Web Services]() Tags: [amazon](), [deployment](), [docker](), [docker-image](), [ecs](), [environment-variables](), [health-checks](), [how-to](), [iam]() ### AI overview A guide to deploying Temporal Workers on Amazon ECS with Fargate. It explains container configuration, health checks, graceful shutdown, task definitions, IAM roles, secret injection through SSM Parameter Store, and outbound connectivity requirements. ### Source excerpt A comprehensive guide to deploying Temporal Workers on Amazon ECS with Fargate. Covers container config, IAM roles, scaling, and cost-saving with Fargate Spot. ## Android Security: mobsfscan DevFeed: [Android Security: mobsfscan]() Original publisher: [Read original article]() Author: Matthew Dolan Published: 2026-01-16T17:51:35Z Content type: tutorial Language: en Sources: [Stories by Matthew Dolan on Medium]() Topics: [Android Security](), [Mobile Security](), [Security](), [GitHub Actions](), [CI/CD](), [GitHub](), [Docker Image]() Tags: [android-app-development](), [android-security](), [androiddev](), [build-secure-apps](), [ci-cd](), [docker-image](), [github](), [github-actions](), [mobile-security](), [security]() ### AI overview A tutorial on using the free, open-source mobsfscan tool to detect insecure code patterns in mobile applications. It focuses on running mobsfscan with GitHub Actions and uploading SARIF results to GitHub's Security tab. ### Source excerpt Not a medium member? "Read for free" Continue reading on Stackademic " ## Secure and Free MinIO Chainguard Containers DevFeed: [Secure and Free MinIO Chainguard Containers]() Original publisher: [Read original article]() Published: 2025-10-27T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [Containers](), [Docker Image](), [Security](), [vulnerability](), [Amazon S3](), [Open Source](), [CI/CD]() Tags: [chainguard-containers](), [ci-cd](), [container](), [containers](), [docker-image](), [minio](), [minio-containers](), [minio-docker-containers](), [object-storage](), [open-source](), [s3](), [security](), [vulnerability](), [zero-cve-minio-containers]() ### AI overview The article explains that MinIO stopped publishing free community container images and describes the resulting maintenance and security concerns for developers. It presents Chainguard's free MinIO and MinIO Client container images as an alternative. ### Source excerpt MinIO pulled its free images--but Chainguard has you covered. Get zero-CVE, continuously built MinIO and MinIO Client containers, free and secure from Chainguard. ## Gradle Now Maintains the Official Docker Image DevFeed: [Gradle Now Maintains the Official Docker Image]() Original publisher: [Read original article]() Author: Laura Kassovic Published: 2025-09-15T04:00:00Z Content type: release Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Docker Image](), [Docker](), [Docker Hub](), [CI/CD](), [GitHub Actions](), [GitLab](), [Jenkins]() Tags: [ci-cd](), [docker](), [docker-hub](), [docker-image](), [github](), [github-actions](), [gitlab](), [gradle](), [jenkins](), [open-source](), [releases](), [support]() ### AI overview Gradle is now the official maintainer of the Gradle Docker image on Docker Hub. The article explains that the image will be kept current with Gradle releases, including security updates, while its usage, CI workflows, and community support continue to evolve. ### Source excerpt We're excited to announce we're now the official maintainer of the Gradle image on Docker Hub. For years, developers have relied on the Gradle Docker image to run builds in containers and CI/CD pipelines. Originally created and maintained by community contributor Keegan Witt, this image became a trusted resource for thousands of teams. Today, we're bringing it under the Gradle organization to ensure it remains reliable, up to date, and aligned with Gradle's release cadence. Why this Matters Docker is a critical part of modern software delivery. Many teams build and test their projects in containerized environments, and CI/CD platforms like GitHub Actions, GitLab CI, and Jenkins depend on official images to provide consistency and trust. By becoming the official maintainer of the Gradle Docker image, we can: Provide timely updates for each Gradle release, including security fixes Ensure alignment with the official Gradle -bin distributions Collaborate with the community to evolve the image for new use cases Provide support through the standard support channels (Slack and Community Forum) This also reduces the burden on individual contributors who've generously kept the image running until now. A big Thank You We want to recognize Keegan Witt, who has maintained the Gradle Docker image for years. His diligent work ensured developers could trust the image long before it became officially supported. We're grateful for his contributions and stewardship! What to Expect Going Forward The official Gradle Docker image will continue to be published under hub.docker.com/_/gradle. Images will be kept aligned with Gradle releases to the greatest extent possible. We'll review and evolve best practices for image usage, including improvements for CI workflows and ephemeral build environments. Using the Image Try the latest official image today: docker pull gradle:latest Or specify a versioned tag: docker pull gradle:9.0.0-jdk17 Get Involved The Gradle Docker image remains open sour ## Troubleshooting 'An Unexpected Error While Executing WSL Command' in Docker Desktop DevFeed: [Troubleshooting 'An Unexpected Error While Executing WSL Command' in Docker Desktop]() Original publisher: [Read original article]() Author: Youssef Salem Published: 2025-08-27T21:53:50Z Content type: tutorial Language: en Sources: [Baeldung - Ops]() Topics: [Windows Subsystem for Linux](), [Docker](), [PowerShell](), [Windows]() Tags: [docker](), [docker-docker-image](), [docker-image](), [powershell](), [troubleshooting](), [tutorial](), [wsl](), [wsl2]() ### AI overview A tutorial explains how to troubleshoot the "An Unexpected Error Was Encountered While Executing a WSL Command" message in Docker Desktop on Windows with the WSL2 backend. It describes how Docker and WSL interact, outlines causes such as stopped services, outdated versions, corrupted distributions, permissions, and resource limits, and begins a step-by-step process using WSL checks and PowerShell commands. ### Source excerpt Discover how Docker and WSL interact in order to troubleshoort this error: "An Unexpected Error While Executing WSL Command" Related Stories What Is the Runtime Performance Cost of a Docker Container? Docker Build and Run Platforms and Resolving exec /usr/bin/sh: exec format error Using a Package Manager in GitHub Actions ## Optimizing Docker Image Builds for Size and Build Time DevFeed: [Optimizing Docker Image Builds for Size and Build Time]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2025-07-31T17:25:43Z Content type: tutorial Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [Docker](), [Docker Image](), [Dockerfile](), [build performance](), [build times](), [Benchmark](), [Go Language]() Tags: [alpine](), [benchmark](), [build-performance](), [build-times](), [docker](), [docker-image](), [docker-image-size](), [go](), [how-to]() ### AI overview This tutorial describes optimizing a Go backend Docker image using Alpine base images and multi-stage builds. The reported changes reduced image size from 1.29 GB to 27.1 MB and build time from 43.8 seconds to 23.6 seconds. ### Source excerpt How to optimize (and benchmark) docker image builds for build time and image size. ## Run and Auto-Update Docker Containers on NixOS DevFeed: [Run and Auto-Update Docker Containers on NixOS]() Original publisher: [Read original article]() Author: Jacek Galowicz Published: 2024-11-21T00:00:00Z Content type: tutorial Language: en Sources: [Nixcademy Blog]() Topics: [Nix](), [Home Assistant](), [Docker Image](), [podman](), [Raspberry Pi](), [systemd](), [Containers]() Tags: [automatically](), [docker](), [docker-image](), [home-assistant](), [how-to](), [pi-4](), [podman](), [raspberry-pi](), [systemd](), [update]() ### AI overview A tutorial showing how to run Home Assistant in a Docker or Podman container on NixOS, including configuration for host networking and a Zigbee USB dongle. It also explains how to automate image updates and prune old images with systemd timers. ### Source excerpt Learn how to run the Home Assistant Docker image on NixOS on a Raspberry Pi 4 or other NixOS machines and how to keep it up to date automatically. ## How to Use the Materialize Emulator DevFeed: [How to Use the Materialize Emulator]() Original publisher: [Read original article]() Published: 2024-10-15T22:00:00Z Content type: tutorial Language: en Sources: [Dennis Felsing]() Topics: [Emulator](), [Docker Image](), [Testing]() Tags: [docker-image](), [emulator](), [how-to](), [qa](), [testing]() ### AI overview This article introduces the Materialize Emulator, a Docker image used during testing to maintain a locally hosted version of Materialize. It directs readers to the Materialize blog for the full post. ### Source excerpt In our last blog about our Quality Assurance (QA) team, we gave an overview of the QA process, including our software and testing methods. One of our key tools during testing is the Materialize Emulator, a Docker image that allows you to maintain a locally hosted version of Materialize. Read the rest of the blog post over on the Materialize blog. ## Deploying applications to Fly.io without Docker DevFeed: [Deploying applications to Fly.io without Docker]() Original publisher: [Read original article]() Author: Harsh Shandilya Published: 2024-10-05T06:30:00Z Content type: tutorial Language: en Sources: [Posts on Harsh Shandilya]() Topics: [fly.io](), [fly](), [Skopeo](), [Docker Image](), [docker registry](), [Nix](), [Command-line interface](), [Deployment]() Tags: [cli](), [deployment](), [docker](), [docker-image](), [docker-registry](), [fly](), [fly-io](), [nix](), [oci-registry](), [skopeo]() ### AI overview A guide to deploying applications on Fly.io without installing Docker locally. It explains how to use Nix to build an OCI image and Skopeo to copy the image archive to Fly.io's registry before deployment. ### Source excerpt A quick guide to deploying apps to Fly.io without a local Docker installation ## Apache Airflow 2.10.0 is here DevFeed: [Apache Airflow 2.10.0 is here]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2024-08-08T00:00:00Z Content type: release Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [releases](), [telemetry](), [configuration](), [data](), [PyPI](), [Docker Image]() Tags: [airflow](), [apache-airflow](), [configuration](), [docker-image](), [pypi](), [release](), [release-notes](), [releases](), [telemetry]() ### AI overview Apache Airflow 2.10.0 is available with default basic telemetry collection, support for multiple executors in one environment, and dynamic dataset scheduling through DatasetAlias. Deployments can opt out of telemetry collection. ### Source excerpt I'm happy to announce that Apache Airflow 2.10.0 is now available, bringing an array of noteworthy enhancements and new features that will greatly serve our community. Details: πŸ“¦ PyPI: https://pypi.org/project/apache-airflow/2.10.0/ πŸ“š Docs: https://airflow.apache.org/docs/apache-airflow/2.10.0/ πŸ›  Release Notes: https://airflow.apache.org/docs/apache-airflow/2.10.0/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:2.10.0" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-2.10.0 Airflow now collects Telemetry data by default With the release of Airflow 2.10.0, we've introduced the collection of basic telemetry data, as outlined here. This data will play a crucial role in helping Airflow maintainers gain a deeper understanding of how Airflow is utilized across various deployments. The insights derived from this information are invaluable in guiding the prioritization of patches, minor releases, and security fixes. Moreover, this data will inform key decisions regarding the development roadmap, ensuring that Airflow continues to evolve in line with community needs. For those who prefer not to participate in data collection, deployments can easily opt out by setting the [usage_data_collection] enabled option to False or by using the SCARF_ANALYTICS=false environment variable. Multiple Executor Configuration (formerly "Hybrid Execution") Each executor comes with its unique set of strengths and weaknesses, typically balancing latency, isolation, and compute efficiency. Traditionally, an Airflow environment is limited to a single executor, requiring users to make trade-offs, as no single executor is perfectly suited for all types of tasks. We are introducing a new feature that allows for the concurrent use of multiple executors within a single Airflow environment. This flexibility enables users to take advantage of the specific strengths of different executors for various tasks, improving overall efficiency and mitigating weaknesses. Users can s ## My first 3 months at incident.io DevFeed: [My first 3 months at incident.io]() Original publisher: [Read original article]() Author: Lambert Le Manh Published: 2024-08-02T15:14:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [data](), [BigQuery](), [Docker](), [Cloud](), [Docker Image](), [ci](), [PostgreSQL](), [cloud-infrastructure](), [Docker Compose]() Tags: [bigquery](), [ci](), [cloud](), [cost](), [data](), [development](), [docker](), [docker-image](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [json](), [outage](), [platform](), [post-mortem](), [postgresql](), [registry](), [slack-incident](), [storage](), [tools]() ### AI overview A Data Engineer reviews their first three months at incident.io, focusing on scaling the development platform, evaluating alternatives to Fivetran for PostgreSQL data ingestion, and reducing platform costs. The work included storing dbt artifacts in Google Cloud Storage and using custom Docker images in CircleCI and production workflows. ### Source excerpt A review of my first three months as Data Engineer on the Data team working on improving the development platform, experimenting with different data ingestion tools, and cutting down on overall platform costs. ## Best practices for writing Dockerfiles - Follow "Filesystem Hierarchy Standard" DevFeed: [Best practices for writing Dockerfiles - Follow "Filesystem Hierarchy Standard"]() Original publisher: [Read original article]() Author: Tom Published: 2024-03-11T00:00:00Z Content type: tutorial Language: en Sources: [Tomasz GΔ…gor]() Topics: [Dockerfile](), [Filesystems](), [Docker Image](), [Containers](), [maintenance]() Tags: [best-practices](), [containers](), [docker](), [docker-container-maintainability](), [docker-image](), [docker-image-organization](), [dockerfile-best-practices](), [dockerfiles](), [fhs-guidelines](), [filesystem-hierarchy-standard](), [linux](), [maintainability](), [volumes]() ### AI overview This tutorial explains how to organize Docker images according to the Filesystem Hierarchy Standard. It covers locations for scripts, binaries, Java artifacts, web content, and temporary or writable files, recommending volumes for files that containers must write. ### Source excerpt Learn best practices for writing Dockerfiles by following the Filesystem Hierarchy Standard (FHS) to enhance organization and maintainability of your Docker images. ## Best practices for patching and deprecating Docker images DevFeed: [Best practices for patching and deprecating Docker images]() Original publisher: [Read original article]() Author: Tom Published: 2024-02-09T00:00:00Z Content type: tutorial Language: en Sources: [Tomasz GΔ…gor]() Topics: [Docker](), [Docker Image](), [Security](), [DevOps]() Tags: [best-practices](), [devops](), [devops-docker-management](), [docker](), [docker-image](), [docker-image-deprecation](), [docker-image-maintenance](), [docker-image-patching](), [docker-security-best-practices](), [docker-update-strategies](), [how-to](), [immutability](), [linux](), [security]() ### AI overview This article presents practices for maintaining Docker images securely and predictably. It recommends deprecating images before deleting them, notifying users of upcoming breaking changes, upgrading base images quarterly, and pinning versions to keep rebuilds stable. ### Source excerpt Learn the best practices for patching and deprecating Docker images to maintain security and stability in your DevOps workflows. ## How Chainguard protects against "Leaky Vessel" container escape vulnerabilities DevFeed: [How Chainguard protects against "Leaky Vessel" container escape vulnerabilities]() Original publisher: [Read original article]() Published: 2024-02-01T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Vulnerabilities](), [container-security](), [chainguard images](), [container images]() Tags: [apko](), [chainguard](), [container-escape](), [container-images](), [container-security](), [docker](), [docker-image](), [leaky-vessel](), [patches](), [runc](), [secure-by-default](), [upgrade]() ### AI overview This article explains the "Leaky Vessel" vulnerabilities affecting runc and BuildKit, including risks of container escape, host filesystem access, and elevated privileges. It describes how Chainguard Images use apko instead of runc or BuildKit and recommends upgrading to patched versions. ### Source excerpt Chainguard's response to 'Leaky Vessel' vulnerabilities: safeguarding container images with innovative, secure-by-default build processes. ## Checking compressed size of Docker image DevFeed: [Checking compressed size of Docker image]() Original publisher: [Read original article]() Author: Tom Published: 2024-01-24T00:00:00Z Content type: tutorial Language: en Sources: [Tomasz GΔ…gor]() Topics: [Docker Image](), [Docker](), [Containers](), [Bash](), [Jenkins]() Tags: [bash](), [bash-function-for-docker](), [compressed-docker-image](), [containers](), [docker](), [docker-image](), [docker-image-metrics](), [docker-image-size](), [docker-optimization](), [docker-performance](), [how-to](), [linux](), [measurements]() ### AI overview The article explains how to use Docker image compressed size as a development metric for estimating download-time impact, while comparing it with uncompressed size to approximate extraction and startup-time impact. It presents a Bash function to simplify the measurements. ### Source excerpt Learn how to check the compressed size of Docker images to optimize download and startup times, using a simple bash function for accurate measurements. ## Trifecta Technology DevFeed: [Trifecta Technology]() Original publisher: [Read original article]() Published: 2024-01-18T09:22:53Z Content type: article Language: en Sources: [Bert Hubert's writings]() Topics: [Open Source](), [Software](), [C++](), [SQLite](), [JavaScript](), [Docker Image]() Tags: [c-plus-plus](), [database](), [docker-image](), [javascript](), [open-source](), [programming](), [technology]() ### AI overview An article explaining the technology behind Trifecta, an open-source image-sharing site designed as a small, self-contained service that users can run themselves. It discusses modern C++ libraries, SQLite, JavaScript, build and distribution choices, type-safe data access, and small Docker images. ### Source excerpt Trifecta is a simple open source image sharing site, built with a combination of modern C++, database and web technologies. Intended to both be useful and make some points. This page sets out to explain the underlying technology of this small yet hopefully useful piece of software. More background can be found on its main page, where you can also read a bit about why I built this software. In short, 1: I need an image sharing site I trust and that does not track its users and 2: I want to show that you can still run a service yourself safely. ## 3 tips for rebuilding a Docker image faster (and save CI seconds!) DevFeed: [3 tips for rebuilding a Docker image faster (and save CI seconds!)]() Original publisher: [Read original article]() Author: Liran Tal Published: 2023-12-28T05:00:00Z Content type: tutorial Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Docker](), [Docker Image](), [Dockerfile](), [Containers](), [CI/CD]() Tags: [ai](), [blog](), [ci](), [ci-cd](), [containers](), [dependency](), [deployment](), [devops](), [docker](), [docker-image](), [engineering](), [image](), [python]() ### AI overview This tutorial presents three ways to rebuild Docker images faster and reduce CI time: add a .dockerignore file, use a dependency lockfile, and group commands. ### Source excerpt Learn about three great ways to rebuild a Docker image faster: Add a .dockerignore file to your repository, use a dependency lockfile, and group commands! [Next page]()