docker 교과서

1부 도커 컨테이너와 이미지 이해하기

docker 교과서 Chapter 2 도커의 기본적인 사용법

• Docker Virtualization: Provides OS-level virtualization for running applications on a virtualized Linux kernel.
• Docker Container: Isolates applications with minimal resources like filesystem, IP address, and hostname.
• Docker Engine: Manages containers via Docker API, with external command access through REST API.
• Docker CLI: A client that interacts with Docker Engine using Docker API.
• docker container run: Runs a container from an image, downloads it if not found locally.
• --interactive/--tty (-i/-t): Opens an interactive terminal session in the container.
• --detach (-d): Runs the container in the background.
• --publish: Maps host port to container port (e.g., 8080:80).
• docker container ls: Lists running containers; -a lists all, including stopped ones.
• docker container top: Displays active processes in a container.
• docker container logs: Shows container's output logs.
• docker container rm: Removes containers; -f force-removes all.
• docker container rmi: Deletes images, independent of container removal.
• docker container exec: Executes commands in a running container, with options like -it for shell access.
• Docker API: Interfaces with Docker Engine via REST API; CLI is one client, enabling remote management and integration with GUI tools.

docker 교과서 Chapter 3 도커 이미지 만들기

• Docker Hub Images: Use pre-shared images from Docker Hub for easier setup.
• Writing Dockerfile: Dockerfile scripts define instructions to build Docker images.
• Building Container Images: Compile the image using Dockerfile commands.
• Understanding Docker Image Layers: Each Dockerfile command creates a new image layer.
• Optimizing Dockerfile with Image Layer Cache: Utilize cached layers to speed up builds by rearranging frequently changed layers last.
• Docker Run -e/--env Flag: Inject environment variables at container startup.
• Why Dockerfile?: Script to package applications into Docker images.
• Docker Image History: Inspect image layers using docker image history.
• Docker Image Layer Sharing: Reuse base layers across images to save space.
• Docker Layer Caching: Optimize builds by placing static instructions early in Dockerfile and variable ones later.
• Manual Container Creation: Use docker container commit to manually create images from container changes.

docker 교과서 Chapter 4 애플리케이션 소스 코드에서 도커 이미지까지

• Docker as Build Tool: Standardizes build environments by defining toolchains (e.g., compilers, linkers) in Dockerfiles.
• Staging in Docker: Manages stages (build, test) with separate base images and uses AS keyword for stage aliasing.
• RUN Instruction: Executes commands inside the container using shell scripts.
• CMD vs ENTRYPOINT: CMD for overridable commands, ENTRYPOINT for fixed commands that always run.
• Docker Virtual Network: Enables container communication via virtual networks using IPs or container names.
• Multi-Stage Build: Reduces image size by separating build tools and runtime; excludes unnecessary tools in final image.
• Optimized Dockerfile: Moves frequently changing files (e.g., index.html) to later stages and uses multi-stage builds to minimize image size.
• CI Integration: Automates building and deploying Docker images with source code using CI pipelines.

docker 교과서 Chapter 5 도커 허브 등 레지스트리에 이미지 공유하기

• Docker Image Naming: Docker images are identified as registry/group/image:tag (e.g., docker.io/diamol/base:latest).
• Docker Tag: Use docker tag to assign names and tags to images, allowing multiple names for the same image ID.
• Docker Image Push: Use docker image push to upload images to a registry (e.g., Docker Hub), with login via docker login.
• Docker Registry as Image: A Docker registry can also be run as a container and used for local image storage and backups.
• Tag Naming Convention: Follows semantic versioning (Major.Minor.Patch), where specific or broader tags can be used (e.g., hello:1, hello:1.2, hello:latest).
• Golden Image: A customized base image with team-specific configurations, often created using LABEL for metadata.
• Docker API for Registry: Manage and verify images using the Docker registry API with requests like GET /v2/_catalog, GET /v2/<name>/tags/list, and DELETE /v2/<name>/manifests/<digest>.

docker 교과서 Chapter 6 도커 볼륨을 이용한 퍼시스턴트 스토리지

• COW Mechanism: Containers share read-only image layers, and changes are written to a unique writable layer (not shared between containers).

• Data Persistence: Container deletion removes the writable layer; Docker volumes or mounts provide persistent storage.

• Docker Volume: A Docker-managed storage solution that allows containers to share data, independent of the host OS. Volumes are consistent across environments.

  • Creation: Use docker volume create to create a volume, and --volume or --mount to attach it to a container.
  • VOLUME vs --volume: Dockerfile VOLUME defines the container’s storage location; --volume specifies both the source and target, allowing for more control.

• Docker Bind Mount: Allows direct mounting of host filesystem directories or files into a container without needing to create a Docker volume.

  • Usage: Bind mount with --volume or --mount options.
  • File Access: Default permissions are read-only for security; modify with the USER command in Dockerfile to manage access.
  • Host Dependence: Bind mounts rely on the host’s file system, unlike volumes, which Docker manages independently.
  • Overwriting Paths: Mounting into an existing directory replaces the container's files at that path with the host's files.

• Volume vs Bind Mount:

  • Volumes are managed by Docker and portable across environments.
  • Bind mounts rely on host-specific directories, making them less portable but providing direct access to host files.

• Linux vs Windows: Linux allows for mounting both directories and single files, while Windows does not support single-file bind mounts.

2부 컨테이너로 분산 애플리케이션 실행하기

docker 교과서 Chapter 7 도커 컴포즈로 분산 애플리케이션 실행하기

• Docker Compose: A tool that simplifies managing multiple containers on a single host by defining services and networks in a declarative docker-compose.yml file.
• version: Specifies the version of Docker Compose being used.
• services: Defines containers (e.g., accesslog, iotd, image-gallery), each of which can have attributes like images, ports, networks, etc.
• networks: Defines the network configuration for containers, with options like external to use an existing network instead of creating a new one.
• depends_on: Specifies the order of container startup, ensuring dependencies start before the current service.
• ports: Maps ports between the host and the container (e.g., 8010:80).
• volumes vs bind mount: volumes are Docker-managed storage, while bind mount links host filesystem directories directly to containers.
• restart Policy: Ensures containers restart after failures or host reboots (e.g., always, unless-stopped).
• scale: Scales out identical containers to distribute load, leveraging Docker's DNS to balance requests.
• Limitations: Docker Compose is limited to single-host environments, and for distributed systems, tools like Docker Swarm or Kubernetes are required.
• Orchestration Alternatives: Docker Swarm or Kubernetes are used when managing multiple hosts or a more complex service architecture.
• Secrets Management: Store sensitive data like passwords or API keys using secrets in docker-compose.yml, linking them securely to services.
• Bind Mount vs Volume: Bind mounts are useful for local development, while Docker volumes are preferred for production due to their independence from the host filesystem.

docker 교과서 Chapter 8 헬스 체크와 디펜던시 체크로 애플리케이션 신뢰성 확보하기

• HEALTHCHECK: Docker instruction to monitor the internal health of a container by running custom commands at set intervals.
• Status Monitoring: Checks for issues like HTTP 500 errors, where the process may be running, but the container is not functioning correctly.
• curl --fail: A command that returns 0 if the endpoint is accessible and a non-zero exit code if there is an error, helping Docker set the health status to either healthy or unhealthy.
• Inspecting Health Logs: Use docker container inspect to view detailed health logs, including ExitCode and Output, which record error codes and responses.
• Dependency Check: Ensures that a service waits for its dependencies (e.g., databases or APIs) to be ready before starting.
• depends_on in Docker Compose: Defines the startup order of services, though it does not guarantee a service's readiness; best for controlling order, not dependency checks.
• Direct Dependency Check in Dockerfile: Uses CMD to execute a curl request to check if a dependent service is accessible, only starting the application if the dependency is met.
• Custom Utility for Dependency Check: Instead of curl, custom-built utilities specific to the application are preferred for dependency checks, enhancing security and reducing unnecessary tools in the container.
• Docker Compose Healthcheck: Defines healthchecks in docker-compose.yml using interval, timeout, and retries to repeatedly check a service’s health.
• Automatic Restart with restart: on-failure: Configures Docker to restart a service if it fails, useful for dependency checks without depends_on by reattempting until dependencies are ready.
• Restart Limitations in Compose: Docker Compose can restart containers based on health status but cannot replace an unhealthy container with a new one, unlike advanced orchestration systems.

docker 교과서 Chapter 9 컨테이너 모니터링으로 투명성 있는 애플리케이션 만들기

• Prometheus: A monitoring tool for collecting, storing, and querying metrics, designed for tracking container health and performance across distributed environments through a standardized API.
• Monitoring Docker Engine: Prometheus can monitor Docker Engine metrics directly, tracking data like container states and failed image builds through /metrics at 127.0.0.1:9323.
• Container-Specific Metrics: Prometheus requires applications to expose their own metrics via Prometheus libraries, supporting custom metrics like response time, error rates, and specific logging events.
• Key Metrics: Recommended metrics include response time (e.g., latency between services or user response times) and simple log counts (e.g., 500 errors), which provide valuable insights with minimal resource usage.
• Prometheus in Golang: Example uses Prometheus Go client library to define Gauge and Counter metrics, exposing these via /metrics.
• Prometheus in NodeJS: Uses prom-client to track metrics like total log requests and unique IP addresses, exposing the /metrics endpoint for scraping.
• Prometheus Graphs: PromQL is used to query and visualize metric data over time. For example, sum functions aggregate data, and without excludes unwanted attributes.
• Dynamic Scraping: Prometheus prometheus.yml configurations allow dynamic scraping of container metrics using dns_sd_configs (for services with multiple IPs) and static_configs (for fixed services).
• Grafana: A dashboard tool that integrates with Prometheus to visualize metrics, enhancing application observability. It uses PromQL to pull and display Prometheus metrics in customized, interactive graphs.
• Service Level Indicators (SLIs): Key performance indicators such as latency, availability, and error rates, essential for monitoring application health. Resources like Google’s Site Reliability Engineering (SRE) guide provide guidance on SLIs.

docker 교과서 Chapter 10 도커 컴포즈를 이용한 여러 환경 구성

• Docker Compose for Environments: Useful in cases where multiple containers need to run on a single Docker Engine, often for smaller services, testing, or development.
• Project Naming: Allows running multiple instances of the same Compose setup by changing the project name (e.g., -p todo-test), helping avoid conflicts by isolating container names.
• Port Conflicts: If containers have predefined ports in their configurations, repeated instances may use random ports. Use docker port <container-name> <inner-port> to retrieve active port mappings.
• Docker Compose Override: Combines multiple Compose files (base and override) to modify configuration for specific environments without changing the base file structure.
• Environment-Specific Overrides: Set up distinct configurations (e.g., docker-compose-dev.yml, docker-compose-test.yml) for dev, test, and user acceptance testing, customizing ports, networks, health checks, and restart policies.
• Environment Variables and Secrets:
  • environment: Defines key-value pairs directly in the Compose file.
  • env_file: Loads environment variables from a file with shell-style syntax.
  • secrets: Declares sensitive data, stored securely and mapped to services in the secrets section.
• Using Local Environment Variables: Use ${VAR} syntax to pull values from the host environment, allowing flexible variable usage without explicit override files.
• YAML Extensions (Anchors and Aliases): Helps reduce redundancy by defining common settings (&anchor) and reusing them (*alias) across multiple services.
• Application Configuration with Docker Compose: Adjust settings per environment for options like databases, port bindings, networks, and application-specific configs (e.g., logging level, cache size).
  Use Case Summary:
  • Development: Local database, custom port (8089), specific image version (e.g., v2).
  • Testing: Separate database service (e.g., PostgreSQL), dedicated volume for persistence, assigned port (8080), and latest image version.

docker 교과서 Chapter 11 도커와 도커 컴포즈를 이용한 애플리케이션 빌드 및 테스트

3부 컨테이너 오케스트레이션을 이용한 스케일링

docker 교과서 Chapter 12 컨테이너 오케스틀이션: 도커 스웜과 쿠버네티스
docker 교과서 Chapter 13 도커 스웜 스택으로 분산 애플리케이션 배포하기
docker 교과서 Chapter 14 업그레이드와 롤백을 이용한 업데이트 자동화
docker 교과서 Chapter 15 보안 원격 접근 및 CI/CD를 위한 도커 설정
docker 교과서 Chapter 16 어디서든 실행할 수 있는 도커 이미지 만들기: 리눅스, 윈도, 인텔, ARM

4부 운영 환경 투입을 위한 컨테이너 준비하기

docker 교과서 Chapter 17 도커 이미지 최적화하기: 보안, 용량, 속도
docker 교과서 Chapter 18 컨테이너의 애플리케이션 설정 관리
docker 교과서 Chapter 19 도커를 이용한 로그 생성 및 관리
docker 교과서 Chapter 20 리버스 프록시를 이용해 컨테이너 HTTP 트래픽 제어하기
docker 교과서 Chapter 21 메시지 큐를 이용한 비동기 통신
docker 교과서 Chapter 22 끝없는 정진