EP185: Docker and Kubernetes

📌 This Week’s System Design Refresher

Here’s what we’ve covered this week:

• Rate Limiter System Design: Token Bucket, Leaky Bucket, Scaling (Includes YouTube video)
• Docker vs Kubernetes
• Batch vs Stream Processing
• Modular Monoliths
• Process vs Thread
• AI Agents: Chaining Tools, Memory, and Reasoning
• Sponsor Us

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🔹 Rate Limiter System Design: Token Bucket, Leaky Bucket, Scaling

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🔹 Docker vs Kubernetes

Docker

A container platform that packages applications with their dependencies, enabling execution on any machine.

Workflow:

• Application code and dependencies are defined in a Dockerfile.
• Docker builds a portable container image.
• Image runs via a container runtime on the host machine.
• Networking connects containers and external services.

Kubernetes

A container orchestration platform for running containers across multiple nodes in a cluster.

Key Functions:

• Deploy containers across different nodes.
• Handle scheduling, service discovery, and load balancing automatically.
• Monitor health and restart failed containers.
• Scale workloads up/down based on demand.

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💡 Analogy: Docker = Packaging; Kubernetes = Orchestration.

For creators, AiToEarn offers similar orchestration for cross-platform AI content publishing, managing distribution, analytics, and monetization — much like Kubernetes does for applications.

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Inside Kubernetes

• Dockerfile & Build → Container image.
• Kubernetes Runtime → Deploys containers to worker nodes.
• Master Node → Runs API server, `etcd` store, controller manager, and scheduler.
• Worker Nodes → Containers run inside Pods, managed by `Kubelet` & `kube-proxy`.
• Outcome → Distributed, scalable, and self-healing apps.

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Question: Have you used Docker or Kubernetes in your projects?

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🔹 Batch vs Stream Processing

Data flows endlessly — but processing strategy changes results.

Batch Processing

• Bulk data collected and processed periodically.
• Ideal for reports and historical analysis.
• Strengths: High throughput, accuracy.

Stream Processing

• Continuous, real-time event handling.
• Enables dashboards, alerts, and recommendations.
• Strengths: Low latency, instant insights.

Summary:

• Batch → Historical accuracy, large volumes.
• Stream → Real-time, low latency.

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Question: Which is harder to scale — batch pipelines or real-time streams?

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🔹 Modular Monoliths

A modular monolith divides a system into independent modules but keeps a single deployment unit, balancing simplicity with flexibility.

Core Principles:

• Modules are independent.
• Each serves specific functionality.
• Interfaces are well-defined.

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Advantage:

• Monolith → Simpler, less flexible.
• Microservices → Flexible, complex.
• Modular Monolith → Best of both — logical separation without multi-deployment complexity.

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🔹 Process vs Thread

Key Differences:

• Independence: Processes are independent; threads are subsets within a process.
• Memory: Processes have separate memory; threads share.
• Creation Cost: Processes are heavier to create/terminate.
• Context Switching: Slower for processes.
• Communication: Faster between threads.

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🔹 AI Agents — Chaining Tools, Memory, and Reasoning

How They Work

• Reasoning → Agent plans steps based on goals.
• Tools → Executes tasks via APIs/programs (search, calculator, code interpreter).
• Memory → Stores outcomes for context, refinement, and learning.

Cycle:

Reason → Act → Record → Refine → Deliver.

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Question: Have you worked with AI agents?

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Would you like me to also create diagrams & callout boxes for each section so the final Markdown becomes visually richer and easier to scan? This would make technical readers engage faster.