### GraphQL Query Example

Source: https://system-design.space/chapter/learning-graphql-book

Demonstrates how to query data in GraphQL. This example fetches user information, including their name, email, and associated posts with titles and creation dates.

```graphql
query {
  user(id: "123") {
    name
    email
    posts {
      title
      createdAt
    }
  }
}
```

--------------------------------

### GraphQL Subscription Example

Source: https://system-design.space/chapter/learning-graphql-book

Shows an example of a GraphQL subscription for real-time updates. This subscription listens for new posts and returns the ID, title, and author's name of the new post.

```graphql
subscription {
  newPost {
    id
    title
    author {
      name
    }
  }
}
```

--------------------------------

### Kubernetes Init Container for Initialization Tasks

Source: https://system-design.space/chapter/kubernetes-patterns-book

Utilizes Init Containers to perform setup tasks before the main application containers start. This is useful for tasks like database migrations, waiting for dependencies, or setting up configurations.

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: myapp-pod
spec:
  containers:
  - name: myapp-container
    image: myapp:latest
    ports:
    - containerPort: 8080
  initContainers:
  - name: init-container
    image: busybox
    command: ['sh', '-c', 'echo Initializing... && sleep 5']
```

--------------------------------

### GraphQL Mutation Example

Source: https://system-design.space/chapter/learning-graphql-book

Illustrates how to modify data using GraphQL mutations. This example shows the creation of a new post with a title and content, and returns the ID and title of the created post.

```graphql
mutation {
  createPost(input: {
    title: "New Post"
    content: "Hello GraphQL!"
  }) {
    id
    title
  }
}
```

--------------------------------

### Kubernetes Deployment Example

Source: https://system-design.space/chapter/cloud-native-book

This YAML manifest defines a Kubernetes Deployment for a microservice named 'my-app'. It specifies the desired number of replicas, labels for selecting pods, and the container image to use, along with resource limits for CPU and memory.

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    spec:
      containers:
      - name: my-app
        image: my-app:v1.2.0
        resources:
          limits:
            memory: "256Mi"
            cpu: "500m"
```

--------------------------------

### Durability Calculation Example (Python)

Source: https://system-design.space/chapter/object-storage-case

Demonstrates a simplified calculation for data durability based on disk failure rates and replication strategy. Assumes annual failure rate (AFR) and the number of replicas across different failure domains.

```python
# Предположим:
# - Annual Failure Rate (AFR) одного диска = 2%
# - 3 реплики в разных failure domains

P_loss_1_disk = 0.02
P_loss_2_disks_simultaneously = P_loss_1_disk * P_loss_1_disk
P_loss_3_disks_before_recovery = P_loss_2_disks_simultaneously * P_loss_1_disk

# Добавляем: разные AZ, быстрое восстановление, мониторинг
# → 99.999999999% durability
```

--------------------------------

### Lifecycle Policy Configuration (JSON)

Source: https://system-design.space/chapter/object-storage-case

An example JSON configuration for defining lifecycle rules in an object storage system. This includes transitions to different storage classes and expiration policies based on object age and prefixes.

```json
{
  "rules": [
    {
      "filter": {"prefix": "logs/"},
      "transitions": [
        {"days": 30, "storageClass": "INFREQUENT_ACCESS"},
        {"days": 90, "storageClass": "GLACIER"}
      ],
      "expiration": {"days": 365}
    }
  ]
}
```