Simple Data Model

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114

from dataclasses import dataclass, asdict
from typing import Optional, List
from datetime import datetime
import json
 
# Define your schema explicitly in code
# The database won't enforce this, but your code will
 
@dataclass
class Address:
    """Embedded address object."""
    street: str
    city: str
    state: str
    postal_code: str
    country: str = "USA"
 
@dataclass
class User:
    """
    User entity schema.
    This is the 'schema' for user data stored in the key-value store.
    The database treats it as opaque bytes; we enforce structure here.
    """
    id: str
    email: str
    name: str
    created_at: datetime
    updated_at: datetime
    
    # Optional fields demonstrate schemaless flexibility
    phone: Optional[str] = None
    address: Optional[Address] = None
    preferences: dict = None
    tags: List[str] = None
    
    def __post_init__(self):
        if self.preferences is None:
            self.preferences = {}
        if self.tags is None:
            self.tags = []
    
    def to_bytes(self) -> bytes:
        """Serialize to bytes for storage."""
        data = asdict(self)
        # Convert datetime to ISO format strings
        data['created_at'] = self.created_at.isoformat()
        data['updated_at'] = self.updated_at.isoformat()
        return json.dumps(data).encode('utf-8')
    
    @classmethod
    def from_bytes(cls, data: bytes) -> 'User':
        """Deserialize from bytes."""
        obj = json.loads(data.decode('utf-8'))
        # Convert ISO strings back to datetime
        obj['created_at'] = datetime.fromisoformat(obj['created_at'])
        obj['updated_at'] = datetime.fromisoformat(obj['updated_at'])
        # Handle nested Address
        if obj.get('address'):
            obj['address'] = Address(**obj['address'])
        return cls(**obj)
 
 
class UserRepository:
    """
    Repository pattern encapsulating all user data access.
    Keeps key design and serialization logic in one place.
    """
    
    def __init__(self, store):
        self.store = store
    
    def _key(self, user_id: str) -> str:
        return f"user:{user_id}"
    
    def save(self, user: User) -> None:
        """Save user to store."""
        key = self._key(user.id)
        value = user.to_bytes()
        self.store.put(key, value)
    
    def get(self, user_id: str) -> Optional[User]:
        """Retrieve user by ID."""
        key = self._key(user_id)
        data = self.store.get(key)
        if data is None:
            return None
        return User.from_bytes(data)
    
    def delete(self, user_id: str) -> bool:
        """Delete user by ID."""
        key = self._key(user_id)
        return self.store.delete(key)
 
 
# Usage example
user = User(
    id="u123",
    email="alice@example.com",
    name="Alice Johnson",
    created_at=datetime.now(),
    updated_at=datetime.now(),
    address=Address(
        street="123 Main St",
        city="San Francisco",
        state="CA",
        postal_code="94102"
    ),
    preferences={"theme": "dark", "notifications": True},
    tags=["premium", "early-adopter"]
)
 
# Serialized size: ~350 bytes
# Fully self-describing JSON that can evolve over time

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73

class MonolithicEntityStore:
    """
    Store complete entities as single values.
    Most common and simplest pattern.
    """
    
    def save_product(self, product: dict) -> None:
        """
        Store complete product entity.
        
        Key: product:{product_id}
        Value: Complete product JSON
        """
        key = f"product:{product['id']}"
        value = json.dumps(product).encode('utf-8')
        self.store.put(key, value)
    
    def get_product(self, product_id: str) -> dict:
        """Retrieve complete product."""
        key = f"product:{product_id}"
        data = self.store.get(key)
        return json.loads(data) if data else None
    
    def update_product(self, product_id: str, updates: dict) -> dict:
        """
        Update specific fields of a product.
        
        Note: This requires read-modify-write, which is NOT atomic.
        For concurrent updates, use CAS (compare-and-swap).
        """
        # Read current state
        product = self.get_product(product_id)
        if product is None:
            raise KeyError(f"Product {product_id} not found")
        
        # Modify
        product.update(updates)
        product['updated_at'] = datetime.now().isoformat()
        
        # Write back (NOT atomic without CAS!)
        self.save_product(product)
        return product
 
 
# Example product entity
product = {
    "id": "p123",
    "sku": "LAPTOP-DELL-XPS15",
    "name": "Dell XPS 15",
    "description": "High-performance laptop...",
    "price_cents": 149999,
    "currency": "USD",
    "category": "electronics/computers/laptops",
    "attributes": {
        "brand": "Dell",
        "screen_size": "15.6",
        "processor": "Intel i7-12700H",
        "ram_gb": 32,
        "storage_gb": 512
    },
    "inventory": {
        "warehouse_01": 45,
        "warehouse_02": 23
    },
    "images": [
        "https://cdn.example.com/products/p123/main.jpg",
        "https://cdn.example.com/products/p123/side.jpg"
    ],
    "created_at": "2024-01-15T10:30:00Z",
    "updated_at": "2024-01-15T10:30:00Z"
}
 
# Stored as: product:p123 → {complete JSON}

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86

class AttributePerKeyStore:
    """
    Store each entity attribute as a separate key.
    Useful for large entities or frequent partial updates.
    """
    
    def save_user_attribute(
        self, 
        user_id: str, 
        attribute: str, 
        value: any
    ) -> None:
        """Store a single user attribute."""
        key = f"user:{user_id}:{attribute}"
        self.store.put(key, json.dumps(value).encode())
    
    def get_user_attribute(self, user_id: str, attribute: str) -> any:
        """Retrieve a single user attribute."""
        key = f"user:{user_id}:{attribute}"
        data = self.store.get(key)
        return json.loads(data) if data else None
    
    def save_user(self, user: dict) -> None:
        """
        Store user as multiple keys.
        Each attribute gets its own key.
        """
        user_id = user['id']
        for attr, value in user.items():
            if attr != 'id':  # Don't store id redundantly
                self.save_user_attribute(user_id, attr, value)
    
    def get_user(self, user_id: str, attributes: List[str] = None) -> dict:
        """
        Retrieve user, optionally only specific attributes.
        
        Uses MGET for efficiency when fetching multiple attributes.
        """
        if attributes is None:
            # Need to know all possible attributes
            # This is a limitation of this pattern
            attributes = ['email', 'name', 'phone', 'address', 'preferences']
        
        keys = [f"user:{user_id}:{attr}" for attr in attributes]
        values = self.store.multi_get(keys)
        
        result = {'id': user_id}
        for attr, key in zip(attributes, keys):
            if values.get(key):
                result[attr] = json.loads(values[key])
        
        return result
    
    def update_user_attribute(
        self, 
        user_id: str, 
        attribute: str, 
        value: any
    ) -> None:
        """
        Update a single attribute atomically.
        
        This is a single key operation - naturally atomic.
        Much more efficient than monolithic read-modify-write.
        """
        self.save_user_attribute(user_id, attribute, value)
 
 
# Key structure for a user:
# user:u123:email      → "alice@example.com"
# user:u123:name       → "Alice Johnson"
# user:u123:phone      → "+1-555-0123"
# user:u123:address    → {"street": "...", "city": "..."}
# user:u123:preferences → {"theme": "dark", ...}
# user:u123:created_at → "2024-01-15T10:30:00Z"
 
# Advantages:
# - Update single field with single PUT (atomic)
# - Read only fields you need (bandwidth savings)
# - No read-modify-write race conditions for field updates
 
# Disadvantages:
# - More keys to manage
# - Must know all field names to read complete entity
# - Multiple round-trips unless using MGET
# - Harder to delete entire entity (need to track all keys)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95

class OneToManyRelationship:
    """
    Pattern for one-to-many relationships.
    Example: User has many Orders
    """
    
    def __init__(self, store):
        self.store = store
    
    # ===== Order Entity Operations =====
    
    def save_order(self, order: dict) -> None:
        """Store order entity."""
        key = f"order:{order['id']}"
        self.store.put(key, json.dumps(order).encode())
    
    def get_order(self, order_id: str) -> dict:
        """Retrieve order by ID."""
        key = f"order:{order_id}"
        data = self.store.get(key)
        return json.loads(data) if data else None
    
    # ===== Relationship Operations =====
    
    def add_order_to_user(self, user_id: str, order_id: str) -> None:
        """
        Add order to user's order list.
        
        Maintains a list of order IDs for the user.
        """
        # Get current order list
        list_key = f"user:{user_id}:orders"
        current = self.store.get(list_key)
        order_ids = json.loads(current) if current else []
        
        # Add new order ID (avoid duplicates)
        if order_id not in order_ids:
            order_ids.append(order_id)
        
        # Save updated list
        self.store.put(list_key, json.dumps(order_ids).encode())
    
    def get_user_order_ids(self, user_id: str) -> List[str]:
        """Get list of order IDs for a user."""
        list_key = f"user:{user_id}:orders"
        data = self.store.get(list_key)
        return json.loads(data) if data else []
    
    def get_user_orders(self, user_id: str) -> List[dict]:
        """
        Get all orders for a user (with full order data).
        
        This requires:
        1. Fetch the list of order IDs
        2. Fetch each order by ID (use MGET for efficiency)
        """
        order_ids = self.get_user_order_ids(user_id)
        
        if not order_ids:
            return []
        
        # Use MGET to fetch all orders in one round-trip
        keys = [f"order:{oid}" for oid in order_ids]
        values = self.store.multi_get(keys)
        
        orders = []
        for key in keys:
            if values.get(key):
                orders.append(json.loads(values[key]))
        
        return orders
    
    def remove_order_from_user(self, user_id: str, order_id: str) -> None:
        """Remove order from user's order list."""
        list_key = f"user:{user_id}:orders"
        current = self.store.get(list_key)
        order_ids = json.loads(current) if current else []
        
        if order_id in order_ids:
            order_ids.remove(order_id)
            self.store.put(list_key, json.dumps(order_ids).encode())
 
 
# Key structure:
# user:u123              → {user entity}
# user:u123:orders       → ["o1", "o2", "o3"]  (list of order IDs)
# order:o1               → {order entity with user_id: "u123"}
# order:o2               → {order entity with user_id: "u123"}
# order:o3               → {order entity with user_id: "u123"}
 
# Access patterns supported:
# - Get user: GET user:u123
# - Get specific order: GET order:o1  
# - Get all user's orders: GET user:u123:orders, then MGET order:o1, order:o2...
# - Get order's user: Get order, extract user_id, GET user:{user_id}

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68

class DenormalizedOrderStore:
    """
    Denormalize frequently-accessed related data into the entity.
    Trades storage space and write complexity for read performance.
    """
    
    def save_order_with_denormalization(self, order: dict, user: dict) -> None:
        """
        Store order with denormalized user info.
        
        Instead of storing just user_id, we embed frequently-accessed
        user fields directly in the order.
        """
        # Denormalize user info into order
        order_with_user = {
            **order,
            "user_id": user["id"],
            # Denormalized user fields (read-only copies)
            "user_name": user["name"],
            "user_email": user["email"],
            "shipping_address": user.get("default_address")
        }
        
        key = f"order:{order['id']}"
        self.store.put(key, json.dumps(order_with_user).encode())
    
    def get_order_with_user_info(self, order_id: str) -> dict:
        """
        Get order with user info - single lookup!
        
        No need to fetch user separately for common display use cases.
        """
        key = f"order:{order_id}"
        data = self.store.get(key)
        return json.loads(data) if data else None
 
 
# Denormalized order entity:
{
    "id": "o123",
    "status": "shipped",
    "total_cents": 14999,
    "items": [...],
    "created_at": "2024-01-15T10:30:00Z",
    
    # Denormalized from User entity
    "user_id": "u456",
    "user_name": "Alice Johnson",
    "user_email": "alice@example.com",
    "shipping_address": {
        "street": "123 Main St",
        "city": "San Francisco",
        "state": "CA",
        "postal_code": "94102"
    }
}
 
# Trade-offs:
# ✅ Single read to display order with user info
# ✅ No cascading lookups needed
# ❌ Data duplication (same user info in every order)
# ❌ Denormalized data can become stale
# ❌ Must update all orders if user changes email
 
# Best for:
# - Read-heavy workloads (orders viewed much more than users updated)
# - Historical accuracy (order should show user info at time of order)
# - Performance-critical paths

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143

class SecondaryIndexedStore:
    """
    Manually maintaining secondary indexes in key-value stores.
    
    For each secondary access pattern, we maintain an index key
    that maps from the lookup value to the primary key.
    """
    
    def __init__(self, store):
        self.store = store
    
    # ===== Primary Entity Storage =====
    
    def _user_key(self, user_id: str) -> str:
        return f"user:{user_id}"
    
    # ===== Secondary Index Keys =====
    
    def _email_index_key(self, email: str) -> str:
        """Index: email → user_id"""
        return f"idx:user:email:{email.lower()}"
    
    def _username_index_key(self, username: str) -> str:
        """Index: username → user_id"""
        return f"idx:user:username:{username.lower()}"
    
    # ===== CRUD with Index Maintenance =====
    
    def create_user(self, user: dict) -> None:
        """
        Create user and all secondary indexes atomically (ideally).
        
        In practice, these are separate operations that could
        partially fail. Consider using transactions if available.
        """
        user_id = user['id']
        
        # 1. Store the primary entity
        self.store.put(
            self._user_key(user_id),
            json.dumps(user).encode()
        )
        
        # 2. Create email index
        self.store.put(
            self._email_index_key(user['email']),
            user_id.encode()
        )
        
        # 3. Create username index
        if user.get('username'):
            self.store.put(
                self._username_index_key(user['username']),
                user_id.encode()
            )
    
    def get_user_by_email(self, email: str) -> dict:
        """
        Look up user by email using secondary index.
        
        This is a two-step lookup:
        1. email → user_id (index lookup)
        2. user_id → user (entity lookup)
        """
        # Step 1: Get user_id from email index
        index_key = self._email_index_key(email)
        user_id_bytes = self.store.get(index_key)
        
        if user_id_bytes is None:
            return None
        
        # Step 2: Get user entity by user_id
        user_id = user_id_bytes.decode()
        return self.get_user_by_id(user_id)
    
    def get_user_by_id(self, user_id: str) -> dict:
        """Primary key lookup."""
        data = self.store.get(self._user_key(user_id))
        return json.loads(data) if data else None
    
    def update_user_email(self, user_id: str, new_email: str) -> None:
        """
        Update email requires index maintenance!
        
        Must:
        1. Delete old email index entry
        2. Update the user entity
        3. Create new email index entry
        """
        # Get current user to find old email
        user = self.get_user_by_id(user_id)
        if user is None:
            raise KeyError(f"User {user_id} not found")
        
        old_email = user['email']
        
        # 1. Delete old index entry
        self.store.delete(self._email_index_key(old_email))
        
        # 2. Update user entity
        user['email'] = new_email
        user['updated_at'] = datetime.now().isoformat()
        self.store.put(
            self._user_key(user_id),
            json.dumps(user).encode()
        )
        
        # 3. Create new index entry
        self.store.put(
            self._email_index_key(new_email),
            user_id.encode()
        )
    
    def delete_user(self, user_id: str) -> None:
        """
        Delete user must clean up all indexes!
        
        Forgetting to clean up indexes leads to:
        - Orphaned index entries pointing to deleted users
        - Uniqueness violations when new users try the same email
        """
        user = self.get_user_by_id(user_id)
        if user is None:
            return
        
        # Delete all index entries
        self.store.delete(self._email_index_key(user['email']))
        if user.get('username'):
            self.store.delete(self._username_index_key(user['username']))
        
        # Delete primary entity
        self.store.delete(self._user_key(user_id))
 
 
# Index key structure:
# user:u123                    → {user entity}
# idx:user:email:alice@ex.com  → "u123"
# idx:user:username:alicej     → "u123"
 
# Lookup flows:
# By ID:       user:u123 → {user}
# By email:    idx:user:email:x@y.com → u123 → user:u123 → {user}
# By username: idx:user:username:alicej → u123 → user:u123 → {user}

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

def create_user_with_unique_email(self, user: dict) -> bool:
    """
    Create user only if email is unique.
    
    Uses set-if-not-exists on the index key to atomically
    check and claim the email.
    """
    email = user['email']
    user_id = user['id']
    
    # Try to claim the email index atomically
    index_key = self._email_index_key(email)
    claimed = self.store.set_if_not_exists(
        index_key, 
        user_id.encode()
    )
    
    if not claimed:
        # Email already taken
        return False
    
    # Email claimed, now safe to create user
    self.store.put(
        self._user_key(user_id),
        json.dumps(user).encode()
    )
    
    return True

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134

class AggregationStore:
    """
    Maintaining pre-computed aggregations in key-value stores.
    
    Every aggregation you need must be explicitly maintained
    as data is written.
    """
    
    def __init__(self, store):
        self.store = store
    
    # ===== Counter Key Patterns =====
    
    def _total_users_key(self) -> str:
        return "stats:users:total"
    
    def _users_by_status_key(self, status: str) -> str:
        return f"stats:users:status:{status}"
    
    def _orders_by_user_count_key(self, user_id: str) -> str:
        return f"stats:user:{user_id}:order_count"
    
    def _revenue_by_date_key(self, date: str) -> str:
        return f"stats:revenue:{date}"
    
    # ===== User Operations with Counter Maintenance =====
    
    def create_user(self, user: dict) -> None:
        """Create user and update all related counters."""
        # Store user entity
        self.store.put(
            f"user:{user['id']}",
            json.dumps(user).encode()
        )
        
        # Update total users counter
        self.store.increment(self._total_users_key())
        
        # Update status counter
        status = user.get('status', 'active')
        self.store.increment(self._users_by_status_key(status))
    
    def delete_user(self, user_id: str) -> None:
        """Delete user and update counters."""
        user = self.get_user_by_id(user_id)
        if not user:
            return
        
        # Delete entity
        self.store.delete(f"user:{user_id}")
        
        # Decrement counters
        self.store.increment(self._total_users_key(), delta=-1)
        status = user.get('status', 'active')
        self.store.increment(self._users_by_status_key(status), delta=-1)
    
    def change_user_status(
        self, 
        user_id: str, 
        old_status: str, 
        new_status: str
    ) -> None:
        """Status change requires counter adjustment."""
        # Decrement old status counter
        self.store.increment(
            self._users_by_status_key(old_status), 
            delta=-1
        )
        # Increment new status counter
        self.store.increment(
            self._users_by_status_key(new_status)
        )
    
    # ===== Order Operations with Counter Maintenance =====
    
    def create_order(self, order: dict) -> None:
        """Create order and update all related counters."""
        order_id = order['id']
        user_id = order['user_id']
        total = order['total_cents']
        date = order['created_at'][:10]  # YYYY-MM-DD
        
        # Store order entity
        self.store.put(
            f"order:{order_id}",
            json.dumps(order).encode()
        )
        
        # Update user's order count
        self.store.increment(self._orders_by_user_count_key(user_id))
        
        # Update daily revenue
        self.store.increment(self._revenue_by_date_key(date), delta=total)
    
    # ===== Reading Aggregations =====
    
    def get_total_users(self) -> int:
        """O(1) lookup for total user count."""
        value = self.store.get(self._total_users_key())
        return int(value) if value else 0
    
    def get_users_by_status(self, status: str) -> int:
        """Count of users in a specific status."""
        value = self.store.get(self._users_by_status_key(status))
        return int(value) if value else 0
    
    def get_user_order_count(self, user_id: str) -> int:
        """How many orders has this user placed?"""
        value = self.store.get(self._orders_by_user_count_key(user_id))
        return int(value) if value else 0
    
    def get_revenue_for_date(self, date: str) -> int:
        """Total revenue for a specific date (in cents)."""
        value = self.store.get(self._revenue_by_date_key(date))
        return int(value) if value else 0
 
 
# Counter key structure:
# stats:users:total           → 15000
# stats:users:status:active   → 14500
# stats:users:status:inactive → 450
# stats:users:status:suspended → 50
# stats:user:u123:order_count → 7
# stats:revenue:2024-01-15    → 1250000  (in cents)
 
# Benefits:
# - O(1) reads for any pre-computed aggregation
# - No expensive COUNT(*) queries
# - Real-time updated as data changes
 
# Challenges:
# - Must remember to update counters on every relevant write
# - Counters can drift if operations fail mid-way
# - Adding new aggregations requires backfilling

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

from datetime import datetime, timedelta
 
class TimeSeriesStore:
    """
    Time-series data patterns for key-value stores.
    """
    
    # ===== Pattern 1: Timestamp-Prefixed Keys (for sorted stores) =====
    
    def store_event(self, event: dict) -> str:
        """
        Store event with timestamp-prefixed key.
        
        Key format: events:{entity_id}:{timestamp}:{event_id}
        
        Timestamp format ensures lexicographic ordering = time ordering.
        Use ISO format or zero-padded epoch for correct sorting.
        """
        entity_id = event['entity_id']
        event_id = event['id']
        timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')
        
        key = f"events:{entity_id}:{timestamp}:{event_id}"
        self.store.put(key, json.dumps(event).encode())
        
        return key
    
    def get_events_in_range(
        self, 
        entity_id: str, 
        start: datetime, 
        end: datetime
    ) -> list:
        """
        Get events in time range using key prefix iteration.
        
        Only works with stores that support sorted key iteration
        (RocksDB, LevelDB, LMDB - NOT basic Redis).
        """
        start_key = f"events:{entity_id}:{start.strftime('%Y%m%d%H%M%S')}"
        end_key = f"events:{entity_id}:{end.strftime('%Y%m%d%H%M%S')}"
        
        # Iterate keys in range (store-specific API)
        events = []
        for key, value in self.store.iterate_range(start_key, end_key):
            events.append(json.loads(value))
        
        return events
    
    # ===== Pattern 2: Time-Bucketed Storage =====
    
    def store_metric(
        self, 
        metric_name: str, 
        value: float, 
        timestamp: datetime = None
    ) -> None:
        """
        Store metric with time bucketing.
        
        Group metrics by hour for efficient retrieval of time ranges.
        Each bucket stores a list/map of fine-grained values.
        """
        if timestamp is None:
            timestamp = datetime.now()
        
        # Bucket key: hourly granularity
        bucket = timestamp.strftime('%Y%m%d%H')
        bucket_key = f"metrics:{metric_name}:{bucket}"
        
        # Within bucket: minute:second key
        fine_key = timestamp.strftime('%M%S')
        
        # Get current bucket or create new
        bucket_data = self.store.get(bucket_key)
        if bucket_data:
            data = json.loads(bucket_data)
        else:
            data = {}
        
        # Store value at fine granularity
        data[fine_key] = value
        
        self.store.put(bucket_key, json.dumps(data).encode())
    
    def get_hourly_metrics(
        self, 
        metric_name: str, 
        hour: datetime
    ) -> dict:
        """Get all metric values for a specific hour."""
        bucket = hour.strftime('%Y%m%d%H')
        bucket_key = f"metrics:{metric_name}:{bucket}"
        
        data = self.store.get(bucket_key)
        return json.loads(data) if data else {}
    
    def get_metrics_for_day(
        self, 
        metric_name: str, 
        date: datetime
    ) -> dict:
        """
        Get all metrics for a day.
        
        Requires 24 key lookups (one per hour bucket).
        """
        all_metrics = {}
        
        for hour in range(24):
            hour_dt = date.replace(hour=hour, minute=0, second=0)
            bucket_key = f"metrics:{metric_name}:{hour_dt.strftime('%Y%m%d%H')}"
            
            data = self.store.get(bucket_key)
            if data:
                hourly_data = json.loads(data)
                for minute_key, value in hourly_data.items():
                    full_key = f"{hour:02d}:{minute_key}"
                    all_metrics[full_key] = value
        
        return all_metrics
 
 
# Key structure for time-bucketed metrics:
# metrics:cpu_usage:2024011510    → {"0000": 45.2, "0001": 46.1, ...}
# metrics:cpu_usage:2024011511    → {"0000": 51.3, "0001": 49.8, ...}
# metrics:cpu_usage:2024011512    → {...}
 
# Benefits of bucketing:
# - Reduces number of keys (60 values per hour bucket vs 60 keys)
# - Efficient retrieval of chunks of time series
# - Natural retention: delete old buckets
# - Compression opportunities within bucket