NoSQL database types (document, key-value, column, graph) in HLD - Architecture Diagram

System Overview - NoSQL database types (document, key-value, column, graph)

This system explains the four main types of NoSQL databases: document, key-value, column, and graph. Each type stores and manages data differently to suit various application needs like flexibility, speed, or complex relationships.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +-------------------------------+-------------------------------+-------------------------------+-------------------------------+
  |                               |                               |                               |                               |
Document DB Service          Key-Value DB Service            Column DB Service              Graph DB Service
  |                               |                               |                               |
  v                               v                               v                               v
Document DB                  Key-Value DB                  Column DB                      Graph DB
  |                               |                               |                               |
Cache (optional)             Cache (optional)             Cache (optional)             Cache (optional)

Components

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway for reliability and scalability.

API Gateway

api_gateway

Routes requests to the correct NoSQL database service based on request type.

Document DB Service

service

Handles requests for document-oriented NoSQL database operations.

Key-Value DB Service

service

Handles requests for key-value store NoSQL database operations.

Column DB Service

service

Handles requests for column-family NoSQL database operations.

Graph DB Service

service

Handles requests for graph NoSQL database operations.

Document DB

database

Stores data as JSON-like documents for flexible schema.

Key-Value DB

database

Stores data as simple key-value pairs for fast lookups.

Column DB

database

Stores data in columns for efficient queries on large datasets.

Graph DB

database

Stores data as nodes and edges to represent relationships.

Cache (optional)

cache

Speeds up read operations by storing frequently accessed data.

Request Flow - 9 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Appropriate DB Service (Document/Key-Value/Column/Graph)

DB Service → Cache (optional)

Cache (optional) → DB Service

DB Service → Cache (optional)

DB Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Cache misses increase, causing more direct database queries and higher latency.

Mitigation:System continues to operate by querying the database directly; cache can be rebuilt over time.

Architecture Quiz - 3 Questions

Which component decides which NoSQL database type to use for a request?

A Load Balancer

B API Gateway

C Cache

D Database

Design Principle

This architecture shows how different NoSQL database types can be integrated behind a unified API Gateway. It uses a Load Balancer for scalability and optional caching to improve read performance. Each database type serves a specific data model need, enabling flexible and efficient data storage.

Practice

1. Which NoSQL database type stores data as flexible JSON-like documents, allowing nested structures?

easy

A. Document database

B. Key-value database

C. Column database

D. Graph database

Solution

1. Step 1: Understand document database structure

   Document databases store data in JSON-like documents that can have nested fields and arrays.

2. Step 2: Compare with other NoSQL types

   Key-value stores use simple key and value pairs, column stores organize data by columns, and graph databases focus on relationships.

3. Final Answer:

   Document database -> Option A

4. Quick Check:

   Nested JSON-like data = Document database [OK]

Hint: Documents = JSON-like nested data [OK]

Common Mistakes:

• Confusing key-value stores with document stores
• Thinking column stores store JSON documents
• Mixing graph databases with document databases

2. Which of the following is the correct way to describe a key-value store?

easy

A. Stores data as interconnected nodes and edges

B. Stores data in tables with rows and columns

C. Stores data as simple pairs of keys and values

D. Stores data as nested JSON documents

Solution

1. Step 1: Define key-value store

   Key-value stores save data as pairs: a unique key and its associated value, like a dictionary.

2. Step 2: Eliminate other options

   Graphs use nodes and edges, column stores use tables with columns, and document stores use nested JSON.

3. Final Answer:

   Stores data as simple pairs of keys and values -> Option C

4. Quick Check:

   Key-value = key and value pairs [OK]

Hint: Key-value = simple pairs [OK]

Common Mistakes:

• Confusing graph databases with key-value stores
• Thinking key-value stores use tables
• Mixing document stores with key-value stores

3. Given a graph database storing people and their friendships, which query result would you expect from a query asking for all friends of 'Alice'?

medium

A. A list of key-value pairs with friend names

B. A set of nodes connected to 'Alice' by edges labeled 'friend'

C. A table with columns for friend names and ages

D. A JSON document with nested friend details

Solution

1. Step 1: Understand graph database query output

   Graph queries return nodes and edges; friends of 'Alice' are nodes connected by 'friend' edges.

2. Step 2: Compare with other data formats

   Key-value pairs, tables, and JSON documents are typical of other NoSQL types, not graph databases.

3. Final Answer:

   A set of nodes connected to 'Alice' by edges labeled 'friend' -> Option B

4. Quick Check:

   Graph query = nodes and edges [OK]

Hint: Graph = nodes connected by edges [OK]

Common Mistakes:

• Expecting JSON documents from graph queries
• Thinking graph data is tabular
• Confusing key-value pairs with graph edges

4. You wrote a query to retrieve data from a column store but got an error. Which mistake likely caused this?

medium

A. Used SQL SELECT syntax without specifying columns

B. Used key-value pairs instead of column names

C. Tried to traverse edges like in a graph database

D. Tried to query nested JSON documents directly

Solution

1. Step 1: Understand column store query requirements

   Column stores require specifying columns in queries; missing columns causes errors.

2. Step 2: Analyze other options

   Nested JSON is for document stores, key-value pairs are for key-value stores, and edge traversal is for graph databases.

3. Final Answer:

   Used SQL SELECT syntax without specifying columns -> Option A

4. Quick Check:

   Column store queries need columns specified [OK]

Hint: Specify columns in column store queries [OK]

Common Mistakes:

• Using document store syntax on column stores
• Trying graph traversal in column stores
• Confusing key-value pairs with columns

5. You need to design a social network app that quickly finds mutual friends between users. Which NoSQL database type is best suited and why?

hard

A. Column database, because it stores data in columns

B. Key-value store, because it is the fastest for any query

C. Document database, because it stores nested user data

D. Graph database, because it efficiently handles relationships

Solution

1. Step 1: Identify the data need for mutual friends

   Finding mutual friends requires exploring relationships between users efficiently.

2. Step 2: Match data need to NoSQL type

   Graph databases specialize in storing and querying relationships like friendships quickly.

3. Step 3: Evaluate other options

   Document stores handle nested data but not relationship queries efficiently; key-value stores are simple but not optimized for relationships; column stores focus on columnar data.

4. Final Answer:

   Graph database, because it efficiently handles relationships -> Option D

5. Quick Check:

   Relationship queries = Graph database [OK]

Hint: Use graph DB for relationship-heavy apps [OK]

Common Mistakes:

• Choosing document DB for relationship queries
• Assuming key-value stores handle complex queries
• Confusing column stores with relationship handling