NoSQL database types (document, key-value, column, graph) in HLD - System Design Exercise

Design: NoSQL Database Types Overview System

Focus on explaining and demonstrating the four NoSQL types with architecture and data flow. Out of scope: detailed implementation of each database engine.

Functional Requirements

FR1: Explain the four main types of NoSQL databases: document, key-value, column, and graph.

FR2: Show use cases for each NoSQL type with simple examples.

FR3: Demonstrate how data is stored and retrieved in each type.

FR4: Support queries typical for each database type.

FR5: Handle up to 10,000 queries per second with average latency under 100ms.

Non-Functional Requirements

NFR1: System must be scalable to handle growing data and query load.

NFR2: Latency for read and write operations should be under 100ms at p99.

NFR3: Availability target of 99.9% uptime.

NFR4: Data consistency can be eventual for some types but must be explained.

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

API layer to accept queries

Separate storage modules for each NoSQL type

Query processor tailored to each database type

Cache layer for frequently accessed data

Monitoring and logging components

Design Patterns

Sharding and partitioning for scaling

Eventual consistency vs strong consistency

Indexing strategies for fast queries

Graph traversal algorithms for graph DB

Document schema design and validation

Reference Architecture

          +---------------------+
          |     Client/API       |
          +----------+----------+
                     |
      +--------------+--------------+
      |     Query Processor Layer     |
      +--------------+--------------+
         |          |          |          |
+--------+  +-------+  +-------+  +-------+
|Document|  |Key-Value| |Column |  | Graph |
|  DB    |  |  DB    | |  DB   |  |  DB   |
+--------+  +--------+ +-------+  +-------+
     |           |         |          |
+----+----+ +----+----+ +--+--+  +----+----+
|Storage  | |Storage  | |Storage| |Storage  |
+---------+ +---------+ +------+ +---------+

Components

Client/API

REST/GraphQL API

Accepts user queries and routes them to the query processor.

Query Processor Layer

Custom middleware

Interprets queries and directs them to the appropriate NoSQL database type.

Document Database

MongoDB or Couchbase

Stores data as JSON-like documents, good for flexible schema and nested data.

Key-Value Database

Redis or DynamoDB

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

Column Database

Cassandra or HBase

Stores data in columns for efficient aggregation and wide tables.

Graph Database

Neo4j or Amazon Neptune

Stores data as nodes and edges for complex relationships and graph queries.

Storage

Distributed storage clusters

Persist data reliably with replication and partitioning.

Request Flow

1. Client sends query to API layer.

2. API forwards query to Query Processor.

3. Query Processor identifies NoSQL type based on query and data model.

4. Query Processor sends query to the corresponding NoSQL database.

5. Database executes query and returns results.

6. Query Processor formats results and sends back to Client.

Database Schema

Document DB: Collections of JSON documents with flexible fields. Key-Value DB: Simple key and value pairs, no fixed schema. Column DB: Tables with rows and dynamic columns grouped in families. Graph DB: Nodes (entities) and edges (relationships) with properties.

Scaling Discussion

Bottlenecks

Query Processor can become a bottleneck under high load.

Storage nodes may face partition hotspots.

Network latency between components can increase with scale.

Consistency delays in eventual consistency models.

Indexing overhead for large datasets.

Solutions

Scale Query Processor horizontally with load balancers.

Use consistent hashing for even data partitioning.

Deploy components in the same region and use CDN for clients.

Use tunable consistency settings per use case.

Implement background index rebuilding and incremental indexing.

Interview Tips

Time: Spend 10 minutes explaining NoSQL types and use cases, 15 minutes on architecture and data flow, 10 minutes on scaling and trade-offs, 10 minutes for questions.

Clear understanding of each NoSQL type and when to use it.

How the system routes queries to the right database type.

Trade-offs between consistency, availability, and partition tolerance.

Scaling strategies for each database type.

Realistic latency and availability targets.

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