Database sharding strategies in HLD - Architecture Diagram

System Overview - Database sharding strategies

This system explains how database sharding splits data across multiple databases to improve performance and scalability. It covers key strategies like horizontal sharding by user ID, range-based sharding, and directory-based sharding to distribute data efficiently.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +-----------------------------+
  |                             |
Shard Router                Directory Service
  |                             |
  +-------------+---------------+
                |
       +--------+--------+
       |                 |
Shard 1             Shard 2
(Database)          (Database)
       |                 |
    Cache 1          Cache 2

Components

User

client

Initiates requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Handles client requests and routes them to appropriate services

Shard Router

service

Determines which shard holds the requested data based on sharding strategy

Directory Service

service

Maintains mapping of data keys to shards for directory-based sharding

Shard 1

database

Stores a subset of the data based on shard key

Shard 2

database

Stores another subset of the data based on shard key

Cache 1

cache

Caches frequently accessed data for Shard 1 to reduce database load

Cache 2

cache

Caches frequently accessed data for Shard 2 to reduce database load

Request Flow - 9 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Shard Router

Shard Router → Directory Service

Shard Router → Cache 1 or Cache 2

Cache 1 or Cache 2 → Shard 1 or Shard 2

Shard 1 or Shard 2 → Cache 1 or Cache 2

Cache 1 or Cache 2 → API Gateway

API Gateway → User

Failure Scenario

Component Fails:Shard 1

Impact:Data stored in Shard 1 becomes unavailable, causing read and write failures for that shard's data. Cache 1 may still serve stale reads if data is cached.

Mitigation:Use database replication for Shard 1 to failover to a replica. Implement retry logic and degrade gracefully by informing users of partial unavailability.

Architecture Quiz - 3 Questions

Which component decides which shard holds the requested data?

A Cache

B Load Balancer

C Shard Router

D User

Design Principle

This architecture demonstrates horizontal scaling by splitting data into shards to improve performance and availability. It uses a shard router to direct requests, caches to reduce database load, and replication to handle failures, following best practices for scalable distributed databases.

Practice

1. Which of the following best describes database sharding?

easy

A. Backing up the entire database to multiple locations for safety.

B. Splitting a large database into smaller, faster, more manageable parts called shards.

C. Encrypting the database to protect sensitive data.

D. Merging multiple databases into one large database.

Solution

1. Step 1: Understand the concept of sharding

   Sharding means dividing a database into smaller parts to improve speed and manageability.

2. Step 2: Compare options with the definition

   Only Splitting a large database into smaller, faster, more manageable parts called shards. describes splitting into smaller parts called shards, which matches the definition.

3. Final Answer:

   Splitting a large database into smaller, faster, more manageable parts called shards. -> Option B

4. Quick Check:

   Sharding = splitting database [OK]

Hint: Sharding means splitting data into parts [OK]

Common Mistakes:

• Confusing sharding with backup or encryption
• Thinking sharding merges databases
• Assuming sharding is only about security

2. Which of the following is a valid shard key choice for hash-based sharding?

easy

A. User ID hashed to assign shard

B. Timestamp of record creation

C. Database server IP address

D. Number of active connections

Solution

1. Step 1: Identify shard key for hash sharding

   Hash sharding uses a hash function on a key like User ID to distribute data evenly.

2. Step 2: Evaluate options

   User ID is a stable, unique key suitable for hashing. Timestamp or server IP are not good shard keys for hashing.

3. Final Answer:

   User ID hashed to assign shard -> Option A

4. Quick Check:

   Hash shard key = User ID [OK]

Hint: Hash shard keys are stable unique IDs [OK]

Common Mistakes:

• Using timestamps which cause uneven shard distribution
• Choosing server IP which is unrelated to data
• Picking dynamic values like active connections

3. Given a range-based sharding on user age groups: shard 1 for ages 0-20, shard 2 for 21-40, shard 3 for 41-60. Which shard will a user aged 35 be stored in?

medium

A. Shard 3

B. Shard 1

C. Shard 2

D. None of the above

Solution

1. Step 1: Understand the age ranges for each shard

   Shard 1: 0-20, Shard 2: 21-40, Shard 3: 41-60.

2. Step 2: Determine which range 35 falls into

   Age 35 is between 21 and 40, so it belongs to Shard 2.

3. Final Answer:

   Shard 2 -> Option C

4. Quick Check:

   35 in 21-40 = Shard 2 [OK]

Hint: Check which range the value fits [OK]

Common Mistakes:

• Choosing shard 1 by mistake for age 35
• Confusing shard 3 range
• Selecting 'None of the above' incorrectly

4. A system uses hash-based sharding on user email addresses. Recently, many users with emails starting with 'a' are causing one shard to overload. What is the likely problem?

medium

A. The hash function is not distributing keys evenly.

B. Emails starting with 'a' are stored in a separate shard by design.

C. Range sharding was used instead of hash sharding.

D. The shard key is not unique.

Solution

1. Step 1: Analyze the shard overload cause

   Hash-based sharding should distribute keys evenly. Overload on one shard means uneven distribution.

2. Step 2: Evaluate options

   The hash function is not distributing keys evenly. explains uneven hash distribution. Emails starting with 'a' are stored in a separate shard by design. is incorrect because hash sharding doesn't group by prefix. Range sharding was used instead of hash sharding. is wrong as range sharding is not used. The shard key is not unique. is unrelated to overload.

3. Final Answer:

   The hash function is not distributing keys evenly. -> Option A

4. Quick Check:

   Uneven hash distribution = overload [OK]

Hint: Uneven load means bad hash distribution [OK]

Common Mistakes:

• Assuming range sharding was used
• Thinking shard key uniqueness causes overload
• Believing emails with same prefix go to same shard by hash

5. You need to shard a global user database to minimize latency and balance load. Which strategy is best to combine geographic location and user ID for sharding?

hard

A. Use range sharding on user ID across all regions.

B. Use only hash sharding on user ID ignoring location.

C. Use directory sharding mapping user ID to random shards.

D. Use geographic sharding first, then hash shard within each region by user ID.

Solution

1. Step 1: Understand the goal

   Minimize latency by serving users from nearby data centers and balance load within each region.

2. Step 2: Evaluate sharding strategies

   Geographic sharding groups users by location to reduce latency. Hash sharding inside each region balances load evenly.

3. Step 3: Compare options

   Use geographic sharding first, then hash shard within each region by user ID. combines geographic and hash sharding effectively. Use only hash sharding on user ID ignoring location. ignores location, increasing latency. Use range sharding on user ID across all regions. uses range sharding which may cause hotspots. Use directory sharding mapping user ID to random shards. uses random mapping, ignoring location and load balance.

4. Final Answer:

   Use geographic sharding first, then hash shard within each region by user ID. -> Option D

5. Quick Check:

   Geo + hash sharding = low latency + balanced load [OK]

Hint: Combine geo then hash for best latency and balance [OK]

Common Mistakes:

• Ignoring geographic location causing high latency
• Using only hash sharding causing uneven latency
• Using range sharding causing load hotspots