Database sharding strategies in HLD - System Design Guide

Problem Statement

When a single database grows too large, it becomes slow to query and update. This causes long wait times for users and risks total failure if the database crashes, since all data is in one place.

Solution

Database sharding splits data into smaller parts called shards, each stored on different servers. Requests are routed to the correct shard based on a key, so queries and writes happen in parallel, improving speed and reliability.

Architecture

Client App

→Shard Router

↓

Shard 2 DB

Shard 2 DB

↓

Shard 3 DB

Shard 3 DB

This diagram shows a client sending requests to a shard router, which directs queries to the correct database shard based on the data key.

Trade-offs

✓ Pros

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Improves database performance by distributing load across multiple servers.

→

Increases system availability since failure of one shard doesn't affect others.

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Enables horizontal scaling by adding more shards as data grows.

→

Reduces contention and lock conflicts by isolating data subsets.

✗ Cons

→

Adds complexity in routing queries to the correct shard.

→

Cross-shard queries are difficult and often inefficient.

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Requires careful shard key selection to avoid uneven data distribution.

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Data rebalancing when adding or removing shards can be complex and costly.

Use when database size or traffic exceeds the capacity of a single server, typically above millions of records or thousands of queries per second.

Avoid if your database is small or traffic is low (under 1,000 queries per second), as sharding complexity outweighs benefits.

Real World Examples

Twitter

Twitter shards user data by user ID to distribute load and reduce latency for timeline queries.

Amazon

Amazon shards product catalog data by product category to enable parallel processing and faster search.

Uber

Uber shards trip data by geographic region to localize data and improve query speed for rides in specific cities.

Alternatives

Vertical partitioning

Splits database tables by columns instead of rows, storing different columns on different servers.

Use when: Choose when different parts of data have very different access patterns or sizes.

Replication

Copies entire database to multiple servers for read scaling and failover, without splitting data.

Use when: Choose when read traffic is high but write traffic is low or can be handled by a single master.

Federated database

Combines multiple independent databases under a unified interface without physically splitting data.

Use when: Choose when integrating legacy or heterogeneous databases without centralizing data.

Summary

Database sharding splits large databases into smaller parts to improve performance and reliability.

It requires careful shard key selection and adds complexity in query routing and data management.

Sharding is best for very large or high-traffic databases but not suitable for small-scale systems.

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