Database sharding strategies in HLD - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the sharding key in a database shard.

HLD

shard_key = request.get('[1]')

Drag options to blanks, or click blank then click option'

Asession_id

Btimestamp

Ctransaction_id

Duser_id

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a timestamp as shard key can cause hotspotting.

Using session_id may not distribute data evenly.

The user_id is commonly used as a sharding key to distribute data evenly across shards.

2fill in blank

medium

Complete the code to calculate the shard number using modulo sharding.

HLD

shard_number = hash(user_id) [1] total_shards

Drag options to blanks, or click blank then click option'

A+

B%

C/

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using division instead of modulo causes float results.

Adding hash and total_shards does not map to a shard.

The modulo operator % is used to map the hash value to a shard number within the total shards.

3fill in blank

hard

Fix the error in the shard lookup function to correctly route requests.

HLD

def get_shard(user_id):
    shard_id = hash(user_id) [1] num_shards
    return shards[shard_id]

Drag options to blanks, or click blank then click option'

A/

B+

C%

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using division returns float, causing index errors.

Multiplying or adding does not limit shard_id range.

The modulo operator % ensures the shard_id is within the valid shard range.

4fill in blank

hard

Fill both blanks to implement range-based sharding for user IDs.

HLD

if user_id [1] 1000:
    shard = shards[[2]]

Drag options to blanks, or click blank then click option'

A<

B>

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using > instead of < reverses the condition.

Assigning shard 1 instead of 0 for this range.

Users with IDs less than 1000 go to shard 0.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension for consistent hashing buckets.

HLD

buckets = [1]: hash([2]) [3] num_buckets for [2] in nodes}

Drag options to blanks, or click blank then click option'

Anode

C%

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of modulo for bucket assignment.

Mismatching key and iteration variable names.

The comprehension maps each node to a bucket using modulo of its hash.

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