Balance calculation algorithm in LLD - Architecture Diagram

System Overview - Balance calculation algorithm

This system calculates user account balances by processing transactions efficiently and accurately. It must handle concurrent updates, ensure data consistency, and provide quick balance queries.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Balance Calculation Service <-> Cache
  |
  v
Database

Components

User

client

Initiates balance queries and transaction requests

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Routes requests to the Balance Calculation Service and handles authentication

Balance Calculation Service

service

Processes transactions, updates balances, and serves balance queries

Cache

cache

Stores recent balance data for fast read access

Database

database

Stores all transaction records and persistent user balance data

Request Flow - 10 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Balance Calculation Service

Balance Calculation Service → Cache

Cache → Balance Calculation Service

Balance Calculation Service → Database

Database → Balance Calculation Service

Balance Calculation Service → Cache

Balance Calculation Service → API Gateway

API Gateway → User

Failure Scenario

Component Fails:Database

Impact:New transactions cannot be recorded; balance updates fail. Cached balances can still be read but may become stale.

Mitigation:Use database replication for failover; rely on cache for read-only balance queries until DB recovers.

Architecture Quiz - 3 Questions

Which component handles user authentication before processing balance requests?

A API Gateway

B Load Balancer

C Cache

D Balance Calculation Service

Design Principle

This architecture uses caching to speed up balance queries and a load balancer to distribute requests evenly. It separates concerns by using an API Gateway for authentication and routing, ensuring scalability and reliability.

Practice

1. What is the main purpose of a balance calculation algorithm in a financial system?

easy

A. To add credits and subtract debits from an initial amount

B. To sort transactions by date

C. To encrypt user data

D. To generate random transaction IDs

Solution

1. Step 1: Understand the role of balance calculation

   The balance calculation algorithm updates the current balance by adding credits and subtracting debits.

2. Step 2: Compare with other options

   Sorting transactions, encrypting data, and generating IDs are unrelated to balance calculation.

3. Final Answer:

   To add credits and subtract debits from an initial amount -> Option A

4. Quick Check:

   Balance calculation = add credits - subtract debits [OK]

Hint: Balance means adding credits and subtracting debits [OK]

Common Mistakes:

• Confusing balance calculation with sorting or encryption
• Thinking balance calculation generates IDs
• Ignoring subtraction of debits

2. Which of the following code snippets correctly initializes a balance variable to zero in a balance calculation algorithm?

easy

A. balance := 0

B. balance = 0

C. balance == 0

D. balance = 'zero'

Solution

1. Step 1: Identify correct assignment syntax

   In most programming languages, = assigns a value. So balance = 0 sets balance to zero.

2. Step 2: Check other options for errors

   := is not standard in many languages, == is a comparison, and assigning a string 'zero' is incorrect for numeric balance.

3. Final Answer:

   balance = 0 -> Option B

4. Quick Check:

   Use = for assignment, not == or := [OK]

Hint: Use single = to assign values in most languages [OK]

Common Mistakes:

• Using == instead of = for assignment
• Using := which is not common in many languages
• Assigning string instead of numeric zero

3. Consider this pseudocode for balance calculation:

balance = 100
transactions = [20, -10, 30, -5]
for t in transactions:
    balance += t
print(balance)

What will be the printed balance?

medium

A. 135

B. 145

C. 155

D. 125

Solution

1. Step 1: Calculate sum of transactions

   Sum = 20 + (-10) + 30 + (-5) = 20 - 10 + 30 - 5 = 35

2. Step 2: Add sum to initial balance

   Initial balance 100 + 35 = 135

3. Final Answer:

   135 -> Option A

4. Quick Check:

   100 + (20 -10 +30 -5) = 135 [OK]

Hint: Add all transactions to initial balance [OK]

Common Mistakes:

• Adding absolute values instead of signed values
• Forgetting to add initial balance
• Miscalculating sum of transactions

4. Identify the bug in this balance calculation code snippet:

balance = 50
transactions = [10, -20, 15]
for t in transactions:
    balance = t
print(balance)

medium

A. The initial balance is not set

B. The transactions list is empty

C. The balance is overwritten instead of updated

D. The loop variable is incorrect

Solution

1. Step 1: Analyze the loop operation

   Inside the loop, balance = t overwrites balance each time instead of adding.

2. Step 2: Understand correct update

   It should be balance += t to add each transaction to balance.

3. Final Answer:

   The balance is overwritten instead of updated -> Option C

4. Quick Check:

   Use += to update balance, not = [OK]

Hint: Use += to add transactions, not = [OK]

Common Mistakes:

• Using = instead of += inside loop
• Assuming transactions list is empty
• Ignoring initial balance

5. You need to design a balance calculation system that handles millions of transactions per day with real-time updates. Which design choice best supports scalability and accuracy?

hard

A. Ignore debits and only add credits to simplify calculation

B. Use a single database transaction to update balance after each transaction

C. Store all transactions in memory and recalculate balance on every request

D. Batch transactions and update balance periodically with distributed processing

Solution

1. Step 1: Consider scalability needs

   Millions of transactions require efficient processing; single updates per transaction cause bottlenecks.

2. Step 2: Evaluate batch processing benefits

   Batching with distributed processing reduces load and maintains accuracy by processing groups of transactions.

3. Step 3: Reject other options

   Single DB transactions cause slowdowns, in-memory recalculation is memory-heavy and slow, ignoring debits causes incorrect balances.

4. Final Answer:

   Batch transactions and update balance periodically with distributed processing -> Option D

5. Quick Check:

   Batch + distributed processing = scalable & accurate [OK]

Hint: Batch and distribute processing for large-scale balance updates [OK]

Common Mistakes:

• Updating balance per transaction causing bottlenecks
• Recalculating balance on every request wasting memory
• Ignoring debits leading to wrong balances