Why Transaction history in LLD? - Purpose & Use Cases

The Big Idea

What if you could instantly find any past transaction without flipping through endless pages?

The Scenario

Imagine you run a small shop and keep track of every sale and return by writing them down in a notebook. When a customer asks about their past purchases, you have to flip through pages, searching for each transaction manually.

The Problem

This manual method is slow and prone to mistakes. You might miss some entries, mix up dates, or lose the notebook. It becomes impossible to quickly answer questions or find patterns in sales, especially as the number of transactions grows.

The Solution

Transaction history systems automatically record every action in a structured way. They store data safely, allow quick searches, and provide clear records. This makes tracking, auditing, and analyzing transactions easy and reliable.

Before vs After

✗ Before

Write each sale on paper; search by flipping pages.

✓ After

Store transactions in a database; query by customer ID or date.

What It Enables

It enables fast, accurate access to all past transactions, supporting better decisions and trust.

Real Life Example

Bank apps show your transaction history instantly, letting you see deposits, withdrawals, and payments without waiting or errors.

Key Takeaways

Manual tracking is slow and error-prone.

Transaction history systems automate and secure records.

They provide quick, reliable access to past data.

Practice

1. What is the main purpose of a transaction history in a system?

easy

A. To record all important actions with details for tracking

B. To speed up the system by caching data

C. To delete old data automatically

D. To encrypt user passwords

Solution

1. Step 1: Understand the role of transaction history

   Transaction history stores records of actions with details like timestamps and IDs.

2. Step 2: Identify the correct purpose

   This helps users and systems track past events clearly and reliably.

3. Final Answer:

   To record all important actions with details for tracking -> Option A

4. Quick Check:

   Transaction history purpose = record actions [OK]

Hint: Transaction history = record actions with details [OK]

Common Mistakes:

• Confusing transaction history with caching
• Thinking it deletes data automatically
• Mixing it with security features like encryption

2. Which of the following is the correct way to uniquely identify each transaction in a history system?

easy

A. Using a timestamp only

B. Using a unique transaction ID

C. Using the user's name

D. Using the transaction amount

Solution

1. Step 1: Identify unique identifiers in transaction history

   Unique transaction IDs ensure each record is distinct and traceable.

2. Step 2: Compare options

   Timestamps alone can repeat; user names and amounts are not unique identifiers.

3. Final Answer:

   Using a unique transaction ID -> Option B

4. Quick Check:

   Unique ID = unique transaction record [OK]

Hint: Unique transaction ID ensures distinct records [OK]

Common Mistakes:

• Assuming timestamp alone is unique
• Using user name as unique key
• Using transaction amount as identifier

3. Given this simplified transaction record list:

transactions = [
  {"id": "t1", "time": "2024-01-01T10:00:00Z"},
  {"id": "t2", "time": "2024-01-01T09:00:00Z"},
  {"id": "t3", "time": "2024-01-01T11:00:00Z"}
]

What is the correct order of transaction IDs if sorted by time ascending?

medium

A. ["t1", "t2", "t3"]

B. ["t2", "t3", "t1"]

C. ["t3", "t1", "t2"]

D. ["t2", "t1", "t3"]

Solution

1. Step 1: Analyze timestamps for each transaction

   t2 = 09:00, t1 = 10:00, t3 = 11:00 in UTC time.

2. Step 2: Sort transactions by ascending time

   Order is t2 (earliest), then t1, then t3 (latest).

3. Final Answer:

   ["t2", "t1", "t3"] -> Option D

4. Quick Check:

   Sorted by time ascending = [t2, t1, t3] [OK]

Hint: Sort by timestamp ascending for correct order [OK]

Common Mistakes:

• Sorting by ID instead of time
• Confusing ascending with descending order
• Ignoring timestamp format

4. You have this code snippet to add a transaction record:

def add_transaction(history, transaction):
    if transaction['id'] not in [t['id'] for t in history]:
        history.append(transaction)
    else:
        print("Duplicate transaction")

history = [{"id": "t1"}]
add_transaction(history, {"id": "t1"})

What is the output when running this code?

medium

A. Duplicate transaction

B. KeyError exception

C. No output, transaction added

D. TypeError exception

Solution

1. Step 1: Check if transaction ID exists in history

   The code checks if 't1' is already in the list of IDs in history.

2. Step 2: Since 't1' exists, print duplicate message

   The else branch runs and prints "Duplicate transaction".

3. Final Answer:

   Duplicate transaction -> Option A

4. Quick Check:

   Duplicate ID detected = print message [OK]

Hint: Check for existing ID before adding to avoid duplicates [OK]

Common Mistakes:

• Assuming transaction is added anyway
• Expecting an exception instead of print
• Confusing list comprehension syntax

5. You want to design a scalable transaction history system for millions of users. Which approach best ensures fast retrieval of a user's transactions sorted by time?

hard

A. Store transactions in separate files per day without indexing

B. Store all transactions in one big list and scan it every time

C. Use a database with an index on user ID and timestamp

D. Keep transactions only in memory without persistence

Solution

1. Step 1: Consider scalability and retrieval speed

   Scanning one big list or files without index is slow for millions of users.

2. Step 2: Use database indexing on user ID and timestamp

   This allows fast queries to get transactions per user sorted by time efficiently.

3. Step 3: Avoid in-memory only storage for persistence and scale

   Memory-only storage risks data loss and limits scale.

4. Final Answer:

   Use a database with an index on user ID and timestamp -> Option C

5. Quick Check:

   Indexing = fast retrieval at scale [OK]

Hint: Index on user ID and timestamp for fast queries [OK]

Common Mistakes:

• Scanning large lists for each query
• Ignoring indexing benefits
• Relying on memory-only storage