ACID Properties

15 min read•Intermediate

Not Started

Master the four fundamental guarantees that databases provide for reliable transaction processing. Learn how ACID properties prevent data corruption and ensure business rule integrity.

Understanding ACID

ACID properties are the foundation of reliable database systems. They ensure that database transactions are processed reliably and maintain data integrity even in the face of errors, power failures, and concurrent access.

Why ACID Matters

Without ACID guarantees, your application could lose money, create duplicate records, violate business rules, or leave the system in an inconsistent state. These properties are essential for any application handling critical data.

Atomicity

All operations in a transaction succeed or all fail

Example

Bank transfer: debit $100 from Account A AND credit $100 to Account B

Without This Property

If credit fails, debit is automatically rolled back

Implementation

Transaction logs, rollback mechanisms

Business Impact

Prevents partial operations that corrupt data integrity

Consistency

Database transitions from one valid state to another

Example

Account balance cannot go negative (business rule)

Without This Property

Transaction rejected if it would violate constraints

Implementation

Constraints, triggers, validation rules

Business Impact

Ensures business rules are never violated

Isolation

Concurrent transactions do not interfere with each other

Example

Two users buying the last item see consistent inventory

Without This Property

Without isolation: both users could purchase the same item

Implementation

Locking mechanisms, MVCC

Business Impact

Prevents race conditions and data corruption

Durability

Committed transactions persist even after system failure

Example

Payment confirmed remains confirmed after power outage

Without This Property

Customer charged but transaction lost

Implementation

Write-ahead logging, disk persistence

Business Impact

Guarantees no data loss after confirmation

Isolation Levels Explained

Isolation isn't binary - databases offer different levels of isolation with trade-offs between consistency and performance. Choose the right level based on your application's requirements.

Read Uncommitted

Fastest

Dirty Reads

Possible

Non-Repeatable

Possible

Phantom Reads

Possible

Best For

Analytics on approximate data

Potential Issues: Dirty reads, Lost updates, Inconsistent data

Read Committed

Fast

Dirty Reads

Prevented

Non-Repeatable

Possible

Phantom Reads

Possible

Best For

Most web applications

Potential Issues: Phantom reads, Non-repeatable reads

Repeatable Read

Moderate

Dirty Reads

Prevented

Non-Repeatable

Prevented

Phantom Reads

Possible

Best For

Financial calculations

Potential Issues: Phantom reads only

Serializable

Slowest

Dirty Reads

Prevented

Non-Repeatable

Prevented

Phantom Reads

Prevented

Best For

Critical financial transactions

Potential Issues: High lock contention, Potential deadlocks

Performance vs Consistency Trade-off

Read Uncommitted

95%performance

20%consistency

Read Committed

80%performance

60%consistency

Repeatable Read

60%performance

80%consistency

Serializable

30%performance

100%consistency

Real-World ACID Scenarios

Understanding how ACID properties apply to common business scenarios helps you design robust applications.

E-commerce Checkout

Customer buying the last item in stock

Without ACID

Two customers could purchase the same item

ACID Solution

Use serializable isolation for inventory updates

Implementation

SELECT ... FOR UPDATE on inventory table

Banking Transfer

Transfer $500 from savings to checking

Without ACID

Money could be debited but not credited

ACID Solution

Wrap both operations in a single transaction

Implementation

BEGIN; UPDATE accounts SET balance = balance - 500 WHERE id = 1; UPDATE accounts SET balance = balance + 500 WHERE id = 2; COMMIT;

User Registration

Create user account with initial preferences

Without ACID

User created but preferences lost

ACID Solution

Create user and preferences in same transaction

Implementation

Use foreign key constraints and transaction boundaries

Order Processing

Create order, update inventory, send notification

Without ACID

Order created but inventory not updated

ACID Solution

Transaction for critical operations, async for notifications

Implementation

Transactional outbox pattern for eventual consistency

ACID Support Across Database Types

Full ACID Support

PostgreSQL

Full ACID compliance, multiple isolation levels

MySQL (InnoDB)

ACID compliant with proper engine choice

Oracle, SQL Server

Enterprise-grade ACID compliance

Partial or Configurable ACID

MongoDB

ACID for single documents, multi-document transactions available

Redis

Atomic operations, limited transaction support

Cassandra, DynamoDB

Eventually consistent, limited ACID guarantees

ACID Performance Considerations

Performance Costs

Logging Overhead

Write-ahead logs for durability

10-15%

Lock Contention

Higher isolation levels

20-50%

Validation Checks

Constraint enforcement

5-10%

Coordination Cost

Distributed transactions

30-70%

Optimization Strategies

Batch Operations

Group multiple operations into single transactions

Read Replicas

Offload read traffic to eventually consistent replicas

Appropriate Isolation

Use the lowest isolation level that meets your requirements

ACID Quick Reference

When to Use Strong ACID

☐ Financial transactions (payments, transfers)
☐ Inventory management (prevent overselling)
☐ User account operations (registration, authentication)
☐ Critical business logic (order processing)
☐ Regulatory compliance (audit trails)

When You Can Relax ACID

☐ Analytics and reporting (eventual consistency OK)
☐ Social media posts (can be eventually consistent)
☐ Search indexes (rebuild-able from source)
☐ Caching layers (temporary data)
☐ Logs and metrics (some loss acceptable)

🎯 ACID Failures and Fixes in Production

Learn from real incidents where ACID violations caused business problems and how they were resolved

Metrics

Time to Disaster

30 minutes

Total Loss

$440 million

Root Cause

Partial deployment

ACID Violation

Atomicity failure

Outcome

Partial deployment caused old code and new code to run simultaneously, creating conflicting orders. No atomic deployment process led to catastrophic system inconsistency.

Lessons Learned

Deployment must be atomic - all systems updated together or none at all
Database and application code changes must be coordinated
Rollback procedures essential for failed deployments
Testing should include partial deployment scenarios

ScenariosClick to explore

Knight Capital Trading Disaster

How lack of transaction atomicity led to $440 million loss in 30 minutes

Amazon DynamoDB Consistency Issue

E-commerce inventory overselling due to eventual consistency

PayPal Double Charging Bug

Race condition in payment processing led to duplicate charges

GitHub Repository Corruption

Database failure during Git operations corrupted repository metadata

Slack Message Ordering Problem

Messages appearing out of order due to distributed transaction issues

Uber Driver Location Inconsistency

Location updates lost during high traffic, causing phantom drivers

Context

How lack of transaction atomicity led to $440 million loss in 30 minutes

Metrics

Time to Disaster

30 minutes

Total Loss

$440 million

Root Cause

Partial deployment

ACID Violation

Atomicity failure

Outcome

Partial deployment caused old code and new code to run simultaneously, creating conflicting orders. No atomic deployment process led to catastrophic system inconsistency.

Key Lessons

•Deployment must be atomic - all systems updated together or none at all
•Database and application code changes must be coordinated
•Rollback procedures essential for failed deployments
•Testing should include partial deployment scenarios

1. Knight Capital Trading Disaster

Context

How lack of transaction atomicity led to $440 million loss in 30 minutes

Metrics

Time to Disaster

30 minutes

Total Loss

$440 million

Root Cause

Partial deployment

ACID Violation

Atomicity failure

Outcome

Partial deployment caused old code and new code to run simultaneously, creating conflicting orders. No atomic deployment process led to catastrophic system inconsistency.

Key Lessons

•Deployment must be atomic - all systems updated together or none at all
•Database and application code changes must be coordinated
•Rollback procedures essential for failed deployments
•Testing should include partial deployment scenarios

2. Amazon DynamoDB Consistency Issue

Context

E-commerce inventory overselling due to eventual consistency

Metrics

Oversold Items

2,000+ products

Customer Impact

15,000 orders

Revenue Loss

$1.2M compensation

Fix Implementation

Strong consistency for inventory

Outcome

Eventually consistent reads on inventory allowed multiple customers to purchase the same limited stock. Switched to strongly consistent reads for inventory operations.

Key Lessons

•Inventory updates require strong consistency - eventual consistency not sufficient
•Different data types have different consistency requirements
•Performance optimization cannot compromise critical business logic
•Compensating transactions needed for when consistency fails

3. PayPal Double Charging Bug

Context

Race condition in payment processing led to duplicate charges

Metrics

Affected Customers

50,000+ users

Duplicate Charges

$12M total

Detection Time

6 hours

Resolution

Serializable isolation

Outcome

Concurrent payment requests with READ COMMITTED isolation allowed duplicate processing. Moved to SERIALIZABLE isolation for payment operations.

Key Lessons

•Payment processing requires highest isolation level (SERIALIZABLE)
•Race conditions in financial systems can be catastrophic
•Idempotency keys essential for payment API design
•Real-time fraud detection must account for race conditions

4. GitHub Repository Corruption

Context

Database failure during Git operations corrupted repository metadata

Metrics

Affected Repositories

3,000 repos

Data Recovery Time

8 hours

Rollback Success

99.8%

Durability Fix

Enhanced WAL

Outcome

Power failure during repository operations led to corruption. Enhanced write-ahead logging and increased fsync frequency ensured durability.

Key Lessons

•Durability requires persistent storage with proper fsync timing
•Git operations must be wrapped in database transactions
•Repository metadata consistency critical for version control
•Backup and recovery procedures must be tested regularly

5. Slack Message Ordering Problem

Context

Messages appearing out of order due to distributed transaction issues

Metrics

Affected Channels

20,000 channels

Message Confusion

4 hour window

User Complaints

5,000+ reports

Solution

Vector clocks

Outcome

Clock skew across servers caused messages to appear out of order. Implemented vector clocks and message ordering guarantees.

Key Lessons

•Distributed systems require logical clocks for ordering
•Message consistency matters for user experience
•ACID properties must be maintained across distributed components
•Real-time systems need additional consistency mechanisms

6. Uber Driver Location Inconsistency

Context

Location updates lost during high traffic, causing phantom drivers

Metrics

Ghost Drivers

15,000 phantom locations

Failed Matches

40,000 riders

Revenue Impact

$800K lost rides

Fix

Transactional updates

Outcome

Non-transactional location updates during peak traffic led to stale driver positions. Implemented atomic location and availability updates.

Key Lessons

•Real-time systems still need transactional guarantees for critical data
•Location and availability must be updated atomically
•High-traffic scenarios expose race conditions
•Eventual consistency insufficient for real-time matching

No quiz questions available

Quiz ID "acid-properties" not found