Database Isolation Levels

What is Isolation?

Isolation is an ACID property that ensures concurrent transactions don’t interfere with each other. Each transaction sees a consistent snapshot of the data, even when other transactions are running simultaneously.

Simple Analogy

Think of isolation like private workspaces in an office. Each employee (transaction) has their own copy of documents. They can work independently without seeing each other’s changes until they “publish” (commit) their work.

---

The Isolation Levels Spectrum

Isolation levels form a spectrum from weakest (most concurrency, most anomalies) to strongest (least concurrency, no anomalies):

---

Level 1: Read Uncommitted

Read Uncommitted is the lowest isolation level. Transactions can read uncommitted data from other transactions.

The Problem: Dirty Reads

Example: Transaction 1 updates balance to 200 but hasn’t committed. Transaction 2 reads 200. Then Transaction 1 rolls back. Transaction 2 saw dirty (uncommitted) data that never actually existed!

When to Use: Almost never. Only for read-only analytics where accuracy isn’t critical.

---

Level 2: Read Committed

Read Committed prevents dirty reads. Transactions can only read committed data.

How It Works

Key Characteristic: Each read sees the latest committed value at the time of the read.

The Problem: Non-Repeatable Reads

Example: Transaction 1 reads balance = 100. Transaction 2 updates it to 200 and commits. Transaction 1 reads again and gets 200. Same transaction, different values!

When to Use: Default in most databases (PostgreSQL, SQL Server). Good balance of consistency and performance.

---

Level 3: Repeatable Read

Repeatable Read prevents dirty reads and non-repeatable reads. A transaction sees a consistent snapshot throughout its lifetime.

How It Works

Key Characteristic: Transaction sees the same data on repeated reads, even if other transactions commit changes.

The Problem: Phantom Reads

Example: Transaction 1 counts active orders (gets 5). Transaction 2 inserts a new active order and commits. Transaction 1 counts again (gets 6). A phantom row appeared!

When to Use: When you need consistent reads of the same rows (e.g., calculating totals, validating constraints).

---

Level 4: Serializable

Serializable is the highest isolation level. Transactions execute as if they ran one at a time (serially).

How It Works

Key Characteristic: Database ensures transactions produce the same result as if they ran serially (one after another).

When to Use: Critical financial transactions, inventory management, any scenario where perfect consistency is required.

---

Isolation Level Comparison

Isolation Level	Dirty Read	Non-Repeatable Read	Phantom Read	Concurrency	Use Case
Read Uncommitted	Possible	Possible	Possible	Highest	Almost never
Read Committed	Prevented	Possible	Possible	High	Default (most DBs)
Repeatable Read	Prevented	Prevented	Possible	⚠️ Medium	Consistent reads
Serializable	Prevented	Prevented	Prevented	Lowest	Critical operations

---

Real-World Examples

Understanding how major companies use isolation levels helps illustrate their practical importance:

Read Committed: E-commerce Inventory Management

The Challenge: E-commerce platforms need to prevent overselling. When multiple users try to buy the last item simultaneously, only one should succeed.

The Solution: Amazon uses Read Committed isolation:

• Scenario: Product has 1 item left. Two users try to buy it simultaneously.
• Read Committed: Each transaction sees committed data only. First transaction commits → second sees 0 items → purchase fails.
• Prevents: Overselling, negative inventory

Example:

• T1: User A reads inventory (1 item) → reserves item
• T2: User B reads inventory (1 item) → tries to reserve
• T1: Commits → inventory = 0
• T2: Tries to commit → fails (inventory already 0)

Impact: Zero overselling incidents. Inventory always accurate. Critical for e-commerce.

Repeatable Read: Financial Reporting

The Challenge: Financial reports need consistent snapshots. A report showing account balances shouldn’t change mid-generation.

The Solution: Banks use Repeatable Read for reporting:

• Scenario: Generate monthly statement. Account has multiple transactions during report generation.
• Repeatable Read: Report sees consistent snapshot. All reads within transaction see same data.
• Prevents: Inconsistent reports, mid-transaction changes

Example:

• T1: Begin report generation
• T1: Read balance at start of month: $1000
• T2: Process transaction: $100 → $900
• T1: Read balance at end of month: Still sees $1000 (consistent snapshot)
• T1: Commit → Report shows consistent data

Impact: Consistent financial reports. No mid-generation changes. Critical for compliance.

Serializable: Ticket Booking Systems

The Challenge: Ticket booking systems need to prevent double-booking. When multiple users try to book the same seat, only one should succeed.

The Solution: Airlines use Serializable isolation:

• Scenario: Last seat on flight. Two users try to book simultaneously.
• Serializable: Transactions execute serially. One completes before other starts.
• Prevents: Double-booking, race conditions

Example:

• T1: User A checks seat availability (available) → locks seat
• T2: User B checks seat availability (waits for T1)
• T1: Books seat → commits
• T2: Sees seat unavailable → booking fails

Impact: Zero double-booking incidents. All bookings are valid. Critical for ticket systems.

Read Uncommitted: Analytics and Monitoring

The Challenge: Analytics systems need real-time data but can tolerate some inconsistency. Performance matters more than perfect accuracy.

The Solution: Analytics platforms use Read Uncommitted for dashboards:

• Scenario: Real-time dashboard showing user count, page views, etc.
• Read Uncommitted: Reads uncommitted data for speed. Some inconsistency acceptable.
• Trade-off: Speed vs accuracy

Example:

• T1: User creates account (uncommitted: +1 user)
• Dashboard: Reads uncommitted count (shows +1 immediately)
• T1: Rolls back (account creation failed)
• Dashboard: Eventually corrects (shows accurate count)

Impact: Real-time dashboards. Fast updates. Acceptable for analytics (not financial data).

PostgreSQL Default: Read Committed

The Challenge: Most applications need balance between consistency and performance.

The Solution: PostgreSQL defaults to Read Committed:

• Why: Good balance. Prevents dirty reads (critical) but allows good concurrency.
• Use case: Most web applications, APIs, general-purpose systems

Example: User updates profile while another user reads it:

• T1: Update profile (uncommitted changes)
• T2: Read profile (sees old committed data, not uncommitted)
• T1: Commit
• T2: Next read sees new data

Impact: Used by millions of applications. Good default for most use cases.

---

Key Takeaways from Real Examples

1. Read Committed - Most common, prevents dirty reads, good for general use
2. Repeatable Read - For consistent snapshots, reporting, financial data
3. Serializable - For critical operations, prevents all anomalies, highest consistency
4. Read Uncommitted - Rarely used, only for analytics where speed > accuracy
5. Choose based on needs - Balance consistency vs performance

---

Example 1: Bank Balance Check (Read Committed)

Isolation Level: Read Committed
Why: User wants to see current balance, doesn’t need perfect consistency for a simple read.

---

Example 2: Inventory Count (Repeatable Read)

Isolation Level: Repeatable Read
Why: Need to read inventory multiple times and ensure it doesn’t change between reads.

---

Example 3: Financial Transfer (Serializable)

Isolation Level: Serializable
Why: Financial transactions require perfect consistency. Can’t have race conditions or anomalies.

---

LLD ↔ HLD Connection

How isolation levels affect your code design:

Choosing Isolation Level

---

Deep Dive: Advanced Isolation Considerations

Deadlocks: The Hidden Cost of High Isolation

Deadlock occurs when two transactions wait for each other’s locks indefinitely.

Deadlock Scenario

Production Impact:

• Frequency: Deadlocks occur ~0.01-0.1% of transactions in high-concurrency systems
• Detection: Databases detect deadlocks automatically (usually less than 1 second)
• Recovery: One transaction aborted, retried by application
• Cost: Aborted transaction wasted work, retry overhead

---

Deadlock Prevention Strategies

Strategy 1: Lock Ordering

Pattern: Always acquire locks in the same order.

Example:

def transfer_money(self, from_id, to_id, amount):
    # Always lock lower ID first
    first_id = min(from_id, to_id)
    second_id = max(from_id, to_id)

    with self.lock(first_id):
        with self.lock(second_id):
            # Transfer logic

Benefit: Prevents circular wait → no deadlocks

---

Strategy 2: Lock Timeout

Pattern: Set timeout on lock acquisition.

Example:

def transfer_money(self, from_id, to_id, amount):
    try:
        # Try to acquire locks with timeout
        if not self.try_lock(from_id, timeout=5):
            raise LockTimeout("Could not acquire lock")
        if not self.try_lock(to_id, timeout=5):
            raise LockTimeout("Could not acquire lock")
        # Transfer logic
    finally:
        self.unlock(from_id)
        self.unlock(to_id)

Benefit: Fails fast instead of deadlocking

---

Strategy 3: Reduce Isolation Level

Pattern: Use lower isolation when possible.

Example:

• Serializable: Highest deadlock risk
• Repeatable Read: Medium deadlock risk
• Read Committed: Lower deadlock risk (fewer locks)

Trade-off: Lower isolation = fewer deadlocks but more anomalies

---

Isolation Level Performance Impact

Lock Contention Analysis

Read Committed:

• Locks: Short-lived (released after read)
• Contention: Low
• Throughput: High (10K-50K TPS)
• Deadlock Risk: Low

Repeatable Read:

• Locks: Held for transaction duration
• Contention: Medium
• Throughput: Medium (5K-20K TPS)
• Deadlock Risk: Medium

Serializable:

• Locks: Range locks, held for transaction duration
• Contention: High
• Throughput: Low (1K-5K TPS)
• Deadlock Risk: High

Production Benchmark (PostgreSQL):

Isolation Level	Read Latency	Write Latency	Throughput	Deadlock Rate
Read Committed	5ms	10ms	20K TPS	0.01%
Repeatable Read	8ms	15ms	10K TPS	0.05%
Serializable	15ms	30ms	3K TPS	0.2%

Key Insight: Each isolation level increase costs 2-3x in performance.

---

Snapshot Isolation: The Middle Ground

Snapshot Isolation is used by many databases (PostgreSQL, MySQL InnoDB) as their default “Repeatable Read” implementation.

How it works:

• Each transaction sees a snapshot of data at transaction start
• Writes create new versions (multi-version concurrency control - MVCC)
• No locks for reads (read from snapshot)
• Locks only for writes (prevent conflicts)

Benefits:

• No read locks: Readers don’t block writers
• No write locks: Writers don’t block readers (until commit)
• Better concurrency: Higher throughput than locking

Trade-offs:

• ⚠️ Storage overhead: Must store multiple versions
• ⚠️ Vacuum required: Old versions must be cleaned up

Production Example: PostgreSQL

• Uses MVCC for all isolation levels
• Vacuum process: Runs periodically to clean old versions
• Performance: 2-5x better than locking-based isolation

---

Read Phenomena: Detailed Analysis

Dirty Read: Real-World Impact

Scenario: Financial reporting

Impact:

• Financial reports: Wrong numbers → regulatory issues
• Analytics: Incorrect insights → bad decisions
• Auditing: Compliance violations

Prevention: Read Committed or higher

---

Non-Repeatable Read: Business Logic Impact

Scenario: Inventory validation

Impact:

• Overselling: Sell more than available
• Double-booking: Book same resource twice
• Race conditions: Business logic fails

Prevention: Repeatable Read or higher

---

Phantom Read: Aggregation Impact

Scenario: Count operations

Impact:

• Statistics: Wrong averages, totals
• Reports: Incorrect aggregations
• Business metrics: Wrong KPIs

Prevention: Serializable (only level that prevents phantoms)

---

Isolation Level Selection: Production Guidelines

Decision Framework

Production Rules:

1. Start with Read Committed (default, works for 90% of cases)
2. Upgrade to Repeatable Read if you read same row twice
3. Upgrade to Serializable only if phantoms cause problems
4. Monitor deadlock rate - if high, consider lowering isolation

---

Database-Specific Isolation Implementations

PostgreSQL Isolation Levels

Read Committed (Default):

• Uses MVCC (Multi-Version Concurrency Control)
• Snapshot: Taken at start of each statement
• Locks: Only for writes
• Performance: Excellent

Repeatable Read:

• Uses MVCC with transaction-level snapshot
• Snapshot: Taken at transaction start
• Locks: Only for writes
• Note: Actually prevents phantoms (stricter than SQL standard!)

Serializable:

• Uses Serializable Snapshot Isolation (SSI)
• Detection: Detects serialization conflicts
• Abort: Aborts conflicting transactions
• Performance: Good (better than locking-based)

---

MySQL InnoDB Isolation Levels

Read Committed:

• Uses MVCC
• Snapshot: Per statement
• Performance: Good

Repeatable Read (Default):

• Uses MVCC with transaction snapshot
• Gap locks: Prevents phantoms (stricter than standard!)
• Performance: Good

Serializable:

• Uses locking (not MVCC)
• Locks: All reads acquire shared locks
• Performance: Poor (much slower)

Key Difference: MySQL’s Repeatable Read is stricter than PostgreSQL’s!

---

Production Best Practices

Practice 1: Keep Transactions Short

Rule: Minimize transaction duration to reduce lock contention.

Bad:

# BAD: Long transaction
def process_order(order):
    with transaction():
        validate_order(order)  # External API call (slow!)
        reserve_inventory(order)
        charge_payment(order)
        # Transaction held during slow API call

Good:

# GOOD: Short transaction
def process_order(order):
    # Do slow work outside transaction
    validation_result = validate_order(order)  # Outside transaction

    # Short transaction
    with transaction():
        reserve_inventory(order)
        charge_payment(order)

Benefit: Reduces lock time → fewer deadlocks, better throughput

---

Practice 2: Use Appropriate Isolation Per Operation

Pattern: Different isolation levels for different operations.

Example:

class OrderService:
    def get_order(self, order_id):
        # Read Committed - simple read
        return self.db.read(order_id, isolation='READ_COMMITTED')

    def validate_inventory(self, product_id):
        # Repeatable Read - need consistent reads
        return self.db.read(product_id, isolation='REPEATABLE_READ')

    def transfer_money(self, from_id, to_id, amount):
        # Serializable - critical operation
        return self.db.transfer(from_id, to_id, amount, isolation='SERIALIZABLE')

Benefit: Optimize each operation for its needs

---

Practice 3: Monitor Isolation-Level Metrics

What to Monitor:

• Deadlock rate: Should be less than 0.1%
• Lock wait time: P95 should be less than 100ms
• Transaction duration: P95 should be less than 1s
• Rollback rate: High rate indicates problems

Production Monitoring:

class IsolationMonitor:
    def track_transaction(self, isolation_level, duration, deadlocked):
        self.metrics.increment(f'transaction.{isolation_level}.total')
        self.metrics.histogram(f'transaction.{isolation_level}.duration', duration)

        if deadlocked:
            self.metrics.increment(f'transaction.{isolation_level}.deadlock')
            self.alert_on_deadlock(isolation_level)

---

Key Takeaways

Remember

• Isolation Levels: Read Uncommitted, Read Committed, Repeatable Read, Serializable
• Read Committed: Default in most databases, prevents dirty reads
• Repeatable Read: Prevents non-repeatable reads, good for consistent reads
• Serializable: Highest level, prevents all anomalies, lowest concurrency
• Trade-off: Higher isolation = fewer anomalies but less concurrency
• LLD Implementation: Choose isolation level based on use case, understand race conditions
• Default is usually fine: Read Committed works for most scenarios
• Deadlocks: Higher isolation = higher deadlock risk, use lock ordering/timeouts
• Performance: Each isolation level increase costs 2-3x in throughput
• Snapshot Isolation: MVCC provides better concurrency than locking
• Best practices: Keep transactions short, use appropriate isolation per operation, monitor metrics

---

What’s Next?

Now that you understand isolation levels, let’s explore how to scale databases to handle more load:

Next up: Scaling Databases — Learn about read replicas, connection pooling, and scaling strategies.

Practice Exercise

Think about a system you’ve built: What isolation level does it use? Could you use a different level for better performance? What anomalies might occur?