Comparison Tables - Consistency Model DeepDive

📖 Understanding the Metrics

Before diving into comparisons, let's understand what each metric means:

💪 Strength

What it measures: How strong the consistency guarantees are.

Strongest: No anomalies, perfect consistency
Strong: Very few anomalies, high consistency
Medium: Some anomalies allowed, balanced
Weak: Many anomalies possible, low consistency
Weakest: Temporary inconsistencies allowed

⏰ Real-time Order

What it measures: Whether operations must respect the order they actually occurred in real time.

✅ Required: If operation A finishes before B starts, all nodes see A before B
❌ Not Required: Operations can appear in different order than real-time

🌐 Global Order

What it measures: Whether all processes see operations in the same order.

✅ Required: All nodes see operations in identical sequence
❌ Not Required: Different nodes can see different orderings

🔗 Causality

What it measures: Whether cause-and-effect relationships are preserved.

✅ Preserved: If A causes B, all nodes see A before B
⚠️ Partial: Some causal relationships preserved
❌ Not Preserved: Causal relationships can be violated

🔄 Availability

What it measures: System's ability to remain operational during failures.

✅ Totally Available: Works during any network partition
✅ Sticky Available: Available for single-user sessions
⚠️ Limited: Available in some partition scenarios
❌ Not Available: Stops working during partitions

📊 Performance Impact

What it measures: How consistency requirements affect system performance.

Latency: Time to complete operations
Throughput: Number of operations per second
Network Overhead: Communication required between nodes
Complexity: Implementation and operational difficulty

Overview Comparison

Model	Strength	Real-time Order	Global Order	Causality	Availability	Primary Use Cases
Strict Serializable	Strongest	✅ Required	✅ Required	✅ Preserved	❌ Not Available	Banking, Financial Systems
Linearizable	Very Strong	✅ Required	✅ Required	✅ Preserved	❌ Not Available	Configuration, Coordination
Sequential	Strong	❌ Not Required	✅ Required	✅ Preserved	❌ Not Available	Distributed Caches
Serializable	Strong	❌ Not Required	✅ Required	✅ Preserved	❌ Not Available	ACID Databases
Snapshot Isolation	Medium-Strong	❌ Not Required	❌ Not Required	⚠️ Partial	⚠️ Limited	MVCC Databases
Repeatable Read	Medium-Strong	❌ Not Required	❌ Not Required	⚠️ Partial	⚠️ Limited	SQL Databases
Causal	Medium	❌ Not Required	❌ Not Required	✅ Preserved	⚠️ Limited	Social Media, Chat
PRAM	Medium-Weak	❌ Not Required	❌ Not Required	⚠️ Per-Process	✅ Available	Distributed Systems
Read Committed	Weak	❌ Not Required	❌ Not Required	❌ Not Preserved	✅ Available	Web Applications
Monotonic Reads	Weak	❌ Not Required	❌ Not Required	❌ Not Preserved	✅ Sticky Available	Session-based Apps
Read Your Writes	Weak	❌ Not Required	❌ Not Required	❌ Not Preserved	✅ Sticky Available	User Profiles
Eventual	Weakest	❌ Not Required	❌ Not Required	❌ Not Preserved	✅ Totally Available	DNS, CDN

Transaction Isolation Levels

Model	Dirty Read	Non-repeatable Read	Phantom Read	Write Skew
Strict Serializable	❌ Prevented	❌ Prevented	❌ Prevented	❌ Prevented
Serializable	❌ Prevented	❌ Prevented	❌ Prevented	❌ Prevented
Repeatable Read	❌ Prevented	❌ Prevented	✅ Possible	✅ Possible
Snapshot Isolation	❌ Prevented	❌ Prevented	❌ Prevented	✅ Possible
Read Committed	❌ Prevented	✅ Possible	✅ Possible	✅ Possible
Read Uncommitted	✅ Possible	✅ Possible	✅ Possible	✅ Possible

Performance Characteristics

Model	Latency	Throughput	Network Overhead	Implementation Complexity
Strict Serializable	Very High	Very Low	Very High	Very Complex
Linearizable	High	Low	High	Complex
Sequential	High	Low	High	Complex
Serializable	High	Low	Medium	Complex
Snapshot Isolation	Medium	Medium	Medium	Medium
Causal	Medium	Medium	Medium	Medium
PRAM	Low	High	Low	Simple
Read Committed	Low	High	Low	Simple
Session Guarantees	Low	High	Low	Simple
Eventual	Very Low	Very High	Very Low	Very Simple

Real-World System Examples

🏦 Financial Systems

Strict Serializable

Google Spanner, FaunaDB, FoundationDB - Critical correctness requirements

⚙️ Configuration Systems

Linearizable

etcd, Consul, Zookeeper - Coordination and leader election

🗄️ ACID Databases

Serializable

PostgreSQL, MySQL - Traditional relational databases

💬 Social Media

Causal

MongoDB (with causal reads) - Preserve conversation flow

🌐 Content Delivery

Eventual

DNS, CDNs, Redis, DynamoDB - High availability priority

🛒 E-commerce

Mixed Models

Cart: Eventual, Inventory: Linearizable, Payments: Strict Serializable

Decision Matrix

🎯 Interactive Decision Guide

Answer these questions to find the right consistency model for your use case:

🚨 Is Absolute Correctness Critical?

Examples: Banking, payments, inventory, medical records

Strict Serializable

✅ Zero tolerance for inconsistencies
✅ ACID compliance required
❌ Accept higher latency (100-500ms)
❌ Lower availability during partitions

⚙️ Need Strong Coordination?

Examples: Configuration systems, leader election, distributed locks

Linearizable

✅ Single-object strong consistency
✅ Real-time ordering guaranteed
⚠️ Medium latency (50-200ms)
❌ Not available during network splits

🔄 Need Global Ordering?

Examples: Distributed caches, replicated logs, event sourcing

Sequential Consistency

✅ All nodes see same operation order
✅ Good for state machine replication
⚠️ Medium latency (20-100ms)
❌ Limited availability during partitions

🔗 Are Cause-Effect Relationships Important?

Examples: Social media, chat, collaborative editing, forums

Causal Consistency

✅ Preserves conversation flow
✅ Good performance (10-50ms)
✅ Available during most partitions
⚠️ Complex to implement correctly

👤 Is Per-User Consistency Enough?

Examples: User profiles, shopping carts, personal settings

Session Guarantees

✅ Users see their own changes
✅ Low latency (5-20ms)
✅ High availability (99.9%+)
❌ No global consistency guarantees

🚀 Is Maximum Performance/Availability Priority?

Examples: DNS, CDNs, analytics, caching, content delivery

Eventual Consistency

✅ Maximum availability (99.99%+)
✅ Lowest latency (1-10ms)
✅ Scales globally
❌ Temporary inconsistencies possible

📊 Quick Reference Matrix

High Correctness

Strict Serializable Linearizable

Balanced Trade-offs

Sequential Causal

High Performance

Session Eventual