Conflict Resolution - Learning Module

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Write Conflicts in Distributed Systems

The Inevitable Reality of Distributed Writes

In a perfect world, every write to a database would happen in perfect isolation—one at a time, in a well-defined order, with instant global visibility. Reality, however, is far more complex. The moment you distribute data across multiple nodes, replicas, or data centers, you enter a world where write conflicts are not just possible—they are inevitable.

Understanding write conflicts is fundamental to designing robust distributed systems. These conflicts arise not from bugs or poor design, but from the fundamental physics of distributed computing: information takes time to travel, nodes can fail independently, and the speed of light is finite. Every distributed database, every multi-master replication setup, every collaborative editing system must confront and resolve these conflicts.

What You Will Learn

By the end of this page, you will understand the fundamental nature of write conflicts, why they occur in distributed systems, how to identify and classify them, and the profound impact they have on system design. This foundation is essential before we explore specific resolution strategies in subsequent pages.

The Anatomy of a Write Conflict

A write conflict occurs when two or more concurrent write operations target the same piece of data without coordination, resulting in a state where the system cannot deterministically decide which value should prevail. Let's dissect the precise conditions that create conflicts.

The Three Prerequisites for Conflict:

For a write conflict to occur, three conditions must be present simultaneously:

Concurrency — Two or more write operations happen 'at the same time' in the distributed sense (neither is aware of the other when it begins)
Same Target — Both writes affect the same logical data item (the same key, the same row, the same document field)
No Coordination — The operations proceed without a synchronization mechanism that would serialize them (no locks, no leader election, no consensus)

Converting Mermaid diagram...

Why 'Concurrent' Is Complex:

In distributed systems, 'concurrent' doesn't mean 'at the exact same nanosecond.' It means causally independent—neither operation could have influenced the other. Consider:

Client A writes to Replica 1 in New York at 10:00:00.000 UTC
Client B writes to Replica 2 in London at 10:00:00.001 UTC

Are these concurrent? The answer depends on perspective:

Physical time: They're 1 millisecond apart
Logical time: If neither replica knew about the other's write when it happened, they're concurrent in the distributed systems sense

This distinction is crucial. Physical clocks can drift (sometimes by seconds), network latency varies, and there's no global time authority in a distributed system. Two operations that appear sequential in wall-clock time may be logically concurrent if the causal relationship cannot be established.

Lamport's Happened-Before Relation

Leslie Lamport's seminal 1978 paper 'Time, Clocks, and the Ordering of Events in a Distributed System' formalized this: event A 'happens before' event B (written A → B) if A could have causally influenced B. Two events are concurrent if neither happened before the other (A || B). This is the theoretical foundation for understanding conflicts.

Sources of Write Conflicts

Write conflicts emerge from specific architectural patterns in distributed systems. Understanding these sources helps you anticipate where conflicts will occur and design appropriate resolution strategies.

Primary Sources of Write Conflicts

•Multi-Master Replication — When multiple nodes can accept writes for the same data, a user in Europe writing to a European replica while a user in Asia writes to an Asian replica creates immediate conflict potential. Popular in: CouchDB, Cassandra, DynamoDB (with certain configurations).
•Leaderless Replication — Systems like Amazon's Dynamo-style databases (Riak, Cassandra) allow any node to accept writes. Without a leader to serialize writes, concurrent updates to the same key naturally conflict.
•Offline-First Applications — Mobile apps, collaborative tools, and edge computing systems that work offline accumulate changes locally, then sync when connectivity returns. Conflicts arise when multiple users modified the same data while disconnected.
•Partition Tolerance (Network Splits) — During a network partition, different sides of the split may continue accepting writes. When the partition heals, the system discovers conflicting writes that occurred in isolation.
•Async Replication Lag — Even in leader-follower systems, if failover occurs before all writes replicate, the new leader may have a stale state. Writes to the new leader conflict with (lost) writes to the old leader.

Conflict Probability by Architecture
Architecture	Conflict Risk	Conflict Frequency	Typical Resolution
Single Leader (sync replication)	Very Low	Rare (only during failover)	Rollback or manual
Single Leader (async replication)	Low	Low (lag-induced)	Last-write-wins or rollback
Multi-Leader	High	Frequent	LWW, vector clocks, or CRDTs
Leaderless (quorum)	Medium-High	Moderate	Read repair, anti-entropy
Offline-First	Very High	Very Frequent	Operational transforms, CRDTs

The Trade-off Spectrum:

Every architectural decision that increases availability or reduces latency also increases conflict potential:

Strong consistency: Zero conflicts, but higher latency and lower availability
Eventual consistency: Lower latency, higher availability, but conflicts are guaranteed

This isn't a flaw—it's a fundamental trade-off governed by the CAP theorem. The key is choosing the right point on this spectrum for your use case and having a robust strategy for the conflicts you'll encounter.

Classification of Conflict Types

Not all conflicts are created equal. Understanding the different types of conflicts helps you choose appropriate resolution strategies and design data models that minimize harmful conflicts.

Write-Write Conflicts (also called update conflicts) occur when two operations concurrently modify the same data item.

Example Scenario:

User A sets balance = balance + 100 (deposit $100)
User B sets balance = balance - 50 (withdraw $50)
Both read balance = 500 before their operation
Both write: A writes 600, B writes 450

The Problem: If we take either value, we lose the other operation. The correct final balance should be 550, but neither 600 nor 450 is correct.

Characteristics:

Most common type of conflict
Often requires semantic understanding to resolve correctly
May result in lost updates if naively resolved
Particularly dangerous for counter/accumulator data

write-write-conflict.pseudo

Conflict Example

// Initial state: { balance: 500 }
 
// Client A's operation (runs on Replica 1)
function deposit(amount) {
    const current = read("balance");  // reads 500
    write("balance", current + amount);  // writes 600
}
deposit(100);
 
// Client B's operation (runs on Replica 2, concurrently)
function withdraw(amount) {
    const current = read("balance");  // reads 500 (hasn't seen A's write)
    write("balance", current - amount);  // writes 450
}
withdraw(50);
 
// After replication sync:
// Replica 1 has: balance = 600
// Replica 2 has: balance = 450
// CONFLICT: Which is correct? Neither! Should be 550.

Detecting Conflicts

Before you can resolve a conflict, you must detect it. Distributed systems employ various mechanisms to identify when concurrent writes have created divergent states.

On-Write Detection

•Version Vectors — Each replica maintains a vector of version counters. When receiving a write, compare vectors to detect concurrent modifications.
•Timestamps Comparison — Compare physical or logical timestamps. Non-equal timestamps for same data version indicate potential conflict.
•Hash Comparison — Compute hashes of data states. Differing hashes for same logical key indicate divergence.
•Merkle Trees — Hierarchical hash structures allow efficient detection of divergent subtrees across large datasets.

On-Read Detection

•Read Repair — During reads, compare responses from multiple replicas. Divergent values reveal conflicts that are then resolved.
•Quorum Reads — Reading from multiple nodes exposes version inconsistencies when values don't match.
•Anti-Entropy Processes — Background processes periodically compare replica states and identify divergences.
•Client-Side Detection — Smart clients compare cached versions with server responses to detect conflicts.

Version Vectors in Depth:

Version vectors are the most rigorous conflict detection mechanism. Each replica maintains a vector: {Replica1: 5, Replica2: 3, Replica3: 7}. When data is modified:

The modifying replica increments its own counter
The vector is attached to the data
On sync, vectors are compared:
- If Vector A dominates Vector B (every element ≥), A supersedes B (no conflict)
- If neither dominates, the writes are concurrent (CONFLICT)

Example:

Vector A: {R1: 5, R2: 3, R3: 7}
Vector B: {R1: 4, R2: 4, R3: 7}

A has R1:5 > R1:4, but B has R2:4 > R2:3. Neither dominates → Concurrent writes detected!

version-vectors.ts
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interface VersionVector {
    [replicaId: string]: number;
}
 
type ComparisonResult = 'DOMINATES' | 'DOMINATED_BY' | 'CONCURRENT' | 'EQUAL';
 
function compareVersionVectors(
    vectorA: VersionVector, 
    vectorB: VersionVector
): ComparisonResult {
    const allKeys = new Set([
        ...Object.keys(vectorA), 
        ...Object.keys(vectorB)
    ]);
    
    let aDominates = true;
    let bDominates = true;
    
    for (const key of allKeys) {
        const a = vectorA[key] ?? 0;
        const b = vectorB[key] ?? 0;
        
        if (a < b) aDominates = false;
        if (b < a) bDominates = false;
    }
    
    if (aDominates && bDominates) return 'EQUAL';
    if (aDominates) return 'DOMINATES';
    if (bDominates) return 'DOMINATED_BY';
    return 'CONCURRENT';  // Neither dominates = CONFLICT
}
 
// Usage example:
const vectorA: VersionVector = { replica1: 5, replica2: 3, replica3: 7 };
const vectorB: VersionVector = { replica1: 4, replica2: 4, replica3: 7 };
 
const result = compareVersionVectors(vectorA, vectorB);
console.log(result);  // Output: 'CONCURRENT' - Conflict detected!

The Impact of Unresolved Conflicts

When conflicts go unresolved or are resolved incorrectly, the consequences cascade through your system. Understanding these impacts underscores why robust conflict resolution is not optional.

Consequences of Poor Conflict Handling

•Data Loss — The most direct impact. When conflicting writes are resolved by 'picking one,' the other write is lost. For banking transactions, this could mean real money disappearing. For medical records, it could mean lost patient information.
•Data Corruption — Naive merge strategies can create data that neither original write intended. Merging {balance: 600} and {balance: 450} might produce {balance: 525} (average), which is still wrong.
•Inconsistent Views — Different users/services see different 'truth.' User A sees their edit succeeded; User B sees something different. This erodes trust and causes confusion.
•Audit Trail Gaps — For compliance-heavy systems (finance, healthcare), losing the history of how data changed is not just inconvenient—it may be legally problematic.
•Cascading Failures — Inconsistent data can trigger downstream errors. An inventory count of -5 (from conflicting decrements) breaks ordering logic. A user with two profiles breaks authentication.
•Silent Failures — Perhaps worst: conflicts that resolve 'successfully' but incorrectly, without anyone noticing until the damage is significant.

Real-World Disaster: Amazon's Shopping Cart

In Amazon's original Dynamo paper, they describe how shopping cart conflicts were resolved by taking the union of all concurrent cart states. This means if you remove an item from your cart during a conflict, it might reappear—frustrating, but Amazon judged it better than items silently disappearing. This is a conscious trade-off between conflict resolution strategies.

Conflict Impact by Domain
Domain	Conflict Example	Potential Impact	Acceptable Strategy
E-commerce Cart	Item quantity conflicts	Customer frustration	Union/addition (items reappear)
Banking	Concurrent transfers	Financial loss	Strong consistency required
Social Media	Concurrent likes/comments	Counter inaccuracy	CRDTs, eventual accuracy
Collaborative Docs	Concurrent text edits	Lost changes	Operational transforms
Inventory	Concurrent stock updates	Overselling, negative stock	Requires coordination
Medical Records	Concurrent updates	Patient safety risk	Strong consistency + audit

The Conflict Resolution Spectrum

Conflict resolution strategies exist on a spectrum from simple but lossy to complex but precise. Your choice depends on data semantics, consistency requirements, and operational complexity tolerance.

Converting Mermaid diagram...

Choosing Your Strategy:

The right conflict resolution strategy depends on multiple factors:

Factor	Favors Simple (LWW)	Favors Complex (CRDTs)
Data semantics	Last value is all that matters	History/accumulation matters
Conflict frequency	Rare (mostly sequential)	Frequent (high concurrency)
Data criticality	Low (logs, metrics)	High (financial, medical)
Team expertise	Limited distributed experience	Strong distributed expertise
Latency requirements	Can tolerate coordination	Needs low latency always

Hybrid Approaches

Production systems often use hybrid approaches: CRDTs for counters and sets, LWW for simple values, and application-level merge for complex business objects. The key is matching the resolution strategy to the semantic requirements of each data type.

Designing for Conflict Minimization

The best conflict is the one that never happens. While you cannot eliminate all conflicts in a distributed system, thoughtful design significantly reduces their frequency and impact.

Conflict Minimization Strategies

•Partition Data by Access Pattern — If User A only modifies their own profile and User B only modifies theirs, concurrent writes don't conflict. Design data models where writes are naturally isolated to specific users, regions, or entities.
•Use Conflict-Free Operations — Instead of setting counter = 5, use increment counter by 1. Increments from different sources can be safely merged. This is the foundation of CRDTs.
•Reduce Replication Factor for Hot Data — If coordination is possible, fewer replicas mean fewer potential conflict sources. Critical data might use strong consistency even in an eventually consistent system.
•Time-Bound Write Windows — For some data, only accept writes within a time window. Old writes are rejected, reducing conflict from delayed replication.
•Immutable/Append-Only Structures — If data is never updated (only appended), conflicts become merge operations. Event sourcing and log-based architectures leverage this principle.
•Pessimistic Locking for Critical Paths — For truly critical operations (financial transactions), acquire locks across replicas before writing. Accept the latency cost for correctness.

conflict-resistant-design.ts
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// CONFLICT-PRONE: Updating shared counter
interface ConflictProne {
    shoppingCart: {
        totalItems: number;  // Concurrent updates conflict!
        items: Item[];
    };
}
 
// CONFLICT-RESISTANT: Each user has own partition
interface ConflictResistant {
    userCarts: {
        [userId: string]: {
            // Only this user modifies their cart
            items: Item[];
        };
    };
}
 
// CONFLICT-FREE: Operation-based updates
interface CartOperation {
    type: 'ADD_ITEM' | 'REMOVE_ITEM' | 'UPDATE_QUANTITY';
    itemId: string;
    userId: string;
    timestamp: number;
    delta?: number;  // For quantity: +1, -1, etc.
}
 
// Operations can be merged: apply all ADDs, then all REMOVEs
// Final state is deterministic regardless of operation order

Summary: Understanding Write Conflicts

Write conflicts in distributed systems are not bugs—they are an inherent consequence of distributing data across space and accepting writes in multiple locations. Let's consolidate what we've learned:

Key Takeaways

•Conflicts Require Three Conditions — Concurrency (in the causal sense), same target data, and no coordination mechanism. Remove any one to prevent conflicts.
•Sources Are Architectural — Multi-master replication, leaderless systems, offline-first apps, and network partitions all generate conflicts by design, not by accident.
•Conflict Types Vary — Write-write, delete-update, insert-insert, and constraint violations each require different handling approaches.
•Detection Precedes Resolution — Version vectors, timestamps, hash comparisons, and read repair are mechanisms to identify when conflicts have occurred.
•Unresolved Conflicts Have Real Cost — Data loss, corruption, inconsistent views, and cascading failures make proper resolution non-optional.
•Resolution Is a Spectrum — From simple LWW to complex CRDTs, choose based on data semantics, criticality, and team capability.
•Design Can Minimize Conflicts — Data partitioning, conflict-free operations, and immutable structures reduce conflict frequency at the architectural level.

What's Next:

Now that we understand the nature and impact of write conflicts, we're ready to explore specific resolution strategies. In the next page, we'll deep-dive into Last-Write-Wins (LWW)—the simplest and most common resolution strategy, its mechanics, trade-offs, and when it's appropriate.

Page Complete

You now understand the fundamental nature of write conflicts in distributed systems—why they occur, how to classify them, how to detect them, and their potential impact. This knowledge is essential for choosing and implementing appropriate resolution strategies in the pages ahead.

Write Conflicts in Distributed Systems

The Inevitable Reality of Distributed Writes

What You Will Learn

The Anatomy of a Write Conflict

The Three Prerequisites for Conflict:

For a write conflict to occur, three conditions must be present simultaneously:

Concurrency — Two or more write operations happen 'at the same time' in the distributed sense (neither is aware of the other when it begins)
Same Target — Both writes affect the same logical data item (the same key, the same row, the same document field)
No Coordination — The operations proceed without a synchronization mechanism that would serialize them (no locks, no leader election, no consensus)

Converting Mermaid diagram...

Why 'Concurrent' Is Complex:

In distributed systems, 'concurrent' doesn't mean 'at the exact same nanosecond.' It means causally independent—neither operation could have influenced the other. Consider:

Client A writes to Replica 1 in New York at 10:00:00.000 UTC
Client B writes to Replica 2 in London at 10:00:00.001 UTC

Are these concurrent? The answer depends on perspective:

Physical time: They're 1 millisecond apart
Logical time: If neither replica knew about the other's write when it happened, they're concurrent in the distributed systems sense

Lamport's Happened-Before Relation

Sources of Write Conflicts

Primary Sources of Write Conflicts

•Multi-Master Replication — When multiple nodes can accept writes for the same data, a user in Europe writing to a European replica while a user in Asia writes to an Asian replica creates immediate conflict potential. Popular in: CouchDB, Cassandra, DynamoDB (with certain configurations).
•Leaderless Replication — Systems like Amazon's Dynamo-style databases (Riak, Cassandra) allow any node to accept writes. Without a leader to serialize writes, concurrent updates to the same key naturally conflict.
•Offline-First Applications — Mobile apps, collaborative tools, and edge computing systems that work offline accumulate changes locally, then sync when connectivity returns. Conflicts arise when multiple users modified the same data while disconnected.
•Partition Tolerance (Network Splits) — During a network partition, different sides of the split may continue accepting writes. When the partition heals, the system discovers conflicting writes that occurred in isolation.
•Async Replication Lag — Even in leader-follower systems, if failover occurs before all writes replicate, the new leader may have a stale state. Writes to the new leader conflict with (lost) writes to the old leader.

Conflict Probability by Architecture
Architecture	Conflict Risk	Conflict Frequency	Typical Resolution
Single Leader (sync replication)	Very Low	Rare (only during failover)	Rollback or manual
Single Leader (async replication)	Low	Low (lag-induced)	Last-write-wins or rollback
Multi-Leader	High	Frequent	LWW, vector clocks, or CRDTs
Leaderless (quorum)	Medium-High	Moderate	Read repair, anti-entropy
Offline-First	Very High	Very Frequent	Operational transforms, CRDTs

The Trade-off Spectrum:

Every architectural decision that increases availability or reduces latency also increases conflict potential:

Strong consistency: Zero conflicts, but higher latency and lower availability
Eventual consistency: Lower latency, higher availability, but conflicts are guaranteed

Classification of Conflict Types

Not all conflicts are created equal. Understanding the different types of conflicts helps you choose appropriate resolution strategies and design data models that minimize harmful conflicts.

Write-Write Conflicts (also called update conflicts) occur when two operations concurrently modify the same data item.

Example Scenario:

User A sets balance = balance + 100 (deposit $100)
User B sets balance = balance - 50 (withdraw $50)
Both read balance = 500 before their operation
Both write: A writes 600, B writes 450

The Problem: If we take either value, we lose the other operation. The correct final balance should be 550, but neither 600 nor 450 is correct.

Characteristics:

Most common type of conflict
Often requires semantic understanding to resolve correctly
May result in lost updates if naively resolved
Particularly dangerous for counter/accumulator data

write-write-conflict.pseudo

Conflict Example

// Initial state: { balance: 500 }
 
// Client A's operation (runs on Replica 1)
function deposit(amount) {
    const current = read("balance");  // reads 500
    write("balance", current + amount);  // writes 600
}
deposit(100);
 
// Client B's operation (runs on Replica 2, concurrently)
function withdraw(amount) {
    const current = read("balance");  // reads 500 (hasn't seen A's write)
    write("balance", current - amount);  // writes 450
}
withdraw(50);
 
// After replication sync:
// Replica 1 has: balance = 600
// Replica 2 has: balance = 450
// CONFLICT: Which is correct? Neither! Should be 550.

Detecting Conflicts

Before you can resolve a conflict, you must detect it. Distributed systems employ various mechanisms to identify when concurrent writes have created divergent states.

On-Write Detection

•Version Vectors — Each replica maintains a vector of version counters. When receiving a write, compare vectors to detect concurrent modifications.
•Timestamps Comparison — Compare physical or logical timestamps. Non-equal timestamps for same data version indicate potential conflict.
•Hash Comparison — Compute hashes of data states. Differing hashes for same logical key indicate divergence.
•Merkle Trees — Hierarchical hash structures allow efficient detection of divergent subtrees across large datasets.

On-Read Detection

•Read Repair — During reads, compare responses from multiple replicas. Divergent values reveal conflicts that are then resolved.
•Quorum Reads — Reading from multiple nodes exposes version inconsistencies when values don't match.
•Anti-Entropy Processes — Background processes periodically compare replica states and identify divergences.
•Client-Side Detection — Smart clients compare cached versions with server responses to detect conflicts.

Version Vectors in Depth:

Version vectors are the most rigorous conflict detection mechanism. Each replica maintains a vector: {Replica1: 5, Replica2: 3, Replica3: 7}. When data is modified:

The modifying replica increments its own counter
The vector is attached to the data
On sync, vectors are compared:
- If Vector A dominates Vector B (every element ≥), A supersedes B (no conflict)
- If neither dominates, the writes are concurrent (CONFLICT)

Example:

Vector A: {R1: 5, R2: 3, R3: 7}
Vector B: {R1: 4, R2: 4, R3: 7}

A has R1:5 > R1:4, but B has R2:4 > R2:3. Neither dominates → Concurrent writes detected!

version-vectors.ts
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interface VersionVector {
    [replicaId: string]: number;
}
 
type ComparisonResult = 'DOMINATES' | 'DOMINATED_BY' | 'CONCURRENT' | 'EQUAL';
 
function compareVersionVectors(
    vectorA: VersionVector, 
    vectorB: VersionVector
): ComparisonResult {
    const allKeys = new Set([
        ...Object.keys(vectorA), 
        ...Object.keys(vectorB)
    ]);
    
    let aDominates = true;
    let bDominates = true;
    
    for (const key of allKeys) {
        const a = vectorA[key] ?? 0;
        const b = vectorB[key] ?? 0;
        
        if (a < b) aDominates = false;
        if (b < a) bDominates = false;
    }
    
    if (aDominates && bDominates) return 'EQUAL';
    if (aDominates) return 'DOMINATES';
    if (bDominates) return 'DOMINATED_BY';
    return 'CONCURRENT';  // Neither dominates = CONFLICT
}
 
// Usage example:
const vectorA: VersionVector = { replica1: 5, replica2: 3, replica3: 7 };
const vectorB: VersionVector = { replica1: 4, replica2: 4, replica3: 7 };
 
const result = compareVersionVectors(vectorA, vectorB);
console.log(result);  // Output: 'CONCURRENT' - Conflict detected!

The Impact of Unresolved Conflicts

When conflicts go unresolved or are resolved incorrectly, the consequences cascade through your system. Understanding these impacts underscores why robust conflict resolution is not optional.

Consequences of Poor Conflict Handling

•Data Loss — The most direct impact. When conflicting writes are resolved by 'picking one,' the other write is lost. For banking transactions, this could mean real money disappearing. For medical records, it could mean lost patient information.
•Data Corruption — Naive merge strategies can create data that neither original write intended. Merging {balance: 600} and {balance: 450} might produce {balance: 525} (average), which is still wrong.
•Inconsistent Views — Different users/services see different 'truth.' User A sees their edit succeeded; User B sees something different. This erodes trust and causes confusion.
•Audit Trail Gaps — For compliance-heavy systems (finance, healthcare), losing the history of how data changed is not just inconvenient—it may be legally problematic.
•Cascading Failures — Inconsistent data can trigger downstream errors. An inventory count of -5 (from conflicting decrements) breaks ordering logic. A user with two profiles breaks authentication.
•Silent Failures — Perhaps worst: conflicts that resolve 'successfully' but incorrectly, without anyone noticing until the damage is significant.

Real-World Disaster: Amazon's Shopping Cart

Conflict Impact by Domain
Domain	Conflict Example	Potential Impact	Acceptable Strategy
E-commerce Cart	Item quantity conflicts	Customer frustration	Union/addition (items reappear)
Banking	Concurrent transfers	Financial loss	Strong consistency required
Social Media	Concurrent likes/comments	Counter inaccuracy	CRDTs, eventual accuracy
Collaborative Docs	Concurrent text edits	Lost changes	Operational transforms
Inventory	Concurrent stock updates	Overselling, negative stock	Requires coordination
Medical Records	Concurrent updates	Patient safety risk	Strong consistency + audit

The Conflict Resolution Spectrum

Conflict resolution strategies exist on a spectrum from simple but lossy to complex but precise. Your choice depends on data semantics, consistency requirements, and operational complexity tolerance.

Converting Mermaid diagram...

Choosing Your Strategy:

The right conflict resolution strategy depends on multiple factors:

Factor	Favors Simple (LWW)	Favors Complex (CRDTs)
Data semantics	Last value is all that matters	History/accumulation matters
Conflict frequency	Rare (mostly sequential)	Frequent (high concurrency)
Data criticality	Low (logs, metrics)	High (financial, medical)
Team expertise	Limited distributed experience	Strong distributed expertise
Latency requirements	Can tolerate coordination	Needs low latency always

Hybrid Approaches

Designing for Conflict Minimization

The best conflict is the one that never happens. While you cannot eliminate all conflicts in a distributed system, thoughtful design significantly reduces their frequency and impact.

Conflict Minimization Strategies

•Partition Data by Access Pattern — If User A only modifies their own profile and User B only modifies theirs, concurrent writes don't conflict. Design data models where writes are naturally isolated to specific users, regions, or entities.
•Use Conflict-Free Operations — Instead of setting counter = 5, use increment counter by 1. Increments from different sources can be safely merged. This is the foundation of CRDTs.
•Reduce Replication Factor for Hot Data — If coordination is possible, fewer replicas mean fewer potential conflict sources. Critical data might use strong consistency even in an eventually consistent system.
•Time-Bound Write Windows — For some data, only accept writes within a time window. Old writes are rejected, reducing conflict from delayed replication.
•Immutable/Append-Only Structures — If data is never updated (only appended), conflicts become merge operations. Event sourcing and log-based architectures leverage this principle.
•Pessimistic Locking for Critical Paths — For truly critical operations (financial transactions), acquire locks across replicas before writing. Accept the latency cost for correctness.

conflict-resistant-design.ts
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// CONFLICT-PRONE: Updating shared counter
interface ConflictProne {
    shoppingCart: {
        totalItems: number;  // Concurrent updates conflict!
        items: Item[];
    };
}
 
// CONFLICT-RESISTANT: Each user has own partition
interface ConflictResistant {
    userCarts: {
        [userId: string]: {
            // Only this user modifies their cart
            items: Item[];
        };
    };
}
 
// CONFLICT-FREE: Operation-based updates
interface CartOperation {
    type: 'ADD_ITEM' | 'REMOVE_ITEM' | 'UPDATE_QUANTITY';
    itemId: string;
    userId: string;
    timestamp: number;
    delta?: number;  // For quantity: +1, -1, etc.
}
 
// Operations can be merged: apply all ADDs, then all REMOVEs
// Final state is deterministic regardless of operation order

Summary: Understanding Write Conflicts

Key Takeaways

•Conflicts Require Three Conditions — Concurrency (in the causal sense), same target data, and no coordination mechanism. Remove any one to prevent conflicts.
•Sources Are Architectural — Multi-master replication, leaderless systems, offline-first apps, and network partitions all generate conflicts by design, not by accident.
•Conflict Types Vary — Write-write, delete-update, insert-insert, and constraint violations each require different handling approaches.
•Detection Precedes Resolution — Version vectors, timestamps, hash comparisons, and read repair are mechanisms to identify when conflicts have occurred.
•Unresolved Conflicts Have Real Cost — Data loss, corruption, inconsistent views, and cascading failures make proper resolution non-optional.
•Resolution Is a Spectrum — From simple LWW to complex CRDTs, choose based on data semantics, criticality, and team capability.
•Design Can Minimize Conflicts — Data partitioning, conflict-free operations, and immutable structures reduce conflict frequency at the architectural level.

What's Next:

Page Complete