Optimistic Locking in Distributed Systems: A Comprehensive Guide

• Optimistic Locking in Distributed Systems: A Comprehensive Guide
  • Introduction
  • What is Optimistic Locking?
  • Why and When is Optimistic Locking Used?
  • Primary Examples of Optimistic Locking in Distributed Systems
    • 1. Microservices with Distributed Databases
    • 2. Event Sourcing and CQRS
    • 3. NoSQL Databases
    • 4. Distributed Caching Systems
    • 5. Blockchain Systems
  • Conflict Resolution in Optimistic Locking
    • 1. Retry the Operation
    • 2. Abort the Transaction
    • 3. Merge the Changes
    • 4. User Intervention
    • 5. Conditional Logic
  • Conclusion

Introduction

Optimistic locking is a concurrency control technique used to handle conflicts that arise when multiple processes or users try to access and modify the same data simultaneously. Unlike pessimistic locking, which prevents conflicts by locking data as soon as it’s read, optimistic locking assumes conflicts will be rare and focuses on detecting and resolving them when they do occur.

This approach is particularly beneficial in systems with high read-to-write ratios, where locking data upfront would result in unnecessary performance degradation.

In this article, we’ll cover:

• What is Optimistic Locking?
• Why and When is Optimistic Locking Used?
• Examples of Optimistic Locking in Distributed Systems
• Conflict Resolution in Optimistic Locking

What is Optimistic Locking?

Optimistic locking is a strategy used to manage data consistency in concurrent systems by assuming that conflicts will occur infrequently. Instead of locking the data when it’s read, the system optimistically allows multiple users or processes to read and work with the same data in parallel. Before the data is written back to the data store, the system checks if the data has been modified by someone else in the meantime. If the data has changed (indicated by a version number or timestamp), the system detects a conflict and resolves it according to predefined rules.

Key Characteristics:

• Version Control: Each record has a version number or a timestamp associated with it. When an update is made, this version is checked. If it matches the version the transaction initially read, the update proceeds; otherwise, a conflict is detected.
• Non-Blocking: The system does not lock the data while it’s being read or modified, leading to better performance in systems where reads vastly outnumber writes.

Why and When is Optimistic Locking Used?

Optimistic locking is ideal for scenarios where the following conditions are met:

1. High Read-to-Write Ratio: In environments where the data is primarily read and updates are rare, optimistic locking allows greater parallelism since the data remains unlocked for most of the time

2. Low Contention: When the likelihood of multiple users or processes modifying the same data concurrently is low, optimistic locking works well since conflicts will rarely occur

3. Performance Requirements: Since optimistic locking doesn’t hold locks during the read and modify phases, it reduces bottlenecks and improves system performance, especially in distributed systems

When Optimistic Locking is Not Ideal:

• High Contention Scenarios: In systems with frequent concurrent updates, optimistic locking may result in a high number of conflicts and retries, reducing performance. In such cases, pessimistic locking (which locks the data during transactions) may be more appropriate.

Primary Examples of Optimistic Locking in Distributed Systems

Optimistic locking is widely used across various types of distributed systems. Below are some common examples:

1. Microservices with Distributed Databases

In microservices architectures, services often need to access and modify shared data stored in distributed databases. Optimistic locking allows services to work with the data concurrently without blocking access.

• Example: In an e-commerce system, multiple users may attempt to place orders simultaneously, affecting inventory levels. Each service instance reads the current inventory and version number, then attempts to update the inventory. Before saving the changes, the service checks the version number to ensure no other service has modified the inventory. If a conflict is detected, the update is retried.

2. Event Sourcing and CQRS

Event sourcing systems capture changes as events rather than directly modifying the state. CQRS (Command Query Responsibility Segregation) separates reads from writes. Optimistic locking is used to ensure that events are processed in the correct order.

• Example: In a banking application using event sourcing, when two concurrent transactions (e.g., a deposit and a withdrawal) affect the same account balance, optimistic locking ensures that the first successful transaction updates the account state. If the state version has changed before the second transaction commits, it detects a conflict and retries.

3. NoSQL Databases

Optimistic locking is also common in distributed NoSQL databases such as MongoDB and DynamoDB, which don’t enforce strict locking during reads but rely on version numbers to manage concurrency.

• Example: In a social media platform, user profiles may be stored in a NoSQL database. When two users simultaneously update the same profile, the system checks the version number before committing the second update. If the version number has changed, indicating the profile was modified by another user, a conflict is detected, and the operation is retried.

4. Distributed Caching Systems

Distributed caches like Redis or Memcached may use optimistic locking to handle concurrent access to shared cached data.

• Example: In a web application, session data might be stored in a Redis cache. If two processes attempt to update the same session data concurrently, optimistic locking ensures that only one update succeeds, preventing potential data loss.

5. Blockchain Systems

Blockchain systems inherently use a form of optimistic locking by relying on distributed consensus to confirm transactions without locking the entire ledger.

• Example: On Ethereum, smart contracts execute based on the assumption that their initial state will not change during execution. However, if another transaction modifies the state, the optimistic locking mechanism detects the conflict, and the smart contract can be retried or re-executed based on the updated state.

Conflict Resolution in Optimistic Locking

When optimistic locking detects a conflict, it must be resolved before the transaction can continue. Below are the common strategies used to resolve conflicts:

1. Retry the Operation

If a conflict is detected, the system can retry the operation after re-reading the latest version of the data. This works well for stateless operations or those where retries are inexpensive.

• Example: A user updates their email address. If another user modifies the profile during this time, the system detects the conflict, re-reads the profile, and retries the update.

2. Abort the Transaction

In scenarios where retrying is not feasible, the system may abort the transaction and notify the user or application to handle the conflict.

• Example: In a banking system, when two concurrent transactions attempt to withdraw funds from the same account, the second transaction might be aborted if a conflict is detected, ensuring that the account is not overdrawn.

3. Merge the Changes

In cases where the changes affect different parts of the same data, the system can merge the changes automatically.

• Example: In a collaborative editing platform, two users may edit different sections of the same document simultaneously. If a conflict is detected, the system merges the non-conflicting changes into a unified version.

4. User Intervention

For complex conflicts that cannot be resolved automatically, the system may ask the user to manually resolve the conflict by choosing between conflicting versions or merging the changes manually.

• Example: In a version control system like Git, when two developers modify the same file, the system may detect a conflict and prompt the developers to manually resolve the changes.

5. Conditional Logic

In some cases, conflict resolution can be based on business logic or priorities. The system may allow the update to proceed based on specific rules, such as prioritizing certain types of transactions.

• Example: In a product catalog, if two users update the price of an item, the system may resolve the conflict based on pre-defined business rules, such as prioritizing updates from a higher-priority service.

Conclusion

Optimistic locking is a powerful concurrency control mechanism that enhances performance in distributed systems by allowing parallel access to data while only resolving conflicts when they occur. It is especially useful in environments with high read-to-write ratios and low contention.

This locking strategy is employed in a variety of distributed systems, including microservices architectures, event sourcing systems, NoSQL databases, and blockchain platforms. By intelligently handling conflicts through retries, merges, or user intervention, optimistic locking ensures that data integrity is maintained without the performance bottlenecks associated with pessimistic locking.

Understanding when and how to implement optimistic locking is critical for designing efficient, scalable distributed systems that can handle concurrency without sacrificing performance.