Navigating Consistency in Distributed Systems: Choosing the Right Trade-Offs

Every distributed system faces a critical question: Should we prioritize consistency or availability? While the answer depends on your use case, understanding the trade-offs is essential for designing systems that meet your users’ needs.

Consistency is the foundation for reliable, scalable distributed systems. Imagine a financial transaction failing due to inconsistent data across nodes or outdated recommendations because of delayed updates. These real-world challenges illustrate the high stakes in balancing consistency, availability, and performance, particularly as systems scale across geographies and handle increasing complexity.

 

The challenges of consistency in distributed systems

Distributed systems operate across multiple nodes over unreliable networks, introducing challenges such as:

• Network failures: Messages can be delayed, dropped, or arrive out of order, leading to inconsistencies between nodes.
• Node crashes: Nodes may fail unexpectedly, potentially losing recent updates before recovering.
• Latency: Communication delays can cause nodes to temporarily hold different versions of the same data, affecting real-time consistency.

 

Enter the CAP Theorem

The CAP Theorem offers a foundational framework for navigating these challenges. It states that a distributed system can guarantee at most two of the following three properties: Consistency (C), Availability (A), and Partition Tolerance (P).

In practice, this means choosing between consistency and availability during network partitions.

 

Beyond CAP: The PACELC Framework

While the CAP Theorem explains trade-offs during a network partition, it does not account for system behavior under normal operation. The PACELC Theorem extends CAP by addressing the else case (E):

• During a Partition (P): The system still faces the CAP trade-off between Availability (A) and Consistency (C).
• Else (E): When no partition exists, the system must choose between low Latency (L) and strong Consistency (C).

PACELC provides a broader perspective, revealing trade-offs beyond rare partition events. Together, CAP and PACELC offer a comprehensive toolkit for understanding distributed system dynamics.

 

Navigating consistency: Models that shape your system

Consistency models describe how distributed systems manage data across multiple nodes. They provide a contract between the system and the programmer, defining how data should behave when read, written or updated. For example:

• Strong consistency guarantees any read reflects the most recent write, regardless of which node is accessed.
• Eventual consistency guarantees all nodes will converge to the same state over time but allows temporary discrepancies between them.

These models help navigate the trade-offs between consistency, availability, and performance. Selecting the right model ensures the system meets technical demands and user expectations, especially under adverse conditions like network partitions or heavy loads.

Let’s explore four commonly used consistency models: Strong Consistency, Sequential Consistency, Causal Consistency, and Eventual Consistency. Understanding the nuances will help you choose the right model for your application’s needs.

 

Strong Consistency

Strong consistency, also known as linearizability, guarantees that all clients see the latest data immediately after a write. Every operation occurs instantaneously at some point between its invocation and completion, behaving as if there were a single copy of the data. This approach guarantees a unified, real-time view of data across all nodes, making it easier to reason about correctness in distributed systems.

Key techniques

• Consensus protocols (e.g. Raft, Paxos): A leader coordinates writes by reaching a quorum among nodes to ensure correctness, even during failures.
• Real-time order (linearizability): Operations are serialized, and their real-time invocation order is respected, meaning a write completed at one node is immediately visible to all subsequent reads across the system.

Trade-offs

• Pros

• • • Guarantees correctness by eliminating stale reads and preventing anomalies like double-spending.
    • Simplifies application logic with consistent data views.

• Cons

• • High latency due to quorum-based operations required for synchronization.
  • Reduced availability during network partitions, as writes may block until consensus is achieved.

Use cases

• Financial transactions and ledgers: Banking and payment applications where real-time correctness and strict ordering of operations are critical to prevent anomalies such as double-spending or inconsistent balances.
• Distributed coordination: Systems like leader election and distributed locks require strict synchronization to ensure tasks or resources are managed consistently across nodes.
• Configuration and metadata management: Distributed systems often rely on shared configuration or metadata (e.g. system settings, quotas, or state information). Strong consistency ensures updates to these values are immediately synchronized across all nodes, preventing conflicting states.

Sequential Consistency

Sequential consistency ensures that all operations occur in a logical order. The execution results are as if all operations were executed individually, maintaining the order in which each client issues operations. Unlike strong consistency, sequential consistency does not enforce real-time ordering, allowing for performance optimizations while preserving predictable behavior.

Key techniques

• Global ordering: All clients observe operations in the same sequence, ensuring no client sees events out of order.

• Relaxed synchronization: Operations do not need to appear instantaneous, reducing overhead compared to strong consistency.

Trade-offs

• Pros

• • • Lower synchronization overhead compared to strong consistency, as strict real-time guarantees are not required.
    • Well-suited for scenarios where the sequence of operations matters more than immediate visibility.

• Cons

• • Slower than eventual consistency due to global ordering constraints.
  • Lack of real-time guarantees can cause anomalies in time-sensitive applications.

Use cases

• Gaming systems: Ensures actions happen in the correct order for all players, such as moves in turn-based games, even if operations aren’t synchronized in real-time.

• Collaborative editing: Guarantees ordered application of updates in shared workspaces, like document editing platforms, ensuring all collaborators’ edits appear in the correct sequence.

 

Causal Consistency

Causal consistency ensures that operations with a cause-and-effect relationship are seen in the correct order across nodes. This model balances consistency and performance, making it ideal for collaborative applications or systems that don’t require strict real-time consistency.

Key techniques

• Dependency tracking: Tools like vector clocks or versioned timestamps capture and respect causal relationships.
• Partial ordering: Causally related events are applied in sequence without enforcing global ordering for unrelated operations.

Trade-offs

• Pros
  • Intuitive behavior for collaborative or real-time systems.
  • Balanced trade-offs between consistency and performance.
• Cons
  • More complexity than eventual consistency.
  • Slightly higher latency due to dependency tracking.
  • Total ordering is not enforced across the entire system.

Use cases

• Collaborative platforms: Tools like Google Docs or shared workspaces, where causally related updates (e.g., editing text and applying formatting) must occur in the correct order.
• Messaging apps: Ensures messages are delivered as they were sent, maintaining logical coherence in conversations without requiring global synchronization.

 

Eventual Consistency

Eventual consistency allows for temporary inconsistencies between nodes, with a guarantee that all nodes will eventually converge to the same state. This model is often employed in AP systems, sacrificing immediate consistency to ensure the system remains responsive during network failures or partitions. In practice, eventual consistency means that updates propagate asynchronously across replicas, leading to temporary discrepancies. However, the system guarantees that all nodes eventually synchronize to the same state.

Key techniques

• Asynchronous replication: Writes propagate in the background, allowing low-latency updates but causing temporary discrepancies.
• Gossip protocols: Nodes communicate in a peer-to-peer fashion to share updates, ensuring all nodes converge to the same data.
• Anti-entropy: Techniques that use data structures like Merkle trees to detect and reconcile differences between replicas.
• Merge conflict resolution: Resolves conflicting writes using strategies like last-write-wins, vector clocks or custom logic.

Session guarantees

Session guarantees aim to improve user experience by ensuring that a client’s actions (writes) are consistently visible to users, even when the system is in an inconsistent state. These guarantees include:

• Read Your Writes (RYW): Once a client performs a write, all subsequent reads within the same session will reflect that write. This method ensures that users see their updates, even when the system is not fully consistent across all nodes.
• Monotonic Reads: Once a client sees a particular value, subsequent reads will never show an older value, preventing backward jumps in the data​.
• Writes Follow Reads: After reading a value, all subsequent operations will see any writes by that client, ensuring logical consistency within the session.

Trade-offs

• Pros
  • High availability and low latency, even during partitions.
  • Scales well for high-demand, read-heavy systems.
• Cons
  • Temporary inconsistencies may lead to stale or conflicting reads.
  • Application logic must handle potential data conflicts.

Use cases

• Web caching: Ensures high-speed data access with tolerable temporary inconsistencies, such as serving stale session data or cached web pages.
• Product recommendations: Provides personalized suggestions without requiring real-time consistency, allowing recommendation systems to sync across nodes gradually.
• Inventory counts: Tracks stock across distributed warehouses or regions, tolerating brief inconsistencies while ensuring eventual convergence to accurate totals.

 

Which model should you choose?

Model	Summary	Best for…	Trade-Offs to Consider
Strong Consistency (Linearizability)	Guarantees all clients see the latest data immediately after a write (real-time order).	Critical systems requiring precise ordering and real-time correctness.	High latency due to quorum-based operations; reduced availability during network partitions.
Sequential Consistency	Ensures all operations are seen in the same order by all clients, but not in real-time.	Systems prioritizing the order of operations over immediate visibility.	Slower than eventual consistency; lacks real-time guarantees for time-sensitive applications.
Causal Consistency	Maintains order for operations with cause-and-effect relationships.	Collaborative systems or messaging apps where logical ordering matters but strict synchronization isn’t needed.	More complexity than eventual consistency; slightly higher latency for tracking dependencies; no total ordering across the system​.
Eventual Consistency	Allows temporary inconsistencies between nodes, ensuring they converge over time.	High-availability systems, such as web caching, social media, and CDNs, where occasional stale reads are acceptable.	Temporary inconsistencies can lead to stale or conflicting reads; requires conflict resolution.

 

Which models does Hazelcast Platform support?

• In AP mode, Hazelcast employs eventual consistency, optimizing for high availability and partition tolerance, making it suitable for systems like web caching and risk analytics that can tolerate temporary inconsistencies.
• In the CP subsystem, Hazelcast ensures strong consistency by providing linearizable guarantees for distributed coordination tasks, which is ideal for financial transactions and systems requiring strict data accuracy and ordering. 

With these two modes, Hazelcast Platform provides the flexibility to choose the right trade-offs between consistency, availability and performance for your application’s needs.

 

Getting started with Hazelcast Platform

Ready to dive in? Check out Hazelcast’s Getting Started Guide, AP / CP Data Structures Guide and a Free Enterprise Trial License. Build scalable, reliable systems today!

 

Further reading

For a deeper dive into consistency models, these seminal research papers provide formal definitions, proofs and key discussions:

• Strong Consistency: Herlihy and Wing’s paper formalizes linearizability, detailing its implications for concurrent systems.
• Sequential Consistency: Lamport’s foundational work defines sequential consistency, where operations maintain a consistent order across clients without real-time guarantees.
• Causal Consistency: Ahamad’s Causal Memory paper explains how causally related operations must be ordered correctly while allowing independent operations to diverge.
• Eventual Consistency: Terry’s research on session guarantees explores Read-Your-Writes and Monotonic Reads, improving usability in weakly consistent systems.

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