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Replicated Caching Pattern

6 min readStatus:Seedling

Core Idea

Replicated Caching Pattern is a distributed caching strategy where cache data is replicated across multiple nodes or servers to ensure high availability, fault tolerance, and improved read performance.

Definition

Replicated Caching Pattern is a distributed caching strategy where cache data is replicated across multiple nodes or servers to ensure high availability, fault tolerance, and improved read performance. This pattern maintains synchronized in-memory caches across services, enabling applications to serve requests even when individual cache nodes fail. The replication can be synchronous (ensuring immediate consistency across replicas) or asynchronous (accepting temporary inconsistencies for higher throughput and lower latency).

Key Characteristics

  • Replication Strategies:

    • Synchronous replication: All replicas receive updates before the write operation completes, ensuring strong consistency but introducing higher latency
    • Asynchronous replication: Primary node accepts writes immediately and propagates to replicas asynchronously, providing lower latency and higher throughput with temporary inconsistency windows
    • Primary-replica topology: One primary node handles writes while read replicas distribute read load
    • Peer-to-peer topology: All nodes can handle both reads and writes, using consensus algorithms for coordination

  • Cache Coherence Protocols:

    • Ensure uniformity of shared data across multiple local caches using snooping or directory-based mechanisms
    • Common protocols include SI, MI, MSI, MESI, MOSI, MEOSI, and MESIF
    • Directory-based systems maintain a common directory that acts as a filter for cache entry permissions and updates
    • Coherence discipline ensures changes in shared data values propagate throughout the system in a timely fashion

  • Failover and Redundancy:

    • Replicated caches implement automatic failover mechanisms to maintain availability during node outages
    • Redis Sentinel promotes replicas to primary when main nodes fail
    • Redis Cluster provides distributed clustering for high availability
    • Hazelcast treats all cluster members equally, with each holding primary data plus backups from other members

  • Embedded vs. Client-Server Architecture:

    • Embedded distributed cache: Cache runs within application JVM, replicating data to other instances—reads are local (fast), writes incur replication overhead
    • Client-server pattern: Applications connect to separate cache server cluster, similar to database architecture, enabling shared cache across multiple applications
    • Sidecar pattern: Cache runs as separate container alongside each service, providing isolation while enabling replication

  • Consistency Trade-offs:

    • Strong Consistency ensures all clients see the same data immediately but reduces availability and increases latency
    • Eventual-Consistency allows replicas to temporarily diverge, providing higher Availability and lower latency
    • Modern systems offer tunable consistency levels (e.g., Cassandra’s ONE to ALL quorum options)
    • Large-scale systems often accept eventual consistency where replicas synchronize over time

Examples

  • Redis Master-Replica Configuration: Primary site backed by one or more replicas that are automatically promoted if the master fails, though Redis may disable replication under heavy load to maintain performance
  • Hazelcast Distributed Cache: All cluster members are equal, holding primary data and backups from other members; supports both active-passive and active-active WAN replication modes
  • Financial Institution Risk Analysis: Uses Hazelcast’s low-latency replicated caching for real-time fraud detection across multiple data centers
  • Twitter Caching Strategy: Employs Redis replication to reduce read load on primary databases while serving high-volume social media requests
  • Hybrid Cache Architecture: Stores frequently-requested “hot” data in Hazelcast in-memory cache while maintaining complete dataset in Redis cache server, balancing latency and capacity

Why It Matters

Replicated caching enables systems to achieve both high performance and high availability—two characteristics critical for modern distributed architectures. Without replication, a single cache node failure would force applications to fall back to slower database queries, creating performance degradation and potential cascading failures. The pattern addresses the fundamental tension between Consistency and availability described in the CAP-Theorem (see Partition-Tolerance): synchronous replication prioritizes consistency, while asynchronous replication prioritizes availability.

The choice of replication strategy directly impacts application behavior and user experience. Systems requiring strict data accuracy (financial transactions, inventory management) benefit from synchronous replication despite the latency penalty. Systems prioritizing responsiveness and scale (social media feeds, content delivery networks) accept eventual consistency through asynchronous replication. Modern cache solutions like Redis and Hazelcast offer configurable replication modes, enabling architects to make per-use-case decisions rather than global trade-offs. The pattern also influences Scalability—horizontal scaling adds cache replicas to distribute read load, while replication overhead can limit write scalability. Understanding these trade-offs is essential for distributed architecture decisions.

Sources

Note

This content was drafted with assistance from AI tools for research, organization, and initial content generation. All final content has been reviewed, fact-checked, and edited by the author to ensure accuracy and alignment with the author’s intentions and perspective.

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