Types of Caches in Coherence - Coherence 3.5 User Guide

This section provides an overview of the types of caches offered by Coherence. More detail is provided in later sections.

Distributed Cache
Replicated Cache
Optimistic Cache
Near Cache
Local Cache
Remote Cache
Summary of Cache Types

Distributed Cache

A distributed, or "partitioned" cache, cache is a clustered, fault-tolerant cache that has linear scalability. Data is partitioned among all the machines of the cluster. For fault-tolerance, partitioned caches can be configured to keep each piece of data on one or more unique machines within a cluster. Distributed caches are the most commonly used caches in Coherence.

Replicated Cache

A replicated cache is a clustered, fault tolerant cache where data is fully replicated to every member in the cluster. This cache offers the fastest read performance with linear performance scalability for reads but poor scalability for writes (as writes must be processed by every member in the cluster). Because data is replicated to all machines, adding servers does not increase aggregate cache capacity.

Optimistic Cache

An optimistic cache is a clustered cache implementation similar to the replicated cache implementation but without any concurrency control. This implementation offers higher write throughput than a replicated cache. It also allows to use an alternative underlying store for the cached data (for example, a MRU/MFU-based cache). However, if two cluster members are independently pruning or purging the underlying local stores, it is possible that a cluster member may have a different store content than that held by another cluster member.

Near Cache

A near cache is a hybrid cache; A near cache is a hybrid cache; it typically fronts a distributed cache or a remote cache with a local cache. Near cache invalidates front cache entries, using configurable invalidation strategy, and provides excellent performance and synchronization. Near cache backed by a partitioned cache offers zero-millisecond local access for repeat data access, while enabling concurrency and ensuring coherency and fail-over, effectively combining the best attributes of replicated and partitioned caches.

Local Cache

A local cache is a cache that is local to (completely contained within) a particular cluster node. While it is not a clustered service, the Coherence local cache implementation is often used in combination with various clustered cache services.

Remote Cache

A remote cache describes any out of process cache accessed by a Coherence*Extend client. All cache requests are sent to a Coherence proxy where they are delegated to one of the other Coherence cache types (Repilcated, Optimistic, Partitioned).

Summary of Cache Types

Numerical Terms:

JVMs = number of JVMs
DataSize = total size of cached data (measured without redundancy)
Redundancy = number of copies of data maintained
LocalCache = size of local cache (for near caches)

	Replicated Cache	Optimistic Cache	Partitioned Cache	Near Cache backed by partitioned cache	LocalCache not clustered
Topology	Replicated	Replicated	Partitioned Cache	Local Caches + Partitioned Cache	Local Cache
Fault Tolerance	Extremely High	Extremely High	Configurable ⁴ Zero to Extremely High	Configurable ⁴ Zero to Extremely High	Zero
Read Performance	Instant ⁵	Instant ⁵	Locally cached: instant ⁵ Remote: network speed ¹	Locally cached: instant ⁵ Remote: network speed ¹	Instant ⁵
Write Performance	Fast ²	Fast ²	Extremely fast ³	Extremely fast ³	Instant ⁵
Memory Usage (Per JVM)	DataSize	DataSize	DataSize/JVMs x Redundancy	LocalCache + [DataSize / JVMs]	DataSize
Memory Usage (Total)	JVMs x DataSize	JVMs x DataSize	Redundancy x DataSize	[Redundancy x DataSize] + [JVMs x LocalCache]	n/a
Coherency	fully coherent	fully coherent	fully coherent	fully coherent ⁶	n/a
Locking	fully transactional	none	fully transactional	fully transactional	fully transactional
Typical Uses	Metadata	n/a (see Near Cache)	Read-write caches	Read-heavy caches w/ access affinity	Local data

Notes:

¹ As a rough estimate, with 100mbit ethernet, network reads typically require ~20ms for a 100KB object. With gigabit ethernet, network reads for 1KB objects are typically sub-millisecond.
² Requires UDP multicast or a few UDP unicast operations, depending on JVM count.
³ Requires a few UDP unicast operations, depending on level of redundancy.
⁴ Partitioned caches can be configured with as many levels of backup as desired, or zero if desired. Most installations use one backup copy (two copies total).
⁵ Limited by local CPU/memory performance, with negligible processing required (typically sub-millisecond performance).
⁶ Listener-based Near caches are coherent; expiry-based near caches are partially coherent for non-transactional reads and coherent for transactional access.