B Types of Caches in Coherence

This appendix provides an overview of the types of caches offered by Coherence.

B.1 Replicated Cache

In a replicated cache service, data is fully replicated to every member in the cluster. Offers the fastest read performance. Clustered, fault-tolerant cache 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.

B.2 Optimistic Cache

Optimistic Cache is a clustered cache implementation similar to the Replicated Cache implementation, but without any concurrency control. This implementation has the highest possible throughput. 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.

B.3 Distributed (Partitioned) Cache

A distributed, or partitioned cache is clustered, fault-tolerant, and 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, two or more unique machines within a cluster.

B.4 Near Cache

A near cache is a hybrid cache; it fronts a fault-tolerant, scalable partitioned 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.

B.5 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)

Table B-1 Summary of Cache Types and Characteristics

	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
Read Performance	Instant 5	Instant 5	Locally cached: instant 5 Remote: network speed 1	Locally cached: instant 5 Remote: network speed 1	Instant 5
Fault Tolerance	Extremely High	Extremely High	Configurable 4 Zero to Extremely High	Configurable 4 Zero to Extremely High	Zero
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
Coherency	fully coherent	fully coherent	fully coherent	fully coherent 6	n/a
Memory Usage (Total)	JVMs x DataSize	JVMs x DataSize	Redundancy x DataSize	[Redundancy x DataSize] + [JVMs x LocalCache]	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:

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.
Requires UDP multicast or a few UDP unicast operations, depending on JVM count.
Requires a few UDP unicast operations, depending on level of redundancy.
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)
Limited by local CPU/memory performance, with negligible processing required (typically sub-millisecond performance).
Listener-based Near caches are coherent; expiry-based near caches are partially coherent for non-transactional reads and coherent for transactional access.