Monday, June 23, 2014

Caching Topologies

There are some common topologies for caching used commonly by cache providers. They are:-

Standalone or in-process Cache
While using an in-process/standalone cache, the cache elements are local to a single instance of an application. In case of applications having multiple instances, there will be as many caches as application instances. Each individual cache may hold data in different states which may lead to inconsistency. However, with a cache eviction policy in place, the data in the cache will be eventually consistent. But this could pose a problem with applications with high volume of cache request and transaction. Standalone cache is a good choice for small applications or where the data in the cache is pre-fetched and seldom changes. So, standalone caches are good candidates for read-only cache with pre-fetched data. Google Guava library is a good example of standalone cache.



Distributed Caching or Partitioned Cache
In distributed or partitioned mode, cache is deployed on a cluster of multiple nodes and offers a single logical view of the cache. Since there is always a single state of the cache cluster, it is never inconsistent. Distributed cache makes use of hash algorithms to determine where in a cluster entries should be stored. Hashing algorithm is configured with the number of copies each cache entry should be maintained cluster-wide. Number of copies represents the tradeoff between performance and consistency of data. The more copies you maintain, the lower will be the performance, but there will be a lower risk of losing data due to server outages. Hash algorithm helps in locating entries without resorting to multicasting requests or maintaining expensive metadata. So, during a put operation, the number of remote calls will be the same as the number of copies maintained. But doing a get operation in the cluster would result in at most 1 remote call. Actually in get operation also the number of remote calls made is equal to the number of copies maintained, but they are made in parallel and whichever call returns first will be accepted. A good example of distributed cache is Memcache.



Replicated Cache

In replicated mode also cache is deployed on a cluster of multiple nodes but any entries added to any of these cache instances will be replicated to all other cache instances in the cluster, and can be retrieved locally from any instance. Replication can be synchronous or asynchronous. Synchronous replication blocks the caller when there is a write operation like put, until the modifications have been replicated successfully to all nodes in a cluster. Asynchronous replication performs replication in the background (the write operation returns immediately). Asynchronous replication is faster since the caller is not blocked. However, when a synchronous replication returns successfully, the caller knows for sure that all modifications have been applied to all cache instances, whereas this is not the case with asynchronous replication. Products like Terracota and Infispan can be configured to run in a replicated mode.

Here is a comparison of the above topologies:-


Replicated Cache
Partitioned Cache
Standalone
Fault Tolerance
Extremely High
Extremely High
No fault tolerance
Read Performance
Extremely fast
Extremely fast
Instant
Write Performance
Fast
Extremely fast
Instant
Typical Uses
Metadata
Read-write caches
Local data

There are some other types of topologies which are basically a hybrid of above approaches. Those approaches are very specific to cache providers. Next I'll try to share my thoughts on different cache access patterns.
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