Chapter 28. High Availability Using Server Hinting

In Red Hat JBoss Data Grid, Server Hinting ensures that backed up copies of data are not stored on the same physical server, rack, or data center as the original. Server Hinting does not apply to total replication because total replication mandates complete replicas on every server, rack, and data center.
Data distribution across nodes is controlled by the Consistent Hashing mechanism. JBoss Data Grid offers a pluggable policy to specify the consistent hashing algorithm. For details see Section 28.1, “ConsistentHashFactories”
Setting a machineId, rackId, or siteId in the transport configuration will trigger the use of TopologyAwareConsistentHashFactory, which is the equivalent of the DefaultConsistentHashFactory with Server Hinting enabled.
Server Hinting is particularly important when ensuring the high availability of your JBoss Data Grid implementation.

28.1. ConsistentHashFactories

Red Hat JBoss Data Grid offers a pluggable mechanism for selecting the consistent hashing algorithm. It is shipped with four implementations but a custom implementation can also be used.
JBoss Data Grid ships with four ConsistentHashFactory implementations:
  • DefaultConsistentHashFactory - keeps segments balanced evenly across all the nodes, however the key mapping is not guaranteed to be same across caches,as this depends on the history of each cache. If no consistentHashFactory is specified this is the class that will be used.
  • SyncConsistentHashFactory - guarantees that the key mapping is the same for each cache, provided the current membership is the same. This has a drawback in that a node joining the cache can cause the existing nodes to also exchange segments, resulting in either additional state transfer traffic, the distribution of the data becoming less even, or both.
  • TopologyAwareConsistentHashFactory - equivalent of DefaultConsistentHashFactory, but automatically selected when the configuration includes server hinting.
  • TopologyAwareSyncConsistentHashFactory - equivalent of SyncConsistentHashFactory, but automatically selected when the configuration includes server hinting.
The consistent hash implementation can be selected via the hash configuration:
<hash consistent-hash-factory="org.infinispan.distribution.ch.SyncConsistentHashFactory"/>
This configuration guarantees caches with the same members have the same consistent hash, and if the machineId, rackId, or siteId attributes are specified in the transport configuration it also spreads backup copies across physical machines/racks/data centers.
It has a potential drawback in that it can move a greater number of segments than necessary during re-balancing. This can be mitigated by using a larger number of segments.
Another potential drawback is that the segments are not distributed as evenly as possible, and actually using a very large number of segments can make the distribution of segments worse.
Despite the above potential drawbacks the SyncConsistentHashFactory and TopologyAwareSyncConsistentHashFactory both tend to reduce overhead in clustered environments, as neither of these calculate the hash based on the order that nodes have joined the cluster. In addition, both of these classes are typically faster than the default algorithms as both of these classes allow larger differences in the number of segments allocated to each node.

28.1.1. Implementing a ConsistentHashFactory

A custom ConsistentHashFactory must implement the org.infinispan.distribution.ch.ConsistenHashFactory interface with the following methods (all of which return an implementation of org.infinispan.distribution.ch.ConsistentHash):

Example 28.1. ConsistentHashFactory Methods

create(Hash hashFunction, int numOwners, int numSegments, List<Address>
members,Map<Address, Float> capacityFactors)
updateMembers(ConsistentHash baseCH, List<Address> newMembers, Map<Address,
Float> capacityFactors)
rebalance(ConsistentHash baseCH)
union(ConsistentHash ch1, ConsistentHash ch2)
Currently it is not possible to pass custom parameters to ConsistentHashFactory implementations.