* Constraints - Configurable conditions under which writes to a table will be rejected. Constraints are written in Java and configurable on a per table basis. * Sharding - Through the use of specialized iterators, Accumulo can be a parallel sharded document store. For example wikipedia could be stored and searched for documents containing certain words. * Large Rows - When reading rows, there is no requirement that an entire row fits into memory. * Namespaces - In version 1.6.0, the concept of table “namespaces” was created to allow for logical grouping and configuration of Accumulo tables. By default, tables are created in a default namespace which is the empty string to preserve the feel for how tables operate in previous versions. One application of table namespaces is placing the Accumulo root and metadata table in an “accumulo” namespace to denote that these tables are used internally by Accumulo. * Volume support - Accumulo 1.6.0 migrated away from configuration of HDFS by using a single HDFS host and directory, to a collection of HDFS URIs (host and path) which allows Accumulo to operate over multiple disjoint HDFS instances. This allows Accumulo to scale beyond the limits of a single namenode. When used in conjunction with HDFS federation, multiple namenodes can share a pool of datanodes.

* Linear and modular scalability. * Strictly consistent reads and writes. * Automatic and configurable sharding of tables * Automatic failover support between RegionServers. * Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables. * Easy to use Java API for client access. * Block cache and Bloom Filters for real-time queries. * Query predicate push down via server side Filters * Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options * Extensible jruby-based (JIRB) shell * Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX