Brown Dwarf: A fully-distributed, fault-tolerant data warehousing system

In this paper we present the Brown Dwarf, a distributed data analytics system designed to efficiently store, query and update multidimensional data over commodity network nodes, without the use of any proprietary tool. Brown Dwarf distributes a centralized indexing structure among peers on-the-fly, reducing cube creation and querying times by enforcing parallelization. Analytical queries are naturally performed on-line through cooperating nodes that form an unstructured Peer-to-Peer overlay. Updates are also performed on-line, eliminating the usually costly over-night process. Moreover, the system employs an adaptive replication scheme that adjusts to the workload skew as well as the network churn by expanding or shrinking the units of the distributed data structure. Our system has been thoroughly evaluated on an actual testbed: it manages to accelerate cube creation up and querying up to several tens of times compared to the centralized solution by exploiting the capabilities of the available network nodes working in parallel. It also manages to quickly adapt even after sudden bursts in load and remains unaffected with a considerable fraction of frequent node failures. These advantages are even more apparent for dense and skewed data cubes and workloads.

► Complete indexing, query processing and update system for data cubes over a distributed environment.
► Distributes a highly effective centralized data structure over a network of commodity PCs.
► Efficient adaptive replication scheme, perceptive to workload skew and node churn.
► 36 times faster cube creation and 60 times faster query resolution compared to the centralized version.
► Prompt adaptation to query skew and resilience to a considerable fraction of node failures.