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We describe a scalable database cluster for the spatial analysis andannotation of high-throughput brain imaging data, initially for 3-d electronmicroscopy image stacks, but for time-series and multi-channel data as well.The system was designed primarily for workloads that build connectomes---neuralconnectivity maps of the brain---using the parallel execution of computervision algorithms on high-performance compute clusters. These services andopen-science data sets are publicly available at http://openconnecto.me.  The system design inherits much from NoSQL scale-out and data-intensivecomputing architectures. We distribute data to cluster nodes by partitioning aspatial index. We direct I/O to different systems---reads to parallel diskarrays and writes to solid-state storage---to avoid I/O interference andmaximize throughput. All programming interfaces are RESTful Web services, whichare simple and stateless, improving scalability and usability. We include aperformance evaluation of the production system, highlighting the effectivenessof spatial data organization.

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience