Java IO has been considerably fast after the introduction of NIO and memory-mapped file offers the fastes t IO operation possible in Java, that's the main reason of Why high-performance Java application should use Memory Mapped files for persisting data. Together with GeographicLib and ESRI's geometry API, it provides an extensive set of geographic and geometric operations for spatial data analysis on the map.Memory Mapped Files in Java is a rather new java concept for many programmers and developers, though it’s been there from JDK 1.4 along with java.nio package. Access to map data is provided with a fast and flexible in-memory map data structure.
JAVA MEMORY DISK MAP SOFTWARE
Barefoot consists of a software library and a (Docker-based) map server that provides access to street map data from OpenStreetMap and is flexible to be used in distributed cloud infrastructures as map data server or side-by-side with Barefoot's stand-alone servers for offline (matcher server) and online map matching (tracker server), or other applications built with Barefoot library. It is designed for use in parallel and distributed systems and, hence, includes a stand-alone map matching server and can be used in distributed systems for map matching services in the cloud. Together with its extensive set of geometric and spatial functions, an in-memory map data structure and basic machine learning functions, it is a versatile basis for scalable location-based services and spatio-temporal data analysis on the map. Both caches are public properties of PINCache, so it's easy to manipulate one or the other separately if necessary.īarefoot - Java library for integrating the map into software and services with state-of-the-art online and offline map matching that can be used stand-alone and in the cloudĪn open source Java library for online and offline map matching with OpenStreetMap. PINCache coordinates them so that objects added to memory are available immediately to other threads while being written to disk safely in the background.
Both PINMemor圜ache and PINDiskCache use locks to protect reads and writes. Objects stored in PINDiskCache remain until you trim the cache yourself, either manually or by setting a byte or age limit.
On iOS, PINMemor圜ache will clear itself when the app receives a memory warning or goes into the background. It is comprised of two self-similar stores, one in memory (PINMemor圜ache) and one on disk (PINDiskCache), all backed by GCD and safe to access from multiple threads simultaneously.
It is a key/value store designed for persisting temporary objects that are expensive to reproduce, such as downloaded data or the results of slow processing. PINCache is a fork of TMCache re-architected to fix issues with deadlocking caused by heavy use. PINCache - Fast, non-deadlocking parallel object cache for iOS, tvOS and OS X The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. This t-digest data structure can be used to estimate quantiles or compute other rank statistics.
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to produce a data structure that is related to the Q-digest. The t-digest algorithm is also very parallel friendly making it useful in map-reduce and parallel streaming applications. T-digest - A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed meansĪ new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means.
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