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Etcd is open-source software, developed at CoreOS under the Apache License. [7] It can be used to perform distributed locks as well. [8] Redis is an open source, Redis Source Available License licensed, advanced key-value cache and store. [9] Redis can be used to implement the Redlock Algorithm for distributed lock management. [10]
The UKV [45] project allows users to use RocksDB on par with LevelDB as the underlying key-value store. It represents a shared abstraction for create, read, update and delete (CRUD) operations common to every storage engine. It augments it with structured bindings for several high-level languages, including Python, Java, and Go.
Solaris Cluster is an example of kernel-level clustering software. Some of the processes it runs are normal system processes on the systems it operates on, but it does have some special access to operating system or kernel functions in the host systems.
Solr (pronounced "solar") is an open-source enterprise-search platform, written in Java.Its major features include full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features [2] and rich document (e.g., Word, PDF) handling.
MySQL Cluster, also known as MySQL Ndb Cluster is a technology providing shared-nothing clustering and auto-sharding for the MySQL database management system. It is designed to provide high availability and high throughput with low latency, while allowing for near linear scalability. [ 3 ]
In computer programming, primary clustering is a phenomenon that causes performance degradation in linear-probing hash tables.The phenomenon states that, as elements are added to a linear probing hash table, they have a tendency to cluster together into long runs (i.e., long contiguous regions of the hash table that contain no free slots).
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In data mining, k-means++ [1] [2] is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.