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Distributed Cache
In computing, a distributed cache is an extension of the traditional concept of cache used in a single locale. A distributed cache may span multiple servers so that it can grow in size and in transactional capacity. It is mainly used to store application data residing in database and web session data. The idea of distributed caching has become feasible now because main memory has become very cheap and network cards have become very fast, with 1 Gbit now standard everywhere and 10 Gbit gaining traction. Also, a distributed cache works well on lower cost machines usually employed for web servers as opposed to database servers which require expensive hardware. An emerging internet architecture known as Information-centric networking (ICN) is one of the best examples of a distributed cache network. The ICN is a network level solution hence the existing distributed network cache management schemes are not well suited for ICN. In the supercomputer environment, distributed cach ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computing
Computing is any goal-oriented activity requiring, benefiting from, or creating computing machinery. It includes the study and experimentation of algorithmic processes, and development of both hardware and software. Computing has scientific, engineering, mathematical, technological and social aspects. Major computing disciplines include computer engineering, computer science, cybersecurity, data science, information systems, information technology and software engineering. The term "computing" is also synonymous with counting and calculating. In earlier times, it was used in reference to the action performed by mechanical computing machines, and before that, to human computers. History The history of computing is longer than the history of computing hardware and includes the history of methods intended for pen and paper (or for chalk and slate) with or without the aid of tables. Computing is intimately tied to the representation of numbers, though mathematical conc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hazelcast
In computing, Hazelcast IMDG is an open source in-memory data grid based on Java. It is also the name of the company developing the product. The Hazelcast company is funded by venture capital and headquartered in Palo Alto, California. In a Hazelcast grid, data is evenly distributed among the nodes of a computer cluster, allowing for horizontal scaling of processing and available storage. Backups are also distributed among nodes to protect against failure of any single node. Hazelcast provides central, predictable scaling of applications through in-memory access to frequently used data and across an elastically scalable data grid. These techniques reduce the query load on databases and improve speed. Hazelcast can run on-premises, in the cloud (Amazon Web Services, Microsoft Azure, Cloud Foundry, OpenShift), virtually (VMware), and in Docker containers. Hazelcast offers technology integrations for multiple cloud configuration and deployment technologies, including Apache jclo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cache Stampede
A cache stampede is a type of cascading failure that can occur when massively parallel computing systems with caching mechanisms come under very high load. This behaviour is sometimes also called dog-piling. To understand how cache stampedes occur, consider a web server that uses memcached to cache rendered pages for some period of time, to ease system load. Under particularly high load to a single URL, the system remains responsive as long as the resource remains cached, with requests being handled by accessing the cached copy. This minimizes the expensive rendering operation. Under low load, cache misses result in a single recalculation of the rendering operation. The system will continue as before, with average load being kept very low because of the high cache hit rate. However, under ''very'' heavy load, when the cached version of that page expires, there may be sufficient concurrency in the server farm that multiple threads of execution will all attempt to render the conte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cache-oblivious Algorithm
In computing, a cache-oblivious algorithm (or cache-transcendent algorithm) is an algorithm designed to take advantage of a processor cache without having the size of the cache (or the length of the cache lines, etc.) as an explicit parameter. An optimal cache-oblivious algorithm is a cache-oblivious algorithm that uses the cache optimally (in an asymptotic sense, ignoring constant factors). Thus, a cache-oblivious algorithm is designed to perform well, without modification, on multiple machines with different cache sizes, or for a memory hierarchy with different levels of cache having different sizes. Cache-oblivious algorithms are contrasted with explicit ''loop tiling'', which explicitly breaks a problem into blocks that are optimally sized for a given cache. Optimal cache-oblivious algorithms are known for matrix multiplication, matrix transposition, sorting, and several other problems. Some more general algorithms, such as Cooley–Tukey FFT, are optimally cache-oblivious ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cache Coherence
In computer architecture, cache coherence is the uniformity of shared resource data that ends up stored in multiple local caches. When clients in a system maintain caches of a common memory resource, problems may arise with incoherent data, which is particularly the case with CPUs in a multiprocessing system. In the illustration on the right, consider both the clients have a cached copy of a particular memory block from a previous read. Suppose the client on the bottom updates/changes that memory block, the client on the top could be left with an invalid cache of memory without any notification of the change. Cache coherence is intended to manage such conflicts by maintaining a coherent view of the data values in multiple caches. Overview In a shared memory multiprocessor system with a separate cache memory for each processor, it is possible to have many copies of shared data: one copy in the main memory and one in the local cache of each processor that requested it. When on ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cache Algorithms
In computing, cache algorithms (also frequently called cache replacement algorithms or cache replacement policies) are optimizing instructions, or algorithms, that a computer program or a hardware-maintained structure can utilize in order to manage a cache of information stored on the computer. Caching improves performance by keeping recent or often-used data items in memory locations that are faster or computationally cheaper to access than normal memory stores. When the cache is full, the algorithm must choose which items to discard to make room for the new ones. Overview The average memory reference time is : T = m \times T_m + T_h + E where : m = miss ratio = 1 - (hit ratio) : T_m = time to make a main memory access when there is a miss (or, with multi-level cache, average memory reference time for the next-lower cache) : T_h= the latency: the time to reference the cache (should be the same for hits and misses) : E = various secondary effects, such as queuing effects in mult ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Velocity (memory Cache)
AppFabric Caching provides an in-memory, distributed cache platform for Windows Server. Microsoft developed AppFabric Caching and released it as part of AppFabric. Architecture AppFabric Caching stores serialized managed objects in a cache cluster. The cache cluster consists of one or more machines that pool their available physical memory. This pooled memory is presented to cache clients as a single source of caching memory. Objects are stored and accessed using an associated key value. AppFabric Caching features must be installed on each server in the cache cluster. Following installation, the AppFabric Configuration Wizard must be used to join each server to the cache cluster. An external file share or database is required to maintain the cache cluster configuration settings. A set of Windows PowerShell commands for Caching provides administration capabilities on the cache cluster. Code examples ''Note that the code samples in this section are shown in C#.'' A common task ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Tarantool
Tarantool is an in-memory computing platform with a flexible data schema, best used for creating high-performance applications. Two main parts of it are an in-memory database and a Lua application server. Tarantool maintains data in memory and ensures crash resistance with write-ahead logging and snapshotting. It includes a Lua interpreter and interactive console, but also accepts connections from programs in several other languages. History Mail.Ru, one of the largest Internet companies in Russia, started the project in 2008 as part of the development of Moy Mir (My World) social network. In 2010 for a project head it hired a former technical lead from MySQL. Open-source contributors have been active especially in the area of external-language connectors for C, Perl, PHP, Python, Ruby, and node.js. Tarantool became part of the Mail.Ru backbone, used for dynamic content such as user sessions, unsent instant messages, task queues, and a caching layer for traditional relation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SafePeak
SafePeak Technologies is a software company founded in 2007 in Israel. It markets products for big data related to relational database management systems. History SafePeak Technologies, originally DCF Technologies Ltd, was founded in 2007 by Vladi Vexler. It operated in stealth mode until 2009. Between 2009 and 2013 the company partnered with distributors and technology partners from Israel (Ness, Valinor), Greece, USA and Hong-Kong. In 2013 SafePeak partnered with Amazon Web Services on Microsoft SQL Server databases. In January 2014 SafePeak Technologies entered into a technology IP acquisition agreement with a USA Boston based company ScaleBase, led by Ram Metser. Technology SafePeak Technologies developed technology for resolving databases scalability and performance of relational databases such as such as the SQL Server and MySQL - automated dynamic caching. The Dynamic Database Caching technology was invented, patented and developed by the SafePeak Technologies. Safe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Redis
Redis (; Remote Dictionary Server) is an in-memory data structure store, used as a distributed, in-memory key–value database, cache and message broker, with optional durability. Redis supports different kinds of abstract data structures, such as strings, lists, maps, sets, sorted sets, HyperLogLogs, bitmaps, streams, and spatial indices. The project was developed and maintained by Salvatore Sanfilippo, starting in 2009. From 2015 until 2020, he led a project core team sponsored by Redis Labs. Salvatore Sanfilippo left Redis as the maintainer in 2020. It is open-source software released under a BSD 3-clause license. In 2021, not long after the original author and main maintainer left, Redis Labs dropped the Labs from its name and now is known simply as "Redis". History The name Redis means Remote Dictionary Server. The Redis project began when Salvatore Sanfilippo, nicknamed ''antirez'', the original developer of Redis, was trying to improve the scalability of his Italian ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Riak
Riak (pronounced "ree-ack" ) is a distributed NoSQL key-value data store based on Amazon's Dynamo paper, including its "tunable AP" approach, that is tunable consistency, to the tradeoffs imposed by the CAP Theorem. Riak offers high availability, fault tolerance, operational simplicity, and scalability. It moved to an entirely open-source project in August 2017, with many of the licensed Enterprise Edition features being incorporated after its acquisition by Bet365. Written in Erlang, Riak has fault tolerant data replication and automatic data distribution across the cluster for performance and resilience. Riak was originally developed by engineers employed by Basho Technologies and maintained by them until 2017 when the rights were sold to bet365 after Basho went into receivership. Riak was originally licensed using a freemium model. It is now completely open-source including all the enterprise features. Riak has a pluggable backend for its core storage, with the default storage ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
