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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 jclouds, Consul, etcd, Eureka, Kubernetes, and Zookeeper. The Hazelcast Cloud Discovery Service Provider Interface (SPI) enables cloud-based or on-premises nodes to auto-discover each other. The Hazelcast platform can manage memory for many different types of applications. It offers an Open Binary Client Protocol to support APIs for any binary programming language. The Hazelcast and open source community members have created client APIs for programming languages that include Java, .NET, C++, Python, Node.js and Go. Typical use-cases for Hazelcast include: Application scaling Cache-as-a-service Cross-JVM communication and shared storage Distributed cache, often in front of a database In-memory processing and Analytics In-memory computing Internet of Things infrastructure Key-value database Memcached alternative with a protocol compatible interface Microservices infrastructure NoSQL data store Spring Cache Web Session clustering Vert.x utilizes it for shared storage. Hazelcast is also used in academia and research as a framework for distributed execution and storage.

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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.

Redis

Redis (ˈrɛdɪs; Remote Dictionary Server) is an open-source in-memory storage, used as a distributed, in-memory key–value database, cache and message broker, with optional durability. Because it holds all data in memory and because of its design, Redis offers low-latency reads and writes, making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database, and one of the most popular databases overall. Redis is used in companies like Twitter, Airbnb, Tinder, Yahoo Adobe, Hulu, and Amazon.

In-memory database

An in-memory database (IMDB, or main memory database system (MMDB) or memory resident database) is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism. In-memory databases are faster than disk-optimized databases because disk access is slower than memory access and the internal optimization algorithms are simpler and execute fewer CPU instructions.

Rack-Scale Memory Pooling for Datacenters

Stanko Novakovic

The rise of web-scale services has led to a staggering growth in user data on the Internet. To transform such a vast raw data into valuable information for the user and provide quality assurances, it is important to minimize access latency and enable in-me ...

EPFL2017

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Data movement between memory and CPU is a well-known energy bottleneck for analytics. Near-Memory Processing (NMP) is a promising approach for eliminating this bottleneck by shifting the bulk of the computation toward memory arrays in emerging stacked DRAM ...

2015

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2015