 | |
| Developer(s) | Hazelcast |
|---|---|
| Stable release | 5.3.6
/ November 9, 2023[1] |
| Repository | |
| Written in | Java |
| Type | in-memory data grid, Data structure store |
| License | Hazelcast: Apache 2.0,[2] Hazelcast Enterprise: Proprietary |
| Website | hazelcast |
In computing, Hazelcast is a unified real-time data platform[3] based on Java that combines a fast data store with stream processing. 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.[4][5][6]
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 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.[7]
YouTube Encyclopedic
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Introducing In-Memory Computing with Hazelcast
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Hazelcast In-Memory Data Grid Overview and Use Cases
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What is the Hazelcast Platform? | Hazelcast Explainer
Transcription
Usage
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[8]
- Microservices infrastructure
- NoSQL data store
- Spring Cache
- Web Session clustering
Vert.x utilizes it for shared storage.[9]
Hazelcast is also used in academia and research as a framework for distributed execution and storage.
- Cloud2Sim[10][11] leverages Hazelcast as a distributed execution framework for CloudSim cloud simulations.
- ElastiCon[12] distributed SDN controller uses Hazelcast as its distributed data store.
- ∂u∂u[13] uses Hazelcast as its distributed execution framework for near duplicate detection in enterprise data solutions.
See also
- Complex event processing
- Distributed data store
- Distributed transaction processing
- Infinispan
- Oracle Coherence
- Ehcache
- Couchbase Server
- Apache Ignite
- Redis
References
- ^ "Release v5.3.6". GitHub. Retrieved 2023-12-20.
- ^ "Licensing". Hazelcast Reference Manual.
- ^ "Streaming and IMDG Coming Together: Hazelcast Platform 5.0 is Released!". Hazelcast. Retrieved 2021-07-14.
- ^ "Home". Hazelcast. Retrieved 2022-08-16.
- ^ Penchikala, Srini (2013-09-18). "Java In-Memory Grid Hazelcast gets VC Funding from Bain Capital". infoq.com. Retrieved 2013-12-11.
- ^ Novet, Jordan (2014-09-18). "Hazelcast adds $11M to grow its business based on an open-source in-memory data grid". VentureBeat. Retrieved 2020-12-28.
- ^ "Hazelcast Clients". Hazelcast Platform Reference Manual.
- ^ "Memcache Client". Hazelcast IMDG Reference Manual.
- ^ Kim, Jaehong (2017-06-16). "Understanding Vert.x Architecture - Part II". Retrieved 2020-12-28.
- ^ Kathiravelu, Pradeeban; Veiga, Luís (9 September 2014). Concurrent and Distributed CloudSim Simulations. IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS). Paris. pp. 490–493. CiteSeerX 10.1.1.714.4924. doi:10.1109/MASCOTS.2014.70.
- ^ Kathiravelu, Pradeeban; Veiga, Luís (8 December 2014). An Adaptive Distributed Simulator for Cloud and MapReduce Algorithms and Architectures. IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), 2014. London. pp. 79–88. doi:10.1109/UCC.2014.16.
- ^ Dixit, Advait Abhay; Hao, Fang; Mukherjee, Sarit; Lakshman, TV; Kompella, Ramana (20 October 2014). ElastiCon: an elastic distributed sdn controller. Tenth ACM/IEEE symposium on Architectures for networking and communications systems. pp. 17–28. Retrieved 2020-12-28.
- ^ Kathiravelu, Pradeeban; Galhardas, Helena; Veiga, Luís (28 October 2015). ∂u∂u Multi-Tenanted Framework: Distributed Near Duplicate Detection for Big Data. On the Move to Meaningful Internet Systems: OTM 2015 Conferences. Rhodes, Greece. pp. 237–256. doi:10.1007/978-3-319-26148-5_14.
External links
This page was last edited on 4 March 2024, at 22:36