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Hazelcast
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 | This article needs additional citations for verification. (March 2019) |
| Hazelcast | |
|---|---|
 | |
| Developer | Hazelcast |
| Stable release | 5.5.0
/ July 26, 2024[1] |
| Written in | Java |
| Type | in-memory data grid, Data structure store |
| License | Hazelcast: Apache-2.0,[2] Hazelcast Enterprise: Proprietary |
| Website | hazelcast |
| Repository | |
In computing, Hazelcast is a unified real-time data platform[3] implemented in Java that combines a fast data store with stream processing. It is also the name of the company that develops 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 can run on-premises, in the cloud (Amazon Web Services, Microsoft Azure, Cloud Foundry, OpenShift), virtually (VMware), and in Docker containers. 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]
Usage
[edit]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
[edit]References
[edit]- ^ "Release v5.5.0". GitHub. Retrieved 2024-07-26.
- ^ "Licensing". Hazelcast Reference Manual.
- ^ "Announcing Hazelcast Platform 5.5 Release". Hazelcast. 30 July 2024. Retrieved 2024-07-30.
- ^ "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
[edit]Hazelcast
View on Grokipediafrom Grokipedia
Hazelcast is an open-source, Java-based unified real-time data platform that combines in-memory data storage with stream processing and distributed computing capabilities, enabling developers to build scalable, low-latency applications for handling data in motion across cloud-native environments.[1][2][3]
Initiated as an open-source project in 2008 by developers including Talip Ozturk to address limitations in traditional data access speeds, Hazelcast evolved from a simple in-memory data grid into a comprehensive platform.[4] The company behind it, Hazelcast Inc., was formally founded in 2012 in Palo Alto, California, with a focus on commercializing the technology for enterprise use.[5] Key milestones include the 2017 integration of real-time stream processing, the 2022 release of unified features supporting machine learning model operationalization, and the 2024 addition of vector search capabilities for AI integration, positioning it as a leader in real-time data solutions.[5][6]
At its core, Hazelcast provides distributed data structures such as maps, queues, and caches that automatically scale across cluster members, offering sub-millisecond access times and resilience through data replication.[2] It supports multiple programming languages including Java, C++, Python, and Node.js, along with protocols like REST and Memcached, making it versatile for microservices, AI applications, and high-throughput event processing.[2] Widely adopted by over 50% of the world's largest banks and companies like JP Morgan, Hazelcast powers use cases in fraud detection, payment processing, and real-time analytics, handling millions of events with minimal latency.[5][1][7]
The Enterprise Edition extends the platform with exclusive features like WAN replication for cross-site data synchronization, encryption at rest via persistence options, and certifications supporting compliance standards such as GDPR and HIPAA through its robust security suite.[100] These enhancements enable seamless integration in mission-critical applications, while core features like distributed primitives and real-time analytics remain available in both editions for foundational use cases.[99]
The Community Edition is commonly adopted by developers and startups for cost-effective experimentation and smaller-scale projects due to its open-source nature.[102] The Enterprise Edition is widely used in production environments, particularly among large organizations requiring high availability, security, and dedicated support for scalable deployments.[103]
Overview
Definition and Core Functionality
Hazelcast is an open-source, Java-based in-memory data grid (IMDG) that functions as a distributed middleware for pooling random access memory (RAM) across networked computers in a cluster, enabling applications to share and process data at high speeds.[2][8] Initially released in 2008 as a simple IMDG focused on distributed data storage, it has evolved into a unified real-time data platform that integrates fast in-memory data storage with stream processing capabilities.[5] This evolution allows Hazelcast to handle both data at rest and data in motion within a single runtime, supporting modern applications in real-time economies that process petabyte-scale workloads.[9] At its core, Hazelcast provides a suite of distributed data structures, such as maps, queues, lists, and caches, which enable low-latency access to data across cluster nodes without requiring developers to manage underlying distribution logic.[2] These structures support elastic scaling, where clusters automatically adjust to increasing data volumes and velocities by adding or removing nodes seamlessly, ensuring consistent performance for high-throughput scenarios.[2] For instance, operations on these data structures can achieve sub-millisecond read and write latencies, making it suitable for latency-sensitive applications like real-time analytics and transaction processing.[10] Key benefits of Hazelcast include its fault tolerance, achieved through automatic data partitioning and replication across nodes, which ensures data availability and reliability even in the event of node failures.[8] Additionally, it supports processing streaming data in motion alongside historical data stored in memory, allowing for immediate insights and actions on combined datasets without the need for separate systems.[9] This combination delivers resilience and scalability, with clusters capable of handling billions of events per second while maintaining millisecond-level responsiveness.[9]Evolution from IMDG to Real-Time Platform
Hazelcast originated as an in-memory data grid (IMDG) in 2008, initially developed by founders Talip Ozturk and Fuad Malikov to address limitations in traditional databases by enabling fast, distributed caching and map-based data storage across clusters.[5] The platform's early focus was on providing scalable, low-latency access to data at rest, serving as a foundational layer for applications requiring high-performance computing without the overhead of disk I/O. By 2017, Hazelcast introduced Jet, a distributed stream and batch processing engine, marking the beginning of its expansion beyond static data management.[11] This evolution accelerated post-2020 with the release of Hazelcast Platform 5.0 in September 2021, which unified the IMDG core with Jet's streaming capabilities and later incorporated AI/ML features, such as vector search in 2024, to support unified real-time analytics.[12][13] In October 2025, version 5.6.0 was released, enhancing vector collections with fault tolerance and backups (in beta), introducing dynamic diagnostic logging without cluster restarts, and improving overall platform performance and resilience.[14][15] Strategically, Hazelcast shifted toward incorporating event stream processing to manage data-in-motion alongside stored data, allowing systems to enrich incoming events with historical context for immediate insights. This integration, realized through Platform 5.0, transformed the IMDG from a passive storage solution into an active processing engine capable of handling continuous data flows in real time.[16] The approach enables instant decision-making in business applications, such as predictive maintenance or dynamic pricing, by combining streaming events with in-memory data structures like IMap for contextual enrichment without separate silos.[17] This progression has profoundly impacted users, evolving Hazelcast from static data storage to dynamic, reactive systems that underpin modern architectures. Organizations now leverage it for microservices orchestration, where low-latency data sharing across distributed nodes ensures seamless scalability. In edge computing scenarios, it supports real-time data aggregation from IoT devices, reducing latency in remote operations. For low-latency fraud detection, the platform processes transaction streams against historical patterns to flag anomalies in milliseconds, enhancing security in financial services.[18] As of November 2025, Hazelcast is positioned as a cornerstone platform for the "real-time economy," empowering businesses to act instantaneously on streaming data for operational intelligence. It is trusted by numerous Fortune 500 companies across industries, driving innovations in automated decision-making and AI-driven applications.[19][20]History
Founding and Early Development
Hazelcast originated as an open-source project in 2008, initiated by Talip Ozturk along with co-founders Enes Akar, Fuad Malikov, and Mehmet Dogan, software engineers in Istanbul, Turkey, to meet the growing demand for efficient distributed caching in Java environments. Ozturk, who had previously served as director of technology at Zaman Media Group, envisioned a simple, embeddable distributed data grid that could handle high-performance data distribution without the complexity of proprietary systems. The project addressed key challenges in scalable Java applications by providing a lightweight alternative for in-memory data management.[21][4][22][23] The initial development began with the first GitHub commit in late 2008, leading to the project's first open-source release in early 2009. Licensed under the Apache 2.0 terms from its inception, Hazelcast prioritized core functionality, centering on the distributed map interface known as IMap. This structure leveraged multicast communication for automatic cluster discovery, enabling nodes to form dynamic clusters seamlessly and distribute data across Java virtual machines. The design emphasized embeddability, allowing developers to integrate it directly into applications without external servers.[24][3][25][26] By 2010, Hazelcast had begun to attract early adopters in the developer community, particularly startups building cloud-native Java applications that required reliable session clustering and caching for high availability. Its simplicity and performance made it suitable for scenarios like web session replication and fast data access in distributed systems. In 2012, the project transitioned into a formal company, Hazelcast Inc., marking the shift from a solo open-source effort to a structured organization poised for broader commercialization.[27]Major Milestones and Acquisitions
Hazelcast 3.0, released in 2013, marked a significant evolution in the platform's architecture through a comprehensive code rewrite comprising 70-80% of the product, enhancing scalability and performance for in-memory data grids.[28] This version laid the groundwork for advanced distributed computing capabilities, including support for continuous queries and entry processing. Subsequent releases built on this foundation; for instance, version 3.6 in 2016 introduced the Hot Restart Store, enabling fast cluster restarts by persisting data states on disk in an optimized format, which supported a range of data structures like maps, caches, and web sessions.[29] In 2017, Hazelcast 3.9 integrated stream processing via the newly introduced Hazelcast Jet engine, allowing for distributed data processing pipelines that combined batch and streaming workloads.[30] The platform continued to advance toward cloud-native deployments with version 4.0 in 2020, which incorporated persistent memory support using technologies like Intel Optane DC for off-heap storage, alongside encryption at rest for the Hot Restart Store and enhanced CP subsystem persistence for linearizable consistency.[31] This release also expanded support for additional programming languages in client libraries. Hazelcast Platform 5.0, generally available in 2021, unified the in-memory data grid (IMDG) and Jet components into a single solution, introducing an integrated SQL engine with support for data manipulation operations like INSERT, UPDATE, and DELETE, as well as advanced aggregations and joins.[32] Building on this, version 5.0 and later incorporated the High-Density Memory Store, an enterprise feature enabling storage of hundreds of gigabytes per node without garbage collection pauses, thus supporting cost-efficient scaling for large datasets.[33] Updates in 2023 and 2024, including Platform 5.5, emphasized extensions for AI and machine learning workloads, such as real-time data enrichment for predictive analytics and improved consistency for AI-driven applications. In October 2025, Platform 5.6 was released, introducing enhancements like CP Snapshot Chunking for better memory efficiency, Dynamic Diagnostic Logging, and optimizations to Vector Search including fault tolerance and performance improvements for AI applications.[34][35] On the corporate front, Hazelcast secured $11 million in Series B funding in 2014, led by Earlybird Venture Capital with participation from Bain Capital Ventures, to accelerate product development and market expansion.[36] The company established its U.S. headquarters in Palo Alto, California, in the same year, facilitating growth in the Silicon Valley ecosystem.[37] Subsequent funding included a $21.5 million round in 2019 led by C5 Capital and a $50 million Series D expansion in 2020, bringing total investment to over $66 million and supporting advancements in real-time computing.[38][39] By 2025, Hazelcast served over 420 enterprise customers, including major banks and telecommunications firms, with tens of thousands of deployed clusters powering mission-critical applications.[40][41] Hazelcast has not pursued acquisitions but has forged strategic partnerships for managed services, including availability on AWS Marketplace for fully managed deployments since 2020 and integration with Microsoft Azure for cloud-native Hazelcast Cloud Enterprise clusters.[42][43] These collaborations enable seamless multi-cloud operations, optimizing latency and scalability for global enterprises.Technical Architecture
Clustering and Data Distribution
Hazelcast clusters are formed by nodes, referred to as members, that automatically discover and join each other using configurable mechanisms such as multicast or TCP/IP.[44] Multicast enables members to find one another via UDP multicast communication on a specified group address and port, suitable for local networks but often restricted in cloud environments. TCP/IP discovery, on the other hand, requires explicit listing of member addresses in configuration and uses TCP for reliable joining, making it ideal for production setups. Lite members, which do not own data partitions but can execute tasks, listen to events, and access distributed structures, join the cluster through declarative configuration in XML or YAML files—such as<lite-member enabled="true"/>—or programmatically via APIs like config.setLiteMember(true).[45] This setup supports dynamic scaling, where adding or removing members triggers automatic rebalancing of data and computations across the cluster to maintain even distribution.[46]
Data distribution in Hazelcast relies on a consistent hashing algorithm to partition objects across cluster members, ensuring balanced load and minimal relocation during changes.[47] By default, Hazelcast uses 271 partitions, with each key hashed and modulo-operated against this count to assign it to a specific partition ID.[47] Partitions are evenly distributed among data-owning members, with one primary replica per partition handling read and write operations, and configurable backup replicas for redundancy—typically a replication factor of 1 (one backup) to 3, though the default is 1 backup for high availability without excessive overhead.[47] Backups can replicate synchronously, blocking until acknowledged, or asynchronously for better performance, and all replicas maintain the same data for fault tolerance.[48]
Fault tolerance is achieved through continuous heartbeat monitoring and automated recovery processes. Members send heartbeats every 1 second by default and use the Phi Accrual Failure Detector to track intervals in a sliding window, calculating a suspicion level (Phi) based on mean and variance; if Phi exceeds the threshold (default 10), the member is deemed failed after a maximum no-heartbeat timeout of 60 seconds.[49] Upon detecting a failure, the master member initiates partition migration, promoting backups to primaries and reassigning replicas to other healthy members, ensuring data consistency and availability without downtime as the cluster rebalances.[47]
Networking in Hazelcast accommodates diverse environments, supporting discovery via multicast for local setups, TCP/IP for explicit configurations, and cloud-specific plugins like Kubernetes for automatic service detection in containerized deployments.[50] Security is integrated through TLS/SSL for encrypting all communications, configurable with custom factories (e.g., <ssl enabled="true">), and authentication mechanisms including default credentials, LDAP, custom plugins, and Kerberos (Enterprise only) for single sign-on (SSO) supporting cluster member and client authentication to verify member identities.[51][52] Additional controls, such as trusted interfaces and outbound port restrictions, further harden the network layer against unauthorized access.[50]
In-Memory Data Structures and Persistence
Hazelcast provides several core in-memory data structures designed for distributed storage and access, enabling scalable applications to manage data efficiently across a cluster. The IMap serves as the primary distributed key-value store, supporting operations such as get, put, and remove while partitioning data across cluster members for load balancing.[53] It includes eviction policies like least recently used (LRU) to manage memory by automatically removing least-accessed entries when limits are reached.[54] The IQueue implements a first-in-first-out (FIFO) collection for distributed queuing, allowing items to be added and polled across members, with data partitioned to ensure availability.[53] Similarly, the ISet offers a distributed set that maintains unique elements without ordering, also partitioned for scalability.[53] For caching needs, the ICache provides a JCache-compliant interface, integrating with the broader ecosystem while supporting eviction based on size or time-to-live.[53] Advanced data models in Hazelcast extend functionality for synchronization and atomic operations. The ILock enables distributed locking to ensure exclusive access to shared resources, preventing concurrent modifications in a cluster environment.[53] The ISemaphore manages concurrent access by distributing permits across members, allowing control over the number of threads that can execute simultaneously.[53] For counters, the IAtomicLong supports atomic increments and decrements on long values, ensuring consistency without locks.[53] These structures, part of the CP subsystem, are available in the enterprise edition and rely on the underlying partitioning mechanism for distribution.[55] Hazelcast also supports custom serialization formats, such as Compact for schema evolution and partial deserialization without full objects and IdentifiedDataSerializable for efficient handling of known types, optimizing data transfer and storage.[56] Persistence options in Hazelcast ensure data durability beyond in-memory storage. Built-in persistence for IMap and ICache writes entries to local disk, allowing recovery after member or cluster restarts, though metadata like time-to-live resets upon restoration.[57] For integration with external systems, the MapStore interface facilitates loading data from and storing to databases via read-through, write-through, and write-behind strategies; write-through synchronously persists changes, while write-behind queues them asynchronously for batching.[58] This supports connectors for relational databases using JDBC and NoSQL stores like MongoDB, enabling hybrid caching where Hazelcast acts as a front-end to persistent backends.[59] Hot restart enhances recovery by loading from disk snapshots, minimizing downtime during planned shutdowns or single-member failures, with options for synchronous flushing to prevent data loss.[57] Memory management features optimize resource usage in Hazelcast's data structures. The High-Density Memory Store, an enterprise capability, stores data off-heap in native memory to bypass Java garbage collection, reducing pause times and enabling large datasets on single JVMs.[33] It applies to IMap and ICache, using configurable allocators for efficient block management.[33] Near Cache complements this by maintaining local copies of frequently accessed IMap entries on members or clients, accelerating reads by avoiding network hops in read-intensive scenarios.[60]Key Features
Distributed Computing Primitives
Hazelcast provides a suite of distributed computing primitives that enable coordinated execution and synchronization across cluster nodes, facilitating scalable processing beyond simple data storage. These primitives leverage the underlying partitioning and replication mechanisms to ensure efficient, fault-tolerant operations on distributed data structures such as maps.[61]Concurrency Controls
Hazelcast's concurrency controls offer distributed implementations of familiar Java synchronization mechanisms, ensuring linearizable operations through the CP Subsystem, which uses Raft consensus for strong consistency. The distributed ReentrantLock allows multiple threads across nodes to acquire locks on shared resources, supporting reentrancy and optional lease times to automatically release locks if a lock holder fails, preventing deadlocks in fault-prone environments.[62][63] The CountDownLatch enables cross-node synchronization by allowing threads to wait until a shared counter reaches zero, coordinating multi-threaded applications that span cluster members via majority-based consensus in the CP group.[64] Similarly, the Semaphore manages a pool of permits for controlling access to limited resources distributed across nodes, using sessions and heartbeats to track caller liveliness and release permits if a session expires.[65]Execution Engines
The IExecutorService implements a distributed version of Java's ExecutorService interface, allowing submission of Serializable Runnable or Callable tasks to specific members, key owners, or the entire cluster for asynchronous execution. Tasks are executed on the target nodes' thread pools, with options to target members owning particular keys for locality, reducing latency in key-based computations. In Hazelcast 5.6.0 (released October 15, 2025), performance of related IMap operations like executeOnKey and executeOnEntries has been improved for efficiency.[66][67][68] EntryProcessor provides an efficient mechanism for in-place updates on map entries, executing custom logic directly on the partition thread where the data resides, thereby avoiding the need to transfer full objects over the network and minimizing serialization overhead. It supports atomic operations on single or multiple entries filtered by predicates, and can be chained for complex transformations akin to MapReduce patterns.[69][70]Aggregation Tools
Hazelcast's aggregation framework enables distributed computation of functions like sum, average, min, and max over map entries using built-in Aggregators, which process data in parallel across partitions and combine partial results for a final aggregate. Custom Aggregators extend this by implementing accumulate, combine, and aggregate phases, supporting efficient queries without retrieving entire datasets to the client. In Hazelcast 5.6.0, new metrics for IMap indexes (e.g., indexesSkippedQueryCount, partitionsIndexed) enhance observability for aggregation performance.[68] For more advanced patterns, EntryProcessor chains facilitate MapReduce-style operations by mapping and reducing data in-place on the cluster, though the dedicated MapReduce API is deprecated in favor of these aggregation and pipeline approaches.[71]Reliability Features
Task partitioning in these primitives routes executions to the owning nodes of relevant keys, ensuring data locality and balanced load distribution across the cluster.[72] Failover is supported through Hazelcast's backup replication, where tasks can seamlessly migrate to backup replicas upon primary node failure, maintaining operation continuity.[61] Configurable thread pools per member, with adjustable sizes and queue capacities, allow tuning for workload-specific performance and resource utilization. In Hazelcast 5.6.0, new TCP write queue metrics (e.g., tcp_connection_out_writeQueuePendingBytes) and enhanced backup promotion logging improve reliability monitoring.[66][68]Stream Processing and Real-Time Analytics
Hazelcast Jet serves as the distributed stream processing framework within Hazelcast, enabling the construction of data pipelines through directed acyclic graph (DAG)-based topologies that model processing stages for efficient parallel execution.[73] These topologies support integration with external systems, including sources such as Apache Kafka for ingesting streaming data and sinks like Elasticsearch for outputting processed results.[74] By leveraging the underlying cluster for distribution, Jet pipelines execute across multiple nodes to handle high-throughput event streams.[73] For real-time operations, Hazelcast Jet provides windowing mechanisms to perform aggregations on unbounded streams, including tumbling windows for non-overlapping fixed intervals, sliding windows that overlap to capture continuous trends, and session windows that group events based on activity gaps.[75] These windows facilitate computations like sums or counts over time-based partitions of data, ensuring timely insights from live feeds. Jet also supports joins between streaming data and historical records stored in in-memory maps, allowing enrichment of incoming events with contextual information for immediate decision-making.[76] Fault tolerance is achieved through periodic distributed snapshots of job state, enabling exactly-once processing guarantees and rapid recovery from node failures by restoring and rescaling pipelines.[77] Hazelcast extends stream processing with analytics capabilities via Hazelcast SQL, which allows declarative querying over streams by mapping sources like Kafka topics and executing continuous jobs powered by the Jet engine.[78] These queries support filtering, windowed aggregations, and stream-to-stream joins, handling late events through configurable lateness policies to maintain accuracy in dynamic environments. For advanced analytics, Jet integrates with machine learning workflows to enable real-time anomaly detection, such as identifying fraudulent patterns in transaction streams by combining event data with predictive models.[79] Performance in Hazelcast Jet emphasizes in-memory processing to deliver sub-second latencies, with benchmarks demonstrating up to 1 billion events per second at a 99th percentile latency of 26 milliseconds in large-scale clusters.[80] Auto-scaling of pipelines occurs dynamically in response to cluster changes, such as adding or removing nodes, by restarting jobs to redistribute workload and adapt to varying loads without manual intervention.[81]Use Cases and Applications
Industry Implementations
Hazelcast has been widely adopted in the financial services sector to support high-performance, real-time operations. For instance, ING Türkiye implemented Hazelcast as a centralized caching layer to scale its microservices architecture, eliminating system latency and enabling massive scalability for digital banking services.[82] In another application, a top U.S. credit card issuer leverages Hazelcast to power real-time fraud detection by storing up to 5TB of customer data in memory, processing 5,000 transactions per second (with scalability to 10,000), and reducing latency to milliseconds, thereby avoiding an estimated $100 million in annual fraud losses.[83] In telecommunications, Hazelcast facilitates efficient handling of customer data and network operations. A leading U.S. communications provider uses Hazelcast IMDG to manage real-time device telemetry and account data, supporting over 1 million daily customer interactions across call centers, websites, and mobile self-service channels, while integrating AI/ML for near real-time issue resolution and scaling to tens of millions of accounts.[84] This deployment has improved Net Promoter Scores from negative to positive and reduced operational costs by minimizing support response times and on-site technician visits. E-commerce platforms rely on Hazelcast for managing peak loads and personalization. The world's second-largest e-commerce retailer employs Hazelcast for in-memory caching to handle burst traffic during high-demand events like Black Friday and Cyber Monday, ensuring seamless inventory management and user experiences under unpredictable volumes.[85] Similarly, a top global e-commerce retailer with $18.3 billion in annual sales uses Hazelcast to build real-time infrastructure that accelerates inventory updates and personalization, supporting auto-scaling to maintain performance during sales surges.[86] In healthcare, Hazelcast enables real-time processing of IoT-generated data for patient care. A healthcare IT company integrates Hazelcast to connect medical devices, electronic health records, and mobile apps via a resilient message bus, facilitating predictive alerts for health risks by analyzing vital signs and historical data in real time across cloud and edge environments.[87] This approach supports scalable, high-speed data operations for monitoring in-patients and outpatient portals, enhancing clinician decision-making without downtime.Integration with Modern Ecosystems
Hazelcast provides native support for deployment on major cloud platforms, including Amazon Web Services (AWS) with Elastic Kubernetes Service (EKS), Microsoft Azure, and Google Cloud Platform (GCP). This integration enables automatic discovery of cluster members in these environments, facilitating seamless scaling and management of distributed clusters.[88] The Hazelcast Platform is available as a managed service on these providers, offering auto-provisioning features such as one-click cluster creation, automated backups to cloud storage like AWS S3, Google Cloud Storage, or Azure Blob Storage, and built-in disaster recovery capabilities.[89] In the data ecosystem, Hazelcast includes connectors that allow it to serve as both a source and sink for popular messaging systems and databases. The Apache Kafka Connector enables streaming, filtering, and transforming events between Hazelcast clusters and Kafka topics, supporting fault-tolerant and transactional guarantees for real-time data pipelines.[90] Similarly, support for RabbitMQ is provided through Kafka Connect Source connectors, which import messages from RabbitMQ queues into Hazelcast for processing.[74] For relational databases, the JDBC Connector facilitates reading from and writing to systems like MySQL, PostgreSQL, and Microsoft SQL Server using standard SQL queries, with automatic batching and connection pooling for efficient data synchronization.[91] Hazelcast also integrates with microservices frameworks, offering compatibility with Spring Boot through dedicated starters for caching and data grids, and with Quarkus via client libraries that enable reactive, cloud-native applications.[92][93] Hazelcast offers official client libraries in multiple programming languages to enable applications to connect to and interact with clusters. These include Java for embedded and client-server topologies, .NET for enterprise integrations, Python for data science workflows, Go for high-performance services, and C++ for low-latency systems.[94] For multi-cluster federation across data centers, WAN replication synchronizes data structures like maps between geographically distributed Hazelcast clusters, supporting active-active or active-passive modes with configurable replication queues to handle network latency and ensure data consistency.[95] To support DevOps practices, Hazelcast provides tools for containerized and automated deployments. The Hazelcast Platform Operator automates cluster lifecycle management on Kubernetes and Red Hat OpenShift, handling provisioning, scaling, upgrades, and rolling restarts declaratively via custom resources.[96] For monitoring, Hazelcast exposes metrics in Prometheus format through its Management Center, allowing integration with Prometheus for collection and Grafana for visualization of cluster health, latency, and throughput dashboards.[97] CI/CD pipelines are streamlined with Helm charts, which package Hazelcast configurations for easy installation and customization on Kubernetes, enabling reproducible deployments across environments.[98]Editions and Support
Community vs. Enterprise Editions
Hazelcast offers two primary editions: the open-source Community Edition and the commercial Enterprise Edition. The Community Edition provides a free, Apache License 2.0-licensed core in-memory data grid (IMDG) with essential features for distributed computing, including basic clustering, standard data structures like maps and lists, and the Jet engine for stream processing.[99] It supports fundamental capabilities such as distributed data storage, advanced caching, out-of-the-box connectors, client libraries, SQL querying, and distributed compute, making it suitable for development and prototyping.[99] Support is limited to community-driven resources, including forums and GitHub issues, without professional assistance or service-level agreements (SLAs).[100] In contrast, the Enterprise Edition is a paid, subscription-based offering that builds on the Community Edition by adding advanced enterprise-grade features and support. It includes enhanced security mechanisms such as role-based access control (RBAC), mutual TLS authentication, Kerberos authentication for single sign-on (SSO) in the cluster, and socket-level interceptors, along with tools for compliance in regulated environments.[100][101] Key additions encompass rolling upgrades for zero-downtime deployments, high-density memory storage for optimized resource utilization, the full Management Center for UI-based monitoring and management (unlimited members), and the advanced CP subsystem for strongly consistent operations.[99] The edition also provides 24/7 professional support with a one-hour SLA response time, up to 30 support contacts, hot fixes, and emergency patches.[100] Pricing is determined via subscription models based on the number of nodes and usage levels, requiring contact with Hazelcast for details.[100]| Feature Category | Community Edition | Enterprise Edition |
|---|---|---|
| Licensing | Free, Apache 2.0 | Subscription-based, requires license key |
| Core IMDG & Jet | Basic clustering, AP data structures, standard Jet for stream processing | All Community features plus CP structures, advanced Jet with job placement control |
| Security | No advanced security features | RBAC, mutual TLS, Kerberos authentication for SSO, interceptors, emergency patches, JAAS, SSL/TLS |
| Business Continuity | Standard persistence (limited) | WAN replication, rolling upgrades, lossless recovery, job upgrades |
| Performance | Standard engine | Thread-per-core engine, high-density memory store |
| Management & Monitoring | Limited Management Center (3 members max) | Unlimited Management Center, Enterprise Operator, third-party integrations |
| Support | Community forums/GitHub | 24/7 professional support, 1-hour SLA, hot fixes |
Development and Community Resources
Hazelcast's open-source ecosystem is primarily hosted on GitHub, where the main repository has garnered over 5,000 stars as of 2025, reflecting strong community interest and adoption.[3] Contributions to the core platform and client libraries, such as those for Java, Python, and other languages, are encouraged through pull requests, with guidelines emphasizing high test coverage, documentation, and adherence to the project's checkstyle configuration.[3] The project operates under an open-source model that welcomes enhancements and bug fixes from the community, fostering collaborative development.[104] Comprehensive documentation is available at docs.hazelcast.com, offering detailed tutorials, API references, and migration guides to assist developers in implementing and upgrading Hazelcast features.[105] These resources are versioned to support transitions from older releases like 3.x through to the latest 5.x and beyond, including tools for data migration between versions.[106] For example, the documentation covers configuration for distributed data structures and integration patterns, with code snippets illustrating practical usage.[107] Community engagement is facilitated through dedicated channels, including the Hazelcast Community Slack workspace, where users and developers discuss implementation challenges, share best practices, and seek real-time assistance.[108] The legacy Google Groups forum, now read-only, directs users to Slack for ongoing conversations, ensuring a centralized hub for support.[109] Additionally, regional user groups, such as the Hazelcast User Group London (HUGL), organize meetups and events in Europe to connect architects, developers, and executives from organizations like HSBC and JPMorgan Chase.[110] These initiatives promote knowledge sharing and networking within the global Hazelcast community. For newcomers, quickstart guides simplify onboarding, with step-by-step instructions for setting up Java and Python clients to connect to a Hazelcast cluster.[111][112] These include code examples for common patterns, such as implementing distributed caching with maps to store and retrieve data efficiently, often using annotations like@Cacheable in Spring Boot applications.[113][114] Developers can quickly prototype by downloading binaries or adding dependencies via Maven or pip, enabling rapid experimentation with in-memory data grids.[115]