[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-4292":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":15,"stars7d":16,"stars30d":17,"stars90d":15,"forks30d":15,"starsTrendScore":16,"compositeScore":18,"rankGlobal":9,"rankLanguage":9,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":20,"topics":23,"createdAt":9,"pushedAt":9,"updatedAt":38,"readmeContent":39,"aiSummary":40,"trendingCount":15,"starSnapshotCount":15,"syncStatus":41,"lastSyncTime":42,"discoverSource":43},4292,"hazelcast","hazelcast\u002Fhazelcast","Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.","https:\u002F\u002Fwww.hazelcast.com",null,"Java",6572,1873,282,1098,0,1,10,40.82,"Other",false,"master",true,[24,25,26,27,28,29,30,31,5,32,33,34,35,36,37],"big-data","caching","data-in-motion","data-insights","distributed","distributed-computing","distributed-systems","hacktoberfest","in-memory","java","low-latency","real-time","scalability","stream-processing","2026-06-12 02:01:01","# Hazelcast\n\n[![Slack](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-chat-green.svg)](https:\u002F\u002Fslack.hazelcast.com\u002F) \n[![javadoc](https:\u002F\u002Fjavadoc.io\u002Fbadge2\u002Fcom.hazelcast\u002Fhazelcast\u002Flatest\u002Fjavadoc.svg)](https:\u002F\u002Fjavadoc.io\u002Fdoc\u002Fcom.hazelcast\u002Fhazelcast\u002Flatest)\n[![Docker pulls](https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Fhazelcast\u002Fhazelcast)](https:\u002F\u002Fhub.docker.com\u002Fr\u002Fhazelcast\u002Fhazelcast)\n\n----\n\n## What is Hazelcast\n\nThe world’s leading companies trust Hazelcast to modernize applications and take instant action on data in motion to create new revenue streams, mitigate risk, and operate more efficiently. Businesses use Hazelcast’s unified **real-time data platform** to process **streaming** data, enrich it with historical context and take instant action with standard or **ML\u002FAI-driven automation** - before it is stored in a database or data lake. \n\nHazelcast is named in the Gartner Market Guide to Event Stream Processing and a leader in the GigaOm Radar Report for Streaming Data Platforms. To join our community of CXOs, architects and developers at brands such as Lowe’s, HSBC, JPMorgan Chase, Volvo, New York Life, and others, visit [hazelcast.com](https:\u002F\u002Fhazelcast.com).\n\n## When to use Hazelcast\n\nHazelcast provides a platform that can handle multiple types of workloads for\nbuilding real-time applications.\n\n* Stateful data processing over streaming data or data at rest\n* Querying streaming and batch data sources directly using SQL\n* Ingesting data through a library of connectors and serving it using\n  low-latency SQL queries\n* Pushing updates to applications on events\n* Low-latency queue-based or pub-sub messaging  \n* Fast access to contextual and transactional data via caching patterns such as\n  read\u002Fwrite-through and write-behind\n* Distributed coordination for microservices\n* Replicating data from one region to another or between data centers in the\n  same region\n\n## Key Features\n\n* [Stateful and fault-tolerant data processing and querying over data streams\n  and data](#stateful-data-Processing) at rest using [SQL](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Fsql\u002Fsql-overview) or dataflow API\n* [A comprehensive library of connectors such as Kafka, Hadoop, S3, RDBMS, JMS\n  and many more](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Fintegrate\u002Fconnectors)\n* Distributed messaging using [pub-sub](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Fdata-structures\u002Ftopic.html) and [queues](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Fdata-structures\u002Fqueue.html)\n* [Distributed, partitioned, queryable key-value store with event listeners,\n  which can also be used to store contextual data for enriching event streams\n  with low latency](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Fdata-structures\u002Fmap)\n* Tight integration for deploying machine learning models with Python to a data\n  processing pipeline\n* Cloud-native, run everywhere architecture\n* Zero-downtime operations with rolling upgrades\n* At-least-once and exactly-once processing guarantees for stream processing\n  pipelines\n* Data replication between data centers and geographic regions using WAN \n* Microsecond performance for key-value point lookups and pub-sub\n* Unique data processing architecture results in 99.99% latency of under 10ms\n  for streaming queries with millions of events per second.\n* Client libraries in [Java](https:\u002F\u002Fgithub.com\u002Fhazelcast\u002Fhazelcast),\n [Python](https:\u002F\u002Fgithub.com\u002Fhazelcast\u002Fhazelcast-python-client), [Node.js](https:\u002F\u002Fgithub.com\u002Fhazelcast\u002Fhazelcast-nodejs-client), [.NET](https:\u002F\u002Fgithub.com\u002Fhazelcast\u002Fhazelcast-csharp-client), [C++](https:\u002F\u002Fgithub.com\u002Fhazelcast\u002Fhazelcast-cpp-client) and [Go](https:\u002F\u002Fgithub.com\u002Fhazelcast\u002Fhazelcast-go-client)\n\n### Stateful Data Processing\n\nHazelcast has a built-in data processing engine called\n[Jet](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Fpipelines\u002Foverview#what-is-the-jet-engine), which can be used to build both streaming\u002Freal-time\nand batch\u002Fstatic data pipelines that are elastic. A single node of Hazelcast has been proven to [aggregate 10 million\nevents per second](https:\u002F\u002Ffoojay.io\u002Ftoday\u002Fsub-10-ms-latency-in-java-concurrent-gc-with-green-threads\u002F) with\nlatency under 10 milliseconds. A cluster of Hazelcast nodes can process [billion\nevents per\nsecond](https:\u002F\u002Fhazelcast.com\u002Fblog\u002Fbillion-events-per-second-with-millisecond-latency-streaming-analytics-at-giga-scale\u002F).\n\n## Get Started\n\nFollow the [Getting Started\nGuide](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Fgetting-started\u002Finstall-hazelcast)\nto install and start using Hazelcast.\n\n## Documentation\n\nRead the [documentation](https:\u002F\u002Fdocs.hazelcast.com\u002F) for\nin-depth details about how to install Hazelcast and an overview of the features.\n\n## Get Help\n\nYou can use [Slack](https:\u002F\u002Fslack.hazelcast.com\u002F) for getting help with Hazelcast.\n\n## How to Contribute\n\nThanks for your interest in contributing! The easiest way is to just send a pull\nrequest.\n\n### Building From Source\n\nBuilding Hazelcast requires at minimum JDK 17. Pull the latest source from the\nrepository and use Maven install (or package) to build:\n\n```bash\n$ git pull origin master\n$ .\u002Fmvnw clean package -DskipTests\n```\n\nIt is recommended to use the included Maven wrapper script.\nIt is also possible to use local Maven distribution with the same \nversion that is used in the Maven wrapper script.\n\nAdditionally, there is a `quick` build activated by setting the `-Dquick` system\nproperty that skips validation tasks for faster local builds (e.g. tests, checkstyle\nvalidation, javadoc, source plugins etc) and does not build `extensions` and `distribution` \nmodules.\n\n### Testing\n\nTake into account that the default build executes thousands of tests which may\ntake a considerable amount of time. Hazelcast has 3 testing profiles:\n\n* Default: \n  ```bash\n  .\u002Fmvnw test\n  ```\nto run quick\u002Fintegration tests (those can be run\n  in parallel without using network by using `-P parallelTest` profile).\n* Slow Tests: \n  ```bash\n  .\u002Fmvnw test -P nightly-build\n  ```\nto run tests that are either slow\n  or cannot be run in parallel.\n* All Tests:\n  ```bash\n  .\u002Fmvnw test -P all-tests\n  ```\nto run all tests serially using\n  network.\n\nSome tests require Docker to run. Set `-Dhazelcast.disable.docker.tests` system property to ignore them.\n\nWhen developing a PR it is sufficient to run your new tests and some \nrelated subset of tests locally. Our PR builder will take care of running\nthe full test suite.\n\n## License\n\nSource code in this repository is covered by one of two licenses:\n\n * [Apache License 2.0](https:\u002F\u002Fdocs.hazelcast.com\u002Fhazelcast\u002Flatest\u002Findex.html#licenses-and-support)\n * [Hazelcast Community\n    License](http:\u002F\u002Fhazelcast.com\u002Fhazelcast-community-license)\n\nThe default license throughout the repository is Apache License 2.0 unless the\nheader specifies another license.\n\n## Acknowledgments\nWe owe (the good parts of) our CLI tool's user experience to\n[picocli](https:\u002F\u002Fpicocli.info\u002F).\n\n## Copyright\n\nCopyright (c) 2008-2026, Hazelcast, Inc. All Rights Reserved.\n\nVisit [www.hazelcast.com](http:\u002F\u002Fwww.hazelcast.com\u002F) for more info.\n","Hazelcast 是一个统一的实时数据平台，结合了流处理和快速数据存储功能，使用户能够即时对动态数据进行操作以获取实时洞察。其核心功能包括基于SQL或数据流API的状态化及容错数据处理、全面的连接器库支持多种数据源接入、分布式消息传递以及可查询的键值存储等。该平台特别适合需要处理流式数据、执行低延迟SQL查询、实现事件驱动的应用更新、提供高速缓存服务以及跨微服务的数据协调等场景。Hazelcast 采用Java开发，具备高可用性、弹性扩展能力和云原生架构，适用于构建要求高性能与实时响应的企业级应用。",2,"2026-06-11 02:59:26","top_language"]