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Databases, search engines, big data and machine learning","三、架构与框架（支撑大型应用）","大数据"],"sub_categories":["Distributed Applications","分布式应用程序","1. Databases and storages","6. 分布式与容错"],"readme":"# Hazelcast\n\n[![Slack](https://img.shields.io/badge/slack-chat-green.svg)](https://slack.hazelcast.com/) \n[![javadoc](https://javadoc.io/badge2/com.hazelcast/hazelcast/latest/javadoc.svg)](https://javadoc.io/doc/com.hazelcast/hazelcast/latest)\n[![Docker pulls](https://img.shields.io/docker/pulls/hazelcast/hazelcast)](https://hub.docker.com/r/hazelcast/hazelcast)\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/AI-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://hazelcast.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/write-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://docs.hazelcast.com/hazelcast/latest/sql/sql-overview) or dataflow API\n* [A comprehensive library of connectors such as Kafka, Hadoop, S3, RDBMS, JMS\n  and many more](https://docs.hazelcast.com/hazelcast/latest/integrate/connectors)\n* Distributed messaging using [pub-sub](https://docs.hazelcast.com/hazelcast/latest/data-structures/topic.html) and [queues](https://docs.hazelcast.com/hazelcast/latest/data-structures/queue.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://docs.hazelcast.com/hazelcast/latest/data-structures/map)\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://github.com/hazelcast/hazelcast),\n [Python](https://github.com/hazelcast/hazelcast-python-client), [Node.js](https://github.com/hazelcast/hazelcast-nodejs-client), [.NET](https://github.com/hazelcast/hazelcast-csharp-client), [C++](https://github.com/hazelcast/hazelcast-cpp-client) and [Go](https://github.com/hazelcast/hazelcast-go-client)\n\n### Stateful Data Processing\n\nHazelcast has a built-in data processing engine called\n[Jet](https://docs.hazelcast.com/hazelcast/latest/pipelines/overview#what-is-the-jet-engine), which can be used to build both streaming/real-time\nand batch/static data pipelines that are elastic. A single node of Hazelcast has been proven to [aggregate 10 million\nevents per second](https://foojay.io/today/sub-10-ms-latency-in-java-concurrent-gc-with-green-threads/) with\nlatency under 10 milliseconds. A cluster of Hazelcast nodes can process [billion\nevents per\nsecond](https://hazelcast.com/blog/billion-events-per-second-with-millisecond-latency-streaming-analytics-at-giga-scale/).\n\n## Get Started\n\nFollow the [Getting Started\nGuide](https://docs.hazelcast.com/hazelcast/latest/getting-started/install-hazelcast)\nto install and start using Hazelcast.\n\n## Documentation\n\nRead the [documentation](https://docs.hazelcast.com/) 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://slack.hazelcast.com/) 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$ ./mvnw 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  ./mvnw test\n  ```\nto run quick/integration tests (those can be run\n  in parallel without using network by using `-P parallelTest` profile).\n* Slow Tests: \n  ```bash\n  ./mvnw 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  ./mvnw 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://docs.hazelcast.com/hazelcast/latest/index.html#licenses-and-support)\n * [Hazelcast Community\n    License](http://hazelcast.com/hazelcast-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://picocli.info/).\n\n## Copyright\n\nCopyright (c) 2008-2025, Hazelcast, Inc. All Rights Reserved.\n\nVisit [www.hazelcast.com](http://www.hazelcast.com/) for more info.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhazelcast%2Fhazelcast","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhazelcast%2Fhazelcast","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhazelcast%2Fhazelcast/lists"}