排版布局测试
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YuCheng Hu
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README.md
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README.md
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<!--- [](https://teamcity.jetbrains.com/viewType.html?buildTypeId=OpenSourceProjects_Druid_Inspections) -->
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---
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[中文文档](https://druid.apache.org/docs/latest/design/) |
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[官方网站](https://druid.apache.org/) |
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[官方文档（英文）](https://druid.apache.org/docs/latest/design/) |
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[开发者邮件地址](https://lists.apache.org/list.html?dev@druid.apache.org) |
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[用户邮件地址](https://groups.google.com/forum/#!forum/druid-user) |
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[Website](https://druid.apache.org/) |
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[Documentation](https://druid.apache.org/docs/latest/design/) |
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[Developer Mailing List](https://lists.apache.org/list.html?dev@druid.apache.org) |
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[User Mailing List](https://groups.google.com/forum/#!forum/druid-user) |
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[Slack](https://s.apache.org/slack-invite) |
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[下载地址](https://druid.apache.org/downloads.html)
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[Twitter](https://twitter.com/druidio) |
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[Download](https://druid.apache.org/downloads.html)
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---
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## Apache Druid
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Apache Druid 是一个高性能的实时分析型数据库。
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### 云原生、流原生的分析型数据库
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Druid专为需要快速数据查询与摄入的工作流程而设计，在即时数据可见性、即席查询、运营分析以及高并发等方面表现非常出色。
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Druid is a high performance real-time analytics database. Druid's main value add is to reduce time to insight and action.
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在实际中[众多场景](Misc/usercase.md)下数据仓库解决方案中，可以考虑将Druid当做一种开源的替代解决方案。
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Druid is designed for workflows where fast queries and ingest really matter. Druid excels at powering UIs, running operational (ad-hoc) queries, or handling high concurrency. Consider Druid as an open source alternative to data warehouses for a variety of use cases.
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### 可轻松与现有的数据管道进行集成
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Druid原生支持从[Kafka](http://kafka.apache.org/)、[Amazon Kinesis](https://aws.amazon.com/cn/kinesis/)等消息总线中流式的消费数据，也同时支持从[HDFS](https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html)、[Amazon S3](https://aws.amazon.com/cn/s3/)等存储服务中批量的加载数据文件。
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### Getting started
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### 较传统方案提升近百倍的效率
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Druid创新地在架构设计上吸收和结合了[数据仓库](https://en.wikipedia.org/wiki/Data_warehouse)、[时序数据库](https://en.wikipedia.org/wiki/Time_series_database)以及[检索系统](https://en.wikipedia.org/wiki/Search_engine_(computing))的优势，在已经完成的[基准测试](https://imply.io/post/performance-benchmark-druid-presto-hive)中展现出来的性能远远超过数据摄入与查询的传统解决方案。
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You can get started with Druid with our [local](https://druid.apache.org/docs/latest/tutorials/quickstart.html) or [Docker](http://druid.apache.org/docs/latest/tutorials/docker.html) quickstart.
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### 解锁了一种新型的工作流程
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Druid为点击流、APM、供应链、网络监测、市场营销以及其他事件驱动类型的数据分析解锁了一种[新型的查询与工作流程](Misc/usercase.md)，它专为实时和历史数据高效快速的即席查询而设计。
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Druid provides a rich set of APIs (via HTTP and [JDBC](https://druid.apache.org/docs/latest/querying/sql.html#jdbc)) for loading, managing, and querying your data.
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You can also interact with Druid via the [built-in console](https://druid.apache.org/docs/latest/operations/druid-console.html) (shown below).
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### 可部署在AWS/GCP/Azure,混合云,Kubernetes, 以及裸机上
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无论在云上还是本地，Druid可以轻松的部署在商用硬件上的任何*NIX环境。部署Druid也是非常简单的，包括集群的扩容或者下线都也同样很简单。
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#### Load data
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> [!TIP]
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> 在国内Druid的使用者越来越多，但是并没有一个很好的中文版本的使用文档。 本文档根据Apache Druid官方文档0.20.1版本进行翻译，目前托管在Github上，欢迎更多的Druid使用者以及爱好者加入翻译行列，为国内的使用者提供一个高质量的中文版本使用文档。
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[](https://druid.apache.org/docs/latest/ingestion/index.html)
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Load [streaming](https://druid.apache.org/docs/latest/ingestion/index.html#streaming) and [batch](https://druid.apache.org/docs/latest/ingestion/index.html#batch) data using a point-and-click wizard to guide you through ingestion setup. Monitor one off tasks and ingestion supervisors.
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#### Manage the cluster
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[](https://druid.apache.org/docs/latest/ingestion/data-management.html)
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Manage your cluster with ease. Get a view of your [datasources](https://druid.apache.org/docs/latest/design/architecture.html), [segments](https://druid.apache.org/docs/latest/design/segments.html), [ingestion tasks](https://druid.apache.org/docs/latest/ingestion/tasks.html), and [services](https://druid.apache.org/docs/latest/design/processes.html) from one convenient location. All powered by [SQL systems tables](https://druid.apache.org/docs/latest/querying/sql.html#metadata-tables), allowing you to see the underlying query for each view.
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#### Issue queries
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[](https://druid.apache.org/docs/latest/querying/sql.html)
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Use the built-in query workbench to prototype [DruidSQL](https://druid.apache.org/docs/latest/querying/sql.html) and [native](https://druid.apache.org/docs/latest/querying/querying.html) queries or connect one of the [many tools](https://druid.apache.org/libraries.html) that help you make the most out of Druid.
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### Documentation
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You can find the [documentation for the latest Druid release](https://druid.apache.org/docs/latest/) on
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the [project website](https://druid.apache.org).
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If you would like to contribute documentation, please do so under
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`/docs` in this repository and submit a pull request.
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### Community
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Community support is available on the
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[druid-user mailing list](https://groups.google.com/forum/#!forum/druid-user), which
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is hosted at Google Groups.
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Development discussions occur on [dev@druid.apache.org](https://lists.apache.org/list.html?dev@druid.apache.org), which
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you can subscribe to by emailing [dev-subscribe@druid.apache.org](mailto:dev-subscribe@druid.apache.org).
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Chat with Druid committers and users in real-time on the `#druid` channel in the Apache Slack team. Please use [this invitation link to join the ASF Slack](https://s.apache.org/slack-invite), and once joined, go into the `#druid` channel.
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### Building from source
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Please note that JDK 8 is required to build Druid.
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For instructions on building Druid from source, see [docs/development/build.md](docs/development/build.md)
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### Contributing
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Please follow the [community guidelines](https://druid.apache.org/community/) for contributing.
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For instructions on setting up IntelliJ [dev/intellij-setup.md](dev/intellij-setup.md)
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### License
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[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)
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