Overview（概述）
Lens provides an Unified Analytics interface. Lens aims to cut the Data Analytics silos by providing a single view of data across multiple tiered data stores and optimal execution environment for the analytical query. It seamlessly integrates Hadoop with traditional data warehouses to appear like one.Lens 提供了一个统一数据分析接口。通过提供一个跨多个数据存储的单一视图来实现数据分析任务切分，同时优化了执行的环境。无缝的集成 Hadoop 实现类似传统数据仓库的功能。
At a high level the project provides these features –该项目主要特性：
• Simple metadata layer which provides an abstract view over tiered data stores• 简单元数据成为数据存储提供抽象视图层
• Single shared schema server based on the Hive Metastore - This schema is shared by data pipelines (HCatalog) and analytics applications.• 单一的共享模式服务器，基于Hive元存储。模式通过数据管道HCatalog和分析应用进行共享：
o OLAP Cube QL which is a high level SQL like language to query and describe data sets organized in data cubes.• OLAP Cube QL 类似 SQL 的高级语言用来查询和描述存放在不同数据立方体 (Cubes) 中的数据集
o A JDBC driver and Java client libraries to issue queries, and a CLI for ad hoc queries.• JDBC 驱动和 Java 客户端库来处理查询
o Lens application server - a REST server which allows users to query data, make schema changes, scheduling queries and enforcing quota limits on queries.• Lens 应用服务器 - 这是一个 REST 服务器允许用户查询数据，更改数据模型，调度查询和查询的配额限制
o Driver based architecture allows plugging in reporting systems like Hive, Columnar data warehouses, Redshift etc.• 基于驱动的架构 允许在报表系统中进行嵌入，例如 Hive、列数据存储、Redshift 等
o Cost based engine selection - allows optimal use of resources by selecting the best execution engine for a given query based on the query cost.• 基于成本算法的引擎选择 - 该算法可优化资源的使用，通过对查询的复杂度自动选择最佳执行引擎
The following diagram shows Lens architecture.Apache Lens 的架构如下：
