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 提供了一個統一數據分析接口。通過提供一個跨多個數據存儲的單一視圖來實現數據分析任務切分，同時優化了執行的環境。無縫的集成 Hadoop 實現類似傳統數據倉庫的功能。
At a high level the project provides these features –該項目主要特性：
• Simple metadata layer which provides an abstract view over tiered data stores• 簡單元數據成爲數據存儲提供抽象視圖層
• Single shared schema server based on the Hive Metastore - This schema is shared by data pipelines (HCatalog) and analytics applications.• 單一的共享模式服務器，基於Hive元存儲。模式通過數據管道HCatalog和分析應用進行共享：
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 類似 SQL 的高級語言用來查詢和描述存放在不同數據立方體 (Cubes) 中的數據集
o A JDBC driver and Java client libraries to issue queries, and a CLI for ad hoc queries.• JDBC 驅動和 Java 客戶端庫來處理查詢
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 應用服務器 - 這是一個 REST 服務器允許用戶查詢數據，更改數據模型，調度查詢和查詢的配額限制
o Driver based architecture allows plugging in reporting systems like Hive, Columnar data warehouses, Redshift etc.• 基於驅動的架構 允許在報表系統中進行嵌入，例如 Hive、列數據存儲、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.• 基於成本算法的引擎選擇 - 該算法可優化資源的使用，通過對查詢的複雜度自動選擇最佳執行引擎
The following diagram shows Lens architecture.Apache Lens 的架構如下：
