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README.md
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README.md
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1. [分布式文件存储系统——HDFS](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hadoop-HDFS.md)
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2. [分布式计算框架——MapReduce](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hadoop-MapReduce.md)
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3. [集群资源管理器——YARN](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hadoop-YARN.md)
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4. [Hadoop单机伪集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/hadoop%E5%8D%95%E6%9C%BA%E7%89%88%E6%9C%AC%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA.md)
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4. [Hadoop单机伪集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/hadoop单机版本环境搭建.md)
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5. [HDFS常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HDFS常用Shell命令.md)
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6. [HDFS Java API的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HDFS-Java-API.md)
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## 二、Hive
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1. [数据仓库Hive简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive.md)
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2. [Linux环境下Hive的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux%E7%8E%AF%E5%A2%83%E4%B8%8BHive%E7%9A%84%E5%AE%89%E8%A3%85%E9%83%A8%E7%BD%B2.md)
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2. [Linux环境下Hive的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux环境下Hive的安装部署.md)
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4. [Hive CLI和Beeline命令行的基本使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HiveCLI和Beeline命令行的基本使用.md)
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5. [Hive 核心概念讲解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive核心概念讲解.md)
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6. [Hive 常用DDL操作](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive常用DDL操作.md)
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@ -94,53 +94,35 @@
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3. [Spark Streaming 整合 Flume](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spark_Streaming整合Flume.md)
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4. [Spark Streaming 整合 Kafka](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spark_Streaming整合Kafka.md)
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## 四、Flink
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TODO
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## 五、Storm
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## 四、Storm
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1. [Storm和流处理简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Storm和流处理简介.md)
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2. [Storm核心概念详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Storm核心概念详解.md)
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3. [Storm单机版本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Storm%E5%8D%95%E6%9C%BA%E7%89%88%E6%9C%AC%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA.md)
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3. [Storm单机版本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Storm单机版本环境搭建.md)
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4. [Storm编程模型详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Storm编程模型详解.md)
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5. [Storm项目三种打包方式对比分析](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Storm三种打包方式对比分析.md)
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6. [Storm集成Redis详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Storm集成Redis详解.md)
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7. [Storm集成HDFS/HBase](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Storm集成HBase和HDFS.md)
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8. [Storm集成Kafka](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Storm集成Kakfa.md)
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## 六、Flume
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## 五、Flink
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1. [Flume简介及基本使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Flume简介及基本使用.md)
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2. [Linux环境下Flume的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux%E4%B8%8BFlume%E7%9A%84%E5%AE%89%E8%A3%85.md)
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3. [Flume整合Kafka](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Flume整合Kafka.md)
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TODO
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## 七、Sqoop
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1. [Sqoop简介与安装](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Sqoop简介与安装.md)
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2. [Sqoop的基本使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Sqoop基本使用.md)
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## 八、Azkaban
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1. [Azkaban简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban简介.md)
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2. [Azkaban3.x 编译及部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Azkaban%203.x%20%E7%BC%96%E8%AF%91%E5%8F%8A%E9%83%A8%E7%BD%B2.md)
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3. [Azkaban Flow 1.0 的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban%20Flow%201.0%20%E7%9A%84%E4%BD%BF%E7%94%A8.md)
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4. [Azkaban Flow 2.0 的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban%20Flow%202.0%20%E7%9A%84%E4%BD%BF%E7%94%A8.md)
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## 九、HBase
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## 六、HBase
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1. [Hbase 简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase简介.md)
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2. [HBase系统架构及数据结构](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%E7%B3%BB%E7%BB%9F%E6%9E%B6%E6%9E%84%E5%8F%8A%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84.md)
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3. [HBase基本环境搭建（Standalone /pseudo-distributed mode）](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hbase%E5%9F%BA%E6%9C%AC%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA.md)
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4. [HBase常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%20Shell.md)
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5. [HBase Java API](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%20Java%20API.md)
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2. [HBase系统架构及数据结构](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase系统架构及数据结构.md)
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3. [HBase基本环境搭建（Standalone /pseudo-distributed mode）](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hbase基本环境搭建.md)
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4. [HBase常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase_Shell.md)
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5. [HBase Java API](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase_Java_API.md)
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6. [Hbase 过滤器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase过滤器详解.md)
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7. [HBase 协处理器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase协处理器详解.md)
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8. [HBase 容灾与备份](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%E5%AE%B9%E7%81%BE%E4%B8%8E%E5%A4%87%E4%BB%BD.md)
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9. [HBase的SQL中间层——Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%E7%9A%84SQL%E5%B1%82%E2%80%94%E2%80%94Phoenix.md)
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10. [Spring/Spring Boot 整合 Mybatis + Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spring%2BMybtais%2BPhoenix%E6%95%B4%E5%90%88.md)
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## 十、Kafka
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8. [HBase 容灾与备份](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase容灾与备份.md)
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9. [HBase的SQL中间层——Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase的SQL中间层_Phoenix.md)
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10. [Spring/Spring Boot 整合 Mybatis + Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spring+Mybtais+Phoenix整合.md)
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## 七、Kafka
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1. [Kafka 核心概念介绍](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Kafka核心概念介绍.md)
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2. [基于Zookeeper搭建Kafka高可用集群](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/基于Zookeeper搭建Kafka高可用集群.md)
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@ -149,7 +131,7 @@ TODO
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5. Kafka 副本机制以及选举原理剖析
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6. Kafka的数据可靠性
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## 十一、Zookeeper
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## 八、Zookeeper
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1. [Zookeeper 简介及核心概念](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Zookeeper简介及核心概念.md)
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2. [Zookeeper单机环境和集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Zookeeper单机环境和集群环境搭建.md)
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@ -157,6 +139,25 @@ TODO
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4. [Zookeeper Java 客户端——Apache Curator](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Zookeeper_Java客户端Curator.md)
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5. [Zookeeper ACL权限控制](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Zookeeper_ACL权限控制.md)
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## 九、Flume
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1. [Flume简介及基本使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Flume简介及基本使用.md)
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2. [Linux环境下Flume的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux下Flume的安装.md)
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3. [Flume整合Kafka](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Flume整合Kafka.md)
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## 十、Sqoop
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1. [Sqoop简介与安装](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Sqoop简介与安装.md)
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2. [Sqoop的基本使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Sqoop基本使用.md)
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## 十一、Azkaban
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1. [Azkaban简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban简介.md)
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2. [Azkaban3.x 编译及部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Azkaban_3.x_编译及部署.md)
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3. [Azkaban Flow 1.0 的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban_Flow_1.0_的使用.md)
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4. [Azkaban Flow 2.0 的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban_Flow_2.0_的使用.md)
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## 十二、Scala
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1. [Scala简介及开发环境配置](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Scala简介及开发环境配置.md)
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@ -174,8 +175,10 @@ TODO
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13. [隐式转换和隐式参数](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Scala隐式转换和隐式参数.md)
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## 十三、公共内容
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1. [大数据应用常用打包方式](https://github.com/heibaiying/BigData-Notes/blob/master/notes/大数据应用常用打包方式.md)
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1. [大数据应用常用打包方式](https://github.com/heibaiying/BigData-Notes/blob/master/notes/大数据应用常用打包方式.md)
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<br>
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## 后记:bookmark_tabs:
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@ -1,252 +1,252 @@
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# Hbase的SQL中间层——Phoenix
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<nav>
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<a href="#一Phoenix简介">一、Phoenix简介</a><br/>
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<a href="#二Phoenix安装">二、Phoenix安装</a><br/>
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<a href="#21-下载并解压">2.1 下载并解压</a><br/>
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<a href="#22-拷贝Jar包">2.2 拷贝Jar包</a><br/>
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<a href="#23-重启-Region-Servers">2.3 重启 Region Servers</a><br/>
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<a href="#24-启动Phoenix">2.4 启动Phoenix</a><br/>
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<a href="#25-启动结果">2.5 启动结果</a><br/>
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<a href="#三Phoenix-简单使用">三、Phoenix 简单使用</a><br/>
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<a href="#31-创建表">3.1 创建表</a><br/>
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<a href="#32-插入数据">3.2 插入数据</a><br/>
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<a href="#33-修改数据">3.3 修改数据</a><br/>
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<a href="#34-删除数据">3.4 删除数据</a><br/>
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<a href="#35-查询数据">3.5 查询数据</a><br/>
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<a href="#36-退出命令">3.6 退出命令</a><br/>
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<a href="#37-扩展">3.7 扩展</a><br/>
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<a href="#四Phoenix-Java-API">四、Phoenix Java API</a><br/>
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<a href="#41-引入Phoenix-core-JAR包">4.1 引入Phoenix core JAR包</a><br/>
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<a href="#42-简单的Java-API实例">4.2 简单的Java API实例</a><br/>
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</nav>
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## 一、Phoenix简介
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Phoenix是HBase的开源SQL层。使得您可以使用标准JDBC API而不是常规HBase客户端API来操作Hbases上的数据。
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Phoenix完全使用Java编写，作为HBase内嵌的JDBC驱动。Phoenix查询引擎会将SQL查询转换为一个或多个HBase scan，并编排并行执行以生成标准的JDBC结果集，同时Phoenix还拥有二级索引等Hbase不具备的特性，这使得Phoenix具有极好的性能表现。
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<div align="center"> <img width="600px" src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-hadoop.png"/> </div>
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## 二、Phoenix安装
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> 我们可以按照官方安装说明进行安装，官方说明如下：
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>
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> - download and expand our installation tar
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> - copy the phoenix server jar that is compatible with your HBase installation into the lib directory of every region server
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> - restart the region servers
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> - add the phoenix client jar to the classpath of your HBase client
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> - download and setup SQuirrel as your SQL client so you can issue adhoc SQL against your HBase cluster
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### 2.1 下载并解压
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官方下载地址: http://phoenix.apache.org/download.html
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官方针对Apache版本和CDH版本的HBase均提供了安装包，按需下载即可。这里我们下载的版本为`4.14.0-cdh5.14.2`
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```shell
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# 下载
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wget http://mirror.bit.edu.cn/apache/phoenix/apache-phoenix-4.14.0-cdh5.14.2/bin/apache-phoenix-4.14.0-cdh5.14.2-bin.tar.gz
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# 解压
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tar tar apache-phoenix-4.14.0-cdh5.14.2-bin.tar.gz
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```
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### 2.2 拷贝Jar包
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按照官方文档的说明，需要将phoenix server jar 添加到所有 Region Servers上 Hbase 安装目录的 lib目录下。
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这里由于我搭建的是Hbase伪集群，所以只需要拷贝到当前机器的HBase的lib目录下。如果是真实集群，则使用scp命令分发到所有Region Servers机器上。
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```shell
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cp /usr/app/apache-phoenix-4.14.0-cdh5.14.2-bin/phoenix-4.14.0-cdh5.14.2-server.jar /usr/app/hbase-1.2.0-cdh5.15.2/lib
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```
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### 2.3 重启 Region Servers
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```shell
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# 停止Hbase
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stop-hbase.sh
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# 启动Hbase
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start-hbase.sh
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```
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### 2.4 启动Phoenix
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在Phoenix解压目录下的`bin`目录下执行如下命令，需要指定Zookeeper的地址:
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+ 如果HBase采用Standalone模式或者伪集群模式搭建，则采用内置的 Zookeeper，默认端口为2181；
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+ 如果是HBase是集群模式并采用自己搭建的Zookeeper集群，则按照自己的实际情况指定端口
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```shell
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# ./sqlline.py hadoop001:2181
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```
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### 2.5 启动结果
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启动后则进入了Phoenix交互式SQL命令行，可以使用`!table`或`!tables`查看当前所有表的信息
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<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/phoenix-shell.png"/> </div>
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## 三、Phoenix 简单使用
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### 3.1 创建表
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```sql
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CREATE TABLE IF NOT EXISTS us_population (
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state CHAR(2) NOT NULL,
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city VARCHAR NOT NULL,
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population BIGINT
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CONSTRAINT my_pk PRIMARY KEY (state, city));
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```
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<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-create-table.png"/> </div>
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|
||||
新建的表会按照特定的规则转换为Hbase上的表，关于表的信息，可以通过Hbase Web UI 进行查看：
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/hbase-web-ui-phoenix.png"/> </div>
|
||||
|
||||
### 3.2 插入数据
|
||||
|
||||
Phoenix 中插入数据采用的是`UPSERT`而不是`INSERT`,因为Phoenix并没有更新操作，插入相同主键的数据就视为更新，所以`UPSERT`就相当于`UPDATE`+`INSERT`
|
||||
|
||||
```shell
|
||||
UPSERT INTO us_population VALUES('NY','New York',8143197);
|
||||
UPSERT INTO us_population VALUES('CA','Los Angeles',3844829);
|
||||
UPSERT INTO us_population VALUES('IL','Chicago',2842518);
|
||||
UPSERT INTO us_population VALUES('TX','Houston',2016582);
|
||||
UPSERT INTO us_population VALUES('PA','Philadelphia',1463281);
|
||||
UPSERT INTO us_population VALUES('AZ','Phoenix',1461575);
|
||||
UPSERT INTO us_population VALUES('TX','San Antonio',1256509);
|
||||
UPSERT INTO us_population VALUES('CA','San Diego',1255540);
|
||||
UPSERT INTO us_population VALUES('TX','Dallas',1213825);
|
||||
UPSERT INTO us_population VALUES('CA','San Jose',912332);
|
||||
```
|
||||
|
||||
### 3.3 修改数据
|
||||
|
||||
```sql
|
||||
-- 插入主键相同的数据就视为更新
|
||||
UPSERT INTO us_population VALUES('NY','New York',999999);
|
||||
```
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-update.png"/> </div>
|
||||
|
||||
### 3.4 删除数据
|
||||
|
||||
```sql
|
||||
DELETE FROM us_population WHERE city='Dallas';
|
||||
```
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-delete.png"/> </div>
|
||||
|
||||
### 3.5 查询数据
|
||||
|
||||
```sql
|
||||
SELECT state as "州",count(city) as "市",sum(population) as "热度"
|
||||
FROM us_population
|
||||
GROUP BY state
|
||||
ORDER BY sum(population) DESC;
|
||||
```
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-select.png"/> </div>
|
||||
|
||||
|
||||
|
||||
### 3.6 退出命令
|
||||
|
||||
```sql
|
||||
!quit
|
||||
```
|
||||
|
||||
|
||||
|
||||
### 3.7 扩展
|
||||
|
||||
从上面的简单操作中我们可以看出，Phoenix 查询语句与我们正常使用的SQL是基本相同的，关于Phoenix 支持的语句、数据类型、函数、序列（和Oracle中序列类似）因为涵盖内容很广，可以参考其官方文档，官方上有详尽的配图说明的：
|
||||
|
||||
+ 语法（Grammar）：https://phoenix.apache.org/language/index.html
|
||||
|
||||
+ 函数（Functions）：http://phoenix.apache.org/language/functions.html
|
||||
|
||||
+ 数据类型（Datatypes）：http://phoenix.apache.org/language/datatypes.html
|
||||
|
||||
+ 序列（Sequences）:http://phoenix.apache.org/sequences.html
|
||||
|
||||
+ 联结查询（Joins）：http://phoenix.apache.org/joins.html
|
||||
|
||||
|
||||
|
||||
## 四、Phoenix Java API
|
||||
|
||||
因为Phoenix遵循JDBC规范，并提供了对应的数据库驱动PhoenixDriver，这使采用Java对其进行操作的时候，就如同对其他关系型数据库（例如 MySQL）操作一样。
|
||||
|
||||
因为在实际的开发中我们通常都是采用第三方框架，比如mybatis,Hibernate,Spring Data 等，很少使用原生Java API操作关系型数据库，所以这里只给出一个简单的查询作为示例，并在下一篇文章中给出Spring boot + mybatis + Phoenix 的整合用例。
|
||||
|
||||
### 4.1 引入Phoenix core JAR包
|
||||
|
||||
如果是maven项目，直接在maven中央仓库找到对应的版本，导入依赖即可
|
||||
|
||||
```xml
|
||||
<!-- https://mvnrepository.com/artifact/org.apache.phoenix/phoenix-core -->
|
||||
<dependency>
|
||||
<groupId>org.apache.phoenix</groupId>
|
||||
<artifactId>phoenix-core</artifactId>
|
||||
<version>4.14.0-cdh5.14.2</version>
|
||||
</dependency>
|
||||
```
|
||||
|
||||
如果是普通项目，则可以从Phoenix 解压目录下找到对应的JAR包，然后手动引入
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/phoenix-core-jar.png"/> </div>
|
||||
|
||||
### 4.2 简单的Java API实例
|
||||
|
||||
```java
|
||||
import java.sql.Connection;
|
||||
import java.sql.DriverManager;
|
||||
import java.sql.PreparedStatement;
|
||||
import java.sql.ResultSet;
|
||||
|
||||
|
||||
public class PhoenixJavaApi {
|
||||
|
||||
public static void main(String[] args) throws Exception {
|
||||
|
||||
// 加载数据库驱动
|
||||
Class.forName("org.apache.phoenix.jdbc.PhoenixDriver");
|
||||
|
||||
/*
|
||||
* 指定数据库地址,格式为 jdbc:phoenix:Zookeeper地址
|
||||
* 如果HBase采用Standalone模式或者伪集群模式搭建，则HBase默认使用内置的Zookeeper，默认端口为2181
|
||||
*/
|
||||
Connection connection = DriverManager.getConnection("jdbc:phoenix:192.168.200.226:2181");
|
||||
|
||||
PreparedStatement statement = connection.prepareStatement("SELECT * FROM us_population");
|
||||
|
||||
ResultSet resultSet = statement.executeQuery();
|
||||
|
||||
while (resultSet.next()) {
|
||||
System.out.println(resultSet.getString("city") + " "
|
||||
+ resultSet.getInt("population"));
|
||||
}
|
||||
|
||||
statement.close();
|
||||
connection.close();
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
结果如下：
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-java-api-result.png"/> </div>
|
||||
|
||||
|
||||
|
||||
# 参考资料
|
||||
|
||||
1. http://phoenix.apache.org/
|
||||
# Hbase的SQL中间层——Phoenix
|
||||
|
||||
<nav>
|
||||
<a href="#一Phoenix简介">一、Phoenix简介</a><br/>
|
||||
<a href="#二Phoenix安装">二、Phoenix安装</a><br/>
|
||||
<a href="#21-下载并解压">2.1 下载并解压</a><br/>
|
||||
<a href="#22-拷贝Jar包">2.2 拷贝Jar包</a><br/>
|
||||
<a href="#23-重启-Region-Servers">2.3 重启 Region Servers</a><br/>
|
||||
<a href="#24-启动Phoenix">2.4 启动Phoenix</a><br/>
|
||||
<a href="#25-启动结果">2.5 启动结果</a><br/>
|
||||
<a href="#三Phoenix-简单使用">三、Phoenix 简单使用</a><br/>
|
||||
<a href="#31-创建表">3.1 创建表</a><br/>
|
||||
<a href="#32-插入数据">3.2 插入数据</a><br/>
|
||||
<a href="#33-修改数据">3.3 修改数据</a><br/>
|
||||
<a href="#34-删除数据">3.4 删除数据</a><br/>
|
||||
<a href="#35-查询数据">3.5 查询数据</a><br/>
|
||||
<a href="#36-退出命令">3.6 退出命令</a><br/>
|
||||
<a href="#37-扩展">3.7 扩展</a><br/>
|
||||
<a href="#四Phoenix-Java-API">四、Phoenix Java API</a><br/>
|
||||
<a href="#41-引入Phoenix-core-JAR包">4.1 引入Phoenix core JAR包</a><br/>
|
||||
<a href="#42-简单的Java-API实例">4.2 简单的Java API实例</a><br/>
|
||||
</nav>
|
||||
|
||||
## 一、Phoenix简介
|
||||
|
||||
Phoenix是HBase的开源SQL层。使得您可以使用标准JDBC API而不是常规HBase客户端API来操作Hbases上的数据。
|
||||
|
||||
Phoenix完全使用Java编写，作为HBase内嵌的JDBC驱动。Phoenix查询引擎会将SQL查询转换为一个或多个HBase scan，并编排并行执行以生成标准的JDBC结果集，同时Phoenix还拥有二级索引等Hbase不具备的特性，这使得Phoenix具有极好的性能表现。
|
||||
|
||||
<div align="center"> <img width="600px" src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-hadoop.png"/> </div>
|
||||
|
||||
|
||||
|
||||
## 二、Phoenix安装
|
||||
|
||||
> 我们可以按照官方安装说明进行安装，官方说明如下：
|
||||
>
|
||||
> - download and expand our installation tar
|
||||
> - copy the phoenix server jar that is compatible with your HBase installation into the lib directory of every region server
|
||||
> - restart the region servers
|
||||
> - add the phoenix client jar to the classpath of your HBase client
|
||||
> - download and setup SQuirrel as your SQL client so you can issue adhoc SQL against your HBase cluster
|
||||
|
||||
### 2.1 下载并解压
|
||||
|
||||
官方下载地址: http://phoenix.apache.org/download.html
|
||||
|
||||
官方针对Apache版本和CDH版本的HBase均提供了安装包，按需下载即可。这里我们下载的版本为`4.14.0-cdh5.14.2`
|
||||
|
||||
```shell
|
||||
# 下载
|
||||
wget http://mirror.bit.edu.cn/apache/phoenix/apache-phoenix-4.14.0-cdh5.14.2/bin/apache-phoenix-4.14.0-cdh5.14.2-bin.tar.gz
|
||||
# 解压
|
||||
tar tar apache-phoenix-4.14.0-cdh5.14.2-bin.tar.gz
|
||||
```
|
||||
|
||||
### 2.2 拷贝Jar包
|
||||
|
||||
按照官方文档的说明，需要将phoenix server jar 添加到所有 Region Servers上 Hbase 安装目录的 lib目录下。
|
||||
|
||||
这里由于我搭建的是Hbase伪集群，所以只需要拷贝到当前机器的HBase的lib目录下。如果是真实集群，则使用scp命令分发到所有Region Servers机器上。
|
||||
|
||||
```shell
|
||||
cp /usr/app/apache-phoenix-4.14.0-cdh5.14.2-bin/phoenix-4.14.0-cdh5.14.2-server.jar /usr/app/hbase-1.2.0-cdh5.15.2/lib
|
||||
```
|
||||
|
||||
### 2.3 重启 Region Servers
|
||||
|
||||
```shell
|
||||
# 停止Hbase
|
||||
stop-hbase.sh
|
||||
# 启动Hbase
|
||||
start-hbase.sh
|
||||
```
|
||||
|
||||
### 2.4 启动Phoenix
|
||||
|
||||
在Phoenix解压目录下的`bin`目录下执行如下命令，需要指定Zookeeper的地址:
|
||||
|
||||
+ 如果HBase采用Standalone模式或者伪集群模式搭建，则采用内置的 Zookeeper，默认端口为2181；
|
||||
+ 如果是HBase是集群模式并采用自己搭建的Zookeeper集群，则按照自己的实际情况指定端口
|
||||
|
||||
```shell
|
||||
# ./sqlline.py hadoop001:2181
|
||||
```
|
||||
|
||||
### 2.5 启动结果
|
||||
|
||||
启动后则进入了Phoenix交互式SQL命令行，可以使用`!table`或`!tables`查看当前所有表的信息
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/phoenix-shell.png"/> </div>
|
||||
|
||||
|
||||
|
||||
## 三、Phoenix 简单使用
|
||||
|
||||
### 3.1 创建表
|
||||
|
||||
```sql
|
||||
CREATE TABLE IF NOT EXISTS us_population (
|
||||
state CHAR(2) NOT NULL,
|
||||
city VARCHAR NOT NULL,
|
||||
population BIGINT
|
||||
CONSTRAINT my_pk PRIMARY KEY (state, city));
|
||||
```
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-create-table.png"/> </div>
|
||||
|
||||
新建的表会按照特定的规则转换为Hbase上的表，关于表的信息，可以通过Hbase Web UI 进行查看：
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/hbase-web-ui-phoenix.png"/> </div>
|
||||
|
||||
### 3.2 插入数据
|
||||
|
||||
Phoenix 中插入数据采用的是`UPSERT`而不是`INSERT`,因为Phoenix并没有更新操作，插入相同主键的数据就视为更新，所以`UPSERT`就相当于`UPDATE`+`INSERT`
|
||||
|
||||
```shell
|
||||
UPSERT INTO us_population VALUES('NY','New York',8143197);
|
||||
UPSERT INTO us_population VALUES('CA','Los Angeles',3844829);
|
||||
UPSERT INTO us_population VALUES('IL','Chicago',2842518);
|
||||
UPSERT INTO us_population VALUES('TX','Houston',2016582);
|
||||
UPSERT INTO us_population VALUES('PA','Philadelphia',1463281);
|
||||
UPSERT INTO us_population VALUES('AZ','Phoenix',1461575);
|
||||
UPSERT INTO us_population VALUES('TX','San Antonio',1256509);
|
||||
UPSERT INTO us_population VALUES('CA','San Diego',1255540);
|
||||
UPSERT INTO us_population VALUES('TX','Dallas',1213825);
|
||||
UPSERT INTO us_population VALUES('CA','San Jose',912332);
|
||||
```
|
||||
|
||||
### 3.3 修改数据
|
||||
|
||||
```sql
|
||||
-- 插入主键相同的数据就视为更新
|
||||
UPSERT INTO us_population VALUES('NY','New York',999999);
|
||||
```
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-update.png"/> </div>
|
||||
|
||||
### 3.4 删除数据
|
||||
|
||||
```sql
|
||||
DELETE FROM us_population WHERE city='Dallas';
|
||||
```
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-delete.png"/> </div>
|
||||
|
||||
### 3.5 查询数据
|
||||
|
||||
```sql
|
||||
SELECT state as "州",count(city) as "市",sum(population) as "热度"
|
||||
FROM us_population
|
||||
GROUP BY state
|
||||
ORDER BY sum(population) DESC;
|
||||
```
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-select.png"/> </div>
|
||||
|
||||
|
||||
|
||||
### 3.6 退出命令
|
||||
|
||||
```sql
|
||||
!quit
|
||||
```
|
||||
|
||||
|
||||
|
||||
### 3.7 扩展
|
||||
|
||||
从上面的简单操作中我们可以看出，Phoenix 查询语句与我们正常使用的SQL是基本相同的，关于Phoenix 支持的语句、数据类型、函数、序列（和Oracle中序列类似）因为涵盖内容很广，可以参考其官方文档，官方上有详尽的配图说明的：
|
||||
|
||||
+ 语法（Grammar）：https://phoenix.apache.org/language/index.html
|
||||
|
||||
+ 函数（Functions）：http://phoenix.apache.org/language/functions.html
|
||||
|
||||
+ 数据类型（Datatypes）：http://phoenix.apache.org/language/datatypes.html
|
||||
|
||||
+ 序列（Sequences）:http://phoenix.apache.org/sequences.html
|
||||
|
||||
+ 联结查询（Joins）：http://phoenix.apache.org/joins.html
|
||||
|
||||
|
||||
|
||||
## 四、Phoenix Java API
|
||||
|
||||
因为Phoenix遵循JDBC规范，并提供了对应的数据库驱动PhoenixDriver，这使采用Java对其进行操作的时候，就如同对其他关系型数据库（例如 MySQL）操作一样。
|
||||
|
||||
因为在实际的开发中我们通常都是采用第三方框架，比如mybatis,Hibernate,Spring Data 等，很少使用原生Java API操作关系型数据库，所以这里只给出一个简单的查询作为示例，并在下一篇文章中给出Spring boot + mybatis + Phoenix 的整合用例。
|
||||
|
||||
### 4.1 引入Phoenix core JAR包
|
||||
|
||||
如果是maven项目，直接在maven中央仓库找到对应的版本，导入依赖即可
|
||||
|
||||
```xml
|
||||
<!-- https://mvnrepository.com/artifact/org.apache.phoenix/phoenix-core -->
|
||||
<dependency>
|
||||
<groupId>org.apache.phoenix</groupId>
|
||||
<artifactId>phoenix-core</artifactId>
|
||||
<version>4.14.0-cdh5.14.2</version>
|
||||
</dependency>
|
||||
```
|
||||
|
||||
如果是普通项目，则可以从Phoenix 解压目录下找到对应的JAR包，然后手动引入
|
||||
|
||||
<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/phoenix-core-jar.png"/> </div>
|
||||
|
||||
### 4.2 简单的Java API实例
|
||||
|
||||
```java
|
||||
import java.sql.Connection;
|
||||
import java.sql.DriverManager;
|
||||
import java.sql.PreparedStatement;
|
||||
import java.sql.ResultSet;
|
||||
|
||||
|
||||
public class PhoenixJavaApi {
|
||||
|
||||
public static void main(String[] args) throws Exception {
|
||||
|
||||
// 加载数据库驱动
|
||||
Class.forName("org.apache.phoenix.jdbc.PhoenixDriver");
|
||||
|
||||
/*
|
||||
* 指定数据库地址,格式为 jdbc:phoenix:Zookeeper地址
|
||||
* 如果HBase采用Standalone模式或者伪集群模式搭建，则HBase默认使用内置的Zookeeper，默认端口为2181
|
||||
*/
|
||||
Connection connection = DriverManager.getConnection("jdbc:phoenix:192.168.200.226:2181");
|
||||
|
||||
PreparedStatement statement = connection.prepareStatement("SELECT * FROM us_population");
|
||||
|
||||
ResultSet resultSet = statement.executeQuery();
|
||||
|
||||
while (resultSet.next()) {
|
||||
System.out.println(resultSet.getString("city") + " "
|
||||
+ resultSet.getInt("population"));
|
||||
}
|
||||
|
||||
statement.close();
|
||||
connection.close();
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
结果如下：
|
||||
|
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<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/Phoenix-java-api-result.png"/> </div>
|
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|
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|
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|
||||
# 参考资料
|
||||
|
||||
1. http://phoenix.apache.org/
|
||||
@ -5,56 +5,47 @@
|
||||
1. [Linux环境下JDK安装](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux下JDK安装.md)
|
||||
2. [Linux环境下Python安装](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux下Python安装.md)
|
||||
|
||||
|
||||
|
||||
### 二、Hadoop
|
||||
|
||||
1. [Hadoop单机版本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/hadoop%E5%8D%95%E6%9C%BA%E7%89%88%E6%9C%AC%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA.md)
|
||||
|
||||
1. [Hadoop单机版本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/hadoop单机版本环境搭建.md)
|
||||
|
||||
2. Hadoop集群环境搭建
|
||||
3. 基于Zookeeper搭建Hadoop的HA集群
|
||||
|
||||
### 三、Spark
|
||||
|
||||
1. [Spark单机版本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Spark单机版本环境搭建.md)
|
||||
|
||||
|
||||
2. Spark集群环境搭建
|
||||
|
||||
### 四、Storm
|
||||
|
||||
1. [Storm单机版本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Storm单机版本环境搭建.md)
|
||||
|
||||
|
||||
2. Storm集群环境搭建
|
||||
|
||||
### 五、Hbase
|
||||
|
||||
1. [Hbase基本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hbase%E5%9F%BA%E6%9C%AC%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA.md)
|
||||
|
||||
1. [Hbase基本环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hbase基本环境搭建.md)
|
||||
|
||||
2. Hbase集群环境搭建
|
||||
|
||||
### 六、Flume
|
||||
|
||||
1. [Linux环境下Flume的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux%E4%B8%8BFlume%E7%9A%84%E5%AE%89%E8%A3%85.md)
|
||||
|
||||
|
||||
1. [Linux环境下Flume的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux下Flume的安装.md)
|
||||
|
||||
### 七、Azkaban
|
||||
|
||||
1. [Azkaban3.x编译及部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Azkaban%203.x%20%E7%BC%96%E8%AF%91%E5%8F%8A%E9%83%A8%E7%BD%B2.md)
|
||||
|
||||
|
||||
1. [Azkaban3.x编译及部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Azkaban_3.x_编译及部署.md)
|
||||
|
||||
### 八、Hive
|
||||
|
||||
1. [Linux环境下Hive的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux%E7%8E%AF%E5%A2%83%E4%B8%8BHive%E7%9A%84%E5%AE%89%E8%A3%85%E9%83%A8%E7%BD%B2.md)
|
||||
|
||||
|
||||
1. [Linux环境下Hive的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux环境下Hive的安装部署.md)
|
||||
|
||||
### 九、Zookeeper
|
||||
|
||||
1. [Zookeeper单机环境和集群环境搭建](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Zookeeper单机环境和集群环境搭建.md)
|
||||
|
||||
|
||||
|
||||
### 十、Kafka
|
||||
|
||||
1. [基于Zookeeper搭建Kafka高可用集群](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/基于Zookeeper搭建Kafka高可用集群.md)
|
||||
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