c#(Windows)怎么用Hbase Phoenix啊

jun471537173 2023-10-10 19:32:15

单表数据量上10亿后，传统关系型数据库查询慢，时序数据库只是按时间来查询快，Hbase Phoenix c#(Windows)支持又不好，不知道怎么选型了。。。有没有一种数据库像Hbase一样有rowkey，支持二级索引，按时间查询像时序数据库那么快，又支持各种sql语法的啊？

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Phoenix技术文档，业界大佬分享技术干货，赶紧学起来。

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C, f( J( @& F0 G 68_脚本分析的过程" ?' q# U7 B/ ~" W, e- I 69_开启和关闭一个进程 70_hadoop常用的命令和关闭防火墙) Q" A0 B3 M8 s3 ? 71_hadoop存储为何是128M 72_hadoop的存储问题 73_hadoop的高可用 74_配置hadoop临时目录 75_hadoop的hdfs的jar包 76_hadoop的存储问题+ B: J K& G* B4 Z 77_hadoop的hdfs常用的命令 78_hadoop的存储过程 79_hadoop的大数据节点% K S, J! U3 W& o2 d) Q 80_hdfs-maven-hdfs API访问8 s8 J# W* l- i% x, ]: L! L 81_hdfs-maven-idea的集成处理 82_hdfs-block大小-副本数设定9 o$ I! k4 |+ ]9 q2 h8 ]# x6 B, S* Y$ W 83_hdfs-网络拓扑-写入剖析2 g4 Z0 j& K; Z, K 84_hdfs-写入剖析2-packet-chunk 85_hdfs-压缩编解码器, u" o: K/ V5 B 86_hdfs-MR原理 87_hdfs-wordcount$ ?% ?& }' U. [0 M9 b 88_hadoop-mapreduce-切片演示-mapper 89_hadoop-mapreduce-url演示1 B% m, V- Z) ~. B9 |9 m2 u 90_job提交流程剖析 91_job split计算法则-读取切片的法则 92_job seqfile5 v! h+ R9 L1 w, U* T6 J# M 93_job 全排序-自定义分区类2 n% h" `: b4 c) C3 J9 S 94_job二次排序5 t3 Z2 R- ]( a: s* c0 Z 95_从db输入数据进行mr计算: L. M4 I6 y, R2 l/ u/ L 96_输出数据到db中 97_NLineInputFormat& u( k1 T& z( O# P, S* y1 Y 98_KeyValueTextInputFormat* p$ O1 z- h, n" e( x1 s& c% z' v 99_join mapper端连接- N, S# O2 }6 m0 T 100_join reduce端连接0 N1 |* R5 n* D8 C+ i 101_hadoop Namenode HA配置8 [( ^7 Q1 W' y3 q 102_avro串行化4 [! T( [, J# e5 h P' w' {% I 103_google pb串行化& S- V% x6 v) {( Y" W 104_hive安装-使用: r/ Q& x. ~6 `- d* Y& R U4 X 105_hive beeline-hiveserver2 106_hive beeline-外部表-内部标 107_hive 分区表-桶表 108_hive word count 109_hive连接查询-union查询-load数据 110_hbase概述 111_zk架构-集群搭建-容灾演练avi 112_zk API-观察者-临时节点-序列节点-leader选举 113_hadoop namenode HA自动容灾" X3 `' ^/ U+ u+ U" F: } b 114_hadoop RM HA自动容灾 115_hbase集群搭建 116_hbase名字空间-表 117_hbase大批量操作7 [! ^" m3 B$ C. {1 S$ h. X 118_hbase架构-表和区域切割( p4 _0 k) J9 A/ ~; [ F 119_hbase架构-区域的合并 120_hbase get-scan-范围指定 121_扫描缓存-超时-切片' O; n; m' P; a6 T/ H$ S! ^ 122_hbase的HA配置演示-和Hadoop的HA集成 123_hbase版本机制 124_hbase-ttl-min-versions-keep-deleted-cells" @- N5 [2 s; S3 T$ H' C 125_keep-deleted-cells控制是否保留删除的shell$ V8 |; Q7 g" ]- C# j% |! y 126_过滤器rowkey-family 127_过滤器-分页-row-col 128_filterList 129_rowkey2 h5 Y+ y9 _1 j0 K0 Q) n 130_区域观察者 131_区域观察者实现和部署" s o7 p+ F& p/ a) ]& W/ ? 132_重写区域观察者的postPut和postScannext方法实现数据统一处理0 H) Q' Z- b; P# K 133_hbase的bulkload命令实现hbase集群之间数据的传输2 D6 d; F6 S8 x+ I/ I0 B0 @ 134_hive同hbase集成，统计hbase数据表信息% Q/ R! Z1 J3 J) k+ H! {6 D# M 135_使用TableInputFormat进行MR编程! m& C6 B/ v6 N" `, I' O& }4 u 136_使用phoenix交互hbase& h* s5 S- ~6 ]: u7 \ 137_squirrel工具. |+ E; g* R9 l3 E 138_flume简介 139_nc收集日志# [3 O7 K& n; f; y( f 140_hdfs sink收集日志到hdfs b9 o, k, j( G4 l! {* u: | 141_使用spooldir实现批量收集/ s8 F* }% o- n6 g& a9 w 142_使用exec结合tail命令实现实时收集 143_使用seq源和压力源实现测试 144_使用avro源 145_导入avro maven-avro-client 146_导入avro maven-avro-client 147_使用hbasesink收集日志到hbase数据库 148_内存通道配置6 U/ X5 L3 ]7 b6 `5 x 149_source的通道选择器-复制策略-multiplexing 150_source的数据流程 151_sinkgroup的处理器-loadbalance- ^6 B0 j4 Z5 f9 d 152_sinkgroup的处理器-failover) y- ^1 Y. ~5 s9 G8 S! ^! a5 o 153_kafka集群安装与启动4 ^; K& j3 @6 p0 M 154_kafka创建主题以及查看主题结构 155_考察zk中kafka结构9 N: Y8 u4 {# m/ z1 d3 H 156_kafka分区服务器服务方式 157_kafka编程API实现生产者和消费者+ w9 l1 N( D8 E% z( D; G 158_kafka手动修改zk的偏移量实现消费处理( w7 s! K9 v7 U3 P7 T4 j 159_kafka与flume集成-source集成- _, G+ K) y% I4 D" q9 \ 160_kafka与flume集成-sink集成4 o6 W; v5 a; p9 s. X% I7 @ 161_kafka与flume集成-channel集成/ x' w3 g3 z& d: w 162_kafka简介!

With Microsoft HDInsight, business professionals and data analysts can rapidly leverage the power of Hadoop on a flexible, scalable cloud-based platform, using Microsoft's accessible business intelligence, visualization, and productivity tools. Now, in just 24 lessons of one hour or less, you can learn all the skills and techniques you'll need to provision, configure, monitor, troubleshoot, and use HDInsight, even if you're new to big data analytics. Each short, easy lesson builds on all that's come before: you'll learn all of HDInsight's essentials as you solve real data analytics problems. Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours covers all this, and much more: Introduction of Big Data, NoSQL systems, its Business Value Proposition and use cases examples Introduction to Hadoop, Architecture, Ecosystem and Microsoft HDInsight Getting to know Hadoop 2.0 and the innovations it provides like HDFS2 and YARN Quickly installing, configuring, and monitoring Hadoop (HDInsight) clusters in the cloud and automating cluster provisioning Customize the HDInsight cluster and install additional Hadoop ecosystem projects using Script Actions Administering HDInsight from the Hadoop command prompt or Microsoft PowerShell Using the Microsoft Azure HDInsight Emulator for learning or development Understanding HDFS, HDFS vs. Azure Blob Storage, MapReduce Job Framework and Job Execution Pipeline Doing big data analytics with MapReduce, writing your MapReduce programs in your choice of .NET programming language such as C# Using Hive for big data analytics, demonstrate end to end scenario and how Apache Tez improves the performance several folds Consuming HDInsight data from Microsoft BI Tools over Hive ODBC Driver - Using HDInsight with Microsoft BI and Power BI to simplify data integration, analysis, and reporting Using PIG for big data transformation workflows step by step Apache HBase on HDInsight, its architecture, data model, HBase vs. Hive, programmatically managing HBase data with C# and Apache Phoenix Using Sqoop or SSIS (SQL Server Integration Services) to move data to/from HDInsight and build data integration workflows for transferring data Using Oozie for scheduling, co-ordination and managing data processing workflows in HDInsight cluster Using R programming language with HDInsight for performing statistical computing on Big Data sets Using Apache Spark's in-memory computation model to run big data analytics up to 100 times faster than Hadoop MapReduce Perform real-time Stream Analytics on high-velocity big data streams with Storm Integration of Enterprise Data Warehouse with Hadoop and Microsoft Analytics Platform System (APS), formally known as SQL Server Parallel Data Warehouse (PDW) Step-by-step instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid problems. By the time you're finished, you'll be comfortable going beyond the book to create any HDInsight app you can imagine! Table of Contents Part I: Understanding Big Data, Hadoop 1.0, and 2.0 Hour 1. Introduction of Big Data, NoSQL, and Business Value Proposition Hour 2. Introduction to Hadoop, Its Architecture, Ecosystem, and Microsoft Offerings Hour 3. Hadoop Distributed File System Versions 1.0 and 2.0 Hour 4. The MapReduce Job Framework and Job Execution Pipeline Hour 5. MapReduce—Advanced Concepts and YARN Part II: Getting Started with HDInsight and Understanding Its Different Components Hour 6. Getting Started with HDInsight, Provisioning Your HDInsight Service Cluster, and Automating HDInsight Cluster Provisioning Hour 7. Exploring Typical Components of HDFS Cluster Hour 8. Storing Data in Microsoft Azure Storage Blob Hour 9. Working with Microsoft Azure HDInsight Emulator Part III: Programming MapReduce and HDInsight Script Action Hour 10. Programming MapReduce Jobs Hour 11. Customizing the HDInsight Cluster with Script Action Part IV: Querying and Processing Big Data in HDInsight Hour 12. Getting Started with Apache Hive and Apache Tez in HDInsight Hour 13. Programming with Apache Hive, Apache Tez in HDInsight, and Apache HCatalog Hour 14. Consuming HDInsight Data from Microsoft BI Tools over Hive ODBC Driver: Part 1 Hour 15. Consuming HDInsight Data from Microsoft BI Tools over Hive ODBC Driver: Part 2 Hour 16. Integrating HDInsight with SQL Server Integration Services Hour 17. Using Pig for Data Processing Hour 18. Using Sqoop for Data Movement Between RDBMS and HDInsight Part V: Managing Workflow and Performing Statistical Computing Hour 19. Using Oozie Workflows and Job Orchestration with HDInsight Hour 20. Performing Statistical Computing with R Part VI: Performing Interactive Analytics and Machine Learning Hour 21. Performing Big Data Analytics with Spark Hour 22. Microsoft Azure Machine Learning Part VII: Performing Real-time Analytics Hour 23. Performing Stream Analytics with Storm Hour 24. Introduction to Apache HBase on HDInsight

上面我们学会了hbase的操作和原理，以及外部集成的mr的计算方式，但是我们在使用hbase的时候，有的时候我们要直接操作hbase做部分数据的查询和插入，这种原生的方式操作在工作过程中还是比较常见的，以上这些方式需要使用外部的框架进行协助处理，其实hbase也对外提供了一个直接的操作方式接口插件Phoenix，它和mr不一样，是直接集成在hbase之中的，通过一个工具使得hbase可以完全支持sql操作，其实我们可以将Phoenix当成是一个sql插件，一个可以写sql完成hbase操作的插件，并且在hb

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