Home

 
Gabriel Reid edited this page on 7 Aug 2013 · 8 revisions

Introduction

The HBase Indexer project provides indexing (via Solr) for content stored in HBase. It provides a flexible and extensible way of defining indexing rules, and is designed to scale.

Indexing is performed asynchronously, so it does not impact write throughput on HBase. SolrCloud is used for storing the actual index in order to ensure scalability of the indexing.

Getting started with the HBase Indexer

  1. Make sure you've got the required software installed, as detailed on the Requirements page.
  2. Follow the Tutorial to get a feel for how to use the HBase Indexer.
  3. Customize your indexing setup as needed using the other reference documentation provided here.

How it works

The HBase Indexer works by acting as an HBase replication sink. As updates are written to HBase region servers, they are "replicated" asynchronously to the HBase Indexer processes.

The indexer analyzes incoming HBase mutation events, and where applicable it creates Solr documents and pushes them to SolrCloud servers.

The indexed documents in Solr contain enough information to uniquely identify the HBase row that they are based on, allowing you to use Solr to search for content that is stored in HBase.

HBase replication is based on reading the HBase log files, which are the precise source of truth of the what is stored in HBase: there are no missing or no extra events. In various cases, the log also contains all the information needed to index, so that no expensive random-read on HBase is necessary (see the read-row attribute in the Indexer Configuration).

HBase replication delivers (small) batches of events. HBase-indexer exploits this by avoiding double-indexing of the same row if it would have been updated twice in a short time frame, and as well will batch/buffer the updates towards Solr, which gives important performance gains. The updates are applied to Solr before confirming the processing of the events to HBase, so that no event loss is possible.

Horizontal scalability

All information about indexers is stored in ZooKeeper. New indexer hosts can always be added to a cluster, in the same way that HBase regionservers can be added to to an HBase cluster.

All indexing work for a single configured indexer is shared over all machines in the cluster. In this way, adding additional indexer nodes allows horizontal scaling.

Automatic failure handling

The HBase replication system upon which the HBase Indexer is based is designed to handle hardware failures. Because the HBase Indexer is based on this system, it also benefits from the same ability to handle failures.

In general, indexing nodes going down or Solr nodes going down will not result in any lost data in the HBase Indexer.

hbase-indexer官网wiki的更多相关文章

  1. eclipse p2更新官网wiki的例子

    官网的cvs好像没了,不过在github上找到一份,可用. https://github.com/anthonydahanne/make-p2-buildable-with-tycho/tree/ma ...

  2. hbases索引技术:Lily HBase Indexer介绍

    Lily HBase Indexer 为hbase提供快速查询,他允许不写代码,快速容易的把hbase行索引到solr.Lily HBase Indexer drives HBase indexing ...

  3. 卸载 Cloudera Manager 5.1.x.和 相关软件【官网翻译】

    问题导读: 1.不同的安装方式,卸载方法存在什么区别?2.不同的操作系统,卸载 Cloudera Manager Server and 数据库有什么区别? 重新安装不完整如果你来到这里,因为你的安装没 ...

  4. dubbo系列一:dubbo介绍、dubbo架构、dubbo的官网入门使用demo

    一.dubbo介绍 Dubbo是阿里巴巴公司开源的一个高性能优秀的服务框架,使得应用可通过高性能的RPC实现服务的输出和输入功能,可以和Spring框架无缝集成.简单地说,dubbo是一个基于Spri ...

  5. OpenTSDB(时序数据库官网)

    官网地址:http://opentsdb.net/ 下载地址:https://github.com/OpenTSDB/opentsdb/releases ----------------------- ...

  6. Java微信扫描支付模式二Demo ,整合官网直接运行版本

    概述 场景介绍 用户使用微信“扫一扫”扫描二维码后,获取商品支付信息,引导用户完成支付. 详细 代码下载:http://www.demodashi.com/demo/13880.html 一.相关配置 ...

  7. caffe官网的部分翻译及NG的教程

    Caffe原来叫:Convolutional Architecture for Fast Feature Embedding 官网的个人翻译:http://blog.csdn.net/fengbing ...

  8. 【原】Zookeeper 概述 + 官网 Overview 翻译

    分布式应用 分布式应用 distributed application可以在给定时间(同时)在网络中的多个系统上运行,通过协调它们以快速有效的方式完成特定任务. (a), (b): a distrib ...

  9. 几个比較好的IT站和开发库官网

    几个比較好的IT站和开发库官网 1.IT技术.项目类站点 (1)首推CodeProject,一个国外的IT站点,官网地址为:http://www.codeproject.com,这个站点为程序开发人员 ...

随机推荐

  1. MySQL数据库(一)-- 数据库介绍、MySQL安装、基础SQL语句

    一.数据库介绍 1.什么是数据库 数据库即存储数据的仓库 2.为什么要用数据库 (1)用文件存储是和硬盘打交道,是IO操作,所以有效率问题 (2)管理不方便 (3)一个程序不太可能仅运行在同一台电脑上 ...

  2. 开放API接口安全处理!

    目录 概念 加密 MD5 Token 开放api参数 重复提交,恶意调用 日志 验证码 开放API接口安全处理! 参考文献: 公钥,私钥和数字签名这样最好理解 (转载) 概念 存在问题: 数据窃取 数 ...

  3. 使用PSCI机制的SMP启动分析

    其他core的入口 文件:arch/arm64/kernel/head.S secondary_entry: 在从bl31切到EL1上的Linux Kernel后: 第595行,在el2_setup中 ...

  4. python类定义的讲解

    python是怎么定义类的,看了下面的文章大家就会了,不用多说,开始学习. 一.类定义: 复制代码代码如下: class <类名>: <语句> 类实例化后,可以使用其属性,实际 ...

  5. opacity兼容性问题

    用来设定元素透明度的 Opacity 是CSS 3里的一个属性.当然现在还只有少部分浏览器支持. 不过各个浏览器都有自己的私有属性来支持,其中包括老版本的Mozilla和Safari: IE: fil ...

  6. PHP 多个字段自增或者自减

    //自增$res=Db::name('accessories') ->where('id',$req['id']) ->inc('number',$req['number']) -> ...

  7. emacs第二天

    setq 和setq-default的区别 cursor-type是一个buffer local 变量 在每一份buffer中都有一份值 如果变量是buffer local 里面的变量 setq-de ...

  8. Linux-day02

    一.安装搜狗输入法 1.更新包安装 setting→language support 2.安装中文语言包,安装Fcitx输入法框架 3.安装搜狗输入法命令: sudo dpkg -i sogoupin ...

  9. Java实现递归阶乘

    public class Factorial{ public static void main(String[] args){ for (int i = -5; i <= 5; i++) { S ...

  10. ML学习笔记(1)

    2019/03/09 16:16 归一化方法: 简单放缩(线性归一化):这种归一化方法比较适用在数值比较集中的情况.这种方法有个缺陷,如果max和min不稳定,很容易使得归一化结果不稳定,使得后续使用 ...