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Scalable—Solr scales by distributing work (indexing and query processing) to multiple
servers in a cluster
Ready to deploy—Solr is open-source, is easy to install and configure, and provides
a preconfigured example to help you get started
Optimized for search—Solr is fast and can execute complex queries in subsecond
speed, often only 10’s of milliseconds
Large volumes of documents—Solr is designed to deal with indexes containing
millions of documents
Text-centric—Solr is optimized for searching natural language text, like emails, web
pages, resumes, PDF documents, and social messages like tweets or blogs
Results sorted by relevance—Solr returns documents in ranked order based on how
relevant each document is to the user’s query
In this book, you’ll learn how to use Solr to design and implement scalable search
solutions. We’ll begin our journey by learning about the types of data and uses cases Solr
supports. This will help you understand where Solr fits into the big picture of modern
application architectures and which problems Solr is designed to solve.
1.1 Why do I need a search engine?
We suspect that because you’re looking at this book, you already have an idea about why
you need a search engine. Therefore, rather than speculate on why you’re considering Solr,
we’ll get right down to the hard questions you need to answer about your data and use cases
in order to decide if a search engine is right for you. In the end, it comes down to
understanding your data and users and then picking a technology that works for both. Let’s
start by looking at the properties of data that a search engine is optimized to handle.
1.1.1 Managing text-centric data
A hallmark of modern application architectures is matching the storage and processing
engine to your data. If you’re a programmer, then you know to select the best data structure
based on how you use the data in an algorithm, that is, you don’t use a linked list when you
need fast random lookups. The same principle applies with search engines. There are four
main characteristics of data search engines like Solr are optimized to handle.
1. Text-centric
2. Read-dominant
3. Document-oriented
4. Flexible schema
A possible fifth characteristic is having a large volume of data to deal with, that is, “big
data,” but our focus is on what makes a search engine special among other NoSQL
technologies. It goes without saying that Solr can deal with large volumes of data.
Licensed to Jiwen Zhang <kevinzhang29@gmail.com>
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2013-05-17
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收藏 8.35MB PDF 举报 
玄月初心2013-11-02虽然不够完整，但仍然不错，谢谢
yunfeng_lin2013-06-17这个试读版，好多内容都没有...不过还是可以看看的
high00482014-01-10虽然不够完整，但仍然有参考意义
