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Searching—The Basic Toolsedit
So far, we have learned how to use Elasticsearch as a simple NoSQL-style distributed document store. We can throw JSON documents at Elasticsearch and retrieve each one by ID. But the real power of Elasticsearch lies in its ability to make sense out of chaos — to turn Big Data into Big Information.
This is the reason that we use structured JSON documents, rather than amorphous blobs of data. Elasticsearch not only stores the document, but also indexes the content of the document in order to make it searchable.
Every field in a document is indexed and can be queried. And it’s not just that. During a single query, Elasticsearch can use all of these indices, to return results at breath-taking speed. That’s something that you could never consider doing with a traditional database.
A search can be any of the following:
-
A structured query on concrete fields like
gender
orage
, sorted by a field likejoin_date
, similar to the type of query that you could construct in SQL - A full-text query, which finds all documents matching the search keywords, and returns them sorted by relevance
- A combination of the two
While many searches will just work out of the box, to use Elasticsearch to its full potential, you need to understand three subjects:
- Mapping
- How the data in each field is interpreted
- Analysis
- How full text is processed to make it searchable
- Query DSL
- The flexible, powerful query language used by Elasticsearch
Each of these is a big subject in its own right, and we explain them in detail in Search in Depth. The chapters in this section introduce the basic concepts of all three—just enough to help you to get an overall understanding of how search works.
We will start by explaining the search
API in its simplest form.
- Elasticsearch - The Definitive Guide:
- Foreword
- Preface
- Getting Started
- You Know, for Search…
- Installing and Running Elasticsearch
- Talking to Elasticsearch
- Document Oriented
- Finding Your Feet
- Indexing Employee Documents
- Retrieving a Document
- Search Lite
- Search with Query DSL
- More-Complicated Searches
- Full-Text Search
- Phrase Search
- Highlighting Our Searches
- Analytics
- Tutorial Conclusion
- Distributed Nature
- Next Steps
- Life Inside a Cluster
- Data In, Data Out
- What Is a Document?
- Document Metadata
- Indexing a Document
- Retrieving a Document
- Checking Whether a Document Exists
- Updating a Whole Document
- Creating a New Document
- Deleting a Document
- Dealing with Conflicts
- Optimistic Concurrency Control
- Partial Updates to Documents
- Retrieving Multiple Documents
- Cheaper in Bulk
- Distributed Document Store
- Searching—The Basic Tools
- Mapping and Analysis
- Full-Body Search
- Sorting and Relevance
- Distributed Search Execution
- Index Management
- Inside a Shard
- You Know, for Search…
- Search in Depth
- Structured Search
- Full-Text Search
- Multifield Search
- Proximity Matching
- Partial Matching
- Controlling Relevance
- Theory Behind Relevance Scoring
- Lucene’s Practical Scoring Function
- Query-Time Boosting
- Manipulating Relevance with Query Structure
- Not Quite Not
- Ignoring TF/IDF
- function_score Query
- Boosting by Popularity
- Boosting Filtered Subsets
- Random Scoring
- The Closer, The Better
- Understanding the price Clause
- Scoring with Scripts
- Pluggable Similarity Algorithms
- Changing Similarities
- Relevance Tuning Is the Last 10%
- Dealing with Human Language
- Aggregations
- Geolocation
- Modeling Your Data
- Administration, Monitoring, and Deployment