- 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
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Mixing It Up
editMixing It Up
editRequiring exact-phrase matches may be too strict a constraint. Perhaps we do want documents that contain “quick brown fox” to be considered a match for the query “quick fox,” even though the positions aren’t exactly equivalent.
We can introduce a degree of flexibility into phrase matching by using the
slop
parameter:
GET /my_index/my_type/_search { "query": { "match_phrase": { "title": { "query": "quick fox", "slop": 1 } } } }
The slop
parameter tells the match_phrase
query how far apart terms are
allowed to be while still considering the document a match. By how far
apart we mean how many times do you need to move a term in order to make
the query and document match?
We’ll start with a simple example. To make the query quick fox
match
a document containing quick brown fox
we need a slop
of just 1
:
Pos 1 Pos 2 Pos 3 ----------------------------------------------- Doc: quick brown fox ----------------------------------------------- Query: quick fox Slop 1: quick ↳ fox
Although all words need to be present in phrase matching, even when using slop
,
the words don’t necessarily need to be in the same sequence in order to
match. With a high enough slop
value, words can be arranged in any order.
To make the query fox quick
match our document, we need a slop
of 3
:
Pos 1 Pos 2 Pos 3 ----------------------------------------------- Doc: quick brown fox ----------------------------------------------- Query: fox quick Slop 1: fox|quick ↵ Slop 2: quick ↳ fox Slop 3: quick ↳ fox