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Fuzzy match Queryedit
The match
query supports fuzzy matching out of the box:
GET /my_index/my_type/_search { "query": { "match": { "text": { "query": "SURPRIZE ME!", "fuzziness": "AUTO", "operator": "and" } } } }
The query string is first analyzed, to produce the terms [surprize, me]
, and
then each term is fuzzified using the specified fuzziness
.
Similarly, the multi_match
query also supports fuzziness
, but only when
executing with type best_fields
or most_fields
:
GET /my_index/my_type/_search { "query": { "multi_match": { "fields": [ "text", "title" ], "query": "SURPRIZE ME!", "fuzziness": "AUTO" } } }
Both the match
and multi_match
queries also support the prefix_length
and max_expansions
parameters.
Fuzziness works only with the basic match
and multi_match
queries. It
doesn’t work with phrase matching, common terms, or cross_fields
matches.
- 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