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Ngrams for Partial Matchingedit
As we have said before, “You can find only terms that exist in the inverted
index.” Although the prefix
, wildcard
, and regexp
queries demonstrated that
that is not strictly true, it is true that doing a single-term lookup is
much faster than iterating through the terms list to find matching terms on
the fly. Preparing your data for partial matching ahead of time will increase
your search performance.
Preparing your data at index time means choosing the right analysis chain, and
the tool that we use for partial matching is the n-gram. An n-gram can be
best thought of as a moving window on a word. The n stands for a length.
If we were to n-gram the word quick
, the results would depend on the length
we have chosen:
-
Length 1 (unigram): [
q
,u
,i
,c
,k
] -
Length 2 (bigram): [
qu
,ui
,ic
,ck
] -
Length 3 (trigram): [
qui
,uic
,ick
] -
Length 4 (four-gram): [
quic
,uick
] -
Length 5 (five-gram): [
quick
]
Plain n-grams are useful for matching somewhere within a word, a technique
that we will use in Ngrams for Compound Words. However, for search-as-you-type,
we use a specialized form of n-grams called edge n-grams. Edge
n-grams are anchored to the beginning of the word. Edge n-gramming the word
quick
would result in this:
-
q
-
qu
-
qui
-
quic
-
quick
You may notice that this conforms exactly to the letters that a user searching for “quick” would type. In other words, these are the perfect terms to use for instant search!
- 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