- 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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Querying a Nested Object
editQuerying a Nested Object
editBecause nested objects are indexed as separate hidden documents, we can’t
query them directly. Instead, we have to use the
nested
query to access them:
GET /my_index/blogpost/_search { "query": { "bool": { "must": [ { "match": { "title": "eggs" } }, { "nested": { "path": "comments", "query": { "bool": { "must": [ { "match": { "comments.name": "john" } }, { "match": { "comments.age": 28 } } ] } } } } ] }}}
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A nested
field can contain other nested
fields. Similarly, a nested
query can contain other nested
queries. The nesting hierarchy is applied
as you would expect.
Of course, a nested
query could match several nested documents.
Each matching nested document would have its own relevance score, but these
multiple scores need to be reduced to a single score that can be applied to
the root document.
By default, it averages the scores of the matching nested documents. This can
be controlled by setting the score_mode
parameter to avg
, max
, sum
, or
even none
(in which case the root document gets a constant score of 1.0
).
GET /my_index/blogpost/_search { "query": { "bool": { "must": [ { "match": { "title": "eggs" } }, { "nested": { "path": "comments", "score_mode": "max", "query": { "bool": { "must": [ { "match": { "comments.name": "john" } }, { "match": { "comments.age": 28 } } ] } } } } ] } } }
If placed inside the filter
clause of a Boolean query, a nested
query behaves
much like a nested
query, except that it doesn’t accept the score_mode
parameter. Because it is being used as a non-scoring query — it includes or excludes,
but doesn’t score — a score_mode
doesn’t make sense since there is nothing to score.