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Querying Geo Shapesedit
The unusual thing about the geo_shape
query is that it allows us to query and filter using shapes, rather than just points.
For instance, if our user steps out of the central train station in Amsterdam, we could find all landmarks within a 1km radius with a query like this:
GET /attractions/landmark/_search { "query": { "geo_shape": { "location": { "shape": { "type": "circle", "radius": "1km", "coordinates": [ 4.89994, 52.37815 ] } } } } }
The query looks at geo-shapes in the |
|
The |
|
The shape is a circle, with a radius of 1km. |
|
This point is situated at the entrance of the central train station in Amsterdam. |
By default, the query (or filter—do the same job) looks for indexed
shapes that intersect with the query shape. The relation
parameter can be
set to disjoint
to find indexed shapes that don’t intersect with the query
shape, or within
to find indexed shapes that are completely contained by the
query shape.
For instance, we could find all landmarks in the center of Amsterdam with this query:
GET /attractions/landmark/_search { "query": { "geo_shape": { "location": { "relation": "within", "shape": { "type": "polygon", "coordinates": [[ [4.88330,52.38617], [4.87463,52.37254], [4.87875,52.36369], [4.88939,52.35850], [4.89840,52.35755], [4.91909,52.36217], [4.92656,52.36594], [4.93368,52.36615], [4.93342,52.37275], [4.92690,52.37632], [4.88330,52.38617] ]] } } } } }
- 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