- 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
WARNING: The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
Filtering by Geo Point
editFiltering by Geo Point
editFour geo-point filters can be used to include or exclude documents by geolocation:
-
geo_bounding_box
- Find geo-points that fall within the specified rectangle.
-
geo_distance
- Find geo-points within the specified distance of a central point.
-
geo_distance_range
- Find geo-points within a specified minimum and maximum distance from a central point.
-
geo_polygon
- Find geo-points that fall within the specified polygon. This filter is very expensive. If you find yourself wanting to use it, you should be looking at geo-shapes instead.
Each filter performs a slightly different calculation to check whether a point falls into the containing area, but the process is similar. The requested area is converted into a range of quad/geohash prefix tokens and used to search the inverted index for documents who share the same tokens.
Geo-filters are relatively expensive — they should be used on as few documents as
possible. First remove as many documents as you can with cheaper filters, like
term
or range
filters, and apply the geo-filters last.
The bool
filter will do this for you automatically. First it
applies any bitset-based filters (see All About Caching) to exclude as many
documents as it can as cheaply as possible. Then it applies the more
expensive geo or script filters to each remaining document in turn.
ElasticON events are back!
Learn about the Elastic Search AI Platform from the experts at our live events.
Register now