- 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This documentation is no longer maintained and may be removed.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.
Geolocation
editGeolocation
editGone are the days when we wander around a city with paper maps. Thanks to smartphones, we now know exactly where we are all the time, and we expect websites to use that information. I’m not interested in restaurants in Greater London—I want to know about restaurants within a 5-minute walk of my current location.
But geolocation is only one part of the puzzle. The beauty of Elasticsearch is that it allows you to combine geolocation with full-text search, structured search, and analytics.
For instance: show me restaurants that mention vitello tonnato, are within a 5-minute walk, and are open at 11 p.m., and then rank them by a combination of user rating, distance, and price. Another example: show me a map of vacation rental properties available in August throughout the city, and calculate the average price per zone.
Elasticsearch offers two ways of representing geolocations: latitude-longitude
points using the geo_point field type, and complex shapes defined in
GeoJSON, using the geo_shape field
type.
Geo-points allow you to find points within a certain distance of another point, to calculate distances between two points for sorting or relevance scoring, or to aggregate into a grid to display on a map. Geo-shapes, on the other hand, are used purely for filtering. They can be used to decide whether two shapes overlap, or whether one shape completely contains other shapes.