- 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This documentation is no longer maintained and may be removed.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.
Geo Shapes
editGeo Shapes
editGeo-shapes use a completely different approach than geo-points. A circle on a computer screen does not consist of a perfect continuous line. Instead it is drawn by coloring adjacent pixels as an approximation of a circle. Geo-shapes work in much the same way.
Complex shapes—such as points, lines, polygons, multipolygons, and polygons with holes,--are “painted” onto a grid of geohash cells, and the shape is converted into a list of the geohashes of all the cells that it touches.
Actually, two types of grids can be used with geo-shapes: geohashes, which we have already discussed and which are the default encoding, and quad trees. Quad trees are similar to geohashes except that there are only four cells at each level, instead of 32. The difference comes down to a choice of encoding.
All of the geohashes that compose a shape are indexed as if they were terms. With this information in the index, it is easy to determine whether one shape intersects with another, as they will share the same geohash terms.
That is the extent of what you can do with geo-shapes: determine the
relationship between a query shape and a shape in the index. The relation
can be one of the following:
-
intersects
- The query shape overlaps with the indexed shape (default).
-
disjoint
- The query shape does not overlap at all with the indexed shape.
-
within
- The indexed shape is entirely within the query shape.
Geo-shapes cannot be used to caculate distance, cannot be used for sorting or scoring, and cannot be used in aggregations.
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