Add Location-based Searching to Site with ElasticSearch

Go beyond simple text search. Learn to filter results by geographic distance, custom polygons, or bounding boxes with Elasticsearch.

#1about 5 minutes

Understanding the fundamentals of the Elasticsearch search engine

Elasticsearch is a read-optimized search engine based on Apache Lucene that operates via REST calls and is part of the ELK stack.

#2about 2 minutes

Setting up a local development environment with Docker

A local Elasticsearch and Kibana environment can be quickly configured and launched using a simple Docker Compose file.

#3about 6 minutes

Defining data structure with indexes and mappings

Data is organized into JSON documents within an index, and its structure is defined by a mapping that specifies data types like text, keyword, and geo_point.

#4about 11 minutes

Performing basic text searches and filters in Kibana

Use `match` queries for ranked text searching and `filter` queries for exact, non-scored matching, which can be combined using a `bool` query.

#5about 3 minutes

Exploring advanced features and efficient data ingestion

Elasticsearch offers fast performance, advanced features like "more like this" searches, and requires bulk inserts for efficient data loading.

#6about 8 minutes

Finding locations within a specific geographic radius

The `geo_distance` filter allows you to find all documents that fall within a specified circular radius from a central latitude and longitude point.

#7about 5 minutes

Sorting search results by proximity to a point

Instead of just filtering, you can use a `geo_distance` sort to order results by their actual distance from a given point, from nearest to farthest.

#8about 2 minutes

Querying for locations inside a custom polygon shape

The `geo_polygon` filter enables searching for documents whose geo-points fall within a custom shape defined by a series of latitude and longitude coordinates.

#9about 2 minutes