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In Lucene 4.x there is an API to fetch index statistics for specific document’s fields.
The following examples shows how to create an index with some random documents and fetch some statistics for a field afterwards ..
The latest snippet from my Lucene examples demonstrates how to achieve a facet search using the Lucene 4.0 API and how easy it is to define multiple category paths to aggregate search results for different possible facets.
In the following example we’re indexing some books as a classical example and create multiple category paths for author, publication date and category afterwards ..
Often in the life of developer’s life there is a scenario where using a relational database tends to get complicated or sometimes even slow – especially when there are fragments with multiple relationships or multiple connections present. This often leads to complex database queries or desperate software engineers trying to handle those problems with their ORM framework.
A possible solution might be to switch from a relational database to a graph database – and – neo4j is our tool of choice here. In the following tutorial we’re going to implement several examples to demonstrate the strengths of a graph database .. from a route planner to a social graph.
Developing plugins for the Confluence Wiki a developer sometimes needs to save additional metadata to a page object using Bandana or the ContentPropertyManager. Wouldn’t it be nice if this metadata was available in the built-in Lucene index?
That is were the Confluence Extractor Module comes into play..
