When building up search engines, indexing tons of data into a schema-less, distributed data store, Elasticsearch has always been a favourite tool of mine.

In addition to its core features, it also offers tools and documentation for us developers when we need to write integration tests for our Elasticsearch powered Java applications.

In the following tutorial I’d like to demonstrate how to implement a small sample application using Elasticsearch under the hood and how to write integration-tests with these tools for this application afterwards.

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Lucene is my favourite search engine library and the more often I use it in my projects the more features or functionality I find that were unknown to me.

Two of those features I’d like to share in the following tutorial is one the one hand the possibility to specify different analyzers on a per-field basis and on the other hand the API to create a simple character based tokenizer and analyzer within a few steps.

Finally we’re going to create a small index- and search application to test both features in a real scenario.

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Encountering the situation that you want to extract meta-data or content from a file – might it be an office document, a spreadsheet or even a mp3 or an image – or you’d like to detect the content type for a given file then Apache Tika might be a helpful tool for you.

Apache Tika supports a variety of document formats and has a nice, extendable parser and detection API with a lot of built-in parsers available.

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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 ..

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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 ..

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In today’s tutorial we’re exploring the world of faceted searches like the one we’re used to see when we’re searching for an item on Amazon.com or other websites. We’re using Hibernate Search here that offers an API to perform discrete as well as range faceted searches on our persisted data.

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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..

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Helo – today I wanted to post a small tutorial for a small index and search operation using the Lucene indexer and Maven for the project setup.

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