ApacheCon EU 2014

What makes an Apache Solr installation high performance? Learn about what's keeping that CPU hot, memory tight, disk screaming and network busy on your Solr installation. Optimize CPU usage, reduce memory and disk contention, unclog that network and learn about hidden gotchas of query and filter performance, DocValues, transaction logs etc.

Although, numerous Apache Solr performance tuning tips are available on the world-wide-web but they are distributed between the official Apache Solr reference guide, Apache Lucene/Solr javadocs, code comments, Jira comments, various books, the mailing-list and many blogs. In many cases, such advice is unsubstantiated with numbers or not annotated with the trade-offs.

Shalin will back each piece of advice with statistics and benchmarks as well as inform you about the trade-offs made so you can make more informed decisions.

Off-heap data structures and native code performance improvements for Apache Solr are being developed as part of the Heliosearch project. This presentation will cover the reasons behind these features, implementation details, and performance impacts. Recently developed features will also be covered (i.e. developed after this abstract was written).

This presentation is about a Semantic Search Engine built on top of an Apache projects stack.
The scope of the system is to extract content from heterogeneus data sources, process and enrich it , index it and search over this content with advanced User Experience.
This result is achieved with the integration of 4 top level Apache projects : ManifoldCF, Stanbol, Marmotta and Solr.

Apache Manifold is used to access different data sources and extract the content from them: it's the engine that moves the main indexer core of the system. The document extracted are processed in a pipe-line manner, here happens the integration with Apache Stanbol and Apache Marmotta that allows the semantic enrichment of the content according to any Linked Data assets.
The last ring of the chain is custom Search API built on top of Apache Solr that allows advanced search features to improve user experience

Nowadays there are plenty of solution to build a search subsystem. The question is how to keep such a system flexible and easy to react on data-driven decisions, constantly improve the quality. In talk are presented lessons learned from our experience of building lean ranking infrastructure, that could be used with data-driven approach in product development. With slides we walk through the process of scaling out the search system from a couple to 13 countries around the world, but keeping flexibility, that allows to test hypothesis on different levels and perform a/b testing in different dimensions.

Web sites without search functionality are unimaginable today - you search for comments and code on github, you look for books in your favourite webshop, you use the search box of your favourite blog to find articles.

When offering your search for your own application - how do you know that your search actually provides a benefit to the user instead of causing lots of frustration over results not found? Only checking that the favourite book about witches of your child is ranked top of all children books clearly doesn’t help.

This talk will walk you through the options of determining search quality - from purely offline metrics that work even before deploying version 1.0 to production to online A/B testing to check continuous improvement. I will highlight some Lucene and Elasticsearch features that can tremendously help you deploy your own search quality checks.

Apache Jackrabbit Oak is the next generation content repository based on the JCR specification, designed to be scalable for high read/write throughput, huge number of nodes in the repository and highly concurrent operations. In this presentation Tommaso Teofili will describe the flexible and pluggable search architecture of Oak which allows to define multiple indices to address specific types of queries with specific constraints for performant indexing and searching. A deeper focus on the Apache Lucene and Apache Solr based index implementations will be given, showing some insights on how they have been integrated to address hierarchical content search together with some performance benchmarks and real life use cases.