Presented by Trey Grainger, Search Technology Development Manager, CareerBuilder
Having “big data” is great, but turning that data into actionable intelligence is where the real value lies. This talk will demonstrate how you can use Solr to build a highly scalable data analytics engine to enable customers to engage in lightning fast, real-time knowledge discovery.
At CareerBuilder, we utilize these techniques to report the supply and demand of the labor force, compensation trends, customer performance metrics, and many live internal platform analytics. You will walk away from this talk with an advanced understanding of faceting, including pivot-faceting, geo/radius faceting, time-series faceting, function faceting, and multi-select faceting. You’ll also get a sneak peak at some new faceting capabilities just wrapping up development including distributed pivot facets and percentile/stats faceting, which will be open-sourced.
The presentation will be a technical tutorial, along with real-world use-cases and data visualizations. After this talk, you'll never see Solr as just a text search engine again.
Presented by Dragan Milosevic, Senior Architect, zanox
Analytics powered by Hadoop is powerful tool and this talk addresses its application in OLAP built on top of Lucene. Many applications use Lucene indexes also for storing data to alleviate challenges concerned with external data sources. The analyses of queries can reveal stored fields that are in most cases accessed together. If one binary compressed field replaces those fields, amount of data to be loaded is reduced and processing of queries is boosted. Furthermore, documents that are frequently loaded together can be identified. If those documents are saved in almost successive positions in Lucene stored files, benefits from file-system caches are improved and loading of documents is noticeably faster.
Large-scale searching applications typically deploy sharding and partition documents by hashing. The implemented OLAP has shown that such hash-based partitioning is not always an optimal one. An alternative partitioning, supported by analytics, has been developed. It places documents that are frequently used together in same shards, which maximizes the amount of work that can be locally done and reduces the communication overhead among searchers. As an extra bonus, it also identifies slow queries that typically point to emerging trends, and suggests the addition of optimized searchers for handling similar queries.
Presented by Marc Sturlese, Architect, Backend engineer, Trovit
In this talk I will explain how we combine a mixed architecture using Hadoop for batch indexing and Storm, HBase and Zookeeper to keep our indexes updated in near real time.Will talk about why we didn't choose just a default Solr Cloud and it's real time feature (mainly to avoid hitting merges while serving queries on the slaves) and the advantages and complexities of having a mixed architecture. Both parts of the infrastucture and how they are coordinated will be explained with details.Finally will mention future lines, how we plan to use Lucene real time feature.
Presented by Timothy Potter, Architect, Big Data Analytics, Dachis Group
My presentation focuses on how we implemented Solr 4.1 to be the cornerstone of our social marketing analytics platform. Our platform analyzes relationships, behaviors, and conversations between 30,000 brands and 100M social accounts every 15 minutes. Combined with our Hadoop cluster, we have achieved throughput rates greater than 8,000 documents per second. Our index currently contains more than 500,000,000 documents and is growing by 3 to 4 million documents per day.
The presentation will include details about:
Search has quickly evolved from being an extension of the data warehouse to being run as a real time decision processing system. Search is increasingly being used to gather intelligence on multi-structured data leveraging distributed platforms such as Hadoop in the background. This session will provide details on how search engines can be abused to use not text, but mathematically derived tokens to build models that implement reflected intelligence. In such a system, intelligent or trend-setting behavior of some users is reflected back at other users. More importantly, the mathematics of evaluating these models can be hidden in a conventional search engine like SolR, making the system easy to build and deploy. The session will describe how to integrate Apache Solr/Lucene with Hadoop. Then we will show how crowd-sourced search behavior can be looped back into analysis and how constantly self-correcting models can be created and deployed. Finally, we will show how these models can respond with intelligent behavior in realtime.
Presented by Liang Shen, Developer, European Bioinformatics Institute
I'm going to introduce a project I built in 2011: Edanz Journal Selector. It's a tool for scholars to find the right journals to publish their manuscripts. It will be a typical “How We Did It” Development Case Study.
We built Edanz Journal Selector based on Solr/Lucene/Hadoop/Hive and deployed it on Amazon web servies. I'm going to share experiences about architecture, cloud and etc. from this project.