Roger Rafanell has graduated as a Computer Science Engineer in Autonomous University of Barcelona and received his M.S. degree in Computer Architecture from the Technical University of Catalonia (UPC). He has been a member of the Distributed Computing group on the Barcelona Supercomputing Center, gaining valuable experience working in scalable systems, parallel programming models and Big Data architectures by participating in several EU research projects. He led the technical team at sisu labs as CTO in charge of designing the Big Data platform by ensuring real-time NLP and sentiment analysis on social data streams.
He's worked in AdTech companies by contributing to the design and implementation of serving strategies for advertisement platforms as well as engineered real-time data ingestion solutions.
He is currently leading the technical architecture of the Search & Relevance platform at letgo by closely collaborating with the data science and discovery teams to improve the search user experience."
Roger Rafanell is speaking at the following session/s
Search and Relevance at Scale for Online Classifieds
A high performing search service implies both having an effective search infrastructure and high search relevance. Seeking for a fault tolerant, self-healing and cost-effective search infrastructure at scale, we built a platform based on Solr search engine with light compressed in-memory indexes, avoiding sharding and decreasing the overall infrastructure needs. To populate the indexes, we use flexible Spark ETL processes, keeping our product catalog and search indexes updated in a near real-time fashion and distributed across high-performant database engines. We aim at getting a high search relevance precision and recall by applying query relaxation and boost solutions on top of the optimized platform. We evaluate the responses both offline and online and finally serve them by service APIs written in highly concurrent Scala frameworks (Finatra).
An example of a performant search platform, getting low costs and increased efficiency, speed and fault tolerance. Use performant search algorithms to increase precision and recall. Design a light, fast and flexible API taking advantage of the infrastructure and search relevance.
Data engineers and data scientists.