Apache: Big Data North America 2017

As Uber continues to grow, our big data systems need to grow in scalability, reliability, and performance, to help Uber make business decisions, give user recommendations, and analyze experiments across all data sources. Since 2016, we put Presto in production. Now Presto is serving ~100K queries per day @ Uber, and it becomes a key component for interactive SQL queries on big data. In this presentation, we would like to talk about our experiences and engineering efforts, we start with general introduction about Hadoop Infrastructure & Analytics @ Uber, then comes a brief introduction to Presto, the Interactive SQL engine for big data. We will focus on how we build the New Parquet Reader for Presto, and the detail techniques, Columnar Reads, Lazy Reads, Nested Column Pruning. We will show performance improvements and Uber's Use Cases. Finally, we would like to share our ongoing work.