Benchmarking the JDBC Bottleneck in Trino

Daniel Abadi's study explores the significant performance bottleneck caused by using JDBC connections in Trino when extracting large amounts of data from traditional database systems like Oracle, MySQL, and PostgreSQL. Through experiments using the TPC-H benchmark, Abadi demonstrates the inefficiencies of JDBC, highlighting a stark contrast in data access speed when comparing JDBC-based extraction to non-JDBC methods, such as those involving HDFS. The experiments reveal that parallel JDBC connections significantly enhance performance, as shown by the use of Starburst's advanced connectors for Oracle, which achieve substantial speedups by leveraging data partitioning. While Starburst provides upgraded connectors for parallel data extraction, alternative strategies, like logical partitioning in Trino, can also mitigate the JDBC bottleneck. Despite these challenges, Abadi underscores Trino's capability to access and process federated data across multiple systems, advocating for strategic actions to optimize performance by addressing the JDBC bottleneck, thereby fully utilizing Trino's potential in accessing data from diverse sources.