Lazy Join Optimizations Without Upfront Statistics - Databricks

Lazy Join Optimizations Without Upfront Statistics

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Modern Data-Intensive Scalable Computing (DISC) systems such as Apache Spark do not support sophisticated cost-based query optimizers because they are specifically designed to process data that resides in external storage systems (e.g. HDFS), or they lack the necessary data statistics. Consequently, many crucial optimizations, such as join order and plan selection, are presently out-of-scope in these DISC system optimizers. Yet, join order is one of the most important decisions a cost-optimizer can make because wrong orders can result in a query response time that can become more than an order-of-magnitude slower compared to the better order. Session hastag: #SFr4

About Matteo Interlandi

Matteo Interlandi recently joined Microsoft CISL as a Research Scientist. Prior to joining Microsoft, Matteo was Postdoctoral Scholar at the University of California, Los Angeles. His research lies in between databases, distributed systems and declarative languages. In particular, he loves to build systems and tools that make it easier to design and implement data-driven distributed applications. Matteo obtained his PhD from the University of Modena and Reggio Emilia, Italy.