An important feature of modern query optimizers is the ability to produce a query plan that is optimal for the underlying data set. This requires the ability to estimate cardinalities and computational costs of intermediate query plan nodes, which is highly dependent on both the query shape and the underlying data distribution. Traditional methods include collecting statistics on base tables and implementing cardinality and computational cost derivation inside the optimizer, which is error-prone for complex query shapes. This paper presents Presto's novel history-based optimization framework (HBO), which collects execution histories and uses them to optimize similar queries in the future. The framework produces accurate estimates for complex query shapes in a lightweight, automated manner, and adapts automatically to changes in underlying data distributions. We present the design and implementation of the HBO framework and provide details on its use in various optimization rules, as well as details on implementing the statistics store on top of a Redis key-value store. We also present the results of running HBO in production in two large data infrastructure organizations (Meta and Uber).

Presto is an open-source distributed SQL query engine that supports analytics workloads involving multiple exabyte-scale data sources. Presto is used for low-latency interactive use cases as well as long-running ETL jobs at Meta. It was originally launched at Meta in 2013 and donated to the Linux Foundation in 2019. Over the last ten years, upholding query latency and scalability with the hyper growth of data volume at Meta as well as new SQL analytics requirements have raised impressive challenges for Presto. A top priority has been ensuring query reliability does not regress with the shift towards smaller, more elastic container allocation, which requires queries to run with substantially smaller memory headroom and can be preempted at any time. Additionally, new demands from machine learning, privacy, and graph analytics have driven Presto maintainers to think beyond traditional data analytics. In this paper, we discuss several successful evolutions in recent years that have improved Presto latency as well as scalability by several orders of magnitude in production at Meta. Some of the notable ones are hierarchical caching, native vectorized execution engines, materialized views, and Presto on Spark. With these new capabilities, we have deprecated or are in the process of deprecating various legacy query engines so that Presto becomes the single piece to serve interactive, ad-hoc, ETL, and graph processing workloads for the entire data warehouse.

Many applications in an IoV setting require transfer of large data between vehicles that cannot be transferred in a single vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) contact. In this paper, we propose a scheme for large sized data transfer between vehicles by breaking up the data into smaller fragments (that can be transferred in a single contact) which are transferred to the destination using any number of other vehicles and roadside units (RSUs) as intermediate nodes. We formulate the problem as a scheduling problem and propose an offline algorithm for it. Detailed simulation results on realistic scenarios are presented to show that the use of other vehicles as intermediate nodes significantly increases the number of successful transfers.