We introduce λ-Tune, a framework that leverages Large Language Models (LLMs) for automated database system tuning. The design of λ-Tune is motivated by the capabilities of the latest generation of LLMs. Different from prior work, leveraging LLMs to extract tuning hints for single parameters, λ-Tune generates entire configuration scripts, based on a large input document, describing the tuning context. λ-Tune generates alternative configurations, using a principled approach to identify the best configuration, out of a small set of candidates. In doing so, it minimizes reconfiguration overheads and ensures that evaluation costs are bounded as a function of the optimal run time. By treating prompt generation as a cost-based optimization problem, λ-Tune conveys the most relevant context to the LLM while bounding the number of input tokens and, therefore, monetary fees for LLM invocations. We compare λ-Tune to various baselines, using multiple benchmarks and PostgreSQL and MySQL as target systems for tuning, showing that λ-Tune is significantly more robust than prior approaches.

In this paper we explore the ability of Large Language Models (LLMs) in analyzing and comparing query plans, and resolving query performance regressions. We present DBG-PT, a query regression debugging framework powered by LLMs. DBG-PT keeps track of query execution instances, and detects slowdowns according to a user-defined regression factor. Once a regression is detected, DBG-PT leverages the capabilities of the underlying LLM in order to compare the regressed plan with a previously effective one, and comes up with tuning knob configurations in order to alleviate the regression. By exploiting textual information of the executed query plans, DBG-PT is able to integrate with close-to-zero implementation effort with any database system that supports the EXPLAIN clause. During the demonstration, we will showcase DBG-PT's ability to resolve query regressions using several real-world inspired scenarios, including plan changes because of index creations/deletions, or configuration changes. Furthermore, users will be able to experiment using ad-hoc, or predefined queries from the Join Order Benchmark (JOB) and TPC-H, and over MySQL and Postgres.

More than 10,000 enterprises worldwide use the big data stack composed of multiple distributed systems. At Unravel, we build the next-generation APM platform for the big data stack, and we have worked with a representative sample of these enterprises that covers most industry verticals. This sample covers the spectrum of choices for deploying the big data stack across on-premises datacenters, private and public cloud deployments, and hybrid combinations of these. In this paper, we present a solution for assisting enterprises planning the migration of their big data stacks from on-premises deployments to the cloud. Our solution is goal driven and adapts to various migration scenarios. We present the system architecture we built and several cloud mapping options. We also describe a demonstration script that involves practical, real-world use-cases of the path to cloud adoption.

Current platforms fail to efficiently cope with the data and task heterogeneity of modern analytics workflows due to their adhesion to a single data and/or compute model. As a remedy, we present IReS, the Intelligent Resource Scheduler for complex analytics workflows executed over multi-engine environments. IReS is able to optimize a workflow with respect to a user-defined policy relying on cost and performance models of the required tasks over the available platforms. This optimization consists in allocating distinct workflow parts to the most advantageous execution and/or storage engine among the available ones and deciding on the exact amount of resources provisioned. Our current prototype supports 5 compute and 3 data engines, yet new ones can effortlessly be added to IReS by virtue of its engine-agnostic mechanisms. Our extensive experimental evaluation confirms that IReS speeds up diverse and realistic workflows by up to 30% compared to their optimal single-engine plan by automatically scattering parts of them to different execution engines and datastores. Its optimizer incurs only marginal overhead to the workflow execution performance, managing to discover the optimal execution plan within a few seconds, even for large-scale workflow instances.

Multi-engine analytics has been gaining an increasing amount of attention from both the academic and the industrial community as it can successfully cope with the heterogeneity and complexity that the plethora of frameworks, technologies and requirements have brought forth. It is now common for a data analyst to combine data that resides on multiple and totally independent engines and perform complex analytics queries. Multi-engine solutions based on SQL can facilitate such efforts, as SQL is a popular standard that the majority of data-scientists understands. Existing solutions propose a middleware that centrally optimizes query execution for multiple engines. Yet, this approach requires manual integration of every primitive engine operator along with its cost model, rendering the process of adding new operators or engines highly inextensible. To address this issue we present MuSQLE, a system for SQL-based analytics over multi-engine environments. MuSQLE can efficiently utilize external SQL engines allowing for both intra and inter engine optimizations. Our framework adopts a novel API-based strategy. Instead of manual integration, MuSQLE specifies a generic API, used for the cost estimation and query execution, that needs to be implemented for each SQL engine endpoint. Our engine API is integrated with a state-of-the-art query optimizer, adding support for location-based, multi-engine query optimization and letting individual runtimes perform sub-query physical optimization. The derived multi-engine plans are executed using the Spark distributed execution framework. Our detailed experimental evaluation, integrating PostgreSQL, MemSQL and SparkSQL under MuSQLE, demonstrates its ability to accurately decide on the most suitable execution engine. MuSQLE can provide speedups of up to 1 order of magnitude for TPCH queries, leveraging different engines for the execution of individual query parts.