Abstract: The Text-to-SQL task has significant application prospects in automating relational database query interfaces. It can reduce user learning costs and improve data query efficiency. However, ...

In a modern enterprise data environment, the increasing volume of data, distributed architecture, and complex application dependencies challenge traditional query-tuning methods. Observability ...

Google is turning its vast public data trove into a goldmine for AI with the debut of the Data Commons Model Context Protocol (MCP) Server — enabling developers, data scientists, and AI agents to ...

Google’s Robby Stein shares new details about the query fan-out technique in AI Mode, explaining how Google generates and executes its own queries. Google’s query fan-out technique issues multiple ...

Abstract: In recent years, the integration of natural language processing (NLP) with database querying has led to the emergence of Text-to-SQL systems, which enable nontechnical users to interact with ...

What is the default strategy for planning and optimizing queries with joins: How does the engine decide the physical join type (e.g., sort-merge join vs. hash join)? How is the join order determined?

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integral to database management, driving new levels of automation and intelligence in how data systems are administered. Modern ...

Workload characterization in DBMS tuning involves analyzing query composition and runtime statistics. Query-level approaches include features from query text, query plans, and data distribution, used ...

Thomas LaRock: "Without a basic understanding of wait events and how the occur, you will waste a lot of time trying and guessing at how to improve the performance of a query." Optimizing SQL Server ...

Modern database systems rely on cost-based query optimizers to come up with good execution plans for input queries. Such query optimizers rely on cost models to estimate the costs of candidate query ...