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There has been a growing demand for making modern cloud-based data analytics systems cost-effective and easy to use. AI-powered intelligent resource scaling is one such effort, aiming at automating scaling decisions for serverless offerings like Amazon ...

Database performance troubleshooting is a complex multi-step process that broadly involves three key stages- (a) Detection: determining what's wrong and when; (b) Root Cause Analysis (RCA): reasoning about why is the performance poor; (c) Resolution: ...

Over the past five decades, the relational database model has proven to be a scaleable and adaptable model for querying a variety of structured data, with use cases in analytics, transactions, graphs, streaming and more. However, most of the world's data ...

Amazon Aurora Serverless is an on-demand, autoscaling configuration for Amazon Aurora with full MySQL and PostgreSQL compatibility. It automatically offers capacity scale-up/down (i.e., vertical scaling) based on a customer database application's needs. ...

Database research and development is heavily influenced by benchmarks, such as the industry-standard TPC-H and TPC-DS for analytical systems. However, these twenty-year-old benchmarks neither capture how databases are deployed nor what workloads modern ...

Modern organizations manage their data with a wide variety of specialized cloud database engines (e.g., Aurora, BigQuery, etc.). However, designing and managing such infrastructures is hard. Developers must consider many possible designs with non-obvious ...

Translating between the SQL dialects of different systems is important for migration and federated query processing. Existing approaches rely on hand-crafted translation rules, which tend to be incomplete and hard to maintain, especially as the number of ...

Cloud data warehouses are today's standard for analytical query processing. Multiple cloud vendors offer state-of-the-art systems, such as Amazon Redshift. We have observed that customer workloads experience highly repetitive query patterns, i.e., users ...

Cloud-based data warehouses are built to be easy to use, requiring minimal intervention from customers as their workloads scale. However, there are still many dimensions of a workload that they do not scale with automatically. For example, in cloud-...

Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution time prediction for many downstream tasks, ranging from high-level ...

Analytic data systems typically use data layouts to improve the performance of scanning and filtering data. Common data layout techniques include single-column sort keys, compound sort keys, and more complex multidimensional data layouts such as the Z-...

The last decade of database research has led to the prevalence of specialized systems for different workloads. Consequently, organizations often rely on a combination of specialized systems, organized in a Data Mesh. Data meshes present significant ...

Separation of compute and storage has become the defacto standard for cloud database systems. First proposed in 2007 for database systems [2], it is now widely adopted by all major cloud providers such as Amazon Redshift, Google BigQuery, and Snowflake. ...

There has been a lot of excitement around using machine learning to improve the performance and usability of database systems. However, few of these techniques have actually been used in the critical path of customer-facing database services. In this ...

Cardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries. They either rely on ...

Social media, self-driving cars, and traffic cameras produce video streams at large scales and cheap cost. However, storing and querying video at such scales is prohibitively expensive. We propose to treat large-scale video analytics as a data ...

In-memory join is an essential operator in any database engine. It has been extensively investigated in the database literature. In this paper, we study whether exploiting the CDF-based learned models to boost the join performance is practical. To the ...

Query driven cardinality estimation models learn from a historical log of queries. They are lightweight, having low storage requirements, fast inference and training, and are easily adaptable for any kind of query. Unfortunately, such models can suffer ...

Hashing is a fundamental operation in database management, playing a key role in the implementation of numerous core database data structures and algorithms. Traditional hash functions aim to mimic a function that maps a key to a random value, which can ...