SQL has been extremely successful as the de facto standard language for working with data. Virtually all mainstream database-like systems use SQL as their primary query language. But SQL is an old language with significant design problems, making it difficult to learn, difficult to use, and difficult to extend. Many have observed these challenges with SQL, and proposed solutions involving new languages. New language adoption is a significant obstacle for users, and none of the potential replacements have been successful enough to displace SQL. In GoogleSQL, we've taken a different approach - solving SQL's problems by extending SQL. Inspired by a pattern that works well in other modern data languages, we added piped data flow syntax to SQL. The results are transformative - SQL becomes a flexible language that's easier to learn, use and extend, while still leveraging the existing SQL ecosystem and existing userbase. Improving SQL from within allows incrementally adopting new features, without migrations and without learning a new language, making this a more productive approach to improve on standard SQL.

F1 Query is a stand-alone, federated query processing platform that executes SQL queries against data stored in different file-based formats as well as different storage systems at Google (e.g., Bigtable, Spanner, Google Spreadsheets, etc.). F1 Query eliminates the need to maintain the traditional distinction between different types of data processing workloads by simultaneously supporting: (i) OLTP-style point queries that affect only a few records; (ii) low-latency OLAP querying of large amounts of data; and (iii) large ETL pipelines. F1 Query has also significantly reduced the need for developing hard-coded data processing pipelines by enabling declarative queries integrated with custom business logic. F1 Query satisfies key requirements that are highly desirable within Google: (i) it provides a unified view over data that is fragmented and distributed over multiple data sources; (ii) it leverages datacenter resources for performant query processing with high throughput and low latency; (iii) it provides high scalability for large data sizes by increasing computational parallelism; and (iv) it is extensible and uses innovative approaches to integrate complex business logic in declarative query processing. This paper presents the end-to-end design of F1 Query. Evolved out of F1, the distributed database originally built to manage Google's advertising data, F1 Query has been in production for multiple years at Google and serves the querying needs of a large number of users and systems.

Aggregate query processing in large data warehouses is computationally intensive. Precomputation is an approach that can be used to speed up aggregate queries. However, in order to make precomputation a truly viable solution to the aggregate query processing problem, it is important to identify the best set of aggregates to precompute and to use these precomputed aggregates effectively. The Red Brick aggregate computation and management system (Red Brick Vista) provides a complete server integrated solution to these problems.

Parallelism is a viable solution to constructing high performance object-oriented database systems. In parallel systems based on a shared-nothing architecture, the database is horizontally declustered across multiple processors, enabling the system to employ multiple processors to speedup the execution time of a query. The placement of objects across the processors has a significant impact on the performance of queries that traverse a few objects. The paper describes and evaluates a greedy algorithm for the placement of objects across the processors of a system. Moreover, it describes two alternative availability strategies and quantifies their performance tradeoff using a trace-driven simulation study.>