Understanding BigQuery enhanced vectorization

Under the hood, there’s a lot of technology and expertise that goes into delivering the performance you get from BigQuery, Google Cloud's data to AI platform. Separating storage and compute provides unique resource allocation flexibility and enables petabyte-scale analysis, while features like compressed storage, compute autoscaling, and flexible pricing contribute to its efficiency. Then there’s the infrastructure — technologies like Borg, Colossus, Jupiter, and Dremel, as we discussed in a previous post.

BigQuery is continually pushing the limits of query price/performance. Google infrastructure innovations such as L4 in Colossus, userspace host networking, optimized BigQuery storage formats, and a cutting-edge data center network have allowed us to do a complete modernization of BigQuery's core data warehousing technology. We do this while adhering to core principles of self-tuning and zero user intervention, to guarantee the best possible price/performance for all queries. Collectively, we group these improvements into ...