'TechCrunch: Google today announced the launch of AlloyDB, a new fully-managed PostgreSQL-compatible database service that the company claims to be twice as fast for transactional workloads as AWS's comparable Aurora PostgreSQL (and four times faster than standard PostgreSQL for the same workloads and up to 100 times faster for analytical queries). [...] AlloyDB is the standard PostgreSQL database at its core, though the team did modify the kernel to allow it to use Google's infrastructure to its fullest, all while allowing the team to stay up to date with new versions as they launch. Andi Gutmans, who joined Google as its GM and VP of Engineering for its database products in 2020 after a long stint at AWS, told me that one of the reasons the company is launching this new product is that while Google has done well in helping enterprise customers move their MySQL and PostgreSQL servers to the cloud with the help of services like CloudSQL, the company didn't necessarily have the right offerings for those customers who wanted to move their legacy databases (Gutmans didn't explicitly say so, but I think you can safely insert 'Oracle' here) to an open-source service. "There are different reasons for that," he told me. "First, they are actually using more than one cloud provider, so they want to have the flexibility to run everywhere. There are a lot of unfriendly licensing gimmicks, traditionally. Customers really, really hate that and, I would say, whereas probably two to three years ago, customers were just complaining about it, what I notice now is customers are really willing to invest resources to just get off these legacy databases. They are sick of being strapped and locked in." Add to that Postgres' rise to becoming somewhat of a de facto standard for relational open-source databases (and MySQL's decline) and it becomes clear why Google decided that it wanted to be able to offer a dedicated high-performance PostgreSQL service. The report also says Google spent a lot of effort on making Postgres perform better for customers that want to use their relational database for analytics use cases. "The changes the team made to the Postgres kernel, for example, now allow it to scale the system linearly to over 64 virtual cores while on the analytical side, the team built a custom machine learning-based caching service to learn a customer's access patterns and then convert Postgres' row format into an in-memory columnar format that can be analyzed significantly faster."' -- source: https://developers.slashdot.org/story/22/05/12/0117241 Cheers, Peter -- Peter Reutemann Dept. of Computer Science University of Waikato, NZ +64 (7) 858-5174 (office) +64 (7) 577-5304 (home office) https://www.cs.waikato.ac.nz/~fracpete/ http://www.data-mining.co.nz/