Best Practices for Index Optimization in Volt Active Data
Indexes provide a classic “space for speed” trade-off. They add to the persistent memory footprint of your application data but they make query filtering significantly faster. They also represent a trade-off that sacrifices write performance for read performance, on the assumption that indexed data will be filtered by queries more frequently than it is modified. […]
Volt Active Data In-Memory Database Achieves Best-In-Class Results, Running in the Cloud, On the YCSB Benchmark
The development team at Volt Active Data recently ran Volt Active Data v4.2 against the Yahoo Cloud Serving Benchmark (YCSB), an industry-standard performance benchmark for cloud databases. We ran our test on as realistic a cluster setup as possible: commodity hardware that we could easily book as spot instances on EC2, with features like durability […]
Aging Out Data From Volt Active Data
Deleting records is often an afterthought when you’re first building a proof of concept with Volt Active Data, but it is an important function that should be given some thought as part of any design. Few applications can retain data forever, and in-memory databases in particular require some careful planning for how much data should [...]
Using The Spring @Schedule Annotation
In a previous life I had a requirement that a web application scanned the expiration date of purchased content and sent one of three emails letting the user know that the item would expire soon, was going to expire very soon and that the item has expired. It fired up at early in the morning […]
Clarifications On The CAP Theorem And Data-Related Errors
There has been another round of online conversations about the CAP theorem as the internet community continues to discuss its implications on networked databases. Coda Hale recently wrote a well received article titled CAP paper by Gilbert and Lynch. Scattered in the larger conversation is a continued mis-perception of my position regarding the CAP theorem. […]
