It’s a sure revolution, one that most people haven’t noticed yet. That’s because of two new, exciting interfaces: eBPF (or BPF for short) and io_uring, the latter added to Linux in 2019 and still in very active development. Those interfaces may look evolutionary, but they are revolutionary in the sense that they will — we bet — completely change the way applications work with and think about the Linux Kernel.
Change Data Capture (CDC) enables users to log all the mutations of data in a selected table(s). It does not capture changes on every table in the database but can be enabled on specific tables that a user is interested in observing.
We came up with a new compaction approach, named Incremental Compaction, that considerably reduces space overhead with a hybrid technique that combines properties from both Size-Tiered and Leveled compaction strategies. It is exclusively available in newer Scylla Enterprise releases (2019.1.4 and above).
Scylla is a highly scalable, highly performant NoSQL database. But just how fast can fast get? And what happens when you run it on a bare metal cloud like Packet? We set out to design a test that would showcase the combined abilities of Scylla as a database and Packet’s fastest instances.
AWS announced their new generation of Graviton2 System on a Chip (SoC), based on the Arm Neoverse N1 core. AWS claims they are much faster than their predecessors, a claim that we put to the test in this article.
For you to get the most out of your big data applications, let’s explore the effects of concurrency in distributed databases and provide you with tools to correctly configure your infrastructure for maximum performance, including client-side parallelism and timeout settings.
In the first part of this blog we’ve learned a bit about compression theory and how some of the compression algorithms work. In this part we focus on practice, testing how the different algorithms supported in Scylla perform in terms of compression ratios and speeds.
In this two-part blog we’ll focus on the problem of storing as much information as we can in the least amount of space as possible. This first part will deal with the basics of compression theory and implementations in Scylla.
In this article we will explore one IoT/time-series classical scenario in which knowledge of how the cache operates can mean the difference between a fully cached workload that will be fast, and a fully storage-bound workload that will of course perform much worse.
Indexing is a useful tool that provides more types of queries on your tables. In principle, columns we wish to be queryable should be declared when the table is created, as part of a table’s primary key. Secondary Indexing is a neat way of making other columns queryable, but it comes with a cost of additional storage space and processing power to maintain the secondary index data coherent with the primary index information.