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.
In practical terms, how do you begin to plan your ultra-efficient Scylla topology? The shift can be intimidating for those coming from a background in Cassandra, or for those coming from a primary-replica architecture, or from a plain old monolithic database implementation. In this post we’ll layout a checklist for designing a Scylla cluster that incorporates both horizontal and vertical scaling.
The I3en family is a class of instances clearly targeted at storage-intensive workloads, but CPUs are still needed to process that data. The largest of the I3en, i3en.24xlarge, ships with 48 cores of the Xeon Platinum 8175M, clocking at 2.50GHz.
In this post we introduce the new Scylla workload prioritization mechanism, explaining the vision behind developing this feature and how it is implemented, and most importantly, we show you test results of how it performs in a real-world setting.
In this article, we will compare Scylla Cloud and Google Cloud Bigtable. We show that Scylla Cloud is one-fifth the cost of Cloud Bigtable under optimal conditions (perfect uniform distribution) and that when applied to Zipfian distribution, the difference grows to 25x.
So you heard about Scylla and its superior performance. Maybe you have experience with Apache Cassandra, and are wondering what parts of that experience will you reuse and what you may have to learn anew. Or maybe you’re coming from a totally different background and want to know how to make Scylla fit best into your application environment. In this article we will cover in detail ten basic principles that help users succeed with Scylla. Some of them are also applicable to Apache Cassandra, and some stand in contrast to Cassandra recommendations. Free your mind, and read on! 1. Monitor […]
Scylla Open Source 3.0 ships with a new format for on-disk representation, SSTable 3.0. In this article, we will discuss some of the benefits that emerge from the adoption of this format and the scenarios in which they apply. We will discuss the differences between the old and new formats, and demonstrate use cases in which the new format has significant advantages, and others where the advantages are much smaller. This is truly a situation of “Your Mileage May Vary.” For example, in one test result below, we were able to show a 53% reduction in table size. Other use […]