Scylla Blog

Scylla 2.3 was just recently released, and you can read more about that here. Aside from the many interesting feature developments like improved support for materialized views and hardware enablement like native support for AWS i3.metal baremetal instance, Scylla 2.3 also delivers even more performance improvements on top of our already industry-leading performance. Most of the performance improvements center around three pillars: Improved CPU scheduling, with more work being tagged and isolated The result of a diligent search for latency-inducing events, known as reactor stalls, particularly in the Scylla cache and in the process of writing SSTables A new, redesigned […]

In June, Miguel Martinez Pedreira, Software engineer at CERN on the ALICE project, and Glauber Costa, VP of Field Engineering at ScyllaDB, teamed up to do a computing seminar to discuss real-time processing of big data with ScyllaDB, examining how Scylla helped the ALICE experiment with their AliEn Global File Catalogue use case. CERN uses the world’s largest and most complex scientific instruments to study the basic constituents of matter – the fundamental particles. The instruments used at CERN are purpose-built particle accelerators and detectors. Accelerators boost beams of particles to high energies before the beams collide with each other or with […]

This article will shed light on the performance penalties of running Scylla on Docker and discuss the tuning steps to solve them.

Scylla 2.2 is a great release. As promised, we will share the performance results for this release and compare it to the previous version of Scylla.

  Benchmarking is no easy task, especially when comparing databases with different “engines” under the hood. You want your benchmark to be fair, to run each database on its optimal setup and hardware, and to keep the comparison as apples-to-apples as possible. (For more on this topic, see our webinar on the “Do’s and Don’ts of Benchmarking Databases.”) We kept this in mind when conducting this Scylla versus Cassandra benchmark, which compares Scylla and Cassandra on AWS EC2, using cassandra-stress as the load generator. Most benchmarks compare different software stacks on the same hardware and try to max out the throughput. […]

Explore the main differences between i3.16xlarge in the i3 family and i3.metal on AWS. Will removing the virtualization layer bring better performance?

KairosDB, a time-series database, provides a simple and reliable tooling to ingest and retrieve chronologically created data, such as sensors’ information or metrics. Scylla provides a large-scale, highly reliable and available backend to store large quantities of time-series data. Together, KairosDB and Scylla provide a highly available time-series solution with an efficiently tailored front-end framework and a backend database with a fast ingestion rate.

Scylla is now available on the Oracle Cloud Marketplace. The ScyllaDB team has completed testing and benchmarking of the bare metal instances available at Oracle Cloud Infrastructure (OCI). Scylla takes advantage of the excellent resources available on OCI bare metal servers: high CPU count, ample amount of DRAM, and fast and large NVMe drives. In our testing, we looked for ease of installation, throughput, and latency performance.

As most earthlings are aware by now, two severe attacks under the names of Meltdown and Spectre were currently disclosed and it affects pretty much everybody living in modern society. Although there is no defense against the more elaborate Spectre, there is a software defense against the more widespread Meltdown. However, there is a catch that set the Internet on fire over the past few days. To protect oneself against Meltdown brings with it a performance penalty of up to 30%.

This is a cross-post from https://www.alexgallego.org/concurrency/smf/2017/12/16/future.html. On June 8, 2016, Avi Kivity came to NYC to present ScyllaDB. During his search for a quick open desk to do some work, I volunteered open spaces we had at Concord1. We talked lock-free algorithms, memory reclamation techniques, threading models, Concord and distributed streaming engines, even C vs C++. Five hours later I was convinced that Seastar was the best systems framework I’d ever come across.