Scylla Blog

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 […]

Scylla Open Source 3.0 is a landmark release for ScyllaDB: Materialized Views and Secondary Indexes are production-ready, and Scylla Open Source 3.0 can now read and write the Cassandra 3.x SSTable (“mc”) format. In addition, Scylla Open Source 3.0 provides a variety of performance improvements to existing functionality. In this article we will explore the nature of a pair of those performance improvements, and the scenarios in which Scylla users can expect to see a significant performance gain. Streaming When one Scylla node needs to transfer data to another, it undertakes a process called streaming. This happens when a new […]

“And now for our main event! Ladies and gentlemen, in this corner, weighing in at 34% of the cloud infrastructure market, the reigning champion and leader of the public cloud…. Amazon!” Amazon has unparalleled expertise at maximizing scalability and availability for a vast array of customers using a plethora of software products. While Amazon offers software products like DynamoDB, it’s database-as-a-service is only one of their many offerings. “In the other corner is today’s challenger — young, lightning quick and boasting low-level Big Data expertise… ScyllaDB!” Unlike Amazon, our company focuses exclusively on creating the best database for distributed data […]

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.