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 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.
“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 […]
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.
If you were forced to choose just one thing that would prompt you to move your mission-critical functionality to a new database, what would it be? Better performance? Worries about future scaling on your existing platform? Easier time for your DevOps? What about awesome support from the company itself? At Scylla Summit 2017, mParticle’s Nayden Kolev explained how all of the above factors started the group one year ago on a fruitful collaboration with Scylla in production.
They may not have time machines or lightsabers, but they do have the Higgs-Boson and they’re looking for the most scalable framework with which to study it. At CERN, the problem of the day is scaling out their AliEn global file catalog and their plans may well involve Scylla.
Snapfish is an industry leader in photo retail with over 100 million members storing over 100PB of data. On a peak shopping day, Snapfish processes 100,000 reads and 7,000 writes per minute. Based on their workload, they need a database that accommodates their high volume but were increasingly finding that their database system was not meeting their performance and scaling needs. They began a search for alternatives and evaluated Scylla as a possible solution.
Scylla 2.0’s New Feature in-depth: Heat Weighted Load Balancing With time, a Scylla cluster adapts to an application’s behavior. Given a steady read-mostly workload, after an initial warm-up period, all nodes will have their caches populated with a working set, and the workload will see a certain cache hit rate and enjoy a certain performance level (throughput and latency).