For a long time, permanent storage has been the bottleneck in most computer systems. Scylla operates under that assumption and includes a fully-featured userspace disk I/O Scheduler that is used to guarantee that different tasks in the database get their fair share of the disk. The I/O Scheduler is the central component at the heart of Scylla’s workload conditioning promise: to automatically adjust the database’s distribution of requests to adapt to the incoming workload. It is capable of providing Quality-of-Service (QoS) among the various tasks in the database and isolating them from each other. Since database systems tend to be […]
Originally published on The New Stack on July 28th, 2017. Recently AWS unleashed a managed cache solution, Amazon DynamoDB Accelerator (DAX), in front of its database. This blog post will discuss the pros and cons of external database caches.
A database like Scylla can be limited by the network, disk I/O or the processor. Which one it is often dynamic and depends on both the hardware configuration and the workload. The only way of dealing with that is to attempt to achieve good throughput and low latency regardless of what is the bottleneck. There are many things that can be done in each of these cases that range from high-level changes in the algorithms to very low-level tweaks. In this post, I am going to take a closer look at fairly recent changes to Scylla which improved the performance […]
How much data can you store in a single Scylla node? A reduced node count translates to ease of operations and lower capital expenses. Using Scylla, developers and database operators can store and retrieve at least twice the amount of data in nodes compared to Apache Cassandra-based systems.
What to expect from Scylla’s performance on low-end hardware Scylla is a reimplementation of Apache Cassandra that has been demonstrated by us and third parties to perform up to 10x better than Apache Cassandra. These performance advantages stem from Scylla’s modern hardware-friendly and ultra-scalable architecture. As a result, Scylla’s performance grows as the hardware size grows. Scaling both up and out offers many advantages: from simplified cluster management to access to generally better hardware and economies of scale. We will address those choices in detail in an upcoming blog post. However, many users have compelling reasons to stay on low-end hardware, […]
Background Samsung MSL (Memory Solutions Lab) recently released benchmark results from a YCSB evaluation they conducted. We are thrilled to share Samsung’s results, which reiterate previous benchmark findings that Scylla performs 10X better than Apache Cassandra. If you have high-end hardware, you can expect the same results. On smaller machines, the difference is in the range of 1.5X to 3X. We recommend using larger machines to reduce both your node count and your Total Cost of Ownership.