A fast in-memory database provides benefits that we all can appreciate such as optimal latency and throughput for workloads. What if you could utilize extremely fast NVMe drives to have similar latency and throughput results? The scope of this blog post is to examine the outcomes of using an in-memory like database combined with NVMe drives to resolve cold-cache and data persistence challenges. In this experiment, various testing scenarios were done with Scylla and Intel® Optane™ SSD DC P4800X drives with a goal of providing a solution with the performance of an in-memory like database without compromises on throughput, latency, […]
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 […]
By default, Scylla SSTables will be compressed when they are written to disk. As mandated by the file format, data is compressed in chunks of a certain size – 4kB if not explicitly set. The size of the chunk is one of the parameters for the compression property to be set at table creation. Chunk-based compression presents trade-offs that users may not be aware of. In this post, I will try to explore what those trade-offs are and how to set them correctly for maximum benefit. As trade-offs imply different results for different loads, we will focus on single-partition read […]
Amazon recently unveiled a new class of machines—the AWS i3 family. Targeted at I/O intensive applications and featuring up to 15TB of fast storage, these machines offer unprecedented power with a great balance between I/O and CPU. At a lower price than the previous i2 family, we expect the i3 family to become the default class for NoSQL workloads. This article will cover i3 instances and provide information about the status of Scylla support for the hardware. Although we don’t yet officially provide i3 AMIs, customers are already running them in production with positive results. Scylla’s native architecture takes advantage […]
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.
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.
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