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. […]
Ola Cabs shares their two-year journey with Scylla and how it lived up to their expectations. Learn how they graduated from using Scylla for very simple and non-critical use cases to running it for their mission-critical flows.
The Intel Memory Group is behind the revolutionary Optane SSD drive that provides breakthrough performance and is 5-8x faster at Low Queue Depths than traditional SSD’s. Intel began working with ScyllaDB staff last year to build a big memory system at high-volume scale. They chose Scylla because they needed a solution that can fully leverage the hardware to derive the best possible performance.
Learn how developers create applications that connect to databases by using the Cassandra libraries available for programming languages. In this post you will learn how to create a sample Node.js application in Docker that connects to the Mutant Monitoring System.
Samsung SDS is a global IT services and solutions company with 57 offices spread across 31 countries. They are tasked with implementing highly performant and scalable systems for a number of Samsung businesses. However, they were experiencing a number of issues at the database layer. For example, their relational database couldn’t meet the performance requirements of several business use cases. As a result, they decided to conduct an in-depth technical evaluation of NoSQL databases.
What fascinates me most about databases is how they can be used for storing time series data. This use case is important for Internet of Things (IoT) devices and data analytics. Everyone should at least be able to relate to a time series database use case as many are likely to have devices in their home collecting and sending data such as a smart thermostat or phone or wrist device gathering your fitness activity. Wouldn’t it be nice to know how the infrastructure works behind the scenes and how to create a time series database? In this post, I will […]
As word about Scylla continues to spread, we’re seeing more and more downloads of our open source software. We’re not always privy to our users’ experiences, but we’re very glad when we have the opportunity to share their results. A recent example of this is from Alexys Jacob of Numberly, who shared his experience evaluating Scylla for production on his personal Blog. In the first installment of a 2-part series, he describes his preparation for a successful POC-based evaluation with the help of the ScyllaDB team.
This is the second post in a series of four about the different compaction strategies available in Scylla. In the previous post, we introduced the Size-Tiered compaction strategy (STCS) and discussed its most significant drawback – its disk-space waste, a.k.a. space amplification. In this post, we will look at Leveled Compaction Strategy (LCS), the first alternative compaction strategy designed to solve the space amplification problem of STCS, and show that it does solve that problem, but unfortunately introduces a new problem – write amplification. The next post in this series will introduce a new compaction strategy, Hybrid Compaction Strategy, which […]
This is the first post in a series of four about the different compaction strategies available in Scylla. The series will look at the good and the bad properties of each compaction strategy, and how to choose the best compaction strategy for your workload. This first post will focus on Scylla’s default compaction strategy, size-tiered compaction strategy.
In the context of graph databases, the performance of your storage backend is paramount. In the world of edges and vertices, graphs (and the data required to support them) can grow exponentially in a point-to-point fashion. In their talk at Scylla Summit 2017, Ted Chang and Chin Huang, both engineers at IBM, decided to add Scylla to the mix of backends which has traditionally included Cassandra and HBase. They ran test scenarios which covered high volume reads and writes, and provided comparative test results for the three backends, along with lessons learned for each.