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.
IBM had previously used only Apache Cassandra and HBase as storage back-ends for the graph databases it makes available on IBM Cloud. Having heard about the advantages of Scylla, IBM’s Open Tech and Performance teams conducted a series of tests to compare Scylla with HBase and Apache Cassandra.
A popular social community with more than 100,000 concurrent active users, IMVU enables people all over the world to interact with each other using 3D avatars on their desktops, tablets, and mobile devices. In order to meet growing requirements for scale, IMVU decided it needed a more performant solution than their previous database architecture of Memcached in front of MySQL and Redis.
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.
Organizations are continuing to adopt Solid State Drives (SSD) in their data centers for optimal performance and lower latencies. With that in mind, it only makes sense to use them with a database solution like Scylla to get the most bang for your buck. One of the popular SSD’s that organizations are adopting now in their data centers is the Samsung Z-SSD drive. In this post, we will go over the Z-SSD and see how Scylla users can benefit from the drives.
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 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.
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.
What’s the deal with prepared statements? A query itself is just a string of text. For example: INSERT INTO tb (key,val) VALUES (“key”, “value”) In this simple example, we inserted two strings in a two-column table. Before that can happen, the CQL statement string (INSERT INTO…) needs to be sent to Scylla, parsed, and assuming no errors in the query, executed. It’s the parsing part that we are concerned with here. Parsing a CQL query is a compute-intensive operation that consumes resources just like anything else you would have a computer do. What if we could do the parsing part […]