After implementing both APIs — CQL and DynamoDB — we, the Scylla developers, are in a unique position to be able to provide an unbiased technical comparison between the two APIs. The goal of this post is to explain some of the more interesting differences between the two APIs, and how these differences affect users and implementers of these APIs.
The remedy to these pain points was Scylla. “Just moving one use case [cart mutations] to Scylla this year we got a huge benefit out of it.” They cut the cluster size from 55 Cassandra nodes to 12 nodes of Scylla, dramatically reducing their EC2 bill.
We came up with a new compaction approach, named Incremental Compaction, that considerably reduces space overhead with a hybrid technique that combines properties from both Size-Tiered and Leveled compaction strategies. It is exclusively available in newer Scylla Enterprise releases (2019.1.4 and above).
ScyllaDB’s Glauber Costa explores cgroups and systemd, and how these can be used to define slices which can be used to protect database performance.
About Scylla’s Alternator Project Alternator is an open source project that gives Scylla compatibility with Amazon DynamoDB™. Our goal is that any application written for Amazon DynamoDB could be run, unmodified, against Scylla with Alternator enabled. Originally, Scylla began as a re-implementation of Apache Cassandra, and it has since proven to be a solid database engine with key performance and TCO benefits over Cassandra. However, we always considered Cassandra to be just a starting point. Now a 5-year old project, Scylla is able to scale to hundreds of machines, petabytes of data and many regions and availability zones. Scylla can […]
Repair is one of several anti-entropy mechanisms in Scylla. It is used to synchronize data across replicas. In this post, we introduce a new repair algorithm coming with Scylla Open Source 3.1 that improves performance by operating at the row-level, rather than across entire partitions.
“Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!” — The Red Queen to Alice, Alice Through the Looking Glass In the world of Big Data, if you are not constantly evolving you are already falling behind. This is at the heart of the Red Queen syndrome, which was first applied to the evolution of natural systems. It applies just as much to the evolution of technology. ‘Now! Now!’ cried the Queen. ‘Faster! Faster!’ […]
This blog post is based on a talk I gave last month at the third annual Scylla Summit in San Francisco. It explains how Scylla ensures that ingestion of data proceeds as quickly as possible, but not quicker. It looks into the existing flow-control mechanism for tables without materialized views, and into the new mechanism for tables with materialized views, which is introduced in Scylla Open Source 3.0. Introduction In this post we look into ingestion of data into a Scylla cluster. What happens when we make a large volume of update (write) requests? We would like the ingestion to […]