Before organizations go into production with Scylla, they must ensure that they are getting the best possible performance so their applications and services will run optimally. One of the many ways to optimize your Scylla deployment is to choose the right compaction strategy. One of the more popular talks at Scylla Summit 2017 was on this subject. Based on that talk, I will explain what compaction is and then explore the different strategies available in Scylla.
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.
When an organization changes their database backend, it is not a simple task and there is usually an interesting story behind it. This was the case with Zenly, a mobile application that lets you know where your friends are in real time. They were using Elasticsearch as their main database to take advantage of its full-text search capabilities. However, Elasticsearch did not perform well for Zenly’s workload, which consists mostly of update operations, and they found it difficult to monitor and locate their data, so they began to look for a database replacement.
Snapfish is an industry leader in photo retail with over 100 million members storing over 100PB of data. On a peak shopping day, Snapfish processes 100,000 reads and 7,000 writes per minute. Based on their workload, they need a database that accommodates their high volume but were increasingly finding that their database system was not meeting their performance and scaling needs. They began a search for alternatives and evaluated Scylla as a possible solution.
mParticle and ScyllaDB attended The NoSQL & NewSQL Database Meetup at the AWS Loft in NYC. Yuan Ren from mParticle gave a nice presentation that explained their journey from Apache Cassandra to Scylla and how they process 50 billion monthly messages with full availability and performance.
Nick Stott is is a software engineer at Compose, with experience developing the company’s application architectures and container orchestration infrastructure.
We have a stellar roster of NoSQL experts lined up for Scylla Summit. Among the speakers is Henrik Johansson, who will cover “Using ScyllaDB for a Microservice Based Pipeline in Go.” Henrik has, after a background in Physics, worked as a software developer with many different things from shuffling financial data to language processing. He Is currently working as Senior Software Developer at Eniro, working with the backend systems for data refinement, enrichment and analysis. Technologies involve everything from the Spring stack to Hadoop, to Apache Flink, as well as newer things such as Go and Docker.
Guest post by By FengLin(MengYe Shen), Mogujie
Ori Lahav started a blog series about Scylla deployment at OutBrain You should check it out