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.
Scylla is now available on the Oracle Cloud Marketplace. The ScyllaDB team has completed testing and benchmarking of the bare metal instances available at Oracle Cloud Infrastructure (OCI). Scylla takes advantage of the excellent resources available on OCI bare metal servers: high CPU count, ample amount of DRAM, and fast and large NVMe drives. In our testing, we looked for ease of installation, throughput, and latency performance.
It is time to start processing data gathered from applications more efficiently. Applications typically gather large amounts of data over time from different sources and data types such as from IoT devices and microservices applications. Traditional data warehouses use ETL (Extract, Transform, Load) strategies which are batch-driven at specific time intervals and each component talked to every other component through messaging queues. This creates a management nightmare because custom scripts move data from their sources to destinations as one-offs along with many single points of failure and there is not a way to analyze the data in real-time. A more […]
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/2017/12/17/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.
When things go wrong with your database solution, a strong understanding of your system’s architecture, capabilities, and limitations will prepare you for making things right as quickly as possible. Regardless of throughput, 99th percentile latency, or any other metric observed during stable operations, what happens during periods of instability can make or break SLAs. All the IOPS in the world won’t save you from misconfigurations, suboptimal architecture, or unforeseen complexities.
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.
The Scylla team is pleased to announce the release of Scylla Enterprise 2017.1.3, a production-ready Scylla Enterprise minor release. Scylla Enterprise 2017.1.3 is a bug fix release for the 2017.1 branch, the latest stable branch of Scylla Enterprise.
For the past two years, we have helped users build fast, resilient, and stable applications with Scylla, an enterprise-grade database solution. During these two years, our early adopters migrated from a variety of database solutions, and while most of the migrations we successfully completed were Apache Cassandra (enterprise and open-source versions), we have seen users migrate from MongoDB, HBase, relational systems such as MySQL and Postgres, and key/value stores like Memcache and Redis. Migration strategies differ between users and systems. In general, we can divide Apache Cassandra-to-Scylla migrations into two main strategies, cold migration and hot migration. Cold Migration […]
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