The team at YugaByte is excited to announce that YugabyteDB 1.1 is officially GA! You can download the latest version from our Quick Start page. New in this release:
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In case you missed the announcement, Ravi Murthy has joined YugaByte as our VP of Engineering. Read on to learn more about his experiences leading the teams who managed the explosive growth of applications and data at Facebook, plus what’s next at YugaByte!
After almost 7 years at Facebook, I am super-excited to start in my new role as YugaByte’s VP of Engineering. This post provides a brief history of my career through the years,
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We have performed our own Jepsen testing on YugabyteDB, and the open-source repository reveals no correctness failures; we’re also working on adding more tests to ensure our database holds true to its promises.
YugabyteDB embraces open source with an Apache 2.0 license and self-governance, focusing on user-driven contributions and sustainable business growth. It aims to balance open source and commercial features, addressing the cloud provider challenge by building high-value, user-loved products.
Time for another update from the engineering team for the YugabyteDB database! Let’s dive in and see all the progress we’ve made.
As we reviewed in “Docker, Kubernetes and the Rise of Cloud Native Databases”, Kubernetes has benefited from rapid adoption to become the de-facto choice for container orchestration. This has happened in a short span of only 4 years since Google open sourced the project in 2014. YugabyteDB’s automated sharding and strongly consistent replication architecture lends itself extremely well to containerized deployments powered by Kubernetes orchestration. In this post we’ll look at the various components involved in getting YugabyteDB up and running as Kubernetes StatefulSets.
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For ever-growing data workloads such as time series metrics and IoT sensor events, running a highly dense database cluster where each node stores terabytes of data makes perfect sense from a cost efficiency standpoint. If we are spinning up new data nodes only to get more storage-per-node, then there is a significant wastage of expensive compute resources. However, running multi-terabyte data nodes with Apache Cassandra as well as other Cassandra-compatible databases (such as DataStax Enterprise) is not an option.
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Apache Cassandra is a distributed open source database that can be referred to as a “NoSQL database” or a “wide column store.” Cassandra was originally developed at Facebook to power its “Inbox” feature and was released as an open source project in 2008. Cassandra is designed to handle “big data” workloads by distributing data, reads and writes (eventually) across multiple nodes with no single point of failure.
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Editor’s note: This post was originally published August 8, 2018 and has been updated as of May 28, 2020.
As we saw in ”How Does Consensus-Based Replication Work in Distributed Databases?”, Raft has become the consensus replication algorithm of choice when it comes to building resilient, strongly consistent systems. YugabyteDB uses Raft for both leader election and data replication. Instead of having a single Raft group for the entire dataset in the cluster,
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Explore how consensus-based replication gets implemented in distributed databases, and dive into Paxos and Raft, the most commonly used leader-based consensus protocols
