Let’s take a look at the evolution of transactions in Apache Cassandra especially in the context of Quorum Reads/Writes, Lightweight Transactions and Secondary Indexes.
Prof. Daniel Abadi, lead inventor of the Calvin transaction management protocol and the PACELC theorem, wrote a thought-provoking post last month titled “NewSQL database systems are failing to guarantee consistency, and I blame Spanner”. The post takes a negative view of software-only Google Spanner derivative databases such as YugabyteDB and CockroachDB that use Spanner-like partitioned consensus for single shard transactions and a two phase commit (2PC) protocol for multi-shard (aka distributed) ACID transactions.
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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.
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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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
After billions of dollars in capital expenditure and reference customers in every major vertical, Google Cloud Platform has finally emerged as a credible competitor to Amazon Web Services and Microsoft Azure when it comes to enterprise-ready cloud infrastructure. While Google Cloud’s compute and storage offerings are easier to understand, making sense of its various managed database offerings is not for the faint-hearted. This post introduces app developers to the major Google Cloud database services,
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In this blog, we dive deeper into Percolator and Spanner as well as the open source databases (YugabyteDB for example) that they have inspired
First-generation NoSQL databases dropped ACID guarantees with the rationale that such guarantees are needed only by old-school enterprises running monolithic, relational applications in a single private data center. And the premise was that modern distributed apps should instead focus on linear database scalability along with low latency, mostly-accurate, single-key-only operations on shared-nothing storage (e.g. those provided by the public clouds).
Application developers who blindly accept the above reasoning are not serving their organizations well.
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Learn the basics of database storage engines, incl. B-tree and LSM tree based storage engines that are popular in the context of modern distributed cloud apps
