YugabyteDB is purpose built for geo-distributed applications that require high availability, high performance and regulatory compliance. In this blog, we are going to “look under the hood,” to explore exactly how YugabyteDB distributes data across multiple clouds, regions and availability zones.
You can join the discussion about the results on HackerNews here.
Last year we published our DIY Jepsen testing results – including the tests and failure modes implemented as well as the bugs found. We recently engaged Kyle Kingsbury, the creator of the Jepsen test suite, for an official analysis and are happy to report that YugabyteDB 1.2 formally passes Jepsen tests using the YCQL API.
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Our previous post dived into the details of the storage layer of YugabyteDB called DocDB, a distributed document store inspired by Google Spanner. This post focuses on Yugabyte SQL (YSQL), a distributed, highly resilient, PostgreSQL-compatible SQL API layer powered by DocDB. A follow-up post will highlight the challenges faced and lessons learned when engineering such a database.
Yugabyte SQL (YSQL) is a distributed and highly resilient SQL layer,
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In this post, we dive into the architecture of the distributed storage layer of YugabyteDB, which is inspired by Google Spanner’s design
The team at YugaByte is excited to announce that YugabyteDB 1.2 is officially GA! You can download the latest version from our Quick Start page.
YugaByte SQL (YSQL) is our PostgreSQL v11 compatible, distributed SQL API. It is ideal for powering microservices that require low latency, internet scale, geographic data distribution and extreme resilience to failures but want the data modeling flexibility of SQL (joins,
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In this post, we comparethe high availability and transactions architecture of PostgreSQL and MongoDB to that of YugabyteDB.
In this post, we reviewed the building blocks of the YugabyteDB and mapped them to the corresponding concepts (if any) in PostgreSQL and MongoDB.
As described in our previous post “How We Built a High Performance Document Store on RocksDB?”, YugabyteDB’s distributed document store (DocDB) uses RocksDB as its per-node storage engine. We made multiple performance and data density related enhancements to RocksDB in the course of embedding it into DocDB’s document storage layer (figure below). These enhancements are distributed as part of the YugabyteDB open source project. The goal of this post is to deep dive into these enhancements for the benefit of engineering teams interested in leveraging RocksDB beyond its original design intent of a fast monolithic key-value store.
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This blog post was co-authored by Mikhail Bautin and Kannan Muthukkaruppan
RocksDB is a popular embeddable persistent key-value store. First open sourced by Facebook in 2012 as a fork of the Google LevelDB project, it has been adapted over the years to a wide range of workloads including database storage engines and application data caching.
In this post, we explain our rationale for selecting RocksDB as a foundational building block for YugabyteDB.
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Observability is absolutely vital to operating business-critical data infrastructure at scale. This is why observability is not an afterthought in the YugabyteDB architecture. Every layer of YugabyteDB exposes metrics in the Prometheus format at the /prometheus-metrics endpoint. Given that Prometheus is essentially the de-facto standard for build-your-own infrastructure monitoring and alerting, you can get started with monitoring YugabyteDB in almost no time.
This post details how to install and configure both YugabyteDB and Prometheus on Docker so that you can run simple queries on the Prometheus UI.
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