The YugaByte Database Blog

Thoughts on open source, cloud native and distributed databases

How to Achieve High Availability, Low Latency & GDPR Compliance in a Distributed SQL Database

Today’s developers understand that the key requirement to converting and retaining customers is all about delivering fast and responsive experiences, while remaining resilient to failures and compliant with data governance regulations. YugaByte DB 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 YugaByte DB distributes data across multiple clouds,

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YugaByte DB 1.2 Passes Jepsen Testing

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 YugaByte DB 1.2 formally passes Jepsen tests using the YCQL API.

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Distributed PostgreSQL on a Google Spanner Architecture – Query Layer

Our previous post dived into the details of the storage layer of YugaByte DB 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.

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Distributed PostgreSQL on a Google Spanner Architecture – Storage Layer

In this post, we’ll dive into the architecture of the distributed storage layer of YugaByte DB, which is inspired by Google Spanner’s design. Our subsequent post covers the Query Layer, where the storage layer meets PostgreSQL as the SQL API. Finally, watch for a follow-up post that highlights the lessons we learned while engineering the SQL capabilities of YugaByte DB.

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YugaByte DB High Availability & Transactions for PostgreSQL & MongoDB Developers

In the first post of our series comparing YugaByte DB with PostgreSQL and MongoDB, we mapped the core concepts in YugaByte DB to the two popular databases. This post is a deeper dive into the high availability and transactions architecture of these databases.

High Availability

Almost all databases including YugaByte DB use replication to ensure that the database remains highly available under failures.

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Mapping YugaByte DB Concepts to PostgreSQL and MongoDB

If you are developing a new distributed application or are extending an existing one with a new set of microservices, chances are you are going to need to store data in a distributed SQL database. The plethora of niche databases that have emerged over the last decade make the task of selecting a database challenging. With many databases, each with its own nomenclature and nuances to choose from,

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Enhancing RocksDB for Speed & Scale

This blog post was co-authored by Mikhail Bautin and Kannan Muthukkaruppan

As described in our previous post “How We Built a High Performance Document Store on RocksDB?”, YugaByte DB’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).

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Basic Introduction to YugaByte DB Components

In this short blog post we are going to give you a quick overview of the components that make up a YugaByte DB universe.

Universe

YugaByte DB is composed of nodes. We collectively refer to this collection of all nodes as a universe. These nodes can be physical machines, virtual machines or containers (e.g. Kubernetes).

Clusters

A YugaByte DB universe is made up of one or more clusters.

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