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A distributed SQL database needs to automatically partition the data in a table and distribute it across nodes. This is known as data sharding and it can be achieved through different strategies, each with its own tradeoffs. In this post, we will examine various data sharding strategies for a distributed SQL database, analyze the tradeoffs, explain the rationale for which of these strategies Yugab

Editor’s note – This is the first part of a two-part series on database storage engines: A Busy Developer’s Guide to Database Storage Engines — The Basics (this post)A Busy Developer’s Guide to Database Storage Engines — Advanced TopicsWhen evaluating operational databases, developers building distributed cloud applications tend to focus on data modeling flexibility, consistency guarantees, linear

Percolator is designed on top of BigTable, Google’s original wide-column NoSQL store first introduced to the world in 2006. It adds cross-shard ACID transactions using a two-phase commit protocol on top of BigTable’s single row atomicity. This enhancement was necessary because the process of updating an index is now divided into multiple concurrent transactions, each of which has to preserve invar

What is Distributed SQL?Distributed SQL is a category of relational databases that combines the core features of traditional SQL and NoSQL systems, being strongly consistent while natively providing ACID transactional support across data centers, availability zones, and regions—in the cloud. It provides a single logical relational database deployed across a cluster of network servers. Distributed

Our previous post in this series “What is Distributed SQL?” highlights the common architectural principles as well as the business benefits of distributed SQL databases. In this post, we compare distributed SQL databases against NewSQL databases so that we can better understand their differences. Before we dive into NewSQL, it is important to understand why NoSQL databases like MongoDB and Apache

You can join the discussion on HackerNews here. We crossed the three year mark of developing the YugabyteDB database in February of 2019. It has been a thrilling journey thus far, but not without its fair share of technical challenges. There were times when we had to go back to the drawing board and even sift through academic research to find a better solution than what we had at hand. In this pos

The database that can be consumed as open source software or as a variety of database-as-a-service offerings