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Category: Distributed SQL

A Primer on ACID Transactions: The Basics Every Cloud App Developer Must Know

A Primer on ACID Transactions: The Basics Every Cloud App Developer Must Know

ACID transactions were a big deal when first introduced formally in the 1980s in monolithic SQL databases such as Oracle and IBM DB2. Popular distributed NoSQL databases of the past decade including Amazon DynamoDB and Apache Cassandra initially focused on “big data” use cases that did not require such guarantees and hence avoided implementing them altogether. However, ACID transactions have made a strong comeback in the last several years with the launch of next-generation distributed databases that have built-in support for them.

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A Busy Developer’s Guide to Database Storage Engines — The Basics

A Busy Developer’s Guide to Database Storage Engines — The Basics

Editor’s note – This is the first part of a two-part series on database storage engines:

When evaluating operational databases, developers building distributed cloud applications tend to focus on data modeling flexibility, consistency guarantees, linear scalability,

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A Busy Developer’s Guide to Database Storage Engines — Advanced Topics

A Busy Developer’s Guide to Database Storage Engines — Advanced Topics

In the first post of this two-part series, we learned about the B-tree vs LSM approach to index management in operational databases. While the indexing algorithm plays a fundamental role in determining the type of storage engine needed, advanced considerations highlighted below are equally important to take into account.

Consistency, Transactions & Concurrency Control

Monolithic databases, which are primarily relational/SQL in nature, support strong consistency and 

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Docker, Kubernetes and the Rise of Cloud Native Databases

Docker, Kubernetes and the Rise of Cloud Native Databases

Containerized Stateful Services Are Here

Results from the 2018 Kubernetes Application Usage Survey should put to rest concerns enterprise users have had around the viability of Docker containers and Kubernetes orchestration for running stateful services such as databases and message queues. Its exciting to see that nearly 40% of respondents are running databases (SQL and/or NoSQL) using Kubernetes. This number will continue to grow in the months ahead.

SQL and NoSQL Databases on Kubernetes (source: Kubernetes Application Survey,

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YugabyteDB 1.0 — A Peek Under The Hood

YugabyteDB 1.0 — A Peek Under The Hood

Modern user-facing apps, like E-Commerce and SaaS, frequently require features from multiple databases (broadly — SQL, NoSQL and a cache) to support their multi-workload needs. App developers are responsible for understanding and managing which pieces of data should be stored in which SQL and NoSQL database. Furthermore, the app is also responsible for moving data across the tiers (e.g. populating the cache on reads and invalidating it on writes). This greatly increases development and operational complexity,

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Yes We Can! Distributed ACID Transactions with High Performance

Yes We Can! Distributed ACID Transactions with High Performance

ACID transactions are a fundamental building block when developing business-critical, user-facing applications. They simplify the complex task of ensuring data integrity while supporting highly concurrent operations. While they are taken for granted in monolithic SQL/relational databases, distributed NoSQL/non-relational databases either forsake them completely or support only a highly restrictive single-row flavor (see sections below). This loss of ACID properties is usually justified with a gain in performance (measured in terms of low latency and/or high throughput).

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Practical Tradeoffs in Google Cloud Spanner, Azure Cosmos DB and YugabyteDB

Practical Tradeoffs in Google Cloud Spanner, Azure Cosmos DB and YugabyteDB

Updated April 2019.

The famed CAP Theorem has been a source of much debate among distributed systems engineers. Those of us building distributed databases are often asked how we deal with it. In this post, we dive deeper into the consistency-availability tradeoff imposed by CAP which is only applicable during failure conditions. We also highlight the lesser-known-but-equally-important consistency-latency tradeoff imposed by the PACELC Theorem that extends CAP to normal operations.

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Yugabyte Has Arrived!

Yugabyte Has Arrived!

Today, we are launching Yugabyte out of stealth and announcing the availability of YugabyteDB’s first public beta release. Yugabyte offers an open-source, cloud-native database for mission-critical applications. Yuga in Sanskrit represents an era or an epoch (about 4.32 million human years), a very long period of time. We picked the name Yugabyte to signify data that lives forever without limits. Our ultimate goal is to simplify data that is critical to businesses.

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Facebook’s User DB — Is it SQL or NoSQL?

Facebook’s User DB — Is it SQL or NoSQL?

Updated March 2019.

Ever wondered which database Facebook (FB) uses to store the profiles of its 2.3B+ users? Is it SQL or NoSQL? How has FB database architecture evolved over the last 15+ years? As an engineer in FB database infrastructure team from 2007 to 2013, I had a front row seat in witnessing this evolution. There are invaluable lessons to be learned by better understanding the database evolution at the world’s largest social network,

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