The Distributed SQL Blog

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

January 25, 2018

ACID Transactions Amazon Web Services Distributed SQL Google Cloud Platform Google Spanner Microsoft Azure Microsoft Azure Cosmos DB

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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Using Redis API as a True Distributed, Fault-Tolerant Database

January 18, 2018

By Karthik Ranganathan

How It Works

Using Redis API as a True Distributed, Fault-Tolerant Database

YugabyteDB’s compatibility with the Redis API enhances data replication and consistency, offering a ‘unified cache + database’ solution. It simplifies data sharding, supports large datasets without full memory reliance, and provides polyglot persistence for diverse workloads.

Scaling YugabyteDB to Millions of Reads and Writes

January 12, 2018

By Bharat Baddepudi

How It Works Performance Benchmarks

Scaling YugabyteDB to Millions of Reads and Writes

Yugabyte’s 50-node cluster benchmark demonstrates linear scalability in YugabyteDB, doubling throughput from 25 to 50 nodes with consistently low latencies. Conducted on Google Cloud Platform, these results highlight YugabyteDB’s cloud-native capabilities, applicable across various cloud and on-premise environments.

Extending Redis API with a Native Time Series Data Type

January 5, 2018

By Pritam Damania

How It Works How To

Extending Redis API with a Native Time Series Data Type

This blog highlights the challenges of handling time series data with vanilla Redis and the efficiency of YugaByte’s TS datatype

Building a Strongly Consistent Cassandra with Better Performance

December 20, 2017

By Robert Pang

ACID Transactions Apache Cassandra How It Works Performance Benchmarks

Building a Strongly Consistent Cassandra with Better Performance

In an earlier blog on database consistency, we had a detailed discussion on the risks and challenges applications face in dealing with eventually consistent NoSQL databases. We also dispelled the myth that eventually consistent DBs perform better than strongly consistent DBs. In this blog, we will look more closely into how YugabyteDB provides strong consistency while outperforming an eventually consistent DB like Apache Cassandra. Note that YugabyteDB retains drop-in compatibility with the Cassandra Query Language (CQL) API.

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YugabyteDB Architecture: Diverse Workloads with Operational Simplicity

November 27, 2017

By Mikhail Bautin

How It Works

YugabyteDB Architecture: Diverse Workloads with Operational Simplicity

This post highlights the architectural considerations that went into the design of the DocDB and YQL layers for the YugabyteDB database.

November 2, 2017

By Karthik Ranganathan

Release Announcements

Yugabyte Has Arrived!

Today, we are launching Yugabyte out of stealth and announcing the availability of YugabyteDB’s first public beta release.

A for Apple, B for Ball, C for “CAP Theorem”

September 28, 2017

By Karthik Ranganathan

How It Works

A for Apple, B for Ball, C for “CAP Theorem”

Dive into the types of consistency referenced by the CAP theorem, learn how they impact an app, and get the facts re: common myths about eventual consistency.

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

August 29, 2017

By Karthik Ranganathan

ACID Transactions Databases Distributed SQL Google Spanner

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

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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August 18, 2017

By Karthik Ranganathan

Compare and contrast

NoSQL vs SQL in 2017

Let’s look at the evolution from RDBMSs to NoSQL databases to handle massive data and complex operations.

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