Raft

August 20, 2019

ACID Transactions Distributed SQL How It Works

Low Latency Reads in Geo-Distributed SQL with Raft Leader Leases

Note: This post contains interactive animations that explain how some of these complex algorithms work. Please view this post in a suitable media (at least 1000px by 600px screen resolution) for best results.

In this blog post, we are going to dive deep into the read performance of Raft – why read performance can take a hit and how it can be improved using leader leases. Additionally, we will also look at how to make the correctness guarantees around leader leases stronger.

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6 Technical Challenges Developing a Distributed SQL Database

April 26, 2019

By Karthik Ranganathan

Amazon Aurora Distributed SQL Google Spanner How It Works Jepsen Tests

6 Technical Challenges Developing a Distributed SQL Database

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.

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Distributed PostgreSQL on a Google Spanner Architecture – Storage Layer

March 18, 2019

By Karthik Ranganathan

Distributed SQL Google Spanner How It Works PostgreSQL YugabyteDB

Distributed PostgreSQL on a Google Spanner Architecture – Storage Layer

In this post, we’ll dive into the architecture of the distributed storage layer of YugabyteDB, 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, here is a follow-up post that highlights the key technical challenges we faced while engineering a distributed SQL database like YugabyteDB.

Logical Architecture

YugabyteDB is comprised of two logical layers,

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How Does the Raft Consensus-Based Replication Protocol Work in YugabyteDB?

August 8, 2018

By Sid Choudhury

ACID Transactions Distributed SQL Google Spanner How It Works

How Does the Raft Consensus-Based Replication Protocol Work in YugabyteDB?

Editor’s note: This post was originally published August 8, 2018 and has been updated as of May 28, 2020.

As we saw in ”How Does Consensus-Based Replication Work in Distributed Databases?”, Raft has become the consensus replication algorithm of choice when it comes to building resilient, strongly consistent systems. YugabyteDB uses Raft for both leader election and data replication. Instead of having a single Raft group for the entire dataset in the cluster,

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August 2, 2018

By Sid Choudhury

ACID Transactions Databases Distributed SQL How It Works

How Does Consensus-Based Replication Work in Distributed Databases?

Editor’s note: This post was originally published August 2, 2018 and has been updated as of May 26, 2020.

Whether it be a WordPress website’s MySQL backend or Dropbox’s multi-exabyte storage system, data replication is at the heart of making data durable and available in the presence of hardware failures such as machine crashes, disk failures, network partitions, and clock skews. The basic idea behind replication is very simple: keep multiple copies of data on physically isolated hardware so that one hardware failure does not impact the others;

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

January 25, 2018

By Sid Choudhury

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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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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Raft

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