Databases

June 28, 2018

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 consider.

Consistency, Transactions, and Concurrency Control

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

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Docker, Kubernetes and the Rise of Cloud Native Databases

June 11, 2018

By Sid Choudhury

CockroachDB Containers Distributed SQL Docker Kubernetes

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.

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Distributed ACID Transactions with High Performance

April 24, 2018

By Karthik Ranganathan

ACID Transactions Databases Distributed SQL Google Spanner How It Works Performance Benchmarks

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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Overcoming MongoDB Sharding and Replication Limitations

March 7, 2018

By Sid Choudhury

How It Works MongoDB YugabyteDB

Overcoming MongoDB Sharding and Replication Limitations

MongoDB’s sharding and replication have improved since it was first an AP database, but it still has limitations for highly scalable and latency-sensitive apps. In this blog, the authors discuss why users switch to YugabyteDB due to distributed ACID transactions and improved scalability.

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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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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Databases

Consistency, Transactions, and Concurrency Control

Containerized Stateful Services Are Here

Explore Distributed SQL and YugabyteDB in Depth