Databases

July 25, 2018

New to Google Cloud Databases? 5 Areas of Confusion That You Better Be Aware of

After billions of dollars in capital expenditure and reference customers in every major vertical, Google Cloud Platform has finally emerged as a credible competitor to Amazon Web Services and Microsoft Azure when it comes to enterprise-ready cloud infrastructure. While Google Cloud’s compute and storage offerings are easier to understand, making sense of its various managed database offerings is not for the faint-hearted. This post introduces app developers to the major Google Cloud database services,

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Implementing Distributed Transactions the Google Way: Percolator vs. Spanner

July 23, 2018

By Sid Choudhury

Google Spanner

Implementing Distributed Transactions the Google Way: Percolator vs. Spanner

In this blog, we dive deeper into Percolator and Spanner as well as the open source databases (YugabyteDB for example) that they have inspired

How DynamoDB’s Pricing Works, Gets Expensive Quickly and the Best Alternatives

July 18, 2018

By Jimmy Guerrero

Amazon DynamoDB Amazon Web Services

How DynamoDB’s Pricing Works, Gets Expensive Quickly and the Best Alternatives

DynamoDB is AWS’s NoSQL alternative to Cassandra, primarily marketed to mid-sized and large enterprises. It works best for those who require a flexible data model, reliable performance, and the automatic scaling of throughput capacity. In a nutshell, DynamoDB’s monthly cost is dictated by data storage, writes and reads. Let’s walk through a synopsis.

11 Things You Wish You Knew Before Starting with DynamoDB

July 10, 2018

By Karthik Ranganathan

Amazon DynamoDB Amazon Web Services

11 Things You Wish You Knew Before Starting with DynamoDB

This post is to help developers and operations engineers understand the precise strengths and weaknesses of DynamoDB, especially when it needs to power a complex, large-scale application.

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

June 28, 2018

By Sid Choudhury

Databases Distributed SQL How It Works

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

Learn the basics of database storage engines, incl. B-tree and LSM tree based storage engines that are popular in the context of modern distributed cloud apps

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

June 28, 2018

By Sid Choudhury

Databases Distributed SQL How It Works

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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Databases

Consistency, Transactions, and Concurrency Control

Containerized Stateful Services Are Here

Explore Distributed SQL and YugabyteDB in Depth