Sid Choudhury, Author at Yugabyte

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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Orchestrating Stateful Apps with Kubernetes StatefulSets

April 17, 2018

By Sid Choudhury

How It Works How To Kubernetes

Orchestrating Stateful Apps with Kubernetes StatefulSets

Kubernetes, the open source container orchestration engine that originated from Google’s Borg project, has seen some of the most explosive growth ever recorded in an open source project. The complete software development lifecycle involving stateless apps can now be executed in a more consistent, efficient and resilient manner than ever before. However, the same is not true for stateful apps — containers are inherently stateless and Kubernetes did not do anything special in the initial days to change that.

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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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Blogs by: Sid Choudhury

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

Consistency, Transactions, and Concurrency Control

Overcoming MongoDB Sharding and Replication Limitations

Explore Distributed SQL and YugabyteDB in Depth