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Author: Sid Choudhury

A Primer on ACID Transactions: The Basics Every Cloud App Developer Must Know

A Primer on ACID Transactions: The Basics Every Cloud App Developer Must Know

ACID transactions were a big deal when first introduced formally in the 1980s in monolithic SQL databases such as Oracle and IBM DB2. Popular distributed NoSQL databases of the past decade including Amazon DynamoDB and Apache Cassandra initially focused on “big data” use cases that did not require such guarantees and hence avoided implementing them altogether. However, ACID transactions have made a strong comeback in the last several years with the launch of next-generation distributed databases that have built-in support for them.

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

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

Editor’s note – This is the first part of a two-part series on database storage engines:

When evaluating operational databases, developers building distributed cloud applications tend to focus on data modeling flexibility, consistency guarantees, linear scalability,

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

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 take into account.

Consistency, Transactions & 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

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.

SQL and NoSQL Databases on Kubernetes (source: Kubernetes Application Survey,

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Orchestrating Stateful Apps with Kubernetes StatefulSets

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 with YugabyteDB

Overcoming MongoDB Sharding and Replication Limitations with YugabyteDB

A few of our early users have chosen to build their new cloud applications on YugabyteDB even though their current primary datastore is MongoDB. Starting with the v3.4 release in Nov 2016, MongoDB has made improvements in its sharding and replication architecture that has allowed it to be re-classified as a Consistent and Partition-tolerant (CP) database and move away from its Available and Partition-tolerant (AP) origins. However, significant limitations remain that make it unsuitable for latency-sensitive,

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

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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