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How Do CRDTs Solve Distributed Data Consistency Challenges?

This is an article about the complexity of maintaining data consistency in distributed environments. It introduces conflict-free replicated data types (CRDTs) as a way to resolve concurrent data changes.

Common Data Consistency Challenges

Consider a situation where there are several distributed entities that each hold a copy of the same data. Data consistency is maintained if those copies continue to match each other, even when one or more of them are updated.

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Data Storage in 2021: Choosing the Right Tools for the Job

Editor’s Note: The following is an article written for and published in DZone’s 2021 Data Persistence Trend Report.

Reading about the death of the relational database seems like a regular occurrence. However, here we are in 2021, and the relational data store is going strong. If we look at the DB-Engines Ranking website, six of the top 10, including the top four spots, are all relational data stores. Evidently, structured, or relational, data storage is here to stay. Yet four of the top spots are held by non-relational engines. Could that mean that relational data storage is really dying?

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Design Exercise: Distributing (Consistent) Data at Scale

The Question

Before I start discussing this topic, I want to talk a bit about the speed of light. That pesky limit basically means that there is an inherent lag in passing information between any two points in space. In your daily life, you can mostly ignore it. The human brain is far too slow to perceive it, and even if you are working with computers, you can usually ignore the speed of light for anything less than about 500 miles.

But the speed of light is merely the hard upper limit of our ability to send information from one location to another. In practice, the lag time between any two computers connected to a network is much higher. In fact, if you are a gamer, you are very well acquainted with that fact.