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On Amazon Prime Video’s move to a monolith May 14, 2023 One size does not fit all: neither cloud nor on-prem Apr 10, 2023 Comparing AWS SQS, SNS, and Kinesis: A Technical Breakdown for Enterprise Developers Feb 11, 2023 Stop Shakespearizing Sep 16, 2022 Using GNU Make with JavaScript and Node.js to build AWS Lambda functions Sep 4, 2022 Monolithic repository vs a monolith Aug 23, 2022 Keep your caching simple and inexpensive Jun 12, 2022 Java is no longer relevant May 29, 2022 There is no such thing as one grand unified full-stack programming language May 27, 2022 Best practices for building a microservice architecture Apr 25, 2022 TypeScript is a productivity problem in and of itself Apr 20, 2022 In most cases, there is no need for NoSQL Apr 18, 2022 Node.js and Lambda deployment size restrictions Mar 1, 2021 Should we abolish Section 230 ? Feb 1, 2021 TDWI 2019: Architecting Modern Big Data API Ecosystems May 30, 2019 Microsoft acquires Citus Data Jan 26, 2019 Which AWS messaging and queuing service to use? Jan 25, 2019 Using Markov Chain Generator to create Donald Trump's state of union speech Jan 20, 2019 Let’s talk cloud neutrality Sep 17, 2018 A conservative version of Facebook? Aug 30, 2018 TypeScript starts where JavaScript leaves off Aug 2, 2017 Design patterns in TypeScript: Chain of Responsibility Jul 22, 2017 I built an ultimate development environment for iPad Pro. Here is how. Jul 21, 2017 Rather than innovating Walmart bullies their tech vendors to leave AWS Jun 27, 2017 Emails, politics, and common sense Jan 14, 2017 Don't trust your cloud service until you've read the terms Sep 27, 2016 I am addicted to Medium, and I am tempted to move my entire blog to it Sep 9, 2016 What I learned from using Amazon Alexa for a month Sep 7, 2016 Amazon Alexa is eating the retailers alive Jun 22, 2016 In search for the mythical neutrality among top-tier public cloud providers Jun 18, 2016 What can we learn from the last week's salesforce.com outage ? May 15, 2016 Why it makes perfect sense for Dropbox to leave AWS May 7, 2016 Managed IT is not the future of the cloud Apr 9, 2016 JavaScript as the language of the cloud Feb 20, 2016 Our civilization has a single point of failure Dec 16, 2015 Operations costs are the Achille's heel of NoSQL Nov 23, 2015 IT departments must transform in the face of the cloud revolution Nov 9, 2015 Setting Up Cross-Region Replication of AWS RDS for PostgreSQL Sep 12, 2015 Top Ten Differences Between ActiveMQ and Amazon SQS Sep 5, 2015 Ten Questions to Consider Before Choosing Cassandra Aug 8, 2015 The Three Myths About JavaScript Simplicity Jul 10, 2015 Big Data is not all about Hadoop May 30, 2015 Smart IT Departments Own Their Business API and Take Ownership of Data Governance May 13, 2015 Guaranteeing Delivery of Messages with AWS SQS May 9, 2015 We Need a Cloud Version of Cassandra May 7, 2015 Building a Supercomputer in AWS: Is it even worth it ? Apr 13, 2015 Ordered Sets and Logs in Cassandra vs SQL Apr 8, 2015 Exploration of the Software Engineering as a Profession Apr 8, 2015 Finding Unused Elastic Load Balancers Mar 24, 2015 Where AWS Elastic BeanStalk Could be Better Mar 3, 2015 Trying to Replace Cassandra with DynamoDB ? Not so fast Feb 2, 2015 Why I am Tempted to Replace Cassandra With DynamoDB Nov 13, 2014 How We Overcomplicated Web Design Oct 8, 2014 Infrastructure in the cloud vs on-premise Aug 25, 2014 Cassandra: a key puzzle piece in a design for failure Aug 18, 2014 Cassandra: Lessons Learned Jun 6, 2014

Cassandra: a key puzzle piece in a design for failure

August 18, 2014

[caption id="attachment_241" align="aligncenter" width="300"]Puzzle.. Photo credit Thomas Leuthard Puzzle..
Photo credit Thomas Leuthard[/caption]

When building out a data center in the cloud (AWS in particular) Cassandra can play a crucial role in the design for failure.

SQL and NoSQL databases have drastically different redundancy profiles:
A NoSQL database (and I hate the term NoSQL with the passion of a billion white hot suns) trades off data consistency for something called partition tolerance. The layman's description of partition tolerance is basically the ability to split your data across multiple, geographically distinct partitions. A relational system can't give you that. A NoSQL system can't give you data consistency. Pick your poison.

Neither Amazon RedShift, nor RDS, nor DynamoDB offer a convenient built-in mechanism for real time cross-region replication. Cassandra, on the other hand, does. So the question now becomes - how do we deal with relational queries ?

Well, write into both. The term NoSQL is atrocious and implies some sort of a zero-sum game where if you use one you cannot use the other. As it turns out, a data store like Cassandra can offer real value in a disaster recovery scenario when using a relational database as a primary querying mechanism.

Both Amazon RDS and Redshift offer multiple availability zones, typically two. Perusing Amazon's own post morterms it is clear that the probability of Amazon losing an entire region is extremely low. However, as Hurricane Sandy has shown, it is certainly possible.

One approach to cross-region redundancy is as follows:

  1. Configure a Cassandra ring that spans both Virginia and California regions. In your primary region you can have a bigger cluster than you do in your backup region.

  2. Configure RDS as you normally would in a multi-AZ configuration.

  3. Write your data into both, RDS and Cassandra. Cassandra writes are very fast and so performance impact of this extra work is minimal.

  4. Proactively build a mechanism to restore RDS out of Cassandra.


So, now, in this setup you have a real-time data backup mechanism. You may go for years without your multi-zone RDS failing but should a catastrophe happen and the entire region is lost - you can quickly recover your RDS in another region in a matter of hours.

As an added bonus you now have a convenient mechanism for data structures and access patterns that may either be inappropriate or put too much of workload on your SQL database. If that is your plan the way I would set this up is to have at least one compute instance backed by fast SSDs per zone in your primary region and a single smaller instance in your backup region backed by a larger slower EBS volume.

Should an emergency arise this set up will allow you to build out a new compute cluster and recover your RDS from its real-time data in a matter of hours. In the meantime you are paying lower costs by not having a complete replica of your primary cluster.