Share your experience using vCloud Air

We use it primarily for real-time applications. In our web application, we added a feature where hundreds of people could play a quiz in real time. 

Instead of using traditional databases like SQL, we implemented Redis to make everything happen in real time – all those quick calculations, data hashing for easy retrieval, and so on. 

It was a live quiz feature, so Redis helped a lot. I also use Redis for caching and similar general use cases.

It improved the performance. For example, data structures like hashmaps in Redis make it a very fast database – much faster than traditional SQL databases. It can perform at significantly higher speeds. Latencies are very low. Our primary focus wasn't on saving money but on improving performance for that specific feature.

So, performance has been the key improvement. Every calculation happened in real time. It improved the performance 10X. 

In our company, we have limited resources, so we can't manage the database ourselves. We use services from Azure for that.  So, Redis integrates well with those services.

We use Azure Cache for Redis.

It performs much better than traditional databases. Our calculations happen in real-time, which was crucial for that quiz feature.

The price could be better. 

I have been using it for more than two years. 

It has been a reliable product for us.

It's been working very well for us. Since our scale isn't huge, it's able to handle our needs without issues.

I've also worked with PostgreSQL, Cassandra, MySQL, and Elasticsearch.

It's tough to find a direct competitor because Redis, Cassandra, PostgreSQL, and Elasticsearch all serve different purposes. Cassandra excels when you structure tables according to your requirements upfront. It offers fast reads. However, the Cassandra Query Language (CQL) isn't as flexible as SQL – there are no joins, for example. It is a very restricted query language. So, we need to carefully design your database tables with future data needs in mind.

Redis is better tested and is used by large companies. I haven't found a direct alternative to what Redis offers. Plus, there are a lot of support and learning resources available, which help you use Redis efficiently.

It's actually quite expensive compared to SQL since Redis uses a system's main RAM, which is costly. And memory can be limited.

We opt for a dedicated server.

Overall, I would rate the solution an eight out of ten. It's been very stable so far and performs well within our system.

I would recommend it, but I would also highlight the cost factor as something to consider.

We may use it as an application database. The application stores the data as documents in the database, which is a preference for our company because it’s a Document DB and a NoSQL database, which are preferred over traditional relational databases.

MongoDB has wrapped up the whole development lifecycle. MongoDB has multiple built-in tools such as MongoDB Shell, Compass, and other tools. It helps the developers to use that specific tool efficiently. Users do not have to worry about finding the tools and then installing and using that specific tool to communicate with their database cluster. MongoDB has a built-in option using MongoDB Shell or Compass for that purpose.

So, it has positively impacted the development speed and productivity.

There are many valuable features, but scalability stands out. It can scale across zones. You can define multiple nodes. They have also partnered with AWS, offering great service with multiple features, including built-in backup, all under the same roof, without the need for external tools.

So, the scalability feature supported our data growth overall. The growth of the database depends on the application side. The database aids in scaling when the application requires more storage. 

It’s configured to scale automatically across zones and regions, ensuring that performance doesn’t degrade even when scaling down.

The scalability aspect is quite difficult to implement. It should be much easier for the end user. You cannot use less than two nodes; you have to use at least two nodes, and they categorize their nodes, like m5, m10, and m20, according to their resource practices, which are also a bit expensive.

The end-user has to learn a bit about it. MongoDB has great content on its site. They call it MongoDB University. They actually have great content for that. Anyone can learn it, but one has to study it before diving into it or starting to use it.

I have been using MongoDB Atlas for almost three years.

The scalability is good. In my team, almost the whole development team is using it. So, there are around five end users. 

I contacted customer service and support for multiple purposes while configuring. The support is quite efficient, and the guidance is quite good. Initially, when I was working on it, I had to communicate with the support team.

So, I had a good experience with the support. 

Positive

The initial setup is not too difficult but can be somewhat tricky. 

It is tricky mainly in terms of configuration, especially if it's not internet-accessible, configuring it to stay within the same data center while allowing developers access without network barriers.

It is worth my money at the end of the day.

The pricing is not that expensive, but it can be, especially when we have deployed it across multiple zones.

If you want to go with NoSQL, I would suggest using MongoDB. 

If you are saving documents and prefer AWS services, AWS also has their DynamoDB for that purpose. I would suggest using AWS service if all of your services are already on AWS.

Overall, I would rate it a seven out of ten.