MongoDB vs. Percona Operator

If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.

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Production ready, robust DB solution built for Kubernetes envs, we was able, for the first time, to ship production db workloads inside k8s. Be sure to check differences with "traditional" MySQL and other clustering solutions. Also be sure to match it with proper k8s storage solutions.

• Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
• You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
• Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.

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• Easy and fast deployment.
• A reliable, fully automated, high-available db lifecycle management solution.
• Exaustive documentation.
• Production ready.

• An aggregate pipeline can be a bit overwhelming as a newcomer.
• There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
• Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.

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• Some differences compared to "traditional" MySQL solution.
• Had some hard times trying to use it combined with pre-existing techs like WordPress, Magento, etc.
• The specific k8s environment features can have an impact on it, like the storage solutions offered by the cloud vendor.

I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.

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NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.

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Easy and fast deployment. A reliable, fully automated, high-available db lifecycle management solution. It requires a bit of learning time for people new to operators ecosystem.

Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.

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Well documented, there was no need to contact support.

While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.

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We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.

For a long time we struggled finding a viable solution to migrate our existing db workloads inside Kubernetes. Before "operators era" proper db workloads required manual management, of course that easily raised administrative overhead. Then the future started to be brighter with the introduction of operators and the "official" Oracle's MySQL Operator, then Presslab's one, finally Percona's operator. Compared to other operators, the last one allowed us to ship production db workloads inside k8s.

• Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
• You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB

• It allowed us to use k8s on our entire infrastructure.
• Decreased administrative overhead around our db services.
• As it is an open source technology, it cut down our db pricings.