CQRS Data Patterns
a guide to cqrs data patterns

A Guide to CQRS Data Patterns

CQRS Data PatternsThere are many types of software, types of software development contracts, and types of software design patterns. The key to avoiding setbacks and keeping your dataflow moving in software design is simplicity. A big part of keeping your work simple is to be clear on the responsibilities of each segment of data in your architecture.

This is particularly true for building and maintaining applications as part of your business model. This is where CQRS data patterns come in as an alternative to traditional CRUD patterns.

But, what is CQRS, and how can you use it? In this article, we’ll take a look at exactly that and give you some advice on avoiding some common mistakes.

 


 

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What is CQRS?


CQRS is the industry shorthand used for Command Query Responsibility Segregation patterns. This means that CQRS patterns are software design patterns that separate queries and commands. They are typically used for data storing applications in the cloud.

Put simply, the answer to the question ‘what is CQRS?’ is that CQRS data patterns separate the operations for writing and reading data.

  • Query: a function that reads new data
  • Command: a function that writes or updates data

CQRS workflow

Practically, commands translate into actions within your application server and are always written as imperative action verbs. For example, a business offering virtual call center services might need to command code that instructs the application server to {delete} old data or {install} new add-ons.

Queries typically read, process, and transfer data for display. They are what makes the command possible, as they read the data written by the command and translate it appropriately.

CQRS data patterns are typically used instead of a traditional system known as create, read, update, and delete or CRUD. This is a more simplistic way of processing data and should generally be used as a first port of call, but it isn’t suitable for every business.

You should be asking yourself ‘what is CQRS?’ and looking into it if your application is considering scaling regularly or needs frequent updates.

 

Why use CQRS Patterns?


Most traditional software systems use the same data model to write and read data. This can work well for simple systems but, as the system grows more complicated, you might find that you struggle to control and track your data sets.

These are some ways in which traditional data models can cause problems in complex data stores:

  • Different interpretations: the query and command functions may result in different interpretations of the same data and cause disruptions in your data flow.
  • Data contention: the data may become scrambled if multiple operations are performed on the same set at the same time.
  • Security: pieces of data are subject to both reading and writing processes within the same set, which brings the potential for incorrectly exposing the data.

One of the major benefits of CQRS patterns comes from event sourcing.

Monitor displaying a graphSource

With separate query and command operations, it’s much simpler to create a chronological log of all changes to your server and reconstruct a past state in the event of a critical error. Other benefits include:

  • Widening user access: CQRS data patterns can be very useful for data sets where many users are accessing and processing it at the same time. It can minimize conflicts with merging data and allow multiple users to make changes to data at the same time, promoting productivity and avoiding workflow disruptions.
  • Read/write imbalance: data is often read a lot more often than it’s written. A CQRS data pattern allows you to scale up the read data processes while leaving the written data processes the same if you’ve got a large imbalance to account for.
  • Keep up with industry regulations: because of the potential for data sharing and leaks, the technology industry regulations are updated often. By being able to make quick updates to your system, you can be sure to always be within these rules when operating.

Try to keep up to date with any developments in the field of data automation and patterns through software publications, and make sure to regularly review your skills.

 

Common Mistakes To Avoid


Any software design process comes with potential pitfalls, particularly if you haven’t worked with this kind of pattern before. These are some of the more common mistakes that you might make with CQRS patterns and how to avoid them.

 

1. Expecting Too Much

CQRS patterns are useful tools, but they’re not a fix-all solution. They can be complex, and they require a high level of expertise to manage properly.

They should only be used in certain situations to make sure that you get the best result from your efforts. These situations are ones where you can use CQRS patterns successfully:

  • Your system evolves frequently or uses frequent updates – very important, as otherwise the system may become too complex and result in too much stale data.
  • You’re struggling to scale up with other pattern methods
  • Your domain is query-oriented
  • You collaborate with other teams or servers
  • You have the capacity to store stale data

The solution: thankfully, the solution here is simply to know when to apply CQRS patterns to your software and when another solution would work better.

 

2. Stale Data

Stale data can be a problem when it comes to CQRS data patterns.

If you’re unfamiliar with the concept, stale data is what occurs when data is read and processed and then altered in some way. When the system attempts to retrieve processed data and is only able to retrieve the old configuration of the data, this is said to be stale data.

Because command and query functions have been separated in a CQRS pattern, the read data may not be updated and may end up being stale. To avoid this, make sure to update the read data alongside the written data.

 

3. Complexity

CQRS systems can be the solution to a lot of different issues thrown up by traditional data patterns. However, they can also lead to increased complexity.

This is especially true if your system includes an event sourcing pattern. In this case, the sequence of events/updates often take the place of actual written data, which helps to optimize performance. The read data is translated into materialized views of the data stored in the read data store.

One of the issues you might encounter here is eventual consistency. The complexity of the system means that there will initially be a delay between generating the event sequence and the data being updated accordingly.

To avoid complexity becoming a problem, be sure to be consistent in updating your data and checking the stores for faults and data loss.

 

4. Updating unnecessarily

You should consider the necessity of CQRS at every stage of implementation. It can be hugely beneficial but time-consuming to implement, so you need to be sure that it is the best course of action before doing this.

CQRS is most suitable for data sets and systems that require continuous and frequent updates, like a communication platform or an application that is compatible with existing devices.

If your business does not do this, you might be better off upgrading your traditional data model.

On the other hand, you may still be using traditional data patterns that aren’t working for you. You might need CQRS without knowing it! For example, you may discover that you need to update your application server more often than planned. Consider reviewing your business and similar ones that have received ISO accreditation to identify any glaring issues with your application.

Cloud workloads

One of the ways that you can work out if you need to update your application more often is by collecting analytics data. For example, a Google Analytics exit rate or other customer satisfaction metrics concerning length and frequency of use. This way, you can identify the areas that might benefit from more frequent updates allowed by CQRS patterns.

 

5. Poor communication

To finish, a quick reminder to communicate! CQRS patterns can be complicated to implement and require a lot of teamwork, so remember to keep your team in the loop of what you’re doing. You’ll need to be able to explain to your team what CQRS is and how to integrate it.

Maintaining your communication infrastructure is key to this, particularly for smaller businesses that might lack the financial capital of larger ones. Understanding the best multi-line phone system for small business and the best way to store your plans and progress are both great first steps toward top-tier communication in your software design team!

 

Next steps


Using CQRS can be complex and requires a deep understanding of your role and the goals of your application to carry out properly.

Part of this preparation might be to join a software engineering community and see what others are saying about this process and how best to manage complications or errors. Above all, be sure to understand the common mistakes that come with considering and implementing CQRS data patterns and how to get around them!

 

About the Writer


Jessica DayJessica Day is the Senior Director for Marketing Strategy at Dialpad, a modern business communications platform that takes every kind of conversation to the next level—turning conversations into opportunities. Jessica is an expert in collaborating with multifunctional teams to execute and optimize marketing efforts, for both company and client campaigns. Jessica has also written for sites such as SendPulse and Recruitee. Here is her LinkedIn.

 

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