Data Consistency in Distributed Enterprise Applications

Data consistency is a crucial aspect of data management, particularly for distributed enterprise applications. It aims to ensure the accuracy, reliability, and timeliness of data distributed across databases and services. Data consistency is a complex problem, but businesses must, at the very least, make a decision about which data consistency tradeoffs they can accept.

What is data consistency?

Data consistency refers to the strategies software engineers use to ensure data is the same across all components of an application at a particular point in time. The goal of data consistency is to make sure that users see the same version of data, no matter where it is stored or accessed from. That means when data is changed in one place, it should also change everywhere else it is stored or replicated. If the data is different, it is considered inconsistent.

While that may seem straightforward, anyone who has studied the CAP theorem knows that it’s anything but. The CAP theorem says a distributed data store can only satisfy two of the following three guarantees: consistency, availability, and partition tolerance. This means that distributed systems must make trade-offs based on their specific requirements.

For instance, a system emphasizing consistency over availability will ensure that all clients always have the same, most recent data. However, this could mean that if certain data cannot be updated, the system might choose to stop serving data at all to maintain consistency.

On the other hand, a system emphasizing availability over consistency will always process queries and respond, even if it can’t guarantee the data is the most recent. This could lead to situations where clients read stale or inconsistent data.

Eventual Consistency

To balance the trade-offs between data consistency and availability, many enterprise systems employ a model known as eventual consistency. An eventually consistent system tolerates data inconsistency for a short period but does achieve consistency in the end. This is often achieved by using an event store, a message broker acting as an event database.

Distributed event-driven applications work by performing a series of asynchronous local transactions before adding each change to an event stream stored in an event store. Each service can then update its own data in a local transaction by consuming events from the event stream.

This approach allows the system to maintain high availability while still working towards data consistency, albeit with a potential delay. It’s a practical compromise that enables distributed systems to function in real-world scenarios.

Why does data consistency matter?

Inconsistent data can lead to several serious problems. Consider a bank with branches in multiple cities. If a customer withdraws money from one branch and the updated balance isn’t immediately propagated to the databases at other branches due to data consistency issues, the customer might be able to withdraw more money than they have in their account. This could lead to financial losses for the bank, regulatory and legal issues, and damage to the bank’s reputation.

In addition to timely propagation, data accuracy is also a key factor in data consistency. In a distributed application, inaccurate data—for example, incomplete data, data with missing records, or data captured using inconsistent formats—can propagate quickly due to the interconnected nature of the system. If one node receives inaccurately recorded data, it can be replicated across other nodes, leading to widespread dissemination of the inaccuracies, resulting in systemic errors and inconsistencies that can be challenging to identify and correct.

How do enterprise apps maintain data consistency?

Data validation is often the first layer in achieving data consistency. It helps to ensure data accuracy by checking data against rules and standards to identify issues and ensure the data meets requirements for accuracy and completeness as it flows into the app.

However, data validation alone isn’t a complete solution for apps with distributed services. When data is ingested via multiple entry points and processed by multiple services, there is a chance that inconsistencies and inaccuracies will make their way into the data stream even though data inputs have been validated.

Downstream consistency checking is another method used to maintain data consistency. It aims to verify that the data stored in each service’s database is consistent, accurate, and complete. But there are significant limitations to this approach. Most notably, it shuts the barn door after the horse has bolted; inaccurate, incomplete, and inconsistent data has already propagated by the time it is identified.

An alternative to downstream consistency checks is to monitor the data stream for consistency, using techniques such as real-time anomaly detection to verify that the data in the stream is correct before it is consumed by other services. Real-time data stream monitoring allows businesses to react immediately to inconsistencies, including by modifying data in-flight or redacting anomalous data before it causes problems.

Data consistency is a critical aspect of distributed enterprise applications. It ensures that data is accurate, reliable, and timely across all services. However, achieving data consistency in distributed systems is a complex task that requires strategies such as eventual consistency, data validation, and real-time data monitoring.