Eventual consistency is a consistency model used in distributed systems, particularly in NoSQL databases. Unlike strong consistency, which guarantees that every read receives the most recent write, eventual consistency allows for temporary inconsistencies. This means that if you write data to one node in a distributed database, it might take some time for that update to propagate to all other nodes. During this period, different clients might read different versions of the data.

Eventual Consistency Model

This relaxed consistency model offers several advantages, especially in highly scalable and available systems:

  • Higher Availability: Systems can continue to accept writes even during network partitions or node failures.
  • Improved Performance: Writes can be acknowledged quickly without waiting for synchronization across all nodes.
  • Scalability: Easier to scale horizontally by adding more nodes.

However, eventual consistency introduces unique challenges for data quality. How do you ensure data accuracy and reliability when there's a possibility of temporary inconsistencies? This post explores the key data quality metrics relevant to NoSQL databases operating under eventual consistency and how different tools can help manage these challenges.

Data Quality Metrics in Eventually Consistent Systems

Traditional data quality metrics like accuracy, completeness, and validity are still relevant, but they need to be considered within the context of eventual consistency. Here are some critical metrics:

  • Recency: How recent is the data being read? In eventually consistent systems, there's a time window during which data might be stale. Measuring the recency of data helps understand the potential for reading outdated information.
  • Convergence Time: How long does it take for all nodes in the distributed system to agree on the same data value after a write? A shorter convergence time means less chance of encountering inconsistencies.
  • Read Repair Rate: How often do reads trigger a repair process to bring inconsistent data into a consistent state? A higher read repair rate can improve consistency but might impact read performance.
  • Error Rate During Convergence: What is the likelihood of errors or conflicts occurring while data is converging across nodes? This metric is crucial for understanding the reliability of the system during periods of inconsistency.

Challenges in Measuring Data Quality

Measuring data quality metrics in NoSQL databases presents distinct challenges compared to traditional relational databases. Several factors contribute to this increased complexity. One key difference lies in the handling of transactions. NoSQL databases often relax the ACID (Atomicity, Consistency, Isolation, Durability) properties that are fundamental to relational databases. This relaxation, while enabling greater scalability and availability, makes it more difficult to guarantee data consistency during updates.

Another challenge stems from the schema flexibility inherent in many NoSQL databases. The schema-less or flexible schema nature of these databases, while offering advantages in terms of data modeling agility, can make it difficult to define and consistently enforce data quality rules. Without a rigid schema, it becomes harder to establish clear expectations for data structure and content.

Finally, the distributed nature of NoSQL databases adds another layer of complexity. Data is frequently distributed across multiple nodes in a cluster, making it challenging to perform comprehensive data quality checks that consider the entire dataset. Gathering and analyzing data from various nodes to assess overall quality requires specialized techniques and tools.

Challenges in Measuring Data Quality
How Datachecks Helps in Eventually Consistent Environments

As an open-source data quality testing tool, Datachecks doesn't directly manage the consistency mechanisms of your NoSQL database. However, it plays a vital role in ensuring data quality after data has converged. In eventually consistent systems, validating that the final, consistent state of the data meets your quality requirements is crucial.

Here's how Datachecks helps:

  • Validating Post-Convergence Data: Datachecks allows you to define checks that verify data correctness after it has had time to propagate across your NoSQL cluster. This ensures that the eventually consistent state of your data is accurate and reliable.
  • Defining Data Quality Rules: You can define complex rules that check for data completeness, accuracy, validity, and other key metrics, even in the face of potential temporary inconsistencies.
  • Monitoring Data Quality Over Time: By running Datachecks regularly, you can track data quality trends and identify any persistent issues that might indicate problems with your data pipelines or consistency mechanisms.
  • Flexible Integration: Datachecks can be easily integrated into your existing workflows and CI/CD pipelines, enabling automated data quality checks.

For example, you can use Datachecks to:

  • Ensure that all required fields in a document are populated.
  • Validate data types and formats.
  • Check for data consistency across related documents or collections.

checks:
 - name: check_order_has_required_fields
   table: orders
   expression: order_id IS NOT NULL AND customer_id IS NOT NULL AND order_date IS NOT NULL
   type: row_condition
   description: Ensure all orders have required fields.

This configuration defines a check to ensure that all orders have the required fields: order_id, customer_id, and order_date. Datachecks supports a wide range of pre-built metrics, including Reliability, Numeric Distribution, Uniqueness, Completeness, and Validity.