Eventual Consistency

What is Eventual Consistency?

Eventual Consistency is a data consistency model used in distributed systems where updates made to data will eventually be propagated and reflected across all replicas. It allows for high availability and scalability without sacrificing data integrity.

How does Eventual Consistency work?

In an eventually consistent system, when a write operation is performed on a replica, it is not immediately applied to all other replicas. Instead, the replicas eventually converge to a consistent state, where all updates are reflected across the system. This convergence can be achieved through mechanisms like gossip protocols, conflict resolution algorithms, or version vectors.

Why is Eventual Consistency important?

Eventual Consistency is important in distributed systems for several reasons:

  • Availability: By allowing replicas to operate independently and handle updates locally, eventual consistency ensures high availability even in the presence of network partitions or failures.
  • Scalability: Eventual consistency enables distributed systems to scale horizontally by adding more replicas and distributing the load, as updates can be processed independently on each replica.
  • Performance: With eventual consistency, write operations can be performed locally on a replica without waiting for synchronous replication across all replicas, resulting in lower latency.
  • Data Processing and Analytics: Eventual consistency is particularly beneficial in data processing and analytics scenarios where real-time insights are not required, allowing for efficient batch processing and analytics on large volumes of data.

The most important Eventual Consistency use cases

Eventual Consistency can be applied to various use cases in distributed systems:

  • Content Delivery Networks (CDNs): CDNs replicate web content across multiple geographically distributed servers, ensuring faster content delivery to end-users. Eventual consistency allows for efficient distribution and synchronization of content across the CDN nodes.
  • Distributed Databases: Eventual consistency is commonly used in distributed databases to achieve scalability and fault tolerance. Updates can be applied locally on a replica and then propagated asynchronously to other replicas.
  • Collaborative Editing: Eventual consistency is crucial in collaborative editing environments where multiple users can edit the same document simultaneously. Changes made by different users are eventually merged into a consistent document.
  • Strong Consistency: In contrast to eventual consistency, strong consistency ensures that all replicas have the most up-to-date data at all times. Achieving strong consistency often comes at the cost of availability and scalability.
  • Conflict Resolution: Conflict resolution mechanisms are used in eventually consistent systems to resolve conflicts that may arise when updates are applied independently on different replicas.
  • Replication: Replication involves creating and maintaining multiple replicas of data across distributed systems to improve availability, fault tolerance, and performance.
  • Data Lakehouse: A data lakehouse is a unified architecture that combines the best features of a data lake (scalability, cost-effectiveness) and a data warehouse (structured querying, performance) to provide a unified data platform for analytics and processing.

Why would Dremio users be interested in Eventual Consistency?

Dremio is a data lakehouse platform that enables organizations to efficiently query and analyze data stored in various data sources. Eventual consistency can be beneficial for Dremio users in several ways:

  • Scalability: Eventual consistency allows Dremio to scale horizontally by distributing query processing across multiple replicas, improving performance and handling large workloads.
  • Data Availability: With eventual consistency, Dremio can ensure high availability of data by allowing replicas to operate independently, even in the presence of network partitions or failures.
  • Batch Processing: Eventual consistency enables efficient batch processing in Dremio by allowing updates to be processed independently on each replica, reducing latency and optimizing resource utilization.
  • Data Integration: Eventual consistency facilitates the integration of data from different sources within Dremio, ensuring eventual convergence of updates across replicas and maintaining data integrity.
get started

Get Started Free

No time limit - totally free - just the way you like it.

Sign Up Now
demo on demand

See Dremio in Action

Not ready to get started today? See the platform in action.

Watch Demo
talk expert

Talk to an Expert

Not sure where to start? Get your questions answered fast.

Contact Us

Ready to Get Started?

Bring your users closer to the data with organization-wide self-service analytics and lakehouse flexibility, scalability, and performance at a fraction of the cost. Run Dremio anywhere with self-managed software or Dremio Cloud.