Copy-On-Write (COW) and Merge-On-Read (MOR) in Open Table Formats: Delta, Hudi, Iceberg

Modern data lakes have become indispensable for handling large-scale, real-time data processing. Open table formats like Apache Hudi, Delta Lake, and Apache Iceberg are key technologies enabling transactional capabilities to the lake, a standard functionality in traditional databases and data warehouses. These formats rely on two primary approaches for managing data updates: Copy-On-Write (COW) and Merge-On-Read (MOR). Each method offers unique benefits and trade-offs, making them suitable for different scenarios.

Copy-On-Write (COW)

Copy-On-Write ensures data updates are applied by rewriting the affected files entirely. A new version is created when a file is updated, leaving older versions intact for use cases like time travel or auditing.

Workflow

1. Identify the file(s) containing the data to be updated.
2. Apply the changes in-memory.
3. Write the modified file(s) back to storage.

COW Approach

Advantages

• Data Consistency: Updates are immediately reflected in the query results, providing strong consistency guarantees.
• Simplicity: Query engines do not require additional runtime merging logic, making reads straightforward.
• Optimized Read Performance: Since updates are applied upfront, queries avoid the computational cost of merging changes during execution.

Disadvantages

• High Write Latency: Rewriting large files is resource-intensive and time-consuming, making this approach less ideal for frequent updates.
• Increased Storage I/O: Repeated rewriting amplifies storage and I/O overhead, especially for substantial datasets.

 Best Fit Use Cases

• Batch Workflows: Works well for scenarios with infrequent updates, such as periodic ETL processes or end-of-day reporting.
• Analytical Queries: Ideal for workloads demanding low-latency, consistent query results.

Merge-On-Read (MOR)

Overview

Merge-On-Read optimizes write performance by storing updates as delta logs rather than modifying base files directly. These deltas are merged with the base data dynamically during query execution.

Workflow

1. Record updates in separate delta files.
2. Combine base files with delta logs at runtime during queries.

MOR Approach

Advantages

• Faster Writes: Logging updates instead of rewriting files significantly improves write throughput.
• Cost-Efficiency: Minimizes immediate I/O and storage requirements by avoiding frequent rewrites.
• Enhanced Features: Facilitates time travel, incremental processing, and real-time analytics.

Disadvantages

• Increased Query Latency: Query engines must perform on-the-fly merging, which can slow down query execution.
• Higher Complexity: The need for runtime merging introduces additional computational and implementation complexity.

Best Fit Use Cases

• Real-Time Processing: Suited for high-throughput workloads such as IoT data ingestion or streaming pipelines.
• Change Data Capture (CDC): Ideal for tracking and querying incremental changes.

Comparing Open Table Formats

Delta Lake

• Primary Approach: COW for robust transactional updates.
• Strengths: Strong ACID guarantees, intuitive for batch analytics, and seamless integration with Apache Spark.
• Limitations: Relatively slower write performance for frequent updates.
• Ideal Scenarios: Batch ETL workflows, machine learning feature stores, and analytics demanding consistency.

Apache Hudi

• Primary Approaches: Supports both COW and MOR, offering flexibility to switch based on workload requirements.
• Strengths: Optimized for both streaming and batch use cases, with indexing for efficient updates and deletes.
• Limitations: Configuration complexity, especially when utilizing MOR.
• Ideal Scenarios: Streaming pipelines, CDC applications, and data lake consolidation.

Apache Iceberg

• Primary Approach: Primarily COW with optional support for incremental changes resembling MOR.
• Strengths: Advanced features like schema evolution, partitioning, and time travel.
• Limitations: Slower write speeds in high-frequency update scenarios compared to Hudi’s MOR implementation.
• Ideal Scenarios: Analytical queries involving schema evolution, compliance audits, and large-scale multi-engine environments.

 Key Insights

• Apache Hudi excels in MOR scenarios, balancing performance and flexibility for streaming and incremental workloads.
• Delta Lake is optimized for COW, prioritizing consistency and simplicity for batch processing and analytics.
• Apache Iceberg provides a robust framework for advanced use cases like schema evolution, multi-engine compatibility, and compliance needs.

By carefully evaluating the trade-offs between Copy-On-Write and Merge-On-Read, organizations can align their data lake strategies with workload requirements to achieve maximum efficiency, scalability, and performance.