Scaling Iceberg Writes with Confidence: A Conflict-Free Distributed Architecture for Fast, Concurrent, Consistent Append-Only Writes

Apache Iceberg is a cornerstone table format in modern data lakehouse systems. It is renowned for its ability to deliver transactional consistency, schema evolution, and snapshot isolation through a metadata-driven architecture. At the heart of Iceberg’s design lies its atomic commit mechanism, which ensures data integrity by serializing metadata updates so that only one writer can modify the table’s state at any given time. This mechanism is essential for maintaining consistency: it prevents concurrent modifications from clashing, guaranteeing that each snapshot reflects a coherent, point-in-time view of the table. While this approach excels in ensuring reliability, it introduces a significant challenge in distributed environments with high-concurrency write operations. 

The serialization of commits can result in commit contention, where multiple writers vie to update the metadata. This leads to operation retries, diminished throughput, and scalability limitations. This bottleneck becomes increasingly critical as the demand for real-time data ingestion intensifies, particularly in systems with many concurrent writers.

In this blog, we will delve into the technical underpinnings of Iceberg’s atomic commit model, explore its scalability constraints, and propose a distributed architecture that separates Parquet file writing from metadata commits to eliminate contention while preserving Iceberg’s consistency guarantees. So, without further ado, let’s dive in. 

The Evolution of Data Lakes–Scale Without Structure

Data lakes emerged in the early 2000s as a solution to traditional data warehouses’ rigidity and cost limitations. By taking advantage of distributed storage systems like HDFS or Amazon S3, data lakes enabled the storage of massive volumes of raw data in its native format. A key advantage was their ability to support parallel writes: multiple ingestion pipelines, such as those fed by Apache Kafka, could write data concurrently without coordination, achieving high throughput. 

However, this design sacrificed consistency. Without transactional semantics, data lakes struggled to provide reliable data views, often resulting in partial writes, ingestion failures, and overlapping modifications that left data indeterminate. These limitations rendered early data lakes unsuitable for use cases demanding correctness and consistency.

The Rise of Lakehouses and Apache Iceberg–Consistency Meets Scale

The lakehouse paradigm combines data lakes' flexibility and scalability with traditional databases' consistency. Apache Iceberg has become a leading table format in this field. 

Iceberg’s architecture is based on immutable snapshots, each providing a consistent, point-in-time view of the table. This design isolates readers from any in-progress write operations. Its key features include:

• ‍Atomic Commits: Every write operation–an append, update, or delete–results in a new snapshot through an atomic metadata update. This process ensures that only one writer can change the table state at a time, maintaining consistency.‍
• Schema Evolution: Iceberg allows seamless changes to the schema without disrupting existing data pipelines.‍
• Time Travel: Users can query historical snapshots for purposes such as auditing, debugging, or ensuring regulatory compliance.

The atomic commit mechanism is the foundation of Iceberg’s consistency model. It functions through a single, indivisible action, typically an atomic file system rename or a check-and-put operation in a metastore to update the table’s metadata pointer. By serializing these updates, Iceberg prevents conflicts arising from concurrent modifications, ensuring that the table’s state transitions smoothly between snapshots. This mechanism ensures that only one writer can modify the table’s state at a time, thus preventing inconsistencies.

Conflict Resolution and Retry in Apache Iceberg

Iceberg pairs its atomic commit model with a conflict resolution and retry mechanism to manage concurrent writes. When multiple writers attempt to commit changes based on the same metadata version, only one succeeds, while the others must retry by rebasing their changes onto the updated version. The feasibility of a retry depends on the operation type:

• Append Operations: These can always be applied to the new version by adding new files to the latest snapshot.
• Replace Operations: These require verification that the files targeted for replacement remain in the table.
• Delete Operations: Deletes targeting specific files must confirm those files remain; expression-based deletes can always be applied.‍
• Schema and Partition Changes: These must ensure that the schema or partition specification has not changed since the base version.

Append-Only Example

Consider a scenario with two writers appending data to an Iceberg table:

• Initial State: Snapshot S1 contains fileA.parquet and fileB.parquet.
• Concurrent Writes:
  
  • Writer X appends fileC.parquet, preparing snapshot S2 based on S1.
  • Writer Y appends fileD.parquet and prepares snapshot S2 based on S1.
• Conflict: Both writers attempt to commit S2. Writer X succeeds in updating the metadata to S2 (fileA.parquet, fileB.parquet, fileC.parquet), while Writer Y fails.
• Resolution:
  
  • Writer Y re-reads the new current snapshot S2.
  • Writer Y adds fileD.parquet to S2 as an append operation, preparing snapshot S3 (fileA.parquet, fileB.parquet, fileC.parquet, fileD.parquet).
  • Writer Y retries the commit, succeeding if no further conflicts arise.

This process ensures a consistent view of the table’s state but can degrade performance in high-concurrency settings due to frequent retries.

The Atomic Commit Bottleneck–Contention at Scale

Although atomic commits and conflict resolution guarantee consistency, they impose a serialization point that hampers scalability in high-concurrency scenarios. For instance, in a system with tens or hundreds of distributed writers attempting commits every second, only one commit succeeds per cycle, forcing the others into retry loops. As concurrency rises, contention intensifies, resulting in:

• Starvation: Some writers face repeated failures, delaying their commits indefinitely.
• Throughput Reduction: The system’s commit rate falls below the target rate.
• Data Risks: Extended retries may trigger timeouts, potentially leading to dropped commits.

This bottleneck is especially pronounced in real-time ingestion pipelines where low latency and high write frequency are essential.

A Conflict-Free Architecture–Scaling Iceberg with Confidence

To overcome this limitation, we propose an architecture that separates data writing from metadata commits, eliminating contention while maintaining consistency:

• Distributed Parquet Writers: Independent writers batch data and write Parquet files to a staging area in S3, using unique partitions or paths to prevent overlap.
• Metadata Queue: After each write, writers send metadata (e.g., file path, partition values) to a distributed queue, such as NATS.
• Centralized Committer: A single-threaded service consumes metadata from the queue, aggregates it, and executes a single atomic commit at fixed intervals (e.g., every 5 seconds).

This design allows writers to operate at maximum throughput without competing for metadata updates. The centralized committer ensures consistency by serializing metadata changes in a controlled, predictable manner.

Real-World Example–IoT Data Ingestion at Scale

Consider an IoT system with thousands of sensors streaming telemetry data into an Iceberg table. In the traditional model, each sensor’s ingestion service would attempt independent commits, causing severe contention. With the proposed architecture:

• Writers: Each service writes Parquet files to S3 (e.g., s3://bucket/staging/sensor123/file.parquet) and queues the metadata.
• Committer: Every 5 seconds, the committer collects queued metadata, constructs a new snapshot, and commits all files atomically.

This configuration enables seamless scaling of ingestion while preserving consistency, providing near-real-time data availability.

Challenges and Considerations

• Writer Failures: If a writer fails after writing a file but before queuing its metadata, the file will not be included in the snapshot. Mitigation options include write-ahead logs or mechanisms to detect orphaned files.
• Committer Reliability: The single-threaded committer must be highly available, potentially requiring failover strategies.
• Append-Only Focus: This architecture is tailored to append-only workloads. Supporting updates and deletes would necessitate additional coordination mechanisms.

Conclusion

Apache Iceberg’s atomic commit model ensures transactional consistency but introduces a scalability bottleneck under high-concurrency conditions. The proposed architecture decouples data writing from metadata commits, eliminating contention and enabling high-throughput, distributed writes while preserving Iceberg’s consistency guarantees. This solution positions a novel architecture to Apache Iceberg Writes to address the demands of modern, real-time data workloads, offering a scalable foundation for the future of lakehouse systems.

‍

‍

‍

‍

‍

‍

‍