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Organizations managing data lakes often face the challenge of enabling concurrent writes from various applications. Traditional methods necessitate additional infrastructure for coordination, leading to increased overheads, costs, and potential performance issues. Developers typically resort to client-side locking mechanisms through databases or dedicated locking services, resulting in complex multi-step processes.

Amazon S3 provides features that effectively tackle these concurrent write issues without the need for external coordination systems. By utilizing conditional writes and enforcing bucket-level policies, organizations can directly implement data consistency validation within their storage framework. Overwrite protection is a key aspect of this functionality. An ETag helps track changes in an object’s content, making it an excellent tool for monitoring object states. This native approach, employing conditional writes with ETag, eliminates the necessity for separate databases or coordination systems while ensuring reliable data consistency.

In this article, we delve into the process of building a multi-writer application using S3’s conditional write operations and bucket-level policy enforcement. We illustrate how to prevent accidental overwrites, implement optimistic locking patterns, and enforce consistency requirements, all without relying on external databases or coordination systems. By adopting these patterns, you can streamline your architecture, reduce expenses, and enhance scalability while preserving data integrity.

Solution Overview

In a standard data lake scenario, both datasets and their associated metadata need management. For instance, a bucket could be structured as follows:

mybucket/
    datasets/
        customer_profiles/
            year=2024/month=01/day=01/profile_data_001.parquet
        location_events/
            year=2024/month=01/day=01/location_events_001.parquet
    metastore/
        dataset_registry.json

This structure supports a data analytics platform that includes two essential datasets:

• The /datasets/ prefix holds parquet files containing customer and location data.
  • The /customer_profiles/ directory stores customer information such as IDs, names, email addresses, signup dates, and activity data.
  • The /location_events/ directory captures geographic data points, including customer IDs, coordinates, and timestamps.
• The /metastore/ prefix contains the dataset_registry.json file, which tracks metadata for all datasets, including file locations, partitioning details, record counts, schema versions, and last updated timestamps.

Historically, data consistency in data lake environments has been managed through older Hive-style implementations, which present two critical consistency requirements:

• Dataset files: When data pipeline processes ingest information from multiple sources, preventing the duplicate uploading of the same data file is vital. Without proper controls, these processes might inadvertently overwrite existing data files.
• Metastore: Each time a dataset is modified, the associated metadata in the registry must be updated accordingly. Multiple applications need to update the registry while ensuring that each one has a consistent view. Without proper controls, one application could overwrite changes made by another, leading to a disjunction between the registry and the actual data files.

Keeping this synchronized relationship between datasets and their metadata is crucial for data integrity and often requires external systems for coordination, increasing latency and operational costs. For example, consider the following flow:

1. A client attempts to acquire a lock via an external service.
2. If successful, the client reads the current state from S3.
3. The client makes modifications.
4. The client writes back to S3.
5. The client releases the lock in the external service.

Amazon S3’s conditional writes allow you to fulfill two distinct consistency requirements directly within the storage layer, establishing a coordinated workflow between dataset files and their registry entries:

• For dataset files: Applications can prevent duplicate uploads by utilizing If-None-Match conditions. This enforces write-once semantics, causing any attempt to upload an existing data file to fail. Data pipeline processes can retry operations safely without creating duplicates.
• For registry files in the metastore: After successfully uploading a dataset file, applications must update the registry. By employing compare-and-swap operations with If-Match using ETag, registry updates will only succeed when the ETag matches the one originally read by the application. This allows multiple applications to update the registry without the need for external coordination.

These operations function together as a coherent unit, ensuring dataset files are written exactly once and that the registry accurately reflects the current state of all dataset files. This coordination is critical as the registry acts as the definitive source for all applications accessing the data lake. Without this, applications might encounter inconsistent views of available data or overlook newly added datasets entirely. Bucket policies can enforce these consistency requirements at both the bucket and prefix levels, allowing for streamlined registry updates using native S3 controls:

1. Retrieve the registry directly from S3.
2. Make the necessary modifications to the registry contents.
3. Upload the modified registry using the If-Match condition to maintain atomic compare-and-swap operations.

Implementation Patterns in a Data Lake Environment

Here, we explore practical implementations of these patterns in a data lake setting, focusing on three common scenarios showcasing the utility of conditional writes:

1. Bucket policy enforcement for conditional writes: Uphold data integrity by validating write operations against established policies.
2. Object creation using If-None-Match: Manage partition boundaries while preventing duplicate entries.
3. Concurrent metadata updates using If-Match: Effectively handle multiple clients updating shared metadata at the same time.

To see these patterns in action, visit the GitHub repository. Additionally, if you’re interested in developing your management skills, check out this insightful blog post on motivating and retaining your team. For a deeper understanding of employee handbooks, refer to this authoritative resource. Lastly, if you’re curious about what a typical first week is like as an Amazon warehouse worker, this is an excellent resource to explore.

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