Amazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 Tables | AWS News Blog (opens in new tab)

Amazon S3 Storage Lens has introduced three significant updates designed to provide deeper visibility into storage performance and usage patterns at scale. By adding dedicated performance metrics, support for billions of prefixes, and direct export capabilities to Amazon S3 Tables, AWS enables organizations to better optimize application latency and storage costs. These enhancements allow for more granular data-driven decisions across entire AWS organizations or specific high-performance workloads.

Enhanced Performance Metric Categories

The update introduces eight new performance-related metric categories available through the S3 Storage Lens advanced tier. These metrics are designed to pinpoint specific architectural bottlenecks that could impact application speed.

• Request and Storage Distributions: New metrics track the distribution of read/write request sizes and object sizes, helping identify small-object patterns that might be better suited for Amazon S3 Express One Zone.
• Error and Latency Tracking: Users can now monitor concurrent PUT 503 errors to identify throttling and analyze FirstByteLatency and TotalRequestLatency to measure end-to-end request performance.
• Data Transfer Efficiency: Metrics for cross-Region data transfer help identify high-cost or high-latency data access patterns, suggesting where compute resources should be co-located with storage.
• Access Patterns: Tracking unique objects accessed per day identifies "hot" datasets that could benefit from higher-performance storage tiers or caching solutions.

Support for Billions of Prefixes

S3 Storage Lens has expanded its analytical scale to support the monitoring of billions of prefixes. This allows organizations with massive, complex data structures to maintain granular visibility without sacrificing performance or detail.

• Granular Visibility: Users can drill down into massive datasets to find specific prefixes causing performance degradation or cost spikes.
• Scalable Analysis: This expansion ensures that even the largest data lakes can be monitored at a level of detail previously limited to smaller buckets.

Integration with Amazon S3 Tables

The service now supports direct export of storage metrics to Amazon S3 Tables, a feature optimized for high-performance analytics. This integration streamlines the workflow for administrators who need to perform complex queries on their storage metadata.

• Analytical Readiness: Exporting to S3 Tables makes it easier to use SQL-based tools to query storage trends and performance over time.
• Automation: This capability allows for the creation of automated reporting pipelines that can handle the massive volume of data generated by prefix-level monitoring.

To take full advantage of these features, users should enable the S3 Storage Lens advanced tier and configure prefix-level monitoring for buckets containing mission-critical or high-throughput data. Organizations experiencing latency issues should specifically review the new request size distribution metrics to determine if batching objects or migrating to S3 Express One Zone would improve performance.