As AI systems become more autonomous and interactive, the demands on data infrastructure are accelerating. From RAG and real-time personalization to anomaly detection and agentic decision-making, modern AI applications depend on ultra-fast, targeted access to the right data at massive scale.
But in most systems, even simple queries require full-table scans, deep partitioning, or pre-aggregation just to deliver acceptable performance - especially as data volumes grow. That’s a critical bottleneck for AI pipelines that need to respond in milliseconds, not minutes.
To solve this, VAST is introducing Sorted Tables for Log-Time Search, a new performance optimization that enables near-logarithmic query speeds (O(logN)) on live, structured datasets. In simple terms, this means query times grow very slowly - even as data volumes explode. By simply sorting tables once upon ingestion, users gain 100x+ acceleration on point queries and key lookups, without indexing, re-clustering, or partition tuning.
Sorted Tables for Log-Time Search is just one example of what’s possible with VAST’s underlying DASE architecture. The VAST AI Operating System offers the first and only unified, all-flash system that brings together storage, database, stream, and vector processing in a single, high-performance environment purpose-built for the real-time demands of modern AI and analytics.
The AI Challenge: Performance Bottlenecks Block Inference
Modern AI workloads depend on blazingly fast, selective access to data that already exists. Whether retrieving a specific document, fetching a set of embeddings, or looking up behavioral signals for scoring, inference often begins with a single question: how fast can I get the row I need?
But traditional systems struggle under this pressure. They rely on full-table scans, brute-force filtering, or rigid indexing strategies that don’t scale, introducing latency, inconsistency, and operational complexity. These performance bottlenecks delay inference, reduce responsiveness, and limit the real-time potential of AI systems in production.
Introducing Sorted Tables: Log-Time Speed Without Complexity
Sorted Tables for Log-Time Search enable AI pipelines to retrieve data with near-logarithmic performance by simply sorting tables once upon ingestion. No re-indexing. No tuning. Just intelligent, infrastructure-level speed that scales with your data.
- 100x Faster Point Lookups for Inference: Instantly retrieve the exact rows or records needed to power AI responses. Benchmarks show 100x speedups on single-key queries and 25x faster results for multi-key access patterns, even at massive scale.
- Sub-Millisecond Access to Context and Features: Retrieve embeddings, feature sets, or reference documents in real time, even from datasets spanning billions to trillions of rows—without relying on caching, materialized views, or heavy filters.
- Always-On Query Speed, No Tuning Required: Forget manual partitioning, secondary indexes, and re-clustering. Sorted Table Search makes your tables immediately and predictably performant, regardless of data growth or structure.
Beyond AI: Faster Analytics at Massive Scale
The performance impact of Sorted Tables extends well beyond AI pipelines. Analytics workloads from ad hoc queries to complex reporting benefit equally from sub-second responsiveness at massive scale, whether powering dashboards in Tableau, Power BI, or internal analytics platforms. Unlike traditional systems that rely on static partitions, materialized views, or caching to approximate speed, Sorted Tables delivers native performance directly on live data.
Point Query Time (Unsorted vs. Sorted Tables):
Take a look at this performance graph. This graph shows how query performance scales with table size. For Sorted tables (in blue), point query times remain nearly flat—even as the dataset grows from zero to 10 billion rows, response times stay close to zero milliseconds.
In contrast, unsorted tables (in orange) show a steep, linear increase in query latency, rising from around 2,500 milliseconds to nearly 10,000 milliseconds as the row count increases, highlighting the dramatic performance benefit of sorted indexing.
Results per Second at Varying Concurrency
This next graph compares throughput (results per second) between Sorted (in blue) and unsorted (in orange) tables as client concurrency increases. For unsorted tables, throughput plateaus around 2,600 results per second, showing little to no scaling with more clients. In contrast, Sorted tables start strong at 50,000 results per second with a single client, and scale linearly, reaching 100,000 results per second with four clients, demonstrating both high efficiency and scalability under load.
Powered by DASE: Pushing Performance to the Extreme
Sorted Tables’ performance gains are only possible because of VAST’s foundation: the DASE architecture. While traditional architectures separate compute and storage or depend on pre-partitioned, replicated data across silos, DASE enables direct, high-speed access to globally shared data without duplication or delay.
By operating directly on this unified, all-flash data fabric, LogN leverages the full potential of the system’s parallelism and storage locality to deliver 100x+ performance gains on point lookups and key-range filters. DASE ensures that these gains scale linearly—even across billions to trillions of rows, making LogN not just fast, but exponentially efficient as your data grows.
Performance that Powers Intelligence
With Sorted Table Search, VAST is redefining how AI systems interact with structured data, turning query performance into a competitive edge for real-time, context-aware intelligence. By delivering 100x+ faster access to the exact signals AI agents and applications need, Sorted Tables transform massive datasets from a bottleneck into an engine for rapid, reliable decision-making.
And this is just one layer of the broader VAST vision: a unified, AI-native platform that brings together storage, database, stream, and vector processing to power the next generation of intelligent systems - without tradeoffs or compromise.
Tired of scanning billions of rows to answer simple queries? Get in touch to learn how Sorted Tables on VAST can power real-time insights across your most critical workloads.
And join the discussion about Sorted Tables on Cosmos, the community for AI practitioners and enthusiasts.
