Heuristic Search Space Partitioning for Low-Latency Multi-Tenant Cloud Queries
Prashant Kumar Pathak, Chandra Biksheswaran Mouleeswaran, Rama Teja Repaka

TL;DR
This paper introduces HSSPS, a query optimization system that partitions search space to significantly reduce latency and improve throughput in large-scale multi-tenant cloud query environments.
Contribution
HSSPS is a novel, schema-agnostic, query-time optimization layer that partitions search space dynamically to reduce latency and increase throughput in multi-tenant cloud systems.
Findings
Achieved 50-97% latency reduction in tests
Real-world deployment reduced P95 latency from 61s to 2s
Improved throughput by 8-10x and reduced active sessions by 41x
Abstract
Large-scale cloud security platforms must continuously query millions of structured cloud resource records distributed across thousands of tenant accounts. Broad, account-spanning queries saturate database infrastructure, producing P95 latencies exceeding 60 seconds. We identify buffer cache pressure as the dominant latency driver: in a controlled experiment, the same query executing with the same plan completed in 3.7 seconds when its working set was memory-resident and 94 seconds when concurrent load had evicted those pages. No query plan optimization can address this; the only effective intervention is reducing the number of pages each query must touch. We present the Heuristic Search Space Partitioning System (HSSPS), a query-time optimization layer that logically partitions the search space through dynamic predicate injection, without schema modification. A two-phase heuristic…
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