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Eliminating Query Latency at Cloud Scale: Heuristic Search Space Partitioning for Multi-Tenant Data

At Palo Alto Networks, broad security queries against hundreds of millions of cloud resource records produced high response times which lead to lower throughput. Traditional fixes — sharding, materialized views — all require schema changes. This talk presents HSSPS (Heuristic Search Space Partitioning System): a query-time optimization layer that logically partitions search space without touching the database schema. Deployed in production and covered by two granted U.S. patents, HSSPS reduced P95 latency by over 95%, increased throughput 8–10×, and cut infrastructure cost by ~50%. A reusable pattern for any multi-tenant system facing the same problem.

22 minutes
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Rama Teja Repaka, Principal Software Engineer at Palo Alto Networks

Rama Teja Repaka is a Principal Software Engineer at Palo Alto Networks with over a decade of experience building large-scale distributed systems. His work spans query processing, search infrastructure, streaming data platforms, and cloud-native systems. He has contributed to the evolution of the Resource Query Language (RQL), developing query parsers, search and indexing capabilities, execution engines, and performance optimizations for complex queries. More recently, he has been building petabyte-scale data ingestion and stream processing systems using Apache Flink, Kafka, Kubernetes, and Java. His interests include distributed systems, information retrieval, stream processing, and AI-powered developer tools.

Prashant Pathak, Sr. Principal Software Performance Engineer at Palo Alto Networks

Prashant Pathak is a Senior Principal Software Performance Engineer at Palo Alto Networks, where he leads query optimization and performance architecture for large-scale cloud security infrastructure. He holds two granted U.S. patents in distributed query optimization with production deployments achieving over 95% query latency reduction and 8-10x throughput improvement. Prior to PAN, Prashant was a Performance and Scalability Engineer at Apple, where he was responsible for multiple Apple Pay transit launches across major cities in Asia and North America, as well as performance infrastructure for Apple Card. Prashant's work spans query optimization, distributed systems performance, cloud infrastructure observability, and machine learning for database cost prediction.

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