Oracle Labs Parallel Graph Analytics (PGX)
Enterprise Graph Analytics APIs, PGQL Optimization & Modern Gradle Infrastructure
Engineering Overview
Developing high-throughput REST and binary APIs for Oracle's Parallel Graph Analytics (PGX) engine, alongside orchestrating enterprise build system migration to Gradle 8.
The Concurrency & Build Legacy
High-scale graph analytics require traversing multi-million node datasets under sub-second SLAs without memory exhaustion. Meanwhile, core analytics libraries were constrained by legacy Gradle 5 builds, causing protracted CI feedback cycles and dependency version locks across engineering squads.
The Engineering Solution
Engineered optimized REST endpoints and memory-mapped buffers for PGX graph query execution in PGQL/SQL, cutting memory footprint by 25%. Spearheaded the Gradle 5 to Gradle 8 upgrade across core analytics modules, cutting CI build times by 40% and authoring ADRs to enforce modern Java concurrency standards.
Technologies & Systems
Measurable Results
-40% CI Build Duration
Modernized build cache and parallelized test execution with Gradle 8, drastically accelerating squad turnaround.
-25% Memory Footprint
Optimized graph query serialization and buffer pooling for high-concurrency PGQL analytic queries.
99.9% Production SLA
Maintained high availability and sub-second query execution across multi-tenant enterprise customer benchmarks.
PGX Architecture & Modernization Diagram
Graph analytics query execution pipeline and modernized Gradle build infrastructure
