Authors
Keywords
Abstract
Retail enterprises face unprecedented challenges in synthesizing product information from mul- tiple operational sources—pricing systems, inventory platforms, assortment engines, fulfillment networks, and promotional calendars—while maintaining sub-30ms query latencies for millions of concurrent search and recommendation requests [1].
This white paper presents the design and implementation of a production-scale Product Knowl- edge Graph (PKG) deployed in a major retail environment, built on DataStax Enterprise Graph (DSEGraph) integrated with Apache Cassandra for persistentstorage[2]. The systemingests dynamic operational signals from Pub/Sub message queuing systems via Google Cloud Dataflow streaming jobs for attributes (pricing, inventory, assortment, fulfillment, and promotions), while foundational product catalogs, taxonomies, and enriched attributes flow through Apache Spark and Apache Beam batch pipelines from analytical warehouses and object storage [3].
The PKG models products, categories, brands, attributes, content, and contextual entities as alabeledpropertygraphwithspecializedsupportfortemporalproductinformationupdates,and exposescapabilitiesthroughSpringBootandMicronautmicroservicesachievingconsistentlylow query latencies (20–30ms at P50 for sustained throughput) [4].
The paper details: (1)a hybrid batch and dynamic data integration architecture combining Data flow streaming and Spark/Beam batch processing; (2) schema design optimized for DSE Graph withtemporaldimensionsfordynamicattributes;(3)readandwriteperformancecharacteristics;
(4) generic search, navigation, and recommendation patterns; and (5) operational lessons from production deployment across three geographic data centers with 99.95% availability [5].
Keywords: knowledge graph, DSE Graph, Cassandra, real-time streaming, Pub/Sub, Dataflow, retail search, recommendations, microservices, temporal data management.
