This article introduces graph databases, explains their advantages over relational databases, and provides concise overviews of popular open‑source options such as Neo4j, ArangoDB, Dgraph, and many others, including key features, performance traits, and download links.
This article introduces graph databases, explains Dgraph concepts and installation via Docker, details Dgraph4j Java client usage including schema setup, querying, mutations, and data monitoring, and explores applications such as equity relationship analysis and data visualization.
This article introduces graph databases as a NoSQL solution, explains property‑graph modeling, compares relational and document stores, evaluates several graph products, and details Dgraph’s architecture, indexing, query language, and real‑world business applications such as knowledge graphs and equity‑relationship analysis.
This article details Beike's journey of storing and querying a 480‑billion‑triple industry graph in milliseconds, covering graph database fundamentals, a comparative evaluation of JanusGraph and Dgraph, the design and deployment of a Docker‑K8s based Dgraph platform, data ingestion pipelines, a custom Graph‑SQL layer, performance testing, optimizations, and future roadmap.
This article presents Beike's experience building a large‑scale graph database platform, covering the need for graph databases, technology selection between JanusGraph and Dgraph, detailed architecture, data ingestion pipelines, query interfaces, performance benchmarks, and future roadmap.
ManishRai Jain recounts his journey from Google’s single‑process Graphd, built for Freebase, to creating Dgraph, a distributed graph‑database that shards SPO triples by predicate, avoids fan‑out broadcasts, and supports deep traversals, illustrating the technical evolution and design choices behind modern scalable graph systems.
