This article explains how Xiaohongshu built the REDgraph graph database to tackle ultra‑large social network queries, compares graph databases with traditional relational databases, showcases a Gremlin example, and highlights the scalability and efficiency benefits of storing relationships as first‑class citizens.
This article presents a comprehensive technical overview of ByteGraph, covering its evolution from 2.0 to 3.0, core graph modeling capabilities, Gremlin query interface, architectural layers, performance bottlenecks, cost‑reduction strategies, and future roadmap for large‑scale graph data services.
This article summarizes ByteDance engineer Chen Chao's DTCC 2022 talk on ByteGraph, covering its purpose, Gremlin query interface, three‑layer architecture, indexing strategies, distributed transaction handling, performance optimizations such as adaptive throttling and write‑amplification reduction, and integration with offline data pipelines.
ByteGraph is ByteDance’s internally developed graph database that stores and queries massive graph data efficiently, featuring a three‑layer architecture of query engine, storage engine, and disk storage, supporting Gremlin, partitioning, indexing, caching, high availability, and integration with online/offline data pipelines.
This article introduces ByteDance’s self‑developed graph database ByteGraph, covering its fundamentals, use‑case scenarios, data model and Gremlin query language, architecture and implementation details, and key challenges such as indexing, hot‑spot handling, resource allocation, high availability, and offline‑online data fusion.
To meet Baidu Chinese’s need for sub‑200 ms responses on multi‑hop queries across millions of dictionary entities, the team replaced MySQL with the open‑source HugeGraph graph database backed by RocksDB, deploying a multi‑master, REST‑enabled architecture with caching, bulk loading, and a data‑intervention platform to ensure fast, reliable traversal of semantic relationships.
This article explains how to use chaos engineering with Gremlin to design, execute, and analyze four experiments that test Kafka broker load, message loss, split‑brain scenarios, and ZooKeeper outages, helping improve the reliability and resilience of Kafka deployments.
This article details ByteGraph, the high‑performance graph database behind TikTok, covering its motivation over relational stores, data model, Gremlin query language, multi‑layer architecture, indexing, hot‑spot handling, and offline‑online data pipelines, while answering common technical questions.
The article offers a comprehensive technical deep‑dive into ByteDance’s home‑grown distributed graph database and graph‑processing engine, ByteGraph, covering its directed‑property graph model, Gremlin query support, multi‑layer architecture, storage strategies for massive data, and real‑world graph‑computing practices.
ByteGraph is ByteDance's home‑grown distributed graph storage and computation platform that supports massive social‑graph workloads with directed‑property models, Gremlin queries, multi‑layer architecture, adaptive KV storage, hot‑spot handling, indexing, and a roadmap toward cloud‑native, HTAP‑capable graph processing.
In this interview, JanusGraph PMC members Florian Hockmann and Jason Plurad discuss the project's origins, compare JanusGraph with Neo4j, share advice for production deployments, outline future expectations for JanusGraph and TinkerPop, and provide practical tips for graph modeling and community contribution.
This article presents an in‑depth technical overview of ByteGraph, ByteDance’s self‑built distributed graph database and its accompanying graph‑computing engine, covering graph data characteristics, the directed‑property graph model, API design, three‑tier system architecture, storage strategies using KV stores and B‑Trees, hotspot handling, indexing, and future research directions.
This article explains the rapid development of knowledge graphs, why graph databases like JanusGraph are preferred over relational databases for large‑scale semantic networks, and provides a step‑by‑step guide covering JanusGraph architecture, schema creation, Gremlin traversal language, server deployment, data import, and query examples.
The article explains how relational databases struggle with many‑to‑many and deep relationship queries, compares popular graph databases, details Titan's modular architecture, data model, Gremlin query examples, storage layout, and demonstrates its successful deployment at Paipaidai for large‑scale fraud detection, achieving over 25% efficiency gains.
Apache TinkerPop, now a top-level Apache project, offers a powerful graph computing framework with Gremlin, supporting real-time transactional processing and batch analytics across languages, scalable from single machines to massive clusters, making it essential for data mining, analysis, and large‑scale graph applications.
