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Dgraph is an open source, scalable, distributed, highly available and fast graph database, designed from ground up to be run in production.
• Production Features • Data Sharing • Horizontal Scalability • Linearizability aka Atomic Consistency • Hot backups in RDF format available using the HTTP interface • HTTP API for queries and mutations • Communication using clients over binary protocol using grpc • Bulk loading of graph data • Optional schema • Geospatial Queries • GraphQL like which responds in JSON • SSL/TLS and auth token based security (support by v1.0)
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Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
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Dgraph can run complex distributed queries involving filters, string matching, pagination, sorting and geolocations blazingly fast.
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Dgraph is an open source, scalable, distributed, highly available and fast graph database, designed from ground up to be run in production. Dgraph started with the idea that every startup should be able to have the same level of technology as run by big giants. Dgraph was designed from ground-up to allow data sharing, horizontal scalability, consistent replication, and a fast and distributed architecture. By building a truly robust piece of technology, Dgraph can have users run only one database, which allows arbitrarily complex queries while providing rock solid performance.
The recent release of Dgraph is packed with new features and improvements. Many of them are related to strings - full text search (with support for 15 languages!) and regular expression matching have been added, and handling of string values in multiple languages was greatly improved. All of these changes make Dgraph an excellent tool for working with multilingual applications. Dgraph does not do any query caching. While Dgraph runs very well on (Linux) Thinkpads, it is designed to be a graph database for production. As such, it allows the ability to share and distribute data over many servers. Consistency and fault tolerance are baked deep into Dgraph , to the point where even tests need to start a single-node Raft cluster. All the writes, irrespective of which replica they end up on, can be read back instantaneously, i.e. linearizable (work in progress, ETA v0.8). A few server crashes would not lose data or affect the end-user queries, making the system highly-available.
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