Dgraph shards the data to horizontally scale to hundreds of servers. It is designed to minimize the number of disk seeks and network calls.
Dgraph is built like a search engine. Queries are broken into sub-queries, which run concurrently to achieve low-latency and high throughput.
With distributed ACID transactions, you can focus on your application logic, instead of worrying about data integrity.
Dgraph automatically runs synchronous replication, so losing a hard disk or a server doesn't affect your services.
Dgraph ensures that data is evenly balanced across servers by automatically moving shards, improving resource utilization for high performance.
Written entirely in Go, Dgraph is available under the Apache 2.0 license for easy adoption.
Quick iteration is important to keep your users happy. An adaptable flexible schema works with you as your application design evolves.
Dgraph provides a user interface, so you can browse and manage your data, making it easier to stay on top of things.
Dgraph internal key-value store, Badger is designed to reduce RAM usage and rely on SSD for performance. That's fast and cheap.
Dgraph can easily scale to multiple machines, or datacenters. Its sharded storage and query processing were specifically designed to minimize the number of network calls.
Dgraph’s commitment to performance, without sacrificing on critically important features like ACID transactions, all while providing excellent community support, has allowed us to focus more on our business logic and less on tuning knobs in our database.
Dgraph benefits from its team’s years of accumulation in the fields of web search, deep traversals, knowledge graph, etc., which effectively solves the problems of graph segmentation and large fan-out problem.
I spent 6 months building a graph API backed with a RDS database. It worked but we were battling several problems. The major roadblock came from the rows of data that had to be touched to get the deep graphs we wanted. After spending a week with Dgraph, we deleted 50K lines of code from our old API and generated a completed API simply by writing a single schema file declaring out types only. The amount of work and time that Dgraph saves is simply unimaginable! And on top of that, Slash makes it even easier with a fully managed service. Making a true graph db has never been easier.
I got the scoop on a new kick-ass GraphQL service that's amazing even in its very early stage. You put the SDL text of your schema and it gives you a usable crud implementation with the push of a button.
See how Dgraph compares to other databases such as Neo4j, TigerGraph, and more.FIND THE ONE THAT’S RIGHT FOR YOUSEE COMPARISON
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