Introduction to DGraph - A Graph Database
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The document provides an introduction to Dgraph, a graph database designed for efficient storage and querying of interconnected data. It outlines the advantages of graph databases over traditional SQL databases, particularly in handling complex relationships, and describes the key components and functionalities of Dgraph. Additionally, it offers guidance on how to get started with Dgraph, including installation methods and basic schema concepts.
Introduction to DGraph - A Graph Database
- 1. Introduction to DGraph - A Graph database Akshansh Jain Software Consultant Knoldus Inc.
- 2. Agenda ● Brief Introduction to Graph database ● Why graph database? ● Applications of graph database ● Introduction to DGraph ● Components of DGraph ● How to get started with DGraph? ● RDF Triple ● DGraph Schema ● DGraph Mutation ● A Glimpse at GraphQL +/-
- 3. Brief Introduction to Graph Database A graph database is essentially a collection of nodes and edges. Each node represents an entity (such as a person or business) and each edge represents a connection or relationship between two nodes. Every node in a graph database is defined by a unique identifier, a set of outgoing edges and/or incoming edges and a set of properties expressed as key/value pairs.
- 5. Why Graph Database? A graph database is a database optimized for storing and querying graphs. When it comes to relationships, graph databases are much faster than SQL databases. SQL databases get bogged down by graph-like data because following edges means joining tables; sometimes big tables; the more edges, the more joins, and the more data needs to be loaded and processed. In a graph database, the edges are a fundamental structure, so following edges is a single lookup, making this operation blazingly fast.
- 6. Applications of Graph database ● Interconnected data, like SQL tables requiring joins ● Advanced search ● Recommendation engines ● Pattern detection ● Networks, like computers, roads, and telecommunications ● Processes, like business and biological processes ● Events and the causality or other links between them ● Structures of firms or markets
- 7. Introduction to DGraph DGraph is a scalable, distributed, highly available and fast graph database.
- 8. Components of DGraph ● DGraph Zero - controls the Dgraph cluster, assigns servers to a group and re-balances data between server groups. ● DGraph Server - hosts predicates and indexes. ● DGraph Ratel - serves the UI to run queries, mutations & altering schema.
- 9. How to get started with DGraph? ● You can pull the docker image from DGraph and run it inside a docker container. ● You can install DGraph from binaries. ● You can download docker-compose.yaml file, provided in DGraph docs, and use docker compose to get DGraph up and running.
- 10. RDF Triple S P O ● Triple - A statement in "subject/predicate/object" form. ● RDF Triple - Every part of the triple has a URI associated with it.
- 11. DGraph Schema ● Schema defines the types of the predicates that are stored by the DGraph. ● Following types are supported by DGraph - ○ default ○ int ○ float ○ string ○ bool ○ dateTime ○ geo ○ password
- 12. DGraph Mutation ● Adding or removing data in DGraph is known as mutation. ● Can be done using triples or JSON.
- 13. A Glimpse of GraphQL +/-
- 14. References ● DGraph docs ● Stackoverflow answer on what are RDF Triples ● Official Slack of DGraph
- 15. THANK YOU!