Graph Database Query Languages You Should Try

Graph query languages (GQLs) are essential for managing and extracting data from graph databases, with each language offering unique features and catering to different needs. GraphQL is a modern alternative for REST-based architectures, providing flexibility and precise data requests, especially beneficial for complex microservices and legacy infrastructures, though it may face performance issues with nested queries. Gremlin, part of Apache TinkerPop, excels in path-oriented graph traversals across different programming languages but can be challenging to read and write for complex queries. Cypher, associated with Neo4j, is intuitive and pattern-based, ideal for data analytics and application development, though it struggles with high write loads and lacks some data types. SPARQL, designed for RDF data, supports federated queries across different repositories but is complex and limited in recursive querying. AQL, the query language for ArangoDB, is human-readable and supports complex computations, yet it lacks data definition capabilities and has execution limitations. Each language has its pros and cons, and the choice depends on the specific data and operations needed.