What is dawgs?

DAWGS is a toolkit that lets you store and query connected data, think relationship maps, network graphs, or linked records, using standard PostgreSQL, the popular open-source database, without needing any special plugins. Normally, working with graph-style data requires a dedicated graph database. This project bridges that gap by translating graph-style queries into something PostgreSQL can understand natively. At its core, the project provides a translation layer. You write queries using openCypher, a widely-used graph query language. If your database supports Cypher natively (like Neo4j), the query runs directly. If you're on PostgreSQL, the toolkit translates your Cypher query into SQL behind the scenes. It also includes command-line tools for exporting and importing graph databases, with support for compression and encryption. This would appeal to teams that already rely on PostgreSQL but want to explore graph-based data models without standing up and maintaining an entirely separate database infrastructure. For example, a security tool tracking how different users, devices, and permissions relate to each other could use this to map those connections without adding operational complexity. It's also useful for anyone who wants to write queries once and run them against either PostgreSQL or Neo4j, since the project supports both as interchangeable backends. The project puts significant effort into ensuring queries perform well and produce consistent results across different database backends. It includes benchmarking tools to compare performance between code changes, and it can verify that PostgreSQL and Neo4j return equivalent results for the same queries. The README emphasizes careful handling of PostgreSQL indexing, ensuring that queries for specific properties actually use database indexes rather than scanning entire tables. This attention to cross-backend equivalence and query optimization suggests the project is built for production use where reliability and performance matter.