Introduction In this paper written by Manish Jain (the founder of Dgraph) he describes Dgraph as: a distributed graph database which provides horizontal scalability, distributed cluster-wide ACID transactions, low-latency arbitrary-depth joins, synchronous replication, high availability, and crash resilience. There are many claims being stated here which frankly I didn’t understand the meaning of the first time I read this sentence. I decided I wanted to better understand these claims with the hope that it would help me understand Dgraph’s architecture and engineering decisions better.
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Introduction In most of the reviews for this post, I was asked why choose a graph database over something else? This is a hard question to answer since my experience right now is limited on the graph database side. My guess is you’re wondering the same thing, so this is my best answer to date. At this point in my career, I would only choose a relational database if I was writing financial software.
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Introduction I have spent a career building business applications that work with databases, starting with SQL and then moving to No-SQL. I’ve been curious how Graph databases work and how to build applications with them. It seems the sweet spot for a Graph database is when your application needs to model the interrelationships of complex and different datasets. I’ve been using No-SQL databases for these types of applications so I am super interested in knowing how Graph databases differ.
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