Graph Databases In Production At Scale Using DGraph with Manish Jain - Episode 44
Data Engineering Podcast
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Introduction
This chapter introduces the guest, Manish Jane, who will be discussing D-Graph, a graph database known for its low latency and high throughput.
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Summary
The way that you store your data can have a huge impact on the ways that it can be practically used. For a substantial number of use cases, the optimal format for storing and querying that information is as a graph, however databases architected around that use case have historically been difficult to use at scale or for serving fast, distributed queries. In this episode Manish Jain explains how DGraph is overcoming those limitations, how the project got started, and how you can start using it today. He also discusses the various cases where a graph storage layer is beneficial, and when you would be better off using something else. In addition he talks about the challenges of building a distributed, consistent database and the tradeoffs that were made to make DGraph a reality.
Preamble
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- Your host is Tobias Macey and today I’m interviewing Manish Jain about DGraph, a low latency, high throughput, native and distributed graph database.
Interview
- Introduction
- How did you get involved in the area of data management?
- What is DGraph and what motivated you to build it?
- Graph databases and graph algorithms have been part of the computing landscape for decades. What has changed in recent years to allow for the current proliferation of graph oriented storage systems?
- The graph space is becoming crowded in recent years. How does DGraph compare to the current set of offerings?
- What are some of the common uses of graph storage systems?
- What are some potential uses that are often overlooked?
- There are a few ways that graph structures and properties can be implemented, including the ability to store data in the vertices connecting nodes and the structures that can be contained within the nodes themselves. How is information represented in DGraph and what are the tradeoffs in the approach that you chose?
- How does the query interface and data storage in DGraph differ from other options?
- What are your opinions on the graph query languages that have been adopted by other storages systems, such as Gremlin, Cypher, and GSQL?
- How is DGraph architected and how has that architecture evolved from when it first started?
- How do you balance the speed and agility of schema on read with the additional application complexity that is required, as opposed to schema on write?
- In your documentation you contend that DGraph is a viable replacement for RDBMS-oriented primary storage systems. What are the switching costs for someone looking to make that transition?
- What are the limitations of DGraph in terms of scalability or usability?
- Where does it fall along the axes of the CAP theorem?
- For someone who is interested in building on top of DGraph and deploying it to production, what does their workflow and operational overhead look like?
- What have been the most challenging aspects of building and growing the DGraph project and community?
- What are some of the most interesting or unexpected uses of DGraph that you are aware of?
- When is DGraph the wrong choice?
- What are your plans for the future of DGraph?
Contact Info
- @manishrjain on Twitter
- manishrjain on GitHub
- Blog
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- DGraph
- Badger
- Google Knowledge Graph
- Graph Theory
- Graph Database
- SQL
- Relational Database
- NoSQL
- OLTP (On-Line Transaction Processing)
- Neo4J
- PostgreSQL
- MySQL
- BigTable
- Recommendation System
- Fraud Detection
- Customer 360
- Usenet Express
- IPFS
- Gremlin
- Cypher
- GSQL
- GraphQL
- MetaWeb
- RAFT
- Spanner
- HBase
- Elasticsearch
- Kubernetes
- TLS (Transport Layer Security)
- Jepsen Tests
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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