Understanding the Power of Graph Databases
summary
Aerospike Graph claims linear scaling to trillions of vertices by solving the supernode partitioning problem (splitting heavy vertices across records) and by using Aerospike’s compound indices for fast entry-point lookups. The system accepts Gremlin compiled to Gremlin bytecode, applies about 20 Aerospike-specific compile optimizations, and targets subsecond traversals at high throughput. Market context: graph DB revenue grows from ~$700M (2018) to ~$2.5B (2023).
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chapters (6)
- 0:00Graph databases intro
- 2:28Graph vs SQL for devs
- 4:12Graph industry trends
- 7:19Scaling graph challenges
- 10:50Evaluating graph solutions
- 13:19Aerospike Graph approach
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