Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 2·5 min read
Relational-Core Graph Analytics Querying graphs at SQL scale, and why the node/edge model is a performance tax, not a truer picture of connected data
TL;DR
ClickGraph and DeltaGraph challenge the notion that graph analytics require specialized engines, demonstrating that a columnar relational engine can outperform native graph engines on analytical queries, especially at scale.
✦ Why It Matters
Consider adopting ClickGraph or DeltaGraph to enhance your graph analytics performance without additional infrastructure costs.
Key Takeaways
- Evaluate your current graph analytics tools for potential inefficiencies.
- Explore ClickGraph and DeltaGraph for improved performance.
- Consider using existing relational schemas for graph queries.
- Benchmark your systems against LDBC Social Network metrics.
How It Works
ClickGraph and DeltaGraph leverage existing relational schemas by translating Cypher queries directly onto tables, columns, and foreign keys. This eliminates the overhead of reconstructing relationships at query time, allowing for efficient execution on platforms like ClickHouse and Databricks.