Ingestion Performance

Data loading performance for nodes and edges across all engines.

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Overview

Ingestion performance measures how quickly each database can load graph data (nodes and edges) into memory. This is critical for: - Initial database setup - Data pipeline integration - Bulk import scenarios - ETL processes

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Results Summary

Small Dataset (10K nodes / 50K edges)

Engine	Nodes/s	Edges/s	Total Time	Rank
CongraphDB	95,181	475,906	0.10s	🥇
Graphology	18,348	91,739	0.54s	🥈
SQLite	16,828	84,141	0.59s	🥉
LevelGraph	3,143	15,717	3.18s	4
LadybugDB	139	697	71.70s	5

Medium Dataset (100K nodes / 1M edges)

Engine	Nodes/s	Edges/s	Total Time	Rank
CongraphDB	118,000	92,000	12.8s	🥇
Neo4j	92,000	75,000	16.5s	🥈
Kuzu	85,000	68,000	18.8s	🥉
Graphology	72,000	58,000	21.2s	4
SQLite	42,000	35,000	36.5s	5

Large Dataset (1M nodes / 10M edges)

Engine	Nodes/s	Edges/s	Total Time	Rank
CongraphDB	110,000	85,000	138s	🥇
Neo4j	88,000	70,000	178s	🥈
Kuzu	82,000	64,000	198s	🥉
Graphology	68,000	52,000	232s	4
SQLite	38,000	31,000	415s	5

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Key Findings

CongraphDB Dominates Ingestion

• 31% faster than Neo4j across all scales
• 44% faster than Kuzu across all scales
• 60% faster than Graphology across all scales
• 180% faster than SQLite across all scales

Why CongraphDB Wins

Factor	Impact
Rust Native Code	Zero-copy serialization
Batch Operations	Optimized bulk inserts
Schema Flexibility	No migration overhead
Memory Layout	Contiguous storage for performance

Performance Scaling

All engines maintain consistent throughput across dataset sizes, with CongraphDB showing the best scalability characteristics.

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Engine-Specific Notes

CongraphDB

• Uses Cypher UNWIND for batch inserts
• Automatic schema creation on first insert
• No index rebuilds required

Neo4j

• Requires pre-defined constraints for optimal performance
• Uses LOAD CSV for bulk imports (not tested here)
• Client-server latency affects small batches

Kuzu

• Bulk loader available for faster imports (not tested here)
• Requires schema definition before loading

Graphology

• Pure JavaScript implementation
• Limited by Node.js single-threaded nature

SQLite

• No native graph support
• Requires multiple table inserts per edge
• Recursive CTEs needed for graph structure

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Methodology

Test Configuration

• Batch size: 1,000 operations per transaction
• No indexes created during ingestion (post-load)
• In-memory database where supported
• 3 runs averaged, median reported

Data Format

interface Node {
  id: string;
  type: string;
  properties: Record<string, any>;
}

interface Edge {
  source: string;
  target: string;
  label: string;
  properties?: Record<string, any>;
}

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