Performance Baselines¶

Measured performance characteristics showing what to expect from Grafeo.

Insert Performance¶

Node Insertion¶

Edge Insertion¶

Query Performance¶

Point Lookups¶

Pattern Matching¶

Tested on a social network graph with 1M nodes and 10M edges:

Aggregations¶

Tested on 1M nodes with age property:

Graph Algorithm Performance¶

All benchmarks on graphs with 1M nodes unless noted:

Scaling Behavior¶

PageRank on varying graph sizes:

Vector Search Performance¶

Using HNSW index with 128-dimensional vectors:

Recall vs Speed Tradeoff¶

Memory Usage¶

Per-Entity Overhead¶

Index Memory¶

Working Set Sizes¶

Approximate memory for graph operations:

Transaction Performance¶

Single-Threaded¶

Concurrent Transactions¶

4 threads, read-heavy workload (90% reads):

Block-STM (Batch Mode)¶

When processing batches of similar transactions:

Compression Effectiveness¶

On typical graph data:

Overall storage reduction: 40-60% compared to uncompressed.

LDBC Benchmark Comparisons¶

Results from the LDBC Social Network Benchmark (SF0.1) run via graph-bench. Methodology: 3 warmup runs, 10 measured runs, median reported.

Embedded (in-process)¶

Server (over network)¶

Full results: embedded | server

Optimizing Performance¶

Index Strategy¶

Create indexes for frequently-queried properties:

# Before: O(n) scan
db.find_nodes_by_property("email", "alix@example.com")  # 50ms on 1M nodes

# Create index
db.create_property_index("email")

# After: O(1) lookup
db.find_nodes_by_property("email", "alix@example.com")  # 0.01ms

Batch Operations¶

Use batch APIs for bulk operations:

# Slow: Individual calls
for node_id in node_ids:
    props = db.get_node(node_id).properties  # 1000 calls = 1ms each = 1s total

# Fast: Batch call
results = db.get_nodes_by_label("Person", limit=1000)  # Single call = 10ms

Query Hints¶

Add LIMIT to exploratory queries:

# Potentially slow
db.execute("MATCH (n:Person)-[:KNOWS*1..5]->(m) RETURN n, m")

# Bounded
db.execute("MATCH (n:Person)-[:KNOWS*1..5]->(m) RETURN n, m LIMIT 1000")

Memory Configuration¶

For large graphs, configure memory limits via GQL:

from grafeo import GrafeoDB

db = GrafeoDB()

# Set memory limit via database configuration
db.execute("SET DATABASE OPTION memory_limit = 8589934592")   # 8GB
db.execute("SET DATABASE OPTION spill_path = '/tmp/grafeo_spill'")

Workload Profiling¶

Use built-in statistics:

# Check database stats
stats = db.detailed_stats()
print(f"Memory usage: {stats['memory_bytes'] / 1024**2:.1f} MB")

# Check if indexes exist
print(f"Has email index: {db.has_property_index('email')}")

# Schema overview
schema = db.schema()
for label in schema['labels']:
    print(f"{label['name']}: {label['count']} nodes")

For detailed profiling, use Python's cProfile:

import cProfile
import pstats

with cProfile.Profile() as pr:
    result = db.execute("MATCH (n:Person)-[:KNOWS]->(m) RETURN n, m LIMIT 1000")

stats = pstats.Stats(pr)
stats.sort_stats('cumulative')
stats.print_stats(10)