>LADDAR // PROJECT DETAILS

PARSING ARCHITECTURE, DIAGRAMS & API ENDPOINTS

>LADDAR // LADDAR

INITIERAR VY

Tillbaka till projekt

Visa på GitHub

Slutförtgoneo4jmemgraphtigergraphdocker

Prestandajämförelse av Grafdatabaser

Testsvit som mäter latens, minnesåtgång och databasprestanda mellan grafdatabaser och relationsdatabaser.

Problem och lösning

Identifiera optimal databasteknik för djupt kapslade sociala nätverk och rekommendationstjänster.

Funktioner

Automatiserade traverseringstester på flera djupnivåer
Jämförelse av latenspercentiler (p50, p95, p99)
Realtidsövervakning av processor- och minnesbelastning
Fullt reproducerbar testmiljö med Docker

Teknikstack

Teknik	Version	Syfte	Varför vald
Go	1.25.1	Primary language	Strong concurrency primitives, fast compilation, good driver ecosystem
neo4j-go-driver/v6	v6.2.0	Bolt protocol client for CognoDB, Neo4j, Memgraph	Official driver, supports Bolt 5.x, parameterized queries
go-echarts/v2	v2.7.2	HTML chart generation	Go-native, generates standalone HTML
godotenv	v1.5.1	Environment configuration	Standard .env loading
net/http	stdlib	REST client for TigerGraph	No external dependency needed

Arkitektur

Benchmark CLI

Go 1.25.1

Dataset Extractor

Data Loader

Summary Generator

Chart Generator

raw/

SNAP soc-Pokec .gz

processed/

nodes.csv, relationships.csv

results/

JSONL, CSV, HTML

CognoDB Cloud

Bolt 5.x, US-East-4

Neo4j AuraDB

Bolt, US-East-1

Memgraph Cloud

Bolt, US East

TigerGraph Savanna

REST/OpenCypher

Benchmark Engine

Adapter pattern with common GraphDatabase interface (16 methods)
5 database adapters (4 active, FalkorDB disabled due to OOM)
Sequential workload executor with warm-up and measured iterations
Concurrent workload executor with N goroutines and sync.WaitGroup
Factory function for adapter creation from configuration

CLI Pipeline

10 CLI entry points: benchmark, captest, charts, cleanup, dataset, load, runall, summary, verify, verify-load
JSONL append-only result output for crash resilience
CSV summary aggregation with percentile calculation
HTML chart generation via go-echarts
Pipeline automation via runall (verify → load → benchmark → report)

Projektstruktur

cmd

benchmark

captest

charts

cleanup

dataset

load

runall

summary

verify

verify-load

internal

benchmark

concurrent.goConcurrent workload executor

measurement.goMeasurement struct + ResultWriter

runner.goSequential workload executor

safe.goPanic-recovery wrapper

database

cognodb

memgraph

neo4j

tigergraph

types

factory.goDatabase factory/registry

dataset

parser.goCSV node/relationship loader

API-slutpunkter

Query Workloads (port 0)

Metod	Rutt	Syfte
Cypher	MATCH (n:Person {id: $id}) RETURN n.id AS id	Point lookup — single node by ID
Cypher	MATCH (n:Person {region: $val}) RETURN n.id AS id LIMIT 10	Indexed lookup — nodes by region
Cypher	MATCH (n:Person {id: $id})-[:FRIEND]->(m:Person) RETURN collect(m.id)	1-hop traversal — direct friends
Cypher	MATCH (n:Person {id: $id})-[:FRIEND]->()-[:FRIEND]->(m:Person) RETURN collect(DISTINCT m.id)	2-hop traversal — friends of friends
Cypher	MATCH (n:Person {id: $id})-[:FRIEND]->()-[:FRIEND]->()-[:FRIEND]->(m:Person) RETURN collect(DISTINCT m.id)	3-hop traversal — extended network
Cypher	MATCH (n:Person) RETURN n.region AS region, count(n) AS cnt ORDER BY cnt DESC LIMIT 10	Aggregation — region distribution
Cypher	CREATE (n:Person {id: $id, region: $region, ...})	Write — create new node

Databasdesign

PERSON

5 cols

idPK

string

region

string

gender

string

age

string

completion_percentage

string

FRIEND_EDGE

2 cols

from_id

string

to_id

string

Person (node label)

Kolumn	Typ	Anteckningar
idPK	STRING	Unique constraint, indexed
region	STRING	Indexed
gender	STRING	Not indexed
age	STRING	Indexed (CognoDB, Neo4j, Memgraph only)
completion_percentage	STRING	Not indexed

49,683 nodes extracted from SNAP soc-Pokec dataset. All properties are strings.

FRIEND (relationship type)

Kolumn	Typ	Anteckningar
source_id	STRING	Person.id (from)
target_id	STRING	Person.id (to)

100,000 directed relationships. No properties on edges. Many-to-many cardinality.

Directed graph: Person --[FRIEND]--> Person. Many-to-many via graph relationships (no junction table). Relationships are first-class citizens in the graph model.

Viktiga designbeslut

Single Bolt Driver for 3 Databases

Beslut: Use neo4j-go-driver/v6 for CognoDB, Neo4j, and Memgraph

Varför: All three support Bolt protocol; Cypher is logically equivalent

Fördelar: Code reuse, consistent behavior across databases, adapter pattern isolates DB-specific behavior

Avvägningar: Cannot test database-specific optimizations; Memgraph has its own Go driver

MaxPoolSize = 1

Beslut: Single connection per benchmark run despite constant defining 10

Varför: Measure single-connection latency; avoid connection pool effects

Fördelar: Clean latency measurement without pool contention

Avvägningar: Doesn't test connection pooling behavior

JSONL for Raw Results

Beslut: One JSON object per line, append-only output

Varför: Stream results without loading all into memory; easy to append across runs

Fördelar: Resilient to crashes (partial results preserved)

Avvägningar: Requires full file scan for aggregation

No Retries on Measured Queries

Beslut: Zero retries for benchmark queries to capture true latency

Varför: Network blips and failures are part of real-world performance

Fördelar: Honest performance measurement including failure modes

Avvägningar: Network blips cause false negatives

Goroutine-Per-Client Concurrency

Beslut: Each concurrent client creates its own database connection

Varför: Simulates real-world multi-client scenarios; avoids shared connection contention

Fördelar: Realistic concurrency testing with independent connections

Avvägningar: Higher resource usage; connection limits become the bottleneck

MERGE for Idempotent Node Loading

Beslut: Use MERGE (upsert) instead of CREATE for node loading

Varför: Makes loading idempotent — running multiple times doesn't create duplicates

Fördelar: Safe re-runs, consistent dataset state

Avvägningar: MERGE is slightly slower than CREATE due to existence checks

Tester

None (no _test.go files)•0 enhetstester

Vad som testas

Capability test matrix (cmd/captest/) — tests all 8 workload types against all databases with small 10-node graph
Connection verification (cmd/verify/) — tests basic connectivity and parameterized queries
Data integrity verification (cmd/verify-load/) — verifies node/relationship counts (49,683 nodes, 100,000 relationships)

Saknad täckning

No unit tests for individual functions (_test.go files do not exist)
No tests for statistical functions (percentile, mean, stddev calculation)
No tests for CSV parsing edge cases (empty datasets, malformed CSVs)
No tests for concurrent behavior
No tests for chart generation correctness
No test framework installed (no testify, gomock)

Viktiga utmaningar

FalkorDB OOM — free tier 100MB RAM limit exceeded by 49K-node dataset, entire adapter disabled
Protocol asymmetry — Bolt (binary) vs REST (HTTP/JSON) confounds direct latency comparison
Resource asymmetry — CognoDB 0.5 vCPU/256MB vs competitors with 2-8x more resources
Free-tier variability — shared resources, auto-pause, time limits affect reproducibility
Ensuring fair comparison across different graph database architectures

Viktiga lärdomar

Adapter pattern for database-agnostic benchmarking with clean interface separation
Goroutine-per-client concurrency model for realistic multi-client simulation
JSONL append-only output for crash-resilient result persistence
Cold-start measurement technique (iteration 0 tagging after warm-up)
Numerical stability in statistical aggregation (percentile calculation)

>LADDAR // PROJECT DETAILS

PARSING ARCHITECTURE, DIAGRAMS & API ENDPOINTS

Tillbaka till projekt

Visa på GitHub

Slutförtgoneo4jmemgraphtigergraphdocker

Prestandajämförelse av Grafdatabaser

Testsvit som mäter latens, minnesåtgång och databasprestanda mellan grafdatabaser och relationsdatabaser.

Problem och lösning

Identifiera optimal databasteknik för djupt kapslade sociala nätverk och rekommendationstjänster.

Funktioner

Automatiserade traverseringstester på flera djupnivåer
Jämförelse av latenspercentiler (p50, p95, p99)
Realtidsövervakning av processor- och minnesbelastning
Fullt reproducerbar testmiljö med Docker

Teknikstack

Teknik	Version	Syfte	Varför vald
Go	1.25.1	Primary language	Strong concurrency primitives, fast compilation, good driver ecosystem
neo4j-go-driver/v6	v6.2.0	Bolt protocol client for CognoDB, Neo4j, Memgraph	Official driver, supports Bolt 5.x, parameterized queries
go-echarts/v2	v2.7.2	HTML chart generation	Go-native, generates standalone HTML
godotenv	v1.5.1	Environment configuration	Standard .env loading
net/http	stdlib	REST client for TigerGraph	No external dependency needed

Arkitektur

Benchmark CLI

Go 1.25.1

Dataset Extractor

Data Loader

Summary Generator

Chart Generator

raw/

SNAP soc-Pokec .gz

processed/

nodes.csv, relationships.csv

results/

JSONL, CSV, HTML

CognoDB Cloud

Bolt 5.x, US-East-4

Neo4j AuraDB

Bolt, US-East-1

Memgraph Cloud

Bolt, US East

TigerGraph Savanna

REST/OpenCypher

Benchmark Engine

Adapter pattern with common GraphDatabase interface (16 methods)
5 database adapters (4 active, FalkorDB disabled due to OOM)
Sequential workload executor with warm-up and measured iterations
Concurrent workload executor with N goroutines and sync.WaitGroup
Factory function for adapter creation from configuration

CLI Pipeline

10 CLI entry points: benchmark, captest, charts, cleanup, dataset, load, runall, summary, verify, verify-load
JSONL append-only result output for crash resilience
CSV summary aggregation with percentile calculation
HTML chart generation via go-echarts
Pipeline automation via runall (verify → load → benchmark → report)

Projektstruktur

cmd

benchmark

captest

charts

cleanup

dataset

load

runall

summary

verify

verify-load

internal

benchmark

concurrent.goConcurrent workload executor

measurement.goMeasurement struct + ResultWriter

runner.goSequential workload executor

safe.goPanic-recovery wrapper

database

cognodb

memgraph

neo4j

tigergraph

types

factory.goDatabase factory/registry

dataset

parser.goCSV node/relationship loader

API-slutpunkter

Query Workloads (port 0)

Metod	Rutt	Syfte
Cypher	MATCH (n:Person {id: $id}) RETURN n.id AS id	Point lookup — single node by ID
Cypher	MATCH (n:Person {region: $val}) RETURN n.id AS id LIMIT 10	Indexed lookup — nodes by region
Cypher	MATCH (n:Person {id: $id})-[:FRIEND]->(m:Person) RETURN collect(m.id)	1-hop traversal — direct friends
Cypher	MATCH (n:Person {id: $id})-[:FRIEND]->()-[:FRIEND]->(m:Person) RETURN collect(DISTINCT m.id)	2-hop traversal — friends of friends
Cypher	MATCH (n:Person {id: $id})-[:FRIEND]->()-[:FRIEND]->()-[:FRIEND]->(m:Person) RETURN collect(DISTINCT m.id)	3-hop traversal — extended network
Cypher	MATCH (n:Person) RETURN n.region AS region, count(n) AS cnt ORDER BY cnt DESC LIMIT 10	Aggregation — region distribution
Cypher	CREATE (n:Person {id: $id, region: $region, ...})	Write — create new node

Databasdesign

PERSON

5 cols

idPK

string

region

string

gender

string

age

string

completion_percentage

string

FRIEND_EDGE

2 cols

from_id

string

to_id

string

Person (node label)

Kolumn	Typ	Anteckningar
idPK	STRING	Unique constraint, indexed
region	STRING	Indexed
gender	STRING	Not indexed
age	STRING	Indexed (CognoDB, Neo4j, Memgraph only)
completion_percentage	STRING	Not indexed

49,683 nodes extracted from SNAP soc-Pokec dataset. All properties are strings.

FRIEND (relationship type)

Kolumn	Typ	Anteckningar
source_id	STRING	Person.id (from)
target_id	STRING	Person.id (to)

100,000 directed relationships. No properties on edges. Many-to-many cardinality.

Directed graph: Person --[FRIEND]--> Person. Many-to-many via graph relationships (no junction table). Relationships are first-class citizens in the graph model.

Viktiga designbeslut

Single Bolt Driver for 3 Databases

Beslut: Use neo4j-go-driver/v6 for CognoDB, Neo4j, and Memgraph

Varför: All three support Bolt protocol; Cypher is logically equivalent

Fördelar: Code reuse, consistent behavior across databases, adapter pattern isolates DB-specific behavior

Avvägningar: Cannot test database-specific optimizations; Memgraph has its own Go driver

MaxPoolSize = 1

Beslut: Single connection per benchmark run despite constant defining 10

Varför: Measure single-connection latency; avoid connection pool effects

Fördelar: Clean latency measurement without pool contention

Avvägningar: Doesn't test connection pooling behavior

JSONL for Raw Results

Beslut: One JSON object per line, append-only output

Varför: Stream results without loading all into memory; easy to append across runs

Fördelar: Resilient to crashes (partial results preserved)

Avvägningar: Requires full file scan for aggregation

No Retries on Measured Queries

Beslut: Zero retries for benchmark queries to capture true latency

Varför: Network blips and failures are part of real-world performance

Fördelar: Honest performance measurement including failure modes

Avvägningar: Network blips cause false negatives

Goroutine-Per-Client Concurrency

Beslut: Each concurrent client creates its own database connection

Varför: Simulates real-world multi-client scenarios; avoids shared connection contention

Fördelar: Realistic concurrency testing with independent connections

Avvägningar: Higher resource usage; connection limits become the bottleneck

MERGE for Idempotent Node Loading

Beslut: Use MERGE (upsert) instead of CREATE for node loading

Varför: Makes loading idempotent — running multiple times doesn't create duplicates

Fördelar: Safe re-runs, consistent dataset state

Avvägningar: MERGE is slightly slower than CREATE due to existence checks

Tester

None (no _test.go files)•0 enhetstester

Vad som testas

Capability test matrix (cmd/captest/) — tests all 8 workload types against all databases with small 10-node graph
Connection verification (cmd/verify/) — tests basic connectivity and parameterized queries
Data integrity verification (cmd/verify-load/) — verifies node/relationship counts (49,683 nodes, 100,000 relationships)

Saknad täckning

No unit tests for individual functions (_test.go files do not exist)
No tests for statistical functions (percentile, mean, stddev calculation)
No tests for CSV parsing edge cases (empty datasets, malformed CSVs)
No tests for concurrent behavior
No tests for chart generation correctness
No test framework installed (no testify, gomock)

Viktiga utmaningar

FalkorDB OOM — free tier 100MB RAM limit exceeded by 49K-node dataset, entire adapter disabled
Protocol asymmetry — Bolt (binary) vs REST (HTTP/JSON) confounds direct latency comparison
Resource asymmetry — CognoDB 0.5 vCPU/256MB vs competitors with 2-8x more resources
Free-tier variability — shared resources, auto-pause, time limits affect reproducibility
Ensuring fair comparison across different graph database architectures

Viktiga lärdomar

Adapter pattern for database-agnostic benchmarking with clean interface separation
Goroutine-per-client concurrency model for realistic multi-client simulation
JSONL append-only output for crash-resilient result persistence
Cold-start measurement technique (iteration 0 tagging after warm-up)
Numerical stability in statistical aggregation (percentile calculation)