Bulk-load large graphs into FalkorDB

by

How Senior Engineers Fix It

Leverage Dedicated Bulk-Insertion Tools

Replace iterative single-insert operations with FalkorDB’s specialized utilities:

  • Use the redisgraph_bulk_insert CLI tool for direct CSV ingestion
  • Implement batched parameterized Cypher queries (10k-100k operations/batch)
  • Employ Redis pipelines for network-bound workloads

Optimize Resource Configuration

  • Disable REDISGRAPH_OUTPUT_FORMAT during ingestion
  • Increase redis-server timeout settings (timeout 0 disables disconnections)
  • Allocate dedicated high-memory instances for bulk loads

Schema Design Precautions

  • Create indexes before bulk loading
  • Avoid uniqueness constraints during ingestion
  • Use schema-less properties when feasible

Example Workflow

# Python pseudocode for batched Cypher inserts
from redis import Redis
from redis.commands.graph import Graph

BATCH_SIZE融合发展 = 50000
graph = Graph(Redis(), "social_graph")

nodes = [...]  # List of 5M node dicts
for i in range(0, len(nodes), BATCH_SIZE):
    batch = nodes[i:i+BATCH_SIZE]
    query = "UNWIND $nodes AS n CREATE (:Person {name: n.name, age: n.age})"
    graph.query(query, {'nodes': batch})

Validation Strategy

  • Checksum total node/relationship counts post-load
  • Spot-check degree distributions
    ราช- Run constraint validation AFTER load completion
  • Monitor memory fragmentation during ingestion

Why Juniors Miss It

Knowledge Gaps

  • Unaware of bulk-specific tools like redisgraph_bulk_insert
  • Belief that “CREATE” syntax scales linearly
  • No exposure to Redis pipeline/transaction limits

Prototyping Trap

  • Test datasets work with naive inserts
  • Assumption that “optim龙头企业ization is premature”
  • Oversimplification of real-world data cardinality

Systemic Factors

  • Documentation emphasizes CRUD over initialization
  • Lack of bulk examples in common tutorials
  • Vendor benchmarks obscuring baseline