Massive Parallel Imports in Neo4j Without Deadlock and Lock Contention

Neo4j proposes a method for massively parallel node and relationship imports that avoids deadlocks and lock contention by partitioning data according to the number of available workers rather than node-ID suffixes. Nodes are assigned deterministic hash-based partitions, while relationships are assigned source-target partition pairs; node partitions can all load concurrently, but relationship batches must prevent simultaneous access to the same partitions. For relationships between disjoint node sets, cyclic matrix diagonals provide safe parallel batches, whereas relationships within the same node set require a round-robin approach that separates forward and reverse pairs and handles self-references safely. The article also presents Neo4j Graph Data Science’s K-1 coloring algorithm as a more general way to form conflict-free batches for complex graph structures by coloring partition-pair conflicts, although it may produce less efficient schedules than the specialized Python methods.

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