Neo4j insights

Excessive young generation garbage collection frequency indicates high allocation rate or undersized young generation, potentially causing cumulative pause time impact and premature promotion to old generation.

Abnormal checkpoint event frequency (too frequent or too infrequent) indicates configuration issues or unusual write patterns that can impact performance and durability.

High page cache eviction rate indicates cache thrashing where frequently accessed data is being evicted and must be reloaded from disk, causing performance degradation.

Large number of idle Bolt connections consuming resources without doing useful work indicates connection leak, improper connection pooling, or application not releasing connections properly.

Sudden increases in query operation time indicate performance degradation from various causes including lock contention, cache misses, inefficient queries, or resource pressure.

Concurrent transaction count approaching or exceeding configured limits can lead to transaction queuing, increased latency, and eventual request rejection.

When page cache hit ratio drops below 95%, queries must fetch data from disk, causing dramatic slowdowns. This creates a cascading effect where slow queries consume more resources, further degrading cache effectiveness.

When checkpoint duration starts approaching the interval between checkpoints, it indicates storage subsystem strain that can slow commit processes and degrade overall database performance.

Divergence between Raft append and applied indexes indicates replication lag in clustered Neo4j, potentially signaling network issues, resource constraints, or follower nodes struggling to keep pace with the leader.

High bolt connections combined with client backend wait time indicates connection pool exhaustion, causing clients to queue for available connections and degrading application response times.

Elevated transaction rollback rate indicates application errors, constraint violations, or deadlocks that waste resources and may signal deeper data integrity or concurrency issues.

Frequent Cypher query replanning events indicate schema changes, statistics updates, or cache eviction forcing query plan regeneration, adding CPU overhead and potentially causing performance variability.

Neo4j store size growing at a rate that will exhaust available disk space can cause catastrophic failures including database corruption, cluster unavailability, and data loss.

Frequent leader changes or leader election failures indicate Raft consensus issues, network partitions, or resource exhaustion causing cluster instability and potential split-brain scenarios.

When Neo4j's old generation heap fills up, garbage collection pauses can last minutes, causing node unavailability, cluster degradation, and forcing disk-intensive resynchronization when the node returns.

Queries with variable-length patterns or unbounded traversals without proper constraints can consume excessive heap memory and cause MemoryLimitExceededException errors or cluster instability.