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Performance

This guide explains how to tune the performance of your Neo4j Enterprise clusters.

Operator Performance Optimizations

The Neo4j Enterprise Operator has been optimized for production environments with several key performance improvements:

Reconciliation Efficiency

  • Optimized Rate Limiting: The controller uses intelligent rate limiting to prevent excessive API calls (~34 reconciliations per minute vs. 18,000+)
  • Status Update Optimization: Status updates only occur when cluster state actually changes, reducing unnecessary API server load
  • ConfigMap Debouncing: 2-minute debounce mechanism prevents restart loops from configuration changes

Resource Management

  • Memory Validation: Automatic validation ensures Neo4j memory settings don't exceed available resources
  • Resource Recommendations: Built-in recommendations for optimal CPU and memory allocation based on cluster size
  • Efficient Monitoring: Lightweight resource monitoring with minimal overhead

Resource Allocation

One of the most important factors for performance is resource allocation. You can configure the CPU and memory resources for your Neo4j pods using the spec.resources field in the Neo4jEnterpriseCluster resource. It is crucial to set both requests and limits for predictable performance.

    resources:
      requests:
        cpu: "2"
        memory: "4Gi"
      limits:
        cpu: "4"
        memory: "8Gi"

Memory Validation and Recommendations

The operator includes intelligent memory validation that:

  • Ensures Neo4j heap settings don't exceed available container memory
  • Provides automatic recommendations for optimal memory allocation
  • Validates memory ratios between heap, page cache, and system overhead
    resources:
      requests:
        memory: "4Gi"    # Minimum for stable operation
      limits:
        memory: "8Gi"    # Allows 4-6GB for Neo4j heap + page cache

JVM Tuning

For advanced use cases, you can tune the JVM settings for your Neo4j pods using environment variables in the spec.env field. This allows you to control settings like heap size, garbage collection, and more.

    env:
      - name: NEO4J_dbms_memory_heap_initial__size
        value: "4G"
      - name: NEO4J_dbms_memory_heap_max__size
        value: "4G"
      - name: NEO4J_dbms_memory_pagecache_size
        value: "2G"

Performance Monitoring

The operator includes built-in performance monitoring capabilities:

Resource Monitoring

  • Real-time tracking of CPU, memory, and storage utilization
  • Neo4j-specific metrics including transaction rates and query performance
  • Automatic detection of resource constraints and bottlenecks

Operational Insights

  • ConfigMap update frequency and debounce effectiveness
  • Controller reconciliation patterns and efficiency metrics
  • Cluster health and readiness status tracking

Best Practices

For Production Deployments

  1. Set appropriate resource limits: Ensure containers have sufficient CPU and memory
  2. Use persistent storage: Configure appropriate storage classes for your workload
  3. Enable monitoring: Monitor both Kubernetes and Neo4j metrics
  4. Plan capacity: Monitor trends and scale manually as needed

For Development Environments

  1. Use smaller resource allocations: Optimize for development machine resources
  2. Enable debug logging: Set log levels for troubleshooting
  3. Use local storage: Consider local storage for faster development cycles