Performance

Executive Summary Performance baseline = safe defaults that work for most workloads, with clear tuning for specific scenarios. This guide covers: sysctl: Kernel parameters (network, filesystem, VM) with production-safe values ulimits: Resource limits (open files, processes, memory locks) CPU Governor: Frequency scaling & power management on servers NUMA: Awareness for multi-socket systems (big apps, databases) I/O Scheduler: NVMe/SSD vs. spinning disk tuning 1. sysctl Kernel Parameters Why sysctl Matters Problem: Default kernel parameters are conservative (fit laptops, embedded systems) Solution: Tune for your workload (databases, web servers, HPC) Trade-off: More throughput vs. latency / memory vs. stability …

Introduction The ELK stack (Elasticsearch, Logstash, Kibana) is powerful for log aggregation and analysis, but requires proper tuning for production workloads. This guide covers Elasticsearch index lifecycle management, Logstash pipeline optimization, and performance best practices. Elasticsearch Index Lifecycle Management (ILM) Understanding ILM ILM automates index management through lifecycle phases: Phases: Hot - Actively writing and querying Warm - No longer writing, still querying Cold - Rarely queried, compressed Frozen - Very rarely queried, minimal resources Delete - Removed from cluster Basic ILM Policy Create policy: …

Introduction Prometheus queries can become slow and resource-intensive as your metrics scale. This guide covers PromQL optimization techniques, recording rules, and performance best practices to keep your monitoring fast and efficient. PromQL Optimization Understanding Query Performance Factors affecting query performance: Number of time series matched Time range queried Query complexity Cardinality of labels Rate of data ingestion Check query stats: # Grafana: Enable query inspector # Shows: Query time, series count, samples processed 1. Limit Time Series Selection Bad (matches too many series): …

Introduction Kafka Producer is a key component for sending messages to a Kafka cluster. Proper producer configuration is critical for achieving high performance and system reliability. Key tuning parameters 1. Batching and Compression # Increase batch size for better throughput batch.size=32768 linger.ms=5 # Enable compression to save bandwidth compression.type=lz4 2. Memory and Buffer # Buffer configuration buffer.memory=67108864 max.block.ms=60000 3. Acknowledgments and Durability # For high reliability acks=all retries=2147483647 enable.idempotence=true Real-world examples High Throughput Scenario For high volume data scenarios: …