Description

This curriculum spans the technical breadth of a multi-phase ELK Stack optimisation engagement, covering the same operational depth as an internal platform team’s playbook for designing, securing, and evolving production-grade clusters.

Module 1: Cluster Architecture Design and Sizing

Selecting primary node types (master-eligible, data, ingest, coordinator) based on workload patterns and fault tolerance requirements.
Determining optimal shard count per index to balance query performance and cluster management overhead.
Calculating memory allocation for heap and filesystem cache based on node RAM and indexing throughput.
Deciding between hot-warm-cold architectures versus flat clusters based on data retention and access frequency.
Planning for rack awareness and zone distribution to meet high availability SLAs in multi-datacenter deployments.
Assessing the impact of index growth rate on future node expansion and shard rebalancing timelines.

Module 2: Index Management and Lifecycle Policies

Defining ILM (Index Lifecycle Management) policies that transition indices from hot to warm based on size or age thresholds.
Configuring rollover conditions for time-series indices to prevent oversized primary shards.
Implementing force merge and shrink operations during index downgrades to reduce segment count and improve search efficiency.
Managing index templates with versioned mappings to support schema evolution without breaking ingestion pipelines.
Setting up data stream routing for multi-tenant environments with isolated retention rules.
Automating deletion of expired indices using ILM delete phases while validating snapshot availability first.

Module 3: Performance Tuning for Search and Indexing

Adjusting refresh_interval based on indexing load and search freshness requirements to reduce segment churn.
Tuning bulk request sizes and concurrency to maximize indexing throughput without triggering circuit breaker exceptions.
Optimizing query cache and request cache settings to reduce redundant aggregations on frequently accessed indices.
Using doc_values selectively to balance storage overhead against aggregation performance.
Diagnosing slow search performance using profile API and identifying expensive queries for rewriting or filtering.
Implementing search routing to limit query scope to relevant shards and reduce cross-node communication.

Module 4: Resource Management and Node Configuration

Setting JVM heap size to 50% of physical RAM, capped at 32GB, to avoid pointer compression penalties.
Configuring thread pools for bulk, search, and write operations to prevent queue saturation under load spikes.
Allocating dedicated ingest nodes with increased CPU for parsing-heavy pipelines using pipelines workers and batch size tuning.
Isolating high-memory workloads (e.g., aggregations) on coordinator nodes with tuned circuit breaker limits.
Disabling swap and configuring mlockall to prevent heap paging in production environments.
Monitoring and adjusting file descriptor limits based on active shard and connection counts.

Module 5: Monitoring, Alerting, and Cluster Health

Deploying Metricbeat to collect node-level metrics (CPU, disk I/O, GC pauses) and correlate with cluster behavior.
Configuring Elasticsearch’s built-in monitoring to ship cluster stats to a separate monitoring cluster.
Creating Kibana alerts for critical conditions such as disk watermark breaches or unassigned shards.
Using the _cluster/allocation/explain API to diagnose shard allocation failures during node outages.
Establishing baselines for normal latency and throughput to detect performance degradation early.
Integrating with external observability tools via OpenTelemetry or custom exporters for centralized logging.

Module 6: Security and Access Governance

Implementing role-based access control (RBAC) with granular index and operation-level permissions.
Configuring API key management for service accounts used by applications and automation tools.
Enforcing TLS for internode and client communications, including certificate rotation procedures.
Restricting snapshot and restore operations to authorized roles and validating repository access controls.
Using query rules and rate limiting to prevent abusive search patterns from degrading cluster performance.
Auditing security events (logins, configuration changes) and exporting logs to a protected index with limited access.

Module 7: Backup, Recovery, and Disaster Planning

Registering shared file system or S3-compatible repositories with proper IAM and network access controls.
Scheduling periodic snapshots with incremental backup strategies to minimize storage and recovery time.
Testing restore procedures on a staging cluster to validate snapshot integrity and mapping compatibility.
Managing snapshot retention using Curator or ILM to avoid unbounded storage growth.
Documenting recovery runbooks for full-cluster restore, including master node reinitialization steps.
Replicating critical indices to a secondary cluster using Cross-Cluster Replication for failover readiness.

Module 8: Scaling and Upgrade Strategies

Planning rolling upgrades with version compatibility checks for plugins, templates, and client drivers.
Adding data nodes incrementally while monitoring shard rebalancing and cluster performance impact.
Migrating from deprecated features (e.g., mapping types, URL-based templates) before major version upgrades.
Testing plugin compatibility in a staging environment prior to production deployment.
Using shrink and split APIs to reconfigure shard counts on legacy indices during cluster modernization.
Validating cluster stability post-upgrade by monitoring GC behavior, query latency, and indexing success rates.