This curriculum spans the technical breadth of a multi-workshop program focused on ELK Stack operational maturity, covering the same depth of configuration, tuning, and governance tasks typically addressed in enterprise-scale deployment and optimization engagements.
Module 1: Infrastructure Sizing and Capacity Planning
- Selecting appropriate instance types for Elasticsearch data nodes based on shard density, heap requirements, and I/O throughput demands.
- Calculating index growth rates using historical ingestion patterns to project storage needs over a 12-month horizon.
- Allocating dedicated master and ingest nodes to isolate control plane operations from indexing and search workloads.
- Implementing time-series index lifecycle policies that align shard size with optimal search performance (typically 20–40 GB per shard).
- Deciding between vertical scaling and horizontal scaling when approaching node memory thresholds.
- Configuring JVM heap size to no more than 50% of system memory while ensuring it does not exceed 32 GB to avoid pointer compression penalties.
Module 2: Index Design and Data Modeling
- Choosing between nested objects and parent-child relationships based on query patterns and update frequency.
- Defining custom dynamic templates to prevent field mapping explosions from unstructured log sources.
- Setting up index aliases to support zero-downtime reindexing during schema migrations.
- Determining optimal shard count per index based on data volume and node count to avoid under-sharding or over-sharding.
- Using index sorting to pre-sort documents on disk for frequently filtered fields, reducing query latency.
- Implementing field data types accurately (e.g., keyword vs. text, scaled_float for metrics) to minimize storage and improve query efficiency.
Module 3: Ingest Pipeline Optimization
- Chaining multiple processors in Logstash or Ingest Pipelines to parse, enrich, and transform data before indexing.
- Offloading parsing logic from Logstash to Filebeat processors to reduce CPU load on central nodes.
- Using conditional statements in pipelines to route or drop low-value logs (e.g., health checks) before indexing.
- Configuring pipeline workers and batch sizes in Logstash to balance throughput and memory consumption.
- Implementing dead-letter queues for failed documents to enable post-failure analysis without data loss.
- Validating Grok patterns against production log samples to prevent pipeline backpressure due to parsing errors.
Module 4: Search Performance and Query Tuning
- Converting wildcard queries to term-level queries using keyword fields and proper analyzers.
- Using _source filtering to retrieve only required fields in high-frequency dashboards.
- Applying request caching selectively for dashboards with static time ranges to reduce segment scans.
- Optimizing aggregations by pre-sizing shard request sizes and using composite aggregations for pagination.
- Diagnosing slow queries using the Profile API and identifying costly components like script evaluations or deep pagination.
- Restricting wildcard field searches (e.g., using *) in production queries to prevent cluster-wide field mapping scans.
Module 5: Index Lifecycle Management (ILM)
- Defining ILM policies that transition indices from hot to warm nodes based on age and access patterns.
- Setting rollover conditions based on index size or age to maintain consistent shard sizing in time-series data.
- Forcing merge operations during the cold phase to reduce segment count and file handle usage.
- Configuring delete phases with retention policies that comply with regulatory requirements and audit trails.
- Using shrink API during the transition to warm phase to consolidate underutilized shards.
- Monitoring ILM explain API output to troubleshoot policy execution delays or step failures.
Module 6: Monitoring and Cluster Health Management
- Deploying Elastic Agent or Prometheus exporters to collect node-level metrics (CPU, disk I/O, GC pauses).
- Setting up alert thresholds for unassigned shards, disk watermark breaches, and thread pool rejections.
- Interpreting thread pool queue sizes to identify bottlenecks in indexing or search execution.
- Using the Cat APIs in automated scripts to audit shard distribution and identify imbalances.
- Enabling slow log logging for search and indexing to capture performance outliers for analysis.
- Regularly auditing cluster settings changes via the audit log to detect unauthorized configuration drift.
Module 7: Security and Access Governance
- Implementing role-based access control (RBAC) to restrict index access based on team or application ownership.
- Configuring field- and document-level security to mask sensitive data (e.g., PII) in shared dashboards.
- Rotating API keys and service account credentials on a quarterly basis in accordance with security policies.
- Enabling TLS between nodes and clients to protect data in transit across network zones.
- Integrating with LDAP or SAML for centralized user authentication and group synchronization.
- Disabling dynamic scripting in production environments to mitigate code injection risks.
Module 8: Cost Control and Resource Isolation
- Deploying dedicated ingest nodes to prevent parsing overhead from impacting search performance.
- Using index-level throttling during peak hours to limit the impact of bulk reindexing operations.
- Allocating indices to specific node groups using shard allocation filtering based on data criticality.
- Enabling compressed storage (default) and evaluating best_compression for cold data tiers.
- Right-sizing replicas based on availability requirements—using 1 replica for high availability, 0 for ephemeral data.
- Implementing circuit breakers with custom limits to prevent queries from triggering out-of-memory errors on nodes.