Why This Cheat Sheet is Valuable? Provides concise reference guidance for both new and experienced users of MonkDB. Bridges the gap between architecture, implementation, and operations — helpful for product adoption, training, and rollout. Tailored specifically for industrial use-cases (mining, rare earth, heavy industry) — not just general database usage. Supports rapid onboarding of development, implementation and operations teams with standardised patterns and checklists.
Prerequisites: OS, memory, disk requirements, network configuration.
MonkDB service startup commands (CLI / Docker / Kubernetes).
Configuration parameters: TLS setup, Pgwire connection, REST API credentials.
Connectors: SCADA historians, OPC-UA, MQTT, RTLS tags, CSV/JSON bulk ingest.
Real-time streaming ingestion best practices: batching, fault-tolerance, checkpointing.
Schema mapping and ingestion templates for time-series, vector, geospatial, document.
Table types:
SQL relational tables (business metadata)
Time-series tables (sensor & RTLS data)
Vector indexes (image/hyperspectral, semantic search)
Geospatial layers (mine topology, asset locations)
Document stores (lab reports, compliance logs)
Sample schemas and naming conventions.
Guidelines for partitioning, index usage, retention policies.
SQL syntax – supported extensions, joins across table types.
Time-series query patterns: rolling windows, delta, event detection.
Vector search patterns: embedding retrieval, K-NN, similarity filtering.
Geospatial query patterns: spatial joins, buffer, containment.
Combined queries: e.g., “find all high-gas zones (time-series) within 500 m of the blast site (geospatial) where hyperspectral anomaly (vector) exists”.
MonkAgent setup steps: domain logic definition, policy encoding, workflows.
Orchestration patterns: advisory → semi-auto → closed-loop.
Agent-agent coordination examples: ventilation agent interacting with fleet agent under energy constraints.
Audit & logging: immutable records, versioning of agent rules, traceability.
Dashboard templates for energy, safety, maintenance KPIs.
M&V templates: shift-wise reports, ROI calculation frameworks.
Governance & compliance: RBAC, RLS, encryption at rest (via encrypted SSD), external policy engine integration.
Scaling best practices: multi-site deployment, data replication, disaster recovery.
Common issues: ingestion lag, vector index build time, query performance bottlenecks.
Performance tuning: partition strategy, indexing, caching recommendations.
Safety & compliance caveats: strict segregation of control logic, fallback to manual override rules.
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