Raw JSON documents containing SKU, Location, and Timestamp
Data / Knowledge library
OpenSearch
indexed document search aggregations and dashboards
Why learn OpenSearch?
Useful for searchable content and operational analytics. Mapping, shard design, and access controls affect search quality and cluster behavior.
Where it came from
OpenSearch provides a search and analytics engine with a companion dashboard application. It supports indexing documents and querying them for retrieval and analysis.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Index documents with explicit field mappings.
- Run a text query and an aggregation.
- Inspect tokenization for a sample field.
- Restrict a test user's index access.
- Measure query behavior as the indexed corpus grows.
Documentation and sources
- Official project documentation (docs.opensearch.org)
Catalog research notes
- Research snapshot: September 9, 2026. This entry summarizes the linked primary documentation; it does not assert a latest release version.
Connect the ideas
Alternatives
Explore next
Real World Example
Optimizing Global Logistics Inventory Visibility
A global logistics provider managing millions of SKU movements across regional distribution centers.
The business problem
The company struggles with fragmented data silos where regional managers cannot perform real-time cross-region inventory analysis, leading to stock imbalances and delayed fulfillment.
How this topic solves it
By centralizing logistics logs and inventory snapshots into OpenSearch, the company can utilize indexed document search to perform complex aggregations across disparate datasets, visualizing the results through OpenSearch Dashboards for unified operational oversight.
- Define index mappings to ensure SKU identifiers and location timestamps are indexed as searchable keywords.
- Ingest real-time logistics event streams into the OpenSearch cluster using a unified index pattern.
- Construct aggregation queries using the Query DSL to calculate inventory levels per region and category.
- Create OpenSearch Dashboards visualizations to map inventory density and identify regional supply gaps.
- Configure index lifecycle management to transition older logistics logs to cost-effective storage tiers.
This diagram illustrates how raw logistics events are transformed into actionable dashboard insights.
Indexed documents optimized for search and aggregation
Aggregation logic for grouping and calculating metrics
Visual representation of aggregated inventory data
- Logistics Events → Ingestion (1:N) → OpenSearch Index
- OpenSearch Index → Search/Aggregate (1:1) → Query DSL
- Query DSL → Visualize (1:N) → Dashboards
Expected business outcome
The logistics team achieves a unified view of global inventory, enabling proactive stock redistribution and improved fulfillment accuracy.
Limitations and checks
The primary limitation is the resource overhead of performing deep aggregations on high-cardinality fields, which can lead to increased latency; this is checked by monitoring the search execution time in the OpenSearch slow logs.