Amazon OpenSearch Service | Overview.

Amazon OpenSearch Service  - Overview.

Scope:

• Intro,
• The Concept of Amazon OpenSearch Service,
• Core Architecture,
• Data flow,
• Storage & Scaling,
• Security,
• Operations & Management,
• Integrations,
• Best Practices,
• Common Use Cases.

Intro:

• Amazon OpenSearch Service is a managed service by Amazon Web Services (AWS).
• Amazon OpenSearch Service simplifies the deployment, operation, and scaling of OpenSearch clusters in the cloud. 
• OpenSearch is a distributed, open-source search and analytics suite used for various data-driven applications.

1. The Concept of Amazon OpenSearch Service

• Amazon OpenSearch Service (AOS) is a managed service for deploying, operating, and scaling OpenSearch clusters (forked from Elasticsearch 7.10 & Kibana).
• Designed for:
• Search & analytics (log search, full-text search, autocomplete, etc.)
• Observability (log & trace analytics, dashboards)
• Security analytics (SIEM use cases, anomaly detection)
• Application monitoring (APM with OpenSearch Dashboards)
• Business intelligence (real-time dashboards over large datasets)

2. Core Architecture

An AOS (Acropolis Operating System) domain (cluster) is made of three main node types:

• Cluster Manager Nodes (Dedicated Masters) – manage cluster state, elections, shard placement.
• Data Nodes – store data and process queries/ingest operations.
• UltraWarm/Cold Nodes – cost-effective storage for infrequently queried data.
• Ingest Nodes – optional, for preprocessing/transforming docs before indexing.

Data flow:

1. Data Ingestion (via API, Kinesis Data Firehose, Logstash, Fluent Bit, Beats, custom apps).
2. Indexing (data split into shards → replicated across nodes).
3. Query/Search (distributed search across shards, results aggregated).
4. Visualization (via OpenSearch Dashboards or BI tools).

3. Storage & Scaling

• Indices → made up of shards (primary + replicas).
• Scaling is done by:
• Vertical Scaling → Bigger instance types.
• Horizontal Scaling → More data nodes/shards.
• Auto-Tune helps optimize performance by adjusting thread pools, cache sizes, and queues automatically.
• Tiered Storage:
• Hot storage → SSD-backed nodes (frequent access).
• UltraWarm → S3-backed cache for logs/analytics.
• Cold storage → Archive in S3, searchable but higher latency.

4. Security

• Encryption:
• At rest (KMS-managed keys).
• In transit (TLS).
• Fine-Grained Access Control (FGAC):
• Document-level security.
• Field-level security.
• Role-based access.
• IAM Integration:
• Sign requests with SigV4.
• Use Cognito for dashboard authentication.
• VPC Deployment:
• Deploy domains inside VPC.
• Private endpoint access only.
• Audit Logs – track user/API activity.

5. Operations & Management

• Deployment Modes:
• Public endpoint (with IAM + FGAC).
• VPC-only (private).
• Availability & Resilience:
• Multi-AZ deployment for fault tolerance.
• Snapshots stored in S3 (automated + manual).
• Monitoring:
• Amazon CloudWatch (metrics).
• OpenSearch Dashboards.
• Trace analytics (with AWS Distro for OpenTelemetry).
• Upgrades:
• Supports in-place version upgrades.
• Blue/Green deployment recommended for production.

6. Integrations

• Data Sources:
• Kinesis, Firehose, CloudWatch Logs, S3, DynamoDB streams, custom apps.
• Visualization:
• OpenSearch Dashboards (fork of Kibana).
• BI tools (Grafana, Amazon QuickSight).
• Machine Learning (ML):
• Built-in anomaly detection (Random Cut Forest).
• k-NN search (vector similarity for AI/ML workloads).
• Observability:
• Metrics, logs, and traces ingestion in one platform.

7. Best Practices

• Shard Strategy:
• Avoid oversharding (too many small shards).
• Aim for shard sizes of 10–50GB.
• High Availability:
• At least 3 cluster managers in multi-AZ.
• Replication factor ≥1.
• Cost Optimization:
• Use UltraWarm/Cold tiers for older data.
• Use Auto-Tune for resource efficiency.
• Security:
• Always use VPC-only domains in production.
• Enforce least privilege IAM + FGAC.
• Performance Tuning:
• Prefer bulk indexing over single doc indexing.
• Monitor hot shards (skewed data distribution).
• Use index lifecycle policies to manage data aging.

8. Common Use Cases

• Log analytics (centralized logging, SIEM).
• Application monitoring (APM + trace analytics).
• E-commerce search engines (full-text search, autocomplete).
• Security analytics (intrusion detection, anomaly detection).
• Vector search (AI-powered semantic search).

Final tip:

•  Amazon OpenSearch Service is the managed search + analytics engine on AWS.
• Amazon OpenSearch Service is built for scale, real-time insights, observability, and search-driven applications—with deep integration into the AWS ecosystem.