DocumentDB | Deep Dive.

 Amazon DocumentDB (with MongoDB compatibility) - Deep Dive.

Scope:

• Intro
• Architecture,
• Features,
• Security,
• Performance,
• Operations,
• Integrations,
• Use cases.

Intro:

• Amazon DocumentDB (with MongoDB compatibility) is a fast, scalable, highly available, and fully managed document database service provided by Amazon Web Services (AWS). 
• Amazon DocumentDB is designed to be compatible with MongoDB APIs, allowing developers to use existing MongoDB applications, drivers, and tools with minimal or no code changes.

1. Overview

• Amazon DocumentDB is a fully managed document database service that is MongoDB-compatible (works with MongoDB APIs, drivers, and tools).
• Designed for scalability, availability, and durability of JSON-like document data.
• Ideal for workloads requiring semi-structured data, flexible schema, hierarchical data, and rapid iteration.

2. Architecture

• Storage & Compute Separation
• Storage layer is distributed, fault-tolerant, SSD-backed, and automatically replicated across 3 AZs.
• Compute layer (instances) handles query processing & caching.
• Cluster Components
• Primary instance → handles read & write.
• Replica instances (up to 15) → read scaling + failover target.
• Cluster volume → shared storage backend, 6-way replicated (2 copies in each AZ).
• Automatic Failover
• If primary fails, replicas auto-promote (failover in ~30s).

3. Key Features

• MongoDB API compatibility (3.6, 4.0, 5.0 versions supported).
• Elastic scaling (scale reads via replicas, scale storage automatically up to 128 TiB).
• Serverless → recently introduced DocumentDB Elastic Clusters for petabyte-scale, auto-sharding.
• Global Clusters → cross-region read replicas for global apps.
• Continuous backup with PITR (Point-In-Time Recovery, up to last 5 min).
• Change Streams → event-driven integration (similar to MongoDB change streams).
• Monitoring & Performance Insights → integration with CloudWatch, Performance Insights, CloudTrail.

4. Security

• Encryption at Rest → AWS KMS managed keys.
• Encryption in Transit → TLS 1.2 support.
• Authentication & Access Control
• IAM authentication.
• Database users + roles.
• Security groups, VPC isolation.
• Audit & Logging → CloudTrail, CloudWatch logs, VPC Flow logs.

5. Performance

• Designed for high throughput & low latency with distributed storage architecture.
• Automatic SSD-based replication reduces I/O bottlenecks.
• Read scaling via up to 15 replicas.
• Connection pooling with Amazon RDS Proxy (optional).
• Best suited for read-heavy, document-based workloads.
• Less efficient for high-write workloads compared to DynamoDB.

6. Operations & Management

• Fully managed (patching, backups, upgrades handled by AWS).
• Backups & PITR → continuous snapshots with 1–35 day retention.
• Monitoring → CloudWatch, Enhanced Monitoring, Performance Insights.
• Automation → CloudFormation, Terraform, CDK, CLI.
• Migration Tools
• AWS Database Migration Service (DMS).
• MongoDB dump/restore tools.

7. Integrations

• Event-driven → integrate Change Streams with Lambda, SQS, Kinesis.
• Analytics → integrate with Athena, Glue, QuickSight via federation.
• ETL & Migration → AWS DMS, Data Pipeline, Glue.
• DevOps → IaC (Terraform, CDK), CI/CD pipelines.

8. Use Cases

• Content Management Systems (CMS) → dynamic JSON storage.
• Catalogs & User Profiles → flexible schema for e-commerce apps.
• IoT & Event Data → ingesting JSON device telemetry.
• Mobile & Gaming Apps → store user/game state in flexible documents.
• Knowledge Graphs / Metadata → store hierarchical metadata with nested JSON.

9. Best Practices

• Use replicas for reads, don’t overload the primary.
• Enable auto-scaling for instance classes.
• Choose Elastic Clusters for large, sharded workloads.
• Use indexes wisely (avoid large unbounded arrays).
• Enable IAM auth + TLS for security.
• Monitor via Performance Insights to tune queries.