overview

• Fully Managed Highly available with replication across 3 AZ
• NoSQL database - not a relational database
• Scales to massive workloads, distributed "serverless" database
• Millions of requests per seconds, trillions of row, 100s of TB of storage
• Fast and consistent in performance
• Single-digit millisecond latency - low latency retrieval
• Integrated with IAM for security, authorization and administration
• Low cost and auto scaling capabilities
• Standard & Infrequent Access (IA) Table Class

DynamoDB - Basics

• DynamoDB is made of Tables
• Each table has a Primary Key (must be decided at creation time)
• Each table can have an infinite number of items (= rows)
• Each item has attributes (can be added over time – can be null)
• Maximum size of an item is 400KB
• Data types supported are:
  • Scalar Types – String, Number, Binary, Boolean, Null
  • Document Types – List, Map
  • Set Types – String Set, Number Set, Binary Set
• Therefore, in DynamoDB you can rapidly evolve schemas

DynamoDB – Read/Write Capacity Modes

• Control how you manage your table’s capacity (read/write throughput)

Provisioned Mode (default)

• You specify the number of reads/writes per second
• You need to plan capacity beforehand
• Pay for provisioned Read Capacity Units (RCU) & Write Capacity Units (WCU)
• Possibility to add auto-scaling mode for RCU & WCU

On-Demand Mode

• Read/writes automatically scale up/down with your workloads
• No capacity planning needed
• Pay for what you use, more expensive ($$$)
• Great for unpredictable workloads, steep sudden spikes

DynamoDB Accelerator - DAX

• Fully-managed, highly available, seamless in- memory cache for DynamoDB
• Help solve read congestion by caching
• Microseconds latency for cached data
• Doesn’t require application logic modification(compatible with existing DynamoDB APIs)
• 5 minutes TTL for cache (default)

DynamoDB Accelerator (DAX) vs. ElastiCache

DynamoDB – Stream Processing

• Ordered stream of item-level modifications (create/update/delete) in a table
• Use cases:
  • React to changes in real-time (welcome email to users)
  • Real-time usage analytics
  • Insert into derivative tables
  • Implement cross-region replication
  • Invoke AWS Lambda on changes to your DynamoDB table

DynamoDB Streams

• 24 hours retention
• Limited # of consumers
• Process using AWS Lambda Triggers, or DynamoDB Stream Kinesis adapter

Kinesis Data Streams (newer)

• I year retention
• High # of consumers
• Process using AWS Lambda, Kinesis Data Analytics, Kineis Data Firehose, AWS Glue Streaming ETL...

DynamoDB Streams

DynamoDB Global Tables

• Make a DynamoDB table accessible with low latency in multiple-regions
• Active-Active replication
• Applications can READ and WRITE to the table in any region
• Must enable DynamoDB Streams as a pre-requisite

DynamoDB – Time To Live (TTL)

• Automatically delete items after an expiry timestamp
• Use cases: reduce stored data by keeping only current items, adhere to regulatory obligations, web session handling…

DynamoDB – Backups for disaster recovery

• Continuous backups using point-in-time recovery (PITR)
  • Optionally enabled for the last 35 days
  • Point-in-time recovery to any time within the backup window
  • The recovery process creates a new table
• On-demand backups
  • Full backups for long-term retention, until explicitely deleted
  • Doesn’t affect performance or latency
  • Can be configured and managed in AWS Backup (enables cross-region copy)
  • The recovery process creates a new table

DynamoDB – Integration with Amazon S3

• Export to S3 (must enable PITR)
  • Works for any point of time in the last 35 days
  • Doesn’t affect the read capacity of your table
  • Perform data analysis on top of DynamoDB
  • Retain snapshots for auditing
  • ETL on top of S3 data before importing back into DynamoDB
  • Export in DynamoDB JSON or ION format
• Import from S3
  • Import CSV, DynamoDB JSON or ION format
  • Doesn’t consume any write capacity
  • Creates a new table
  • Import errors are logged in CloudWatch Logs