DynamoDB

Summary

• AWS proprietary technology, managed serverless NoSQL database, millisecond latency
• Capacity modes: provisioned capacity with optional auto-scaling or on-demand capacity
• Can replace ElastiCache as a key/value store (storing session data for example, using TTL feature)
• Highly Available, Multi AZ by default, Read and Writes are decoupled, transaction capability
• DAX cluster for read cache, microsecond read latency
• Security, authentication and authorization is done through IAM
• Event Processing: DynamoDB Streams to integrate with AWS Lambda, or Kinesis Data Streams
• Global Table feature: active-active setup
• Automated backups up to 35 days with PITR (restore to new table), or on-demand backups
• Export to S3 without using RCU within the PITR window, import from S3 without using WCU
• Great to rapidly evolve schemas

Use Case: Serverless applications development (small documents 100s KB), distributed serverless cache

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

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 inmemory 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)

DAX vs ElastiCache

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 vs Kinesis Data Streams

DynamoDB Streams	Kinesis Data Streams
24 hours retention	1 year retention
Limited number of consumers	High number of consumers
Process using AWS Lambda Triggers, or DynamoDB Stream Kinesis adapter	Process using AWS Lambda, Kinesis Data Analytics, Kineis Data Firehose, AWS Glue Streaming ETL…

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

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…

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

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

Building a Serverless API