by Mike Gillespie, Solutions Architect, AWS Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.

1. Pop-up Loft
Amazon ElastiCache Deep Dive:
High Performance Applications
Mike Gillespie,
Solutions Architect

2. What to expect from this session
• Amazon ElastiCache overview
• Amazon ElastiCache security and encryption
• Amazon ElastiCache usage patterns
• Real-time Dashboard Demo
• Hands On Lab

3. Amazon
ElastiCache
µs is the new ms

4. Amazon ElastiCache: High Performance
Applications

5. Session
management
Database caching APIs
(HTTP responses)
IOT
Streaming data
analytics
(Filtering/aggregation)
Pub/sub
Social media
(Sentiment analysis)
Standalone
database
(Metadata store)
Leaderboards
Usage patterns

6. Amazon
Kinesis
Analytics
Amazon
Kinesis
Streams
Amazon
Kinesis
Streams
Amazon
ElastiCache
(Redis)
cleansed
stream
Streaming data enrichment/processing
Datasources
raw
stream
Subscribers
AWS Lambda function 1
Continual data
filtering/enrichment
Real-time
pub/sub
AWS Lambda function 2

7. Big data architectures using Redis
Amazon Kinesis
DataSources
AWS Lambda
Apache Storm
on EMR
Spark Streaming
on Amazon EMR
Amazon
Kinesis app
Amazon
EC2
AWS IoT
Amazon
ElastiCache
Collect
Store
Process
Amazon
S3
Apache Kafka
AWS
Lambda
Custom
app
Spark on
Amazon
EMR
Analyze

8. Near Real-time Dashboard Demo

9. Scenario:
• High-Volume E-Commerce Website
• Data Streaming with Amazon Kinesis Streams
• Need insight into the real-time sales analytics
data for the current hour and day
• Very Latency Sensitive
• Current Relational DBs are already maxed out.
Amazon
ElastiCache

10. Metrics:
• Daily Order Count
• Unique Items Sold
• Product Leaderboard
• Recently Orders
• Historical Sales Revenue by Day
Amazon
ElastiCache

11. Dashboard

12. Why ElastiCache:
• Very Complex SQL Queries on Relational DBs
• Millisecond Performance
• Elasticsearch: Complex queries and
computationally expensive
• Kinesis Analytics: Query Complexity,
Performance, and Data Retention.
Amazon
ElastiCache

13. Ingestion Architecture
Kinesis Stream AWS Lambda Amazon ElastiCache
Redis

14. Dashboard Architecture
Application Load
Balancer
Amazon ECS
Fargate
Amazon ElastiCache
Redis

15. Daily Order Count
To increment our order count on March 11, 2018, we use the command:
INCR orders:20180311
We can then retrieve the value in integer format as follows:
GET orders:20180311
Computational Complexity O(1)

16. Number of Daily Unique Items Sold
This metric will track the number of order lines per day:
SADD orders:items:20180311 123456789
EXPIRE orders:items:20180311 60*60*24*7
The expire command will manage the purging of data as it ages out.
SCARD orders:items:20180311
Computational Complexity O(1)

17. Leaderboard: Most Popular Products
This metric will track the most popular products on a given day:
ZINCRBY orders:items:popular 2 123456789
To build a report in the most popular items sold on that day:
ZREVRANGE orders:items:popular 0 9
Computational Complexity: O(log(N)) where N is the number of elements
in the sorted set.

18. Latest Products Purchased
This metric will track the orders as they are processed and list the most
recent orders processed in real time:
LPUSH orders:items:latest 123456789
LTRIM orders:items:latest 0 99
The LTRIM handles the purge of list of the 100 most recent orders.
LRANGE orders:items:latest 0 9
Computational Complexity: O(1)

19. Historical Sales Revenue
This metric will track total sales revenue in the last week.
ZADD sales:revenue:days 0 20180311
Now, increment hourly sales revenue:
HINCRBYFLOAT sales:revenue:20180311 10 244.56
To generate the report, get all the keys for the week:
ZREVRANGE sales:revenue:days 0 6
For each day in range: HGETALL sales:revenue:20180311
Computational Complexity: O(n) where n number of keys in the hash