In unserem Vortrag tauchen wir tiefer in die Serverless-Welt ein und zeigen wie eine produktionsreife Serverless-Anwendung mithilfe von AWS-Cloud mit dem Technologie-Stack API Gateway, SNS, Lambda and DynamoDB aufgebaut werden kann. Dabei gehen wir auf die Herausforderungen der jeweiligen Services ein, wie "cold start" bei Lamda oder "provisioned throughput" und "adaptice capacity" bei DynamoDB. Dabei zeigen wir, welche Strategien und Wege es gibt, damit umzugehen. Außerdem behandeln wir solche Themen wie Implementierung von Aggregationslogik und (Scheduled) Auto Scaling bei DynamoDB. Am Ende werfen wir einen Blick in die Zukunft und sprechen über die erste relationale serverless Datenbank "Aurora Serverless"

1. Serverless on AWS
Understanding The Hard Parts
10.08.2019
by Elmar Warken and Vadym Kazulkin, ip.labs GmbH

2. Contact
Vadym Kazulkin, ip.labs GmbH
v.kazulkin@iplabs.de
www.xing.com/profile/Vadym_Kazulkin
@VKazulkin, @ServerlessBonn (Meetup)
Elmar Warken, ip.labs GmbH
e.warken@iplabs.de
www.xing.com/profile/Elmar_Warken

3. ip.labs GmbH

4. Revenue Share Project

5. Revenue Share Calculator Service Requirements
1. Automatic calculation for the complete previous month on the 1st of each
month for each customer
2. Manual calculation via a simple GUI
• for the current month so far
• recalculation of a user selected month
3. Calculation results as downloads, download link per email
4. Archive with calculated results for each customer
5. For internal use only!

6. Serverless decision for RevShare project
• spike workloads only at the beginning of a month
• service is idle more than 99% of the time
➔ We use Function as a Service, to never pay for idle.
6

7. RS core
calculator
Turnover info
input data:
order data & calculation rules
output data:
RS calculation & input data
email with path to
download RS
calculation result
Central DB
Management
Server
extract customer, year
and month from the
file path
Key example: revsharetool-storage-demo/input-data/1000000/import/201907/revshare-final.json
RS calculator app
customer, year, month

8. AWS
X-Ray
AWS
Lambda
Amazon API
Gateway

9. RevenueShare Service Lambda creation

10. Implementing „Revenue Share Calculation“ Lambda with Java SDK

11. RevenueShare Service Lambda memory setting

12. Selecting memory size for a Lambda function
• Price 0,00001667 USD per GB-second
• CPU power allocation proportional to amount of memory
• more „GB“ will often just improve performance and not increase costs!
• cheaper GB setting may save costs, when lambda function
often has to wait
12

13. XRay activation
13

14. XRay activation in Java
14

15. Lambda profiling with X-Ray
15

16. Request references from X-Ray and CloudWatch
16

17. Billed Duration and Max Memory Used
17

18. Lambda free tier
• 136170 sec. per month with the most powerful CPU setting
• = more than 1h per day
• with max 1 call every 2 sec.
18

19. Considerations for fast Lambda functions (<500ms)
• for billing, execution time will be rounded to 100 ms
• reducing real execution time from 105 ms to 95 ms can safe 50% costs
• might be possible by increasing memory by e.g. only 10%
• fee for the mere call: 0,20$ per million
• 1 million executions of 1 GB function with 1 sec runtime: > 16$
• 1 million executions of 128 MB function with 99 ms runtime: 0,208$
19

20. Amazon API
Gateway
• Expose Lambda functions as RESTful web
service
• API Management functionality built in:
• authorization and access control for API calls with IAM
• request monitoring and analytics
• overload protection
• version management
• APIs as products (SaaS, AWS Marketplace)
• SDK generation
Amazon API Gateway

21. API Gateway for different AWS services
• Access different AWS
services in a consistent
manner

22. https endpoints for Lambda functions
https://pe2r5mu2n3.execute-api.us-east-1.amazonaws.com/Beta/calculate
Application
Lambda
function

23. API Gateway for Lambda function in RevShare App

24. Cost influence of API gateway
24
0,408 1,032
1,656
2,28
2,904
3,528
4,152
4,776
3,908
4,532
5,156
5,78
6,404
7,028
7,652
8,276
100 MS 400MS 700MS 1000MS 1300MS 1600MS 1900MS 2200MS
duration per call
cost per million API calls in $ for a 128 MB
function
without API Gateway with API Gateway

25. Defining APIs for Lambdas – manual approach
Resources
Operations

26. Automated API definitions with „Serverless“ framework
26

27. Automated API definitions with „Serverless“ framework
27

28. 28

30. Aggregations
Source: Advanced Design Patterns for Amazon DynamoDB https://www.youtube.com/watch?v=jzeKPKpucS0

31. Aggregations with DynamoDB Streams and Lambda

32. Aggregations with DynamoDB Streams and Lambda

33. Aggregations with DynamoDB Streams
„revshare-calculation-aggregator“ Lambda

34. Our Journey of (Auto-) Scaling Options

35. Understanding DynamoDB Provisioned Throughput
DynamoDB works by provisioning throughput at the
table level. The throughput is set up as follows:
• Each write capacity unit (WCU) gives 1KB/s of
write throughput
• Each read capacity unit (RCU) gives 4KB/s of
read throughput
• Read and write throughput are independent

36. Partitioning Math
By Capacity = (Total RCU)/3000 + (Total WCU)/1000
By Size = Total size / 10 GB
Number of partitions = CEILING (MAX (Capacity, Size))

37. Partitioning Math
• Max RCUs per partition=3000
• Max WCUs per partition=1000
• Throughput (RCU & WCU) is uniformly spread
across partitions
• In case of exceeding throughput per partition -
ProvisionedThroughputExceededException

38. Partitioning Math
Table Size=40 GB , RCUs=1500, WCUs=400
By Capacity = 1500/3000 + 400/1000=0.9
By Size = 40 GB / 10 GB =4
Number of partitions = CEILING (MAX (0.9,4)) =4
RCUs per partition=1500/4 =375
WCUs per partition=400/4 =100

39. DynamoDB (Auto)-Scaling options
• Configured auto scaling
• Scheduled auto scaling
• On demand capacity

40. Configured Auto Scaling Example
Source: Yan Cui: "The problems with DynamoDB Auto Scaling and how it might be improved"
https://hackernoon.com/the-problems-with-dynamodb-auto-scaling-and-how-it-might-be-improved-a92029c8c10b

41. Scheduled Auto Scaling Example
aws application-autoscaling put-scheduled-action
--service-namespace dynamodb
--schedule "cron (50 1 1 * *)" // on the 1st of each month at 1:50 a.m.
--scheduled-action-name IncreaseReadCapacity
--resource-id arn:aws:dynamodb:region:account-id:your-table-name
--scalable-dimension dynamodb:table:ReadCapacityUnits
--scalable-targe-action MinCapacity=4,MaxCapacity=20
aws application-autoscaling delete-scheduled-action
--service-namespace dynamodb
--schedule "cron (30 3 1 * *)" // on the 1st of each month at 3:30 a.m.
--scheduled-action-name IncreaseReadCapacity
--resource-id arn:aws:dynamodb:region:account-id:your-table-name
--scalable-dimension dynamodb:table:ReadCapacityUnits

42. Database Capacity Planing Challenges
Source: „A Deep Dive into What's New for Amazon DynamoDB (DAT201) - AWS re:Invent 2018”
https://de.slideshare.net/AmazonWebServices/a-deep-dive-into-whats-new-for-amazon-dynamodb-dat201-aws-reinvent-2018

43. New Feature : DynamoDB on Demand Capacity
Source: https://aws.amazon.com/de/blogs/aws/amazon-dynamodb-on-demand-no-capacity-planning-and-pay-per-request-
pricing/

44. New Feature : DynamoDB on Demand Capacity
Source: „A Deep Dive into What's New for Amazon DynamoDB (DAT201) - AWS re:Invent 2018”
https://de.slideshare.net/AmazonWebServices/a-deep-dive-into-whats-new-for-amazon-dynamodb-dat201-aws-reinvent-2018

45. New Feature : DynamoDB on Demand Capacity
Source: „A Deep Dive into What's New for Amazon DynamoDB (DAT201) - AWS re:Invent 2018”
https://de.slideshare.net/AmazonWebServices/a-deep-dive-into-whats-new-for-amazon-dynamodb-dat201-aws-reinvent-2018

46. DynamoDB on Demand Capacity Limits
• 40,000 read request units and 40,000 write
request units per table in most regions
• DynamoDB On-Demand provisions capacity to
handle two times the past peak traffic
Source: „Understanding the scaling behaviour of DynamoDB OnDemand tables”
https://theburningmonk.com/2019/03/understanding-the-scaling-behaviour-of-dynamodb-ondemand-tables/

47. DynamoDB on Demand Capacity Use Cases
• You can’t predict your traffic patterns
• You are not worried about a runaway bill
(DynamoDB On-Demand pricing is about 6.94x
the cost of provisioned capacity)
Source: „DynamoDB On-Demand: When, why and how to use it in your serverless applications” by Alex DeBrie
https://serverless.com/blog/dynamodb-on-demand-serverless/

48. DynamoDB Capacities:
reserved vs provisioned vs on demand
1. If you have steady, predictable traffic, choose
reserved capacity
2. If you have variable, predictable traffic, choose
provisioned capacity
• Use (auto)-scaling
3. If you have variable, unpredictable traffic, choose
on-demand capacity
Source: „DynamoDB On-Demand: When, why and how to use it in your serverless applications” by Alex DeBrie
https://serverless.com/blog/dynamodb-on-demand-serverless/

49. DynamoDB canonical use cases
• Key-value lookups on well-distributed records
• Avoiding complex queries (and joins)

50. DynamoDB is not the right choice for
each solution

51. Challenges with DynamoDB
• Think of the access pattern in advance
• Difficult to change access patterns (partition&sort key)
& table structure afterwards

52. Amazon Aurora Serverless
(GA for MySql and Postgres)

53. What Is Amazon Aurora Serverless?

54. Amazon Aurora
GA MySql/Postgres
Source: “AWS Summit - London | twitch.tv/aws | Aurora Serverless Preview Update”
https://www.youtube.com/watch?v=J3A_TJZosu8

55. What Is Amazon Aurora Serverless?

56. Amazon Aurora Serverless Architeсture
Source: https://aws.amazon.com/de/blogs/aws/amazon-aurora-postgresql-serverless-now-generally-available/

57. Amazon Aurora Serverless Settings
Source: https://aws.amazon.com/de/blogs/aws/amazon-aurora-postgresql-serverless-now-generally-available/

58. Amazon Aurora Serverless (Auto-)Scaling
Source: https://aws.amazon.com/de/blogs/aws/amazon-aurora-postgresql-serverless-now-generally-available/

59. Use cases for Amazon Aurora Serverless
• Reasons to stay with relational database
• Seldom usage (weekly jobs) + additional spikes
• Dev/Test Database
• Noticable initial latencies acceptable
• Aurora and therefore Lambda are behind VPC
• Drastically increases also Lambda cold start
Source: Ajay Nair „Become a Serverless Black Belt” https://www.youtube.com/watch?v=oQFORsso2go

60. Amazon Aurora Serverless Data API
as beta for MySql available
Sources: https://docs.aws.amazon.com/de_de/AmazonRDS/latest/AuroraUserGuide/data-api.html
Jeremy Daly: „Aurora Serverless Data API: A First Look“ https://www.jeremydaly.com/aurora-serverless-data-api-a-first-look/

61. Amazon Aurora Serverless Data API Example
const AWS = require('aws-sdk')
const RDS = new AWS.RDSDataService()
exports.test = async (event, context) => {
const params = {
awsSecretStoreArn: 'arn:aws:secretsmanager:us-east-1:XXXXXXXXX:secret:test/data-api/mysql-test',
dbClusterOrInstanceArn: 'arn:aws:rds:us-east-1:XXXXXXXXXXX:cluster:test-data-api',
sqlStatements: `SELECT * FROM mytable WHERE id = :id`,
parameters: [ { name: 'id‘ , value: { “longValue“: 1 } } ],
database: 'test_data_api'
}
let data = await RDS.executeStatement(params).promise()
console.log(JSON.stringify(data, null, 2))
61
Source: Jeremy Daly: „Aurora Serverless Data API: An (Updated) First Look“ https://www.jeremydaly.com/aurora-serverless-data-
api-a-first-look/

62. Questions?

63. www.iplabs.de
Thank You!