This document provides an overview of batch processing options on AWS, focusing on AWS Batch and Amazon ECS. It discusses the challenges of running batch workloads, why the cloud is suitable, and how containers help. It then details the key features and architectures of AWS Batch and Amazon ECS for batch processing, including scheduling, scaling, and integration with other AWS services. The document concludes with best practices like leveraging Spot Instances and event-based task scheduling.

2. WIFI: awsDevDay | PASS: CodeHappy
U P N E X T :
Introduction to Batch
Processing on AWS

3. T H A N K S T O O U R F R I E N D S A T :

4. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Pierre Steckmeyer, Solutions Architect
August 1, 2017
Introduction to Batch
Processing on AWS

5. • Batch processing – overview and challenges
• Why run batch workloads in the cloud
• Overview of AWS batch solutions
• Deep dive look at AWS Batch and Amazon ECS
• Best practices review
Agenda

6. Challenges of Running Batch Workloads
• Typically resource intensive
• Time constraint for completion
• Potential impact to concurrent batch jobs
• Scaling infrastructure resources
• Ensuring effective resource utilization and cost savings
• Fragile and unreliable

7. What Batch Workloads Need
Reliability Easy Development Easy Deployment
High Efficiency Low Ops Load Cost Effective

8. Why the cloud makes sense for batch workloads
Reliable Scalable Pay as you goInfrastructure as
code

9. Why containers make sense for batch workloads
• Simple to model
• Polyglot
• Image is the version
• Do one thing well
• You build it, you run it
• Black box

10. Options for batch workloads on AWS
AWS Batch Amazon ECS

11. Introducing AWS Batch
• Fully managed batch primitives
• Focus on your applications (shell scripts,
Linux executables, Docker images) and
their resource requirements
• We take care of the rest!

12. IAM role for the
AWS Batch job
Input files
Queue of
runnable jobs
S3 events trigger a
Lambda function that submits
an AWS Batch job
AWS Batch
compute environments
AWS Batch
job output
Typical AWS Batch Job Architecture
Job definition
Job resource requirements
and other parameters
AWS Batch execution
Application
image
AWS Batch
Scheduler

13. Amazon EC2 Container Service (ECS) is a highly
scalable, high performance container management
service that supports Docker containers and allows you
to easily run applications on a managed cluster of
Amazon EC2 instances.
Introducing Amazon ECS

14. Cluster Management Made Easy
Nothing to run
Complete state
Control and monitoring
Scale

15. Performance at Scale

16. Flexible Container Placement
Applications
Batch jobs
Multiple schedulers

17. Designed for Use with Other AWS Services
Elastic Load Balancing
Amazon Elastic Block Store
Amazon Virtual Private Cloud
AWS Identity and Access Management
AWS CloudTrail
Spot Fleet

18. Security
Your own EC2 instances in a VPC
with all its security features to
provide a high level of isolation.

19. Amazon ECS
EC2 INSTANCES
LOAD
BALANCER ECS
AGENT
TASK
Container
TASK
Container
ECS
AGENT
TASK
Container
TASK
Container AGENT COMMUNICATION
SERVICE
Amazon
ECS
API
CLUSTER MANAGEMENT
ENGINE
KEY/VALUE STORE
ECS
AGENT
TASK
Container
TASK
Container
LOAD
BALANCER
Internet

20. Running batch workloads on
ECS

21. File put into
S3 bucket
Amazon
Simple Queue
Service
Output to S3
bucket
Amazon ECS provisions compute
clusters and schedules tasks based
on demand
Batch worker
task polls
SQS for new
jobs
Queue load is
communicated to
ECS
Containerized
batch worker
processes file
Basic batch workflow with ECS

22. Trigger Batch Processing with Lambda
Amazon ECS
Availability Zone Availability Zone
Container Instance
AutoScaling Group
Task A
AWS Lambda
Amazon
S3 Bucket
(Source)
ecs:RunTask
Amazon
S3 Bucket
(Target)
Amazon
S3 Bucket
Object
Amazon
CloudWatch
AWS CloudTrail
Container Instance

23. Fleet of workers with ECS with SQS
Amazon ECS
Availability Zone Availability Zone
SQS queue
Container Instance Container Instance
AutoScaling Group
Task A
AWS Lambda
Amazon
S3
DynamoDB
Amazon
Kinesis
ecs:RunTask
Amazon
CloudWatch
AWS CloudTrail

24. Long-running Batch Jobs
• Utilize Spot
Instances
• EC2 Spot Blocks for
Defined-Duration
Workloads
• ECS event stream
for CloudWatch
Events
• Service Scaling and
Monitoring
Amazon ECS
Availability Zone Availability Zone
Container Instance Container Instance
AutoScaling Group
Task A Task B
Task C
Amazon
CloudWatch
AWS CloudTrail

25. Get the Best Value for EC2 Capacity – Spot
Instances
• Since Spot instances typically cost 50-90% less than
On-Demand, you can increase your compute capacity
by 2-10x within the same budget
• Or you could save 50-90% on your existing workload
• Either way, you should try it!

26. Best Practices
• Store state and inputs, outputs in S3 or another
datastore
• Minimize dependencies between task definitions (should
be independent of each other)
• Use Spot Instances and Spot fleets for long-running
batch jobs
• Monitor cluster state with ECS APIs
• Share pools of resources
• Auto Scaling, VPC, IAM, scheduled Reserved Instances

28. Serving
Maps at
Scale on
AWS
Powers over 5,000 apps in categories ranging from social to mobility
Reaches more than 200 million users each month and growing

29. C4 R3 M4R3 R3
R3 R3 R3
M4 M4
M4 M4 M4
C4 C4
C4 C4 C4
Map Service Search Service Directions Service

30. C4
ECS Cluster
R3 M4R3 R3
R3 R3 R3
M4 M4
M4 M4 M4
C4 C4
C4 C4 C4
Map Service Search ServiceDirections Service

31. Deploying
with the
Old Way
R3 R3 R3
R3 R3 R3
Git SHA 56fb514
R3 R3 R3
R3 R3 R3
Git SHA 168f73e

32. Deploying
on ECS
Deploys take minutes instead of hours
Can iterate and ship new features faster
Rollbacks are faster

33. Reduce
Waste with
Better
Instance
Packing
Map
service
CPU 55%
Mem 5%
Search
service
CPU 25%
Mem 75%
Combined
services
with ECS
CPU 80%
Mem 80%

34. Stability On
Spot with
Instance
Diversity
C4
ECS Cluster
R3 R3 R3
R3 R3 R3
M4
M4
M4 M4
M4 M4
C4 C4
C4 C4 C4
Map Service Search ServiceDirections Service
Spot Fleet
C4
C4
R3
R3

35. 25%
Fewer instances
80-90%
Savings per month on EC2

36. 21
Services
2000
Tasks
1.3 billion
Requests per day

37. Time and Event-Based Task Scheduling
• Schedule on fixed time intervals (e.g.: number of minutes, hours, or days)
• or use cron expressions.
• Set Amazon ECS as a CloudWatch Events target

38. Time and Event-Based Task Scheduling
• Schedule on fixed time intervals (e.g.: number of minutes, hours, or days)
• or use cron expressions.
• Set Amazon ECS as a CloudWatch Events target

39. Summary
• Cloud and containers are a great way to run batch
workloads
• Two options on AWS: Batch and ECS
• Why AWS Batch:
• Managed Batch Processing environment
• Why ECS:
• DIY Batch Processing
• Very flexible Time and Event based Task Scheduling

40. Thank You!

41. Don’t Forget Evaluations!