New AWS Services for Bioinformatics - Athena, Batch, Step Functions, Glue and QuickSight

1. Lynn Langit
New AWS Services
For bioinformatics pipelines
Feb 2017

2. New AWS Services
• Useful for scaling bioinformatics pipelines
• Announced at re:Invent (Nov 2016)
• Athena
• Step Functions
• Batch
• Glue
• QuickSight

3. Starting Point for CSIRO

4. Serverless AWS Lambda Application

5. Public Genomic Datasets

6. About AWS Athena
Serverless SQL queries on S3 data

8. AWS Athena Information
• Add table (structure) to database via DDL from input file(s)
• Write and execute SQL query
• Optionally save query
• Optionally review query history
• View results
• Optionally download result set to .csv

9. Athena - Demo

10. Athena Genomics Query Example

11. About AWS Step Functions
Serverless visual workflows for Lambdas

12. AWS Step Functions
1. Define steps and services (activities or lambdas)
2. Verify step execution(s)
3. Monitor and scale
“Your application as a state machine.”

13. AWS Step Functions – 1. Define Steps/Services

14. AWS Step Functions – 2. Verify step execution

15. Step Functions - Demo

17. About AWS Batch
Fully managed batch processing at scale

18. What is batch computing?
Run jobs asynchronously and automatically across one or
more computers.
Jobs may dependencies, making the sequencing and
scheduling of multiple jobs complex and challenging.

19. What is AWS Batch?
Fully Managed
No software to install or
servers to manage.
Integrated with AWS
Batch jobs can easily and
securely interact with
services such as Amazon S3,
DynamoDB, and Rekognition
Cost-optimized
Provisioning
Auto provisions compute
resources tailored to the job
needs using EC2 & EC2 Spot

20. AWS Batch Concepts
1. Jobs
1. Job Definitions
2. Job Queues
3. Job States
2. Compute Environments
3. Scheduler
Short Video -- here

21. Jobs
Jobs are the unit of work executed by AWS Batch as containerized
applications running on Amazon EC2.
Containerized jobs can reference a container image, command, and
parameters or users can simply provide a .zip containing their
application and we will run it on a default Amazon Linux container.
$ aws batch submit-job --job-name variant-calling
--job-definition gatk --job-queue genomics

22. Massively parallel jobs
• Now - users can submit a large number of independent “simple jobs.”
• Soon – AWS will add support for “array jobs” that run many copies of an
application against an array of elements.
Array jobs are an efficient way to run:
• Parametric sweeps
• Monte Carlo simulations
• Processing a large collection of objects
NOTE: These use cases are possible today, simply submit more jobs.

23. Example Genomics
Workflow

24. Workflows, Pipelines, and Job Dependencies
Jobs can express a dependency on the successful
completion of other jobs or specific elements of an
array job.
Use your preferred workflow engine and language to
submit jobs. Flow-based systems simply submit jobs
serially, while DAG-based systems submit many jobs
at once, identifying inter-job dependencies.
$ aws batch submit-job –depends-on 606b3ad1-aa31-48d8-92ec-f154bfc8215f ...

25. Job Definitions
Batch Job Definitions specify how jobs are to be run. While each job
must reference a job definition, many parameters can be overridden.
Some of the attributes specified in a job definition:
• IAM role associated with the job
• vCPU and memory requirements
• Mount points
• Container properties
• Environment variables
$ aws batch register-job-definition --job-definition-name gatk
--container-properties ...

26. Job Queues
Jobs are submitted to a Job Queue, where they reside until they are
able to be scheduled to a compute resource. Information related to
completed jobs persists in the queue for 24 hours.
$ aws batch create-job-queue --job-queue-name genomics
--priority 500 --compute-environment-order ...

27. Compute Environments
Mapped from job queues to run containerized batch jobs.
• Managed CEs - you describe your requirements (instance types,
min/max/desired vCPUs, and EC2 Spot bid as a % of On-Demand),
AWS launches & scales resources for you. Pick specific instance types,
instance families or simply choose “optimal”
• Unmanaged CEs - you can launch and manage your own resources. Your
instances need to include the ECS agent and run supported versions of Linux
and Docker. AWS Batch will then create an Amazon ECS cluster which can
accept the instances you launch. Jobs can be scheduled to your Compute
Environment as soon as your instances are healthy and register with the
ECS Agent.
$ aws batch create-compute-environment --compute-
environment-name unmanagedce --type UNMANAGED ...

28. AWS Batch Scheduler
The Scheduler evaluates when, where, and
how to run jobs that have been submitted to
a job queue.
Jobs run in approximately the order in which
they are submitted as long as all
dependencies on other jobs have been met.

29. Queued Job States
• SUBMITTED: Accepted into the queue, but not yet evaluated for execution
• PENDING: Your job has dependencies on other jobs which have not yet
completed
• RUNNABLE: Your job has been evaluated by the scheduler and is ready to run
• STARTING: Your job is in the process of being scheduled to a compute
resource
• RUNNING: Your job is currently running
• SUCCEEDED: Your job has finished with exit code 0
• FAILED: Your job finished with a non-zero exit code or was cancelled or
terminated.

30. AWS Batch Actions
• CancelJob: Marks jobs that are not yet STARTING as
FAILED.
• TerminateJob: Cancels jobs that are currently waiting in the
queue. Stops jobs that are in a STARTING or RUNNING state
and transitions them to FAILED.
NOTE: Requires a “reason” which is viewable via DescribeJobs
$ aws batch cancel-job --reason “Submitted to wrong queue”
--jobId= 8a767ac8-e28a-4c97-875b-e5c0bcf49eb8

31. AWS Batch Data Types
• ComputeEnvironmentDetail
• ComputeEnvironmentOrder
• ComputeResource
• ContainerProperties
• ContainerPropertiesResource
• CounterProperties
• Host
• Job
• JobDefinition
• JobQueueDetail
• MountPoint
• Parameter
• Ulimit
• Volume

32. Batch - Demo

33. AWS Batch Pricing and Functionality
There is no charge for AWS Batch; you only pay for the
underlying resources that you consume!
NOTE: Support for Array Jobs, retries, and jobs executed as AWS Lambda
functions coming soon!

34. Use the Right Tool for the Job
Not all batch workloads are the same…
• ETL and Big Data processing/analytics?
• Consider EMR, Data Pipeline, Redshift, and related services.
• Lots of small Cron jobs? AWS Batch is a great way to execute these jobs, but
you will likely want a workflow or job-scheduling system to orchestrate job
submissions.
• Efficiently run lots of big and small compute jobs on heterogeneous
compute resources? Use AWS Batch

35. Example: DNA Sequencing

36. Example: Genomics on Unmanaged Compute Environments

37. Fully Managed Integrated with AWS Cost-optimized
Resource Provisioning
AWS Batch summarized

38. About AWS Glue
Serverless managed, scalable ETL

39. AWS Glue
1. Build a data catalog
1. Discover and use your datasets via a Hive-compatible metastore
2. Store versions, connection and credential info
3. Use crawlers to auto-generate schema from S3 data & partitions
2. Generate and edit transforms using PySpark
3. Schedule and run your jobs
1. On schedule, event or lambda
NOTE: Glue is announced, but no beta as of yet…video from re:Invent -- here

43. An aside…
EC2 Elastic GPUs

45. About AWS QuickSight
Quick and easy data dashboards

48. Resources for new AWS Services
• Athena (SQL query on S3) – here
• Batch (Optimized, chained EC2 batches) – here
• Glue (Scaled ETL) -- here
• Step Functions (Lambda workflows) – here
• QuickSight (Data Dashboards) – here
• Full list of AWS services announced at re:Invent 2016 -- here