This document provides an overview and agenda for a presentation on real-time data processing using AWS Lambda. The presentation covers serverless real-time data processing concepts, processing streaming data with Lambda and Kinesis, a streaming data processing demo, a data processing pipeline with Lambda and MapReduce, and a big data processing solution demo. It also discusses a customer story of Fannie Mae using distributed computing with Lambda for financial modeling. Key topics include serverless processing of real-time streaming data, a map-reduce model for serverless distributed computing, benchmarks of serverless distributed computing, and Fannie Mae's journey migrating their high performance computing workloads to AWS Lambda.

1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Tara E. Walker | Technical Evangelist | @taraw
Presented by Randall Hunt
May 2017
SMC303
Real-Time Data Processing Using
AWS Lambda

2. Agenda
What’s Serverless Real-Time Data Processing?
Processing Streaming Data with Lambda and Kinesis
Streaming Data Processing Demo
Data Processing Pipeline with Lambda and MapReduce
Building a Big Data Processing Solution Demo
What’s Serverless Real-Time Data Processing?
Serverless Processing of Real-Time Streaming Data
Serverless Data Processing with Distributed Computing
Customer Story:
Fannie Mae-Distributed Computing with Lambda

3. What’s Serverless Real-Time
Data Processing?

4. AWS Lambda
Efficient performance at scale Easy to author, deploy,
maintain, secure & manage. Focus on business logic
to build back-end services that perform at scale.
Bring Your Own Code: Stateless, event-driven code
with native support for Node.js, Java, Python and C#
languages.
No Infrastructure to manage: Compute without
managing infrastructure like Amazon EC2 instances
and Auto Scaling groups.
Cost-effective: Automatically matches capacity to
request rate. Compute cost 100 ms increments.
Triggered by events: Direct Sync & Async API calls,
AWS service integrations, and 3rd party triggers.

5. Amazon
S3
Amazon
DynamoDB
Amazon
Kinesis
AWS
CloudFormation
AWS
CloudTrail
Amazon
CloudWatch
Amazon
Cognito
Amazon
SNS
Amazon
SES
Cron
events
DATA STORES ENDPOINTS
CONFIGURATION REPOSITORIES EVENT/MESSAGE SERVICES
Lambda Event Sources
… more on the way!
AWS
CodeCommit
Amazon
API Gateway
Amazon
Alexa
AWS
IoT
AWS Step
Functions

6. Serverless Real-Time Data Processing Is..
Capture Data
Streams
IoT Data
Financial
Data
Log Data
No servers to
provision or
manage
EVENT SOURCE
Node.js
Python
Java
C#
Process Data
Streams
FUNCTION
Clickstream
Data
Output
Data
DATABASE
CLOUD
SERVICES

7. Amazon
DynamoDB
Amazon
Kinesis
Amazon
S3
Amazon
SNS
ASYNCHRONOUS PUSH MODEL
STREAM PULL MODEL
Lambda Real-Time Event Sources
Amazon
Alexa
AWS
IoT
SYNCHRONOUS PUSH MODEL
Mapping owned by Event Source
Mapping owned by Lambda
Invokes Lambda via Event Source API
Lambda function invokes when new
records found on stream
Resource-based policy permissions
Lambda Execution role policy permissions
Concurrent executions
Sync invocation
Async Invocation
Sync invocation
Lambda polls the streams
HOW IT WORKS

8. Serverless Processing of
Real-Time Streaming Data

9. Amazon Kinesis
Real-Time: Collect real-time data streams and
promptly respond to key business events and
operational triggers. Real-time latencies.
Easy to use: Focus on quickly launching data
streaming applications instead of managing
infrastructure.
Amazon Kinesis Offering: Managed services for
streaming data ingestion and processing.
• Amazon Kinesis Streams: Build applications
that process or analyze streaming data.
• Amazon Kinesis Firehose: Load massive
volumes of streaming data into Amazon S3
and Amazon Redshift.
• Amazon Kinesis Analytics: Analyze data
streams using SQL queries.

10. Processing Real-Time Streams: Lambda + Amazon Kinesis
Streaming data sent to Amazon
Kinesis and stored in shards
Multiple Lambda functions can be
triggered to process same Amazon
Kinesis stream for “fan out”
Lambda can process data and store
results ex. to DynamoDB, S3
Lambda can aggregate data to
services like Amazon Elasticsearch
Service for analytics
Lambda sends event data and
function info to Amazon CloudWatch
for capturing metrics and monitoring
Amazon
Kinesis
AWS
Lambda
Amazon
CloudWatch
Amazn
DynamoDB
AWS
Lambda
Amazon
Elasticsearch Service
Amazon
S3

11. Processing Streams: Set Up Amazon Kinesis Stream
Streams
Made up of Shards
Each Shard ingests/reads data up to 1 MB/sec
Each Shard emits/writes data up to 2 MB/sec
Each shard supports 5 reads/sec
Data
All data is stored and is replayable for 24 hours
Make sure partition key distribution is even to optimize parallel throughput
Partition key used to distribute PUTs across shards, choose key with more groups than
shards
Best Practice
Determine an initial size/shards to plan for expected maximum demand
 Leverage “Help me decide how many shards I need” option in Console
 Use formula for Number Of Shards:
max(incoming_write_bandwidth_in_KB/1000, outgoing_read_bandwidth_in_KB / 2000)

12. Processing Streams: Create Lambda functions
Memory
CPU allocation proportional to the memory configured
Increasing memory makes your code execute faster (if CPU bound)
Increasing memory allows for larger record sizes processed
Timeout
Increasing timeout allows for longer functions, but longer wait in case of errors
Permission model
Execution role defined for Lambda must have permission to access the stream
Retries
With Amazon Kinesis, Lambda retries until the data expires
(24 hours)
Best Practice
Write Lambda function code to be stateless
Instantiate AWS clients & database clients outside the scope of the function handler

13. Processing Streams: Configure Event Source
Amazon Kinesis mapped as event source in Lambda
Batch size
Max number of records that Lambda will send to one invocation
Not equivalent to effective batch size
Effective batch size is every 250 ms – Calculated as:
MIN(records available, batch size, 6MB)
Increasing batch size allows fewer Lambda function invocations with more
data processed per function
Best Practices
Set to “Trim Horizon” for reading from start of
stream (all data)
Set to “Latest” for reading most recent data (LIFO) (latest data)

14. Processing streams: How It Works
Polling
Concurrent polling and processing per shard
Lambda polls every 250 ms if no records found
Will grab as much data as possible in one GetRecords call (Batch)
Batching
Batches are passed for invocation to Lambda through
function parameters
Batch size may impact duration if the Lambda function
takes longer to process more records
Sub batch in memory for invocation payload
Synchronous invocation
Batches invoked as synchronous RequestResponse type
Lambda honors Amazon Kinesis at least once semantics
Each shard blocks in order of synchronous invocation

15. Processing streams: Tuning throughput
If put / ingestion rate is greater than the theoretical throughput, your
processing is at risk of falling behind
Maximum theoretical throughput
# shards * 2MB / Lambda function duration (s)
Effective theoretical throughput
# shards * batch size (MB) / Lambda function duration (s)
… …
Source
Amazon Kinesis
Destination
1
Lambda
Destination
2
FunctionsShards
Lambda will scale automaticallyScale Amazon Kinesis by splitting or merging shards
Waits for responsePolls a batch

16. Processing streams: Tuning Throughput w/ Retries
Retries
Will retry on execution failures until the record is expired
Throttles and errors impacts duration and directly impacts throughput
Best Practice
Retry with exponential backoff of up to 60s
Effective theoretical throughput with retries
( # shards * batch size (MB) ) / ( function duration (s) * retries until expiry)
… …
Source
Amazon Kinesis
Destination
1
Lambda
Destination
2
FunctionsShards
Lambda will scale automaticallyScale Amazon Kinesis by splitting or merging shards
Receives errorPolls a batch
Receives error
Receives success

17. Processing streams: Common observations
Effective batch size may be less than configured during low throughput
Effective batch size will increase during higher throughput
Increased Lambda duration -> decreased # of invokes and GetRecord calls
Too many consumers of your stream may compete with Amazon Kinesis read
limits and induce ReadProvisionedThroughputExceeded errors and metrics
Amazon
Kinesis
AWS
Lambda

18. Processing streams: Monitoring with Cloudwatch
• GetRecords: (effective throughput)
• PutRecord : bytes, latency, records, etc
• GetRecords.IteratorAgeMilliseconds: how old your
last processed records were
Monitoring Amazon Kinesis Streams
Monitoring Lambda functions
• Invocation count: Time function invoked
• Duration: Execution/processing time
• Error count: Number of Errors
• Throttle count: Number of time function throttled
• Iterator Age: Time elapsed from batch received &
final record written to stream
• Review All Metrics
• Make Custom logs
• View RAM consumed
• Search for log events
Debugging
AWS X-Ray
Coming soon!

19. Serverless Data Processing with
Distributed Computing
10101101
11001010

20. Serverless Distributed Computing: Map-Reduce Model
Why Serverless Data Processing with Distributed
Computing?
Remove Difficult infrastructure management
 Cluster administration
 Complex configuration tools
Enable simple, elastic, user-friendly distributed data
processing
 Eliminate complexity of state management
 Bring Distributed Computing power to the masses

21. Serverless Distributed Computing: Map-Reduce Model
Why Serverless Data Processing with Distributed
Computing?
Eliminate utilization concerns
 Makes code simpler by removes complexities of multi-
threading processing to optimize server usage
 Cost-effective option to run ad hoc MapReduce jobs
Easier, automatic horizontal scaling
 Provide ability to process scientific and analytics
applications

22. Serverless Distributed Computing: MapReduce
Input Bucket
1
2
Driver
job state
Mapper Functions
map phase
S3
event
source
mapper
output
3 Coordinator
4
Reducer step 1
reducer output
5
recursively
create
n‘th reducer
step
ResultFinal Reducer
reduce phase
6

23. Serverless Distributed Computing: PyWren
PyWren Prototype Developed at University of California, Berkeley
Uses Python with AWS Lambda stateless functions for large scale data
analytics
Achieved @ 30-40 MB/s write and read performance per-core to S3
object store
Scaled to 60-80 GB/s across 2800 simultaneous functions

24. Serverless Distributed Computing: Benchmark
Using Amazon MapReduce Reference Architecture Framework
with Lambda
Dataset
Queries:
 Scan query (90 M Rows, 6.36 GB of data)
 Select query on Page Rankings
 Aggregation query on UserVisits ( 775M rows, ~127GB of
data)
Rankings
(rows)
Rankings
(bytes)
UserVisits
(rows)
UserVisits
(bytes)
Documents
(bytes)
90 Million 6.38 GB 775 Million 126.8 GB 136.9 GB

25. Serverless Distributed Computing: Benchmark
Using Amazon MapReduce Reference Architecture Framework
with Lambda
Subset of the Amplab benchmark ran to compare with other data
processing frameworks
Performance Benchmarks: Execution time for each workload in seconds
TECHNOLOGY SCAN 1A SCAN 1B AGGREGATE 2A
Amazon Redshift (HDD) 2.49 2.61 25.46
Serverless MapReduce 39 47 200
Impala - Disk - 1.2.3 12.015 12.015 113.72
Impala - Mem - 1.2.3 2.17 3.01 84.35
Shark - Disk - 0.8.1 6.6 7 151.4
Shark - Mem - 0.8.1 1.7 1.8 83.7
Hive - 0.12 YARN 50.49 59.93 730.62
Tez - 0.2.0 28.22 36.35 377.48

26. Fannie Mae: Distributed
Computing with Lambda

27. © 2017 Fannie Mae. Trademarks of Fannie Mae. 28
© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Bin Lu, Fannie Mae
4/18/2017
High Performance Computing Using
AWS Lambda for Financial Modeling

28. © 2017 Fannie Mae. Trademarks of Fannie Mae. 295/19/2017
Fannie Mae Business
Fannie Mae is a leading source of financing for mortgage
lenders:
• Providing access to affordable mortgage financing in all
market conditions.
• Effectively managing and reducing risk to our business,
taxpayers, and the housing finance system.
In 2016, Fannie Mae provided $637B in liquidity to the
mortgage market, enabling
• 1.1M home purchase ,
• 1.4 M refinancing,
• 724K rental housing units.

29. © 2017 Fannie Mae. Trademarks of Fannie Mae. 305/19/2017
Fannie Mae Financial Modeling
Financial Modeling is a Monte-Carlo simulation process to project future cash flows , which is used for managing
the mortgage risk on daily basis:
• Underwriting and valuation
• Risk management
• Financial reporting
• Loss mitigation and loan removal
~10 Quadrillion (10𝑥1015
) of cash flow
projections each month in hundreds of
economic scenarios.

30. © 2017 Fannie Mae. Trademarks of Fannie Mae. 315/19/2017
Fannie Mae Financial Modeling Infrastructure
High Performance Computing grids is the key infrastructure component for financial modeling at Fannie Mae.
Fannie Mae existing HPC grids no longer meet our growing business needs:
• It is 7 years old with limited computing capacity, limited IO capacity, limited storage and complex API.
• It takes more than half a year to add incremental compute capacity and develop any new application.
We are looking for a new HPC facility to react to the rapidly changing market!
• Unlimited computing resources and unlimited storage.
• Serverless infrastructure with simple distributed computing API.
• Efficient cost model.

31. © 2017 Fannie Mae. Trademarks of Fannie Mae. 325/19/2017
Fannie Mae’s Journey to AWS Serverless HPC Service
In 2016, Fannie Mae began to work with AWS to build the first serverless HPC computing platform in the
industry using Lambda service. This is also the first pilot program for Fannie Mae to develop an AWS cloud
native application.
Once the infrastructure is setup, we are able to develop a new application within a month and provision the
compute resources within minutes.
In March 2017, Fannie Mae successfully deployed the first financial modeling application to preproduction and
ran on 15,000 concurrent executions

32. © 2017 Fannie Mae. Trademarks of Fannie Mae. 335/19/2017
Fannie Mae’s Serverless HPC Performance
Lambda service configuration:
• Initial burst rate = 2,000, incremental rate = 100 per minute,
throttle limit = 15,000.
• Lambda ramps up automatically from 2,000 to 15,000 concurrent
executions.
Application Result:
• One simulation run of ~ 20 million mortgages takes 2 hours, >3
times faster than the existing process.
• The performance does not degrade during the ramp up period.
• Lambdas’ CPU efficiency is close to 100%. Actual elapsed time is
consistent with the estimated elapsed time based on Lambda billing
time.
Number of New
Lambda Invocations
every 5 Mins
Maximum
Concurrent
Lambdas =
15,000

33. © 2017 Fannie Mae. Trademarks of Fannie Mae. 345/19/2017
Simple Serverless HPC Reference Architecture
Map-reduce framework is used for simple parallel workload:
• Input file in S3 input bucket is split using EC2 to n triggers, which are saved in S3 event bucket.
• Lambda automatically ramps up n concurrent executions and writes outputs to S3 mapper bucket.
• EC2 is used to aggregate outputs and write final result to S3 reducer bucket.
Amazon S3
Input
Amazon EC2
Splitter
…
AWS Lambda
Mappers
Amazon EC2
Reducer
AmazonS3
Mapper Result
Amazon S3
Reducer Result
…
Amazon S3
Event

34. © 2017 Fannie Mae. Trademarks of Fannie Mae. 355/19/2017
Complex Serverless HPC Reference Architecture
Breakdown complex workload into multiple simple ones:
…

35. © 2017 Fannie Mae. Trademarks of Fannie Mae. 365/19/2017
Benefit of Serverlesss HPC Service
Cost Effective
• Never pay for idle. The cost is based on actual vCPU usage, not elapsed time or maximum processing capacity
of the infrastructure.
• Performance improvement at zero cost: 1 Lambda x 15,000 hours = 15,000 Lambda x 1 hour.
Shorter Time to Market
• Ability to burst to cloud immediately to access additional computing resources.
• Ability to focus on business needs. No server to manage and no complex distributed computing code to write.
Most Complete Data Analytics Platform
• Streamlined integration with big data platform and BI tools / Data Lake.
• Business resiliency.

36. © 2017 Fannie Mae. Trademarks of Fannie Mae. 375/19/2017
Considerations and Next Step
Considerations:
• Maximize S3 performance by distributing the key names to evenly distribute objects across the partitions.
• Set up a separate AWS account for unlimited Lambda access / IP addresses.
• Adopt microservice architecture to migrate one business function/application at a time.
• Integrate with AWS big data analytics platform for accessing unlimited storage and state of art business
analytical tools.
Next step:
• Production migration of the first application in Q2 2017.
• Complete migration of primary loan performance modeling applications to AWS in early 2018.

37. Real-time Data Processing with
Lambda: Next Steps

38. Data Processing with AWS: Next steps
 Learn more about AWS Serverless at
https://aws.amazon.com/serverless
 Explore the AWS Lambda Reference Architecture on GitHub:
 Real-Time Streaming:
https://github.com/awslabs/lambda-refarch-
streamprocessing
 Distributed Computing Reference Architecture
(serverless MapReduce)
https://github.com/awslabs/lambda-refarch-mapreduce

39. Data Processing with AWS: Next steps
 Create an Amazon Kinesis stream. Visit the Amazon Kinesis
Console and configure a stream to receive data Ex. data from
Social media feeds.
 Create & test a Lambda function to process streams from Amazon
Kinesis by visiting Lambda console. First 1M requests each month
are on us!
 Read the Developer Guide and try the Lambda and Amazon
Kinesis Tutorial:
 http://docs.aws.amazon.com/lambda/latest/dg/with-
kinesis.html
 Send questions, comments, feedback to the AWS Lambda Forums

40. Thank you!
Tara E. Walker
AWS Technical Evangelist
@taraw