Real-time data processing serverless architecture can eliminate the need to provision and manage servers required to process files or streaming data in real time. In this session, we will cover the fundamentals of using AWS Lambda to process data in real-time from push sources such as AWS Iot and pull sources such as Amazon DynamoDB Streams or Amazon Kinesis. We'll also discuss best practices and do a deep dive into AWS Lambda real-time stream processing.

1. Real-Time Processing Using AWS Lambda
Presenter: Sidhartha Chauhan, Solution Architect
Author: Cecilia Deng, SDE
March 2017 – AWS Loft New York

2. What to Expect from the Session
• What kinds of real time events can trigger lambda?
• How does Lambda pull and process streams?
• What are some stream processing behaviors?
• Hear how Thomson Reuters went real time with AWS Lambda

3. Flavors of real time event sources
Asynchronous Invoke
Push Event Source
Synchronous Invoke
Push Event Source
Stream
Pull Event Source
S3
async invoke
Alexa skill
sync invoke
Pull then sync invoke
DynamoDB
Update Stream

4. Real-time push

5. Real-time push
• Who?
• Any integrator that uses AWS Lambda invoke API
• E.g., Amazon S3, Amazon SNS, Amazon Alexa, AWS IoT
• What?
• Event sources sending events to Lambda for processing
• How?
• Real-time triggered events owned by event source
• Real-time processing owned by Lambda invoke methods

6. Real-time push
Synchronous Invoke
Push Event Source
Asynchronous Invoke
Push Event Source

7. Real-time pull

8. Real-time pull
• Who?
• Amazon Kinesis and DynamoDB update streams
• What?
• Lambda grabbing events from a stream for processing
• How?
• Mapping maintained by Lambda
• Real-time triggered events owned by DDB or Kinesis producer
• Real-time processing owned by Lambda stream polling component and
invoke methods

9. Real-time pull
Stream
Pull Event Source

10. Processing streams

11. Processing streams: Kinesis setup
• Streams
▪ Made up of shards
▪ Each shard supports writes up to 1 MB/s
▪ Each shard supports reads up to 2 MB/s
▪ Each shard supports 5 reads/s
• Data
▪ All data is stored and replayable for 24 hours by default
▪ Make sure partition key distribution is even to optimize parallel throughput
▪ Pick a key with more groups than shards

12. Processing streams: Lambda setup
Memory
▪ CPU is proportional to the memory
configured
▪ More memory means faster execution,
if CPU bound
▪ More memory means larger sized
record batches can be processed
Timeout
• Increasing timeout allows for longer functions, but more wait in case of errors
Permission model
• The execution role defined for Lambda must have permission to access the
stream

13. Processing streams: event source setup
• Batch size
▪ Max number of records that Lambda will send in one invocation
▪ Not equivalent to how many records Lambda gets from Kinesis
▪ Effective batch size is
• MIN(records available, batch size, 6 MB)
▪ Increasing batch size allows fewer Lambda function invocations with
more data processed per function

14. Processing streams: event source setup
• Starting Position:
▪ The position in the stream where Lambda starts reading
▪ Set to “Trim Horizon” for reading from start of stream (all data)
▪ Set to “Latest” for reading most recent data (LIFO) (latest data)

15. Processing streams: event source setup
Amazon
Kinesis 1
AWS
Lambda 1
Amazon
CloudWatch
Amazon
DynamoDB
AWS
Lambda 2 Amazon
S3
• Multiple functions can be mapped to one
stream
• Multiple streams can be mapped to one
Lambda function
• Each mapping is a unique key pair Kinesis
stream to Lambda function
• Each mapping has unique shard iterators
Amazon
Kinesis 2

16. Processing streams: under the hood
• Event received by Lambda function is a collection of records from the
stream
{ "Records": [ {
"kinesis": {
"partitionKey": "partitionKey-3",
"kinesisSchemaVersion": "1.0",
"data": "SGVsbG8sIHRoaXMgaXMgYSB0ZXN0IDEyMy4=",
"sequenceNumber": "49545115243490985018280067714973144582180062593244200961" },
"eventSource": "aws:kinesis",
"eventID": "shardId-
000000000000:49545115243490985018280067714973144582180062593244200961",
"invokeIdentityArn": "arn:aws:iam::account-id:role/testLEBRole",
"eventVersion": "1.0",
"eventName": "aws:kinesis:record",
"eventSourceARN": "arn:aws:kinesis:us-west-2:35667example:stream/examplestream",
"awsRegion": "us-west-2" } ] }

17. Processing streams: under the hood
• Polling
▪ Concurrent polling and processing per shard
▪ Currently, polls every 1s for DDB Streams if no records found
▪ Currently, polls every 250 ms for DDB Streams if no records found
▪ Grab as much as possible in one GetRecords call
• Batching
▪ Sub batch in memory for invocation payload
• Synchronous invocation
▪ Batches invoked as synchronous RequestResponse type
▪ Lambda honors Kinesis at least once semantics
▪ Each shard blocks on in order synchronous invocation

18. Processing streams: under the hood
• Per Shard:
▪ Lambda calls GetRecords with max limit from Kinesis (10 k or 10 MB)
▪ If no record, wait some time
▪ From in memory, sub batches and formats records into Lambda payload
▪ Invoke Lambda with synchronous invoke
… …
Source
Kinesis Lambda Polling Logic
Shards
Lambda will scale automaticallyScale Kinesis by adding shards
Batch sync invokesPolls

19. Processing streams: how it works
▪ Lambda blocks on ordered processing for each individual shard
▪ Increasing # of shards with even distribution allows increased concurrency
▪ Batch size may impact duration if the Lambda function takes longer to process
more records
… …
Source
Kinesis Lambda Polling Logic
Shards
Lambda will scale automaticallyScale Kinesis by adding shards
Batch sync invokesPolls

20. Processing streams: under the hood
▪ Retry execution failures until the record is expired
▪ Retry with exponential backoff up to 60 s
▪ Throttles and errors impacts duration and directly impacts throughput
Kinesis
…
Source
Scale Kinesis by adding shards
Lambda Polling Logic
Lambda will scale automatically
Polls
invoke fail
invoke fail
invoke success
Batch sync invokes

21. Processing streams: under the hood
▪ Maximum theoretical throughput:
# shards * 2 MB / (s)
▪ Effective theoretical throughput:
• ( # shards * batch size (MB) ) / ( function duration (s) * retries until expiry)
▪ If put / ingestion rate is greater than the theoretical throughput, consider increasing
number of shards of optimizing function duration to increase throughput

22. Processing streams: how it looks
•GetRecords (effective throughput): bytes, latency, records, etc.
•PutRecord: bytes, latency, records, etc.
•GetRecords.IteratorAgeMilliseconds: how old your last processed records were. If high,
processing is falling behind. If close to 24 hours, records are close to being dropped.

23. Processing streams: how it looks
Amazon CloudWatch Metrics
• Invocation count
• Duration
• Error count
• Throttle count
Amazon CloudWatch Logs
• All Metrics
• Custom logs
• RAM consumed

24. Processing streams: how it looks
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 Kinesis read limits and
induce ReadProvisionedThroughputExceeded errors and metrics

25. ANALYSING USAGE OF THOMSON REUTERS
PRODUCTS WITH AWS
Anders Fritz & Marco Pierleoni

26. CHALLENGE
• To identify and define a solution for usage analytics tracking that enables product
teams to take ownership of the usage data collected. In addition to tracking and
visualizing usage data it had to;
1. Cross reference Usage
with Business data
4. Require Limited
Maintenance.
3. Auto Scale as data
flow fluctuates.
2. Follow TR Security &
Compliance rules.
5. Launch in 5 months.

27. SOLUTION

28. SOLUTION

29. SOLUTION

30. SOLUTION

31. SOLUTION

32. SOLUTION

33. SOLUTION

34. • Product Insight is live – adoption rate high.
• Tested 4,000 requests per second while targeting 5bn requests / month.
• Since March – very little maintenance required
• No Outages
• No Downtime
• Cloudwatch monitor everything.
• Latency – Data visible on chart within 10 seconds
• BrExit and US elections tested autoscaling.
• US elections ~16m events – normally ~ 6-8m events / day.
• UK EU referendum (BrExit) ~ 10m events – normally ~ 5m events / day
OUTCOME

35. EVENTS CAPTURED
UK EU Referendum June 23rd (BrExit)
time
#events

36. EVENTS CAPTURED
US Elections November 8th
time
#events

37. aws.amazon.com/activate
Everything and Anything Startups
Need to Get Started on AWS