Real-time Data Processing Using AWS Lambda

2. Agenda Why Serverless? How can AWS Serverless Services be used for Realtime Data Processing?

3. The serverless compute manifesto • Functions are the unit of deployment and scaling. • No machines, VMs, or containers visible in the programming model. • Permanent storage lives elsewhere. • Scales per request. Users cannot over- or under-provision capacity. • Never pay for idle (no cold servers/containers or their costs). • Implicitly fault-tolerant because functions can run anywhere. • BYOC – Bring your own code. • Metrics and logging are a universal right. Why Serverless?

4. Hardware OS Dependencies Framework Your CodeLambda “Undifferentiated Heavy Lifting” Configuration, Updates, Security/Hardening e.g. Apache, MySQL e.g. PHP, Django, Ruby-on-Rails

5. GENERATE STORE ANALYZE SHARE BATCH PROCESSING GENERATE  SHARE STREAM PROCESSING

6. Data Streaming Architecture & Workflow Smart Devices Click Stream Log Data

7. AWS Lambda: Overview Lambda functions: a piece of code with stateless execution Triggered by events: • Direct Sync and Async API calls • AWS Service integrations • 3rd party triggers • And many more … Makes it easy to: • Perform data-driven auditing, analysis, and notification • Build back-end services that perform at scale

8. High performance at any scale; Cost-effective and efficient No Infrastructure to manage Pay only for what you use: Lambda automatically matches capacity to your request rate. Purchase compute in 100ms increments. Bring Your Own Code “Productivity focused compute platform to build powerful, dynamic, modular applications in the cloud” Run code in a choice of standard languages. Use threads, processes, files, and shell scripts normally. Focus on business logic, not infrastructure. You upload code; AWS Lambda handles everything else. Benefits of AWS Lambda for building a serverless data processing engine 1 2 3

9. Amazon Kinesis: Overview Managed services for streaming data ingestion and processing Amazon Kinesis Streams Build your own custom applications that process or analyze streaming data Amazon Kinesis Firehose Easily load massive volumes of streaming data into Amazon S3 and Redshift Amazon Kinesis Analytics Easily analyze data streams using standard SQL queries

10. Benefits of Amazon Kinesis for stream data ingestion and continuous processing Real-time Ingest Highly Scalable Durable Replay-able Reads Continuous Processing GetShardIterator and GetRecords(ShardIterator) Allows checkpointing/ replay Enables multi concurrent processing KCL, Firehose, Analytics, Lambda Enable data movement into many Stores/ Processing Engines Managed Service Low end-to-end latency

11. AWS Lambda and Amazon Kinesis integration How it Works Pull event source model: ▪ Kinesis mapped as Event Source in Lambda ▪ Lambda polls the stream and batches available records ▪ Batches are passed for invocation to Lambda through function param What this means: ▪ Fleet of pollers running a flavour of KCL ▪ No resource policy

12. AWS Lambda and Amazon Kinesis integration How it Works Event structure: Event received by Lambda function is a collection of records from Kinesis 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" } ] }

13. AWS Lambda and Amazon Kinesis integration How it Works Synchronous invocation: ▪ Lambda invoked as synchronous RequestResponse type ▪ Lambda honors Kinesis at least once semantics ▪ Each shard blocks on in order synchronous invocation

14. AWS Lambda and Amazon Kinesis integration How it Works Amazon Kinesis 1 AWS Lambda 1 Amazon CloudWatch Amazon DynamoD B 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

15. DEMO

16. Creating a Kinesis stream Streams ▪ Made up of Shards ▪ Each Shard supports writes up to 1MB/s ▪ Each Shard supports reads up to 2MB/s across maximum 5 transactions/s Data ▪ All data is stored and replayable for 24 hours ▪ A Partition Key is supplied by producer and used to distribute the PUTs across Shards (using MD5 function to hash to 128-bit integers) ▪ A unique Sequence # is returned to the Producer upon a successful PUT call ▪ Make sure partition key distribution is even to optimize parallel throughput ▪ Pick a key with more groups than shards

17. Creating a Lambda function Memory: ▪ CPU and disk 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 more wait in case of errors Retries: ▪ For Kinesis, Lambda retries until the data expires (default 24 hours) Permission model: • The execution role defined for Lambda must have permission to access the stream

18. Configuring the Event Source Batch size: ▪ Max number of records that Lambda will send to one invocation ▪ Not equivalent to how many records Lambda will poll ▪ Effective batch size is every 250 ms MIN(records available, batch size, 6MB) ▪ Increasing batch size allows fewer Lambda function invocations with more data processed per function

19. Configuring the Event Source 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)

20. Processing Per Shard: ▪ Lambda calls GetRecords with max limit from Kinesis (10k or 10MB) ▪ If no record, wait 250 ms ▪ From in memory, sub batches and formats records into Lambda payload ▪ Invoke Lambda with synchronous invoke … … Source Kinesis Destination 1 Lambda Poller Destination 2 Functions Shards Lambda will scale automaticallyScale Kinesis by adding shards Batch sync invokesPolls

21. Processing ▪ 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 Destination 1 Lambda Poller Destination 2 Functions Shards Lambda will scale automaticallyScale Kinesis by adding shards Batch sync invokesPolls

22. Processing ▪ Maximum theoretical throughput : # shards * 2MB / (s) ▪ Effective theoretical throughput : # shards * batch size (MB) / Lambda function duration (s) ▪ If put / ingestion rate is greater than the theoretical throughput, your processing is at risk of falling behind Common observations: ▪ Effective batch size may be less than configured during low throughput ▪ Effective batch size will increase during higher throughput ▪ Number of invokes and GetRecord calls will decrease with increased Lambda duration

23. Processing ▪ Retry execution failures until the record is expired ▪ Retry with exponential backoff up to 60s ▪ Throttles and errors impacts duration and directly impacts throughput ▪ Effective theoretical throughput : ( # shards * batch size (MB) ) / ( function duration (s) * retries until expiry) Kinesis Destination 1 Destination 2 … … Source Functions Shards Lambda will scale automaticallyScale Kinesis by adding shards Receives errorPolls Receives error Receives success Lambda Poller

24. Monitoring Kinesis •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.

25. Monitoring Lambda •Monitoring: available in Amazon CloudWatch Metrics • Invocation count • Duration • Error count • Throttle count •Debugging: available in Amazon CloudWatch Logs • All Metrics • Custom logs • RAM consumed • Search for log events

26. Best Practices ▪ Create enough shards for parallel processing ▪ Distribute load evenly across shards ▪ Monitor and address Lambda errors and throttles ▪ Monitor Kinesis limits and throttles

27. Best Practices Create different Lambda functions for each task, associate to same Kinesis stream Log to CloudWatch Logs Push to SNS

28. Get Started: Data Processing with AWS Next Steps 1. Create your first Kinesis stream. Configure hundreds of thousands of data producers to put data into an Amazon Kinesis stream. Ex. data from Social media feeds. 2. Create and test your first Lambda function. Use any third party library, even native ones. First 1M requests each month are on us! 3. Read the Developer Guide, AWS Lambda and Kinesis Tutorial, and resources on GitHub at AWS Labs • http://docs.aws.amazon.com/lambda/latest/dg/with-kinesis.html • https://github.com/awslabs/lambda-streams-to-firehose

Real-time Data Processing Using AWS Lambda

Amazon Web Services

Real-time Data Processing Using AWS Lambda