Tune your function’s resources Only a memory control - % of CPU core and network capacity allocated to a function proportionally Is your code CPU, network or memory-bound? If so, it could be cheaper to choose more memory > Memory, > Cores, > Network
Lambda best practices Minimize your package size & use only needed SDK modules Put your dependency (e.g. jar files) in a separate directory Improve dependency injection with smaller and simpler IoC frameworks that load quickly on startup, like Dagger2 Leverage smaller and faster frameworks like jackson-jr for Java data binding Use environment variables to modify operational behavior Secure secrets/tokens/passwords with Parameter Store and AWS Secrets Manager
AWS Serverless Application Model (SAM) AWS CloudFormation extension (Macro) to simplify serverless apps New serverless resource types: functions, APIs, and tables Local testing with SAM CLI github.com/awslabs/serverless-application-model
Choose the right API endpoint type Edge optimized: reduce latency from anywhere on the Internet AWS Region API Gateway Internet edge location edge location edge location CloudFront Distribution API Gateway Managed
Choose the right API endpoint type Regional AWS us-east-2 API Gateway Internet AWS us-west-2 API Gateway Route 53 Lambda DynamoDB Lambda DynamoDB Global Tables
Regional API Gateway Internet API Gateway Route 53 Lambda DynamoDB Lambda DynamoDB Global Tables Lambda@Edge CloudFront Choose the right API endpoint type AWS us-east-2 AWS us-west-2
Streaming with Amazon Kinesis Collect, process, and analyze video and data streams in real time Kinesis Data Firehose SQL Kinesis Data Analytics Kinesis Data Streams Kinesis Video Streams
Kinesis Data Streams and Lambda # of shards corresponds to concurrent invocations of Lambda function Batch size sets maximum # of records per invocation (min 1, max 10K) Data Stream Processor Function Streaming source Other AWS services
Real-time analytics Data Stream Kinesis Data Analytics: Time window aggregation Kinesis Data Firehose: Error stream S3: Error records Record producers Lambda: Alert function DynamoDB SNS: Notifications
CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM" SELECT STREAM "device_id", STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '10' MINUTE) as "window_ts", SUM("measurement") as "sample_sum", COUNT(*) AS "sample_count" FROM "SOURCE_SQL_STREAM_001" GROUP BY "device_id", STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '10' MINUTE); Kinesis Data Analytics Aggregation 10-minute tumbling window Kinesis Data Analytics: Time window aggregation Source stream Destination stream(s)
Data lake characteristics Collect, store, process, consume, and analyze organizational data Structured, semi-structured, and unstructured data Decoupled compute and storage Fast automated ingestion Schema on-read Complementary to data warehouses
Athena – Just SQL (presto) Query duration: 44.66 seconds Data scanned: 169.53GB Cost*: $0.85 * $5/TB or $0.005/GB SELECT gram, year, sum(count) FROM ngram WHERE gram = 'just say no' GROUP BY gram, year ORDER BY year ASC;
Pywren - http://pywren.io Python library developed by UCI (University of California, Berkeley) Up to 40 TFLOPS of peak compute power Over 700 GB/sec of read and 500 GB/sec of write performance using S3 “numpywren: Serverless Linear Algebra” https://arxiv.org/pdf/1810.09679.pdf
M L F R A M E W O R K S & I N F R A S T R U C T U R E The Amazon ML Stack: Broadest & Deepest Set of Capabilities A I S E R V I C E S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D C O M P R E H E N D M E D I C A L L E X R E K O G N I T I O N V I D E O Vision Speech Chatbots A M A Z O N S A G E M A K E R B U I L D T R A I N F O R E C A S T T E X T R A C T P E R S O N A L I Z E D E P L O Y Pre-built algorithms & notebooks Data labeling (G R O U N D T R U T H ) One-click model training & tuning Optimization ( N E O ) One-click deployment & hosting M L S E R V I C E S F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e E C 2 P 3 & P 3 d n E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E Models without training data (REINFORCEMENT LEARNING) Algorithms & models ( A W S M A R K E T P L A C E ) Language Forecasting Recommendations NEW NEW NEW NEW NEW NEW NEW NEW NEW
Intelligent call center chatbot Amazon Connect Customer Amazon Lex Lambda: Chatbot Processing DynamoDB: Customer Data SNS: SMS Messaging Customer calls Connect to reschedule an appointment Connect calls Lex chatbot Lex chatbot calls Lambda function to get customer preferences and fulfil Intents Lambda function sends text message confirmation via SNS Customer receives appointment confirmation text message Lambda function writes updates to DynamoDB
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