Serverless Architectural Patterns and Best Practices | AWS
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This document discusses serverless architectural patterns and best practices using AWS services like AWS Lambda, Amazon API Gateway, Amazon S3, Amazon DynamoDB, and Amazon Kinesis. It covers four common patterns: 1) 3-tier web applications, 2) batch processing, 3) stream processing, and 4) automation. For each pattern, it discusses considerations, example architectures, and best practices. It provides guidance on designing resilient and scalable serverless applications and systems.
![Lambda considerations and best practices
Custom CloudWatch metrics
• 40 KB per POST
• Default Acct Limit of 150 TPS
• Consider aggregating with Kinesis
def put_cstate ( iid, state ):
response = cwclient.put_metric_data(
Namespace='AWSx/DirectConnect',
MetricData=[
{
'MetricName':'ConnectionState',
'Dimensions': [
{
'Name': 'ConnectionId',
'Value': iid
},
],
'Value': state,
'Unit': 'None’
…](https://image.slidesharecdn.com/awsusergroupdresden-serverlessarchitecturalpatternsandbestpractices2017-08-24-170829095253/85/Serverless-Architectural-Patterns-and-Best-Practices-AWS-8-320.jpg)
Serverless Architectural Patterns and Best Practices | AWS
- 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Daniel Geske <[email protected]> 24 August 2017 Serverless Architectural Patterns and Best Practices
- 2. Agenda Serverless characteristics and practices 3-tier web application Batch processing Stream processing Operations automation Wrap-up/Q&A
- 3. Spectrum of AWS offerings AWS Lambda Amazon Kinesis Amazon S3 Amazon API Gateway Amazon SQS Amazon DynamoDB AWS IoT Amazon EMR Amazon ElastiCache Amazon RDS Amazon Redshift Amazon Elasticsearch Service Managed Serverless Amazon EC2 “On EC2” Amazon Cognito Amazon CloudWatch
- 4. Serverless patterns built with functions Functions are the unit of deployment and scale Scales per request—users cannot over or under-provision Never pay for idle Skip the boring parts; skip the hard parts
- 5. Lambda considerations and best practices AWS Lambda is stateless—architect accordingly • Assume no affinity with underlying compute infrastructure • Local filesystem access and child process may not extend beyond the lifetime of the Lambda request
- 6. Lambda considerations and best practices Can your Lambda functions survive the cold? • Instantiate AWS clients and database clients outside the scope of the handler to take advantage of connection re-use. • Schedule with CloudWatch Events for warmth • ENIs for VPC support are attached during cold start import sys import logging import rds_config import pymysql rds_host = "rds-instance" db_name = rds_config.db_name try: conn = pymysql.connect( except: logger.error("ERROR: def handler(event, context): with conn.cursor() as cur: Executes with each invocation Executes during cold start
- 7. Lambda considerations and best practices How about a file system? • Don’t forget about /tmp (512 MB scratch space) exports.ffmpeg = function(event,context) { new ffmpeg('./thumb.MP4', function (err, video) { if (!err) { video.fnExtractFrameToJPG('/tmp’) function (error, files) { … } … if (!error) console.log(files); context.done(); ...
- 8. Lambda considerations and best practices Custom CloudWatch metrics • 40 KB per POST • Default Acct Limit of 150 TPS • Consider aggregating with Kinesis def put_cstate ( iid, state ): response = cwclient.put_metric_data( Namespace='AWSx/DirectConnect', MetricData=[ { 'MetricName':'ConnectionState', 'Dimensions': [ { 'Name': 'ConnectionId', 'Value': iid }, ], 'Value': state, 'Unit': 'None’ …
- 9. Pattern 1: 3-Tier Web Application
- 10. Web application Data stored in Amazon DynamoDB Dynamic content in AWS Lambda Amazon API Gateway Browser Amazon CloudFront Amazon S3
- 11. Amazon API Gateway AWS Lambda Amazon DynamoDB Amazon S3 Amazon CloudFront • Bucket Policies • ACLs • OAI • Geo-Restriction • Signed Cookies • Signed URLs • DDOS IAM AuthZ IAM Serverless web app security • Throttling • Caching • Usage Plans Browser
- 12. Amazon API Gateway AWS Lambda Amazon DynamoDB Amazon S3 Amazon CloudFront • Bucket Policies • ACLs • OAI • Geo-Restriction • Signed Cookies • Signed URLs • DDOS IAMAuthZ IAM Serverless web app security • Throttling • Caching • Usage Plans Browser Amazon CloudFront • HTTPS • Disable Host Header Forwarding AWS WAF
- 13. Amazon API Gateway AWS Lambda Amazon DynamoDB Amazon S3 Amazon CloudFront • Access Logs in S3 Bucket• Access Logs in S3 Bucket • CloudWatch Metrics- https://aws.amazon.com/ cloudfront/reporting/ Serverless web app monitoring AWS WAF • WebACL Testing • Total Requests • Allowed/Blocked Requests by ACL logslogs • Invocations • Invocation Errors • Duration • Throttled Invocations • Latency • Throughput • Throttled Reqs • Returned Bytes • Documentation • Latency • Count • Cache Hit/Miss • 4XX/5XX Errors Streams AWS CloudTrail Browser Custom CloudWatch Metrics & Alarms
- 14. Serverless web app lifecycle management AWS SAM (Serverless Application Model) - blog AWS Lambda Amazon API Gateway AWS CloudFormation Amazon S3 Amazon DynamoDB Package & Deploy Code/Packages/ Swagger Serverless Template Serverless Template w/ CodeUri package deploy CI/CD Tools
- 15. Amazon API Gateway best practices Use mock integrations Signed URL from API Gateway for large or binary file uploads to S3 Use request/response mapping templates for legacy apps and HTTP response codes Asynchronous calls for Lambda > 30s
- 16. Root/ /{proxy+} ANY Your Node.js Express app Greedy variable, ANY method, proxy integration Simple yet very powerful: • Automatically scale to meet demand • Only pay for the requests you receive
- 17. Pattern 2: Batch Processing
- 18. Characteristics Large data sets Periodic or scheduled tasks Extract Transform Load (ETL) jobs Usually non-interactive and long running Many problems fit MapReduce programming model
- 19. Serverless batch processing AWS Lambda: Splitter Amazon S3 Object Amazon DynamoDB: Mapper Results AWS Lambda: Mappers …. …. AWS Lambda: Reducer Amazon S3 Results
- 20. Considerations and best practices Cascade mapper functions Lambda languages vs. SQL Speed is directly proportional to the concurrent Lambda function limit Use DynamoDB/ElastiCache/S3 for intermediate state of mapper functions Lambda MapReduce Reference Architecture
- 21. Cost of serverless batch processing 200 GB normalized Google Ngram data-set Serverless: • 1000 concurrent Lambda invocations • Processing time: 9 minutes • Cost: $7.06
- 22. Pattern 3: Stream Processing
- 23. Stream processing characteristics • High ingest rate • Near real-time processing (low latency from ingest to process) • Spiky traffic (lots of devices with intermittent network connections) • Message durability • Message ordering
- 24. Serverless stream processing architecture Sensors Amazon Kinesis: Stream Lambda: Stream Processor S3: Final Aggregated Output Lambda: Periodic Dump to S3 CloudWatch Events: Trigger every 5 minutes S3: Intermediate Aggregated Data Lambda: Scheduled Dispatcher KPL: Producer
- 25. Fan-out pattern • Number of Amazon Kinesis Streams shards corresponds to concurrent Lambda invocations • Trade higher throughput & lower latency vs. strict message ordering Sensors Amazon Kinesis: Stream Lambda: Dispatcher KPL: Producer Lambda: Processors Increase throughput, reduce processing latency
- 26. More about fan-out pattern • Keep up with peak shard capacity • 1000 records / second, OR • 1 MB / second • Consider parallel synchronous Lambda invocations • Rcoil for JS (https://github.com/sapessi/rcoil) can help • Dead letter queue to retry failed Lambda invocations
- 27. Best practices • Tune batch size when Lambda is triggered by Amazon Kinesis Streams – reduce number of Lambda invocations • Tune memory setting for your Lambda function – shorten execution time • Use KPL to batch messages and saturate Amazon Kinesis Stream capacity
- 28. Monitoring Amazon Kinesis Stream metric GetRecords.IteratorAgeMilliseconds maximum
- 29. Amazon Kinesis Analytics Sensors Amazon Kinesis: Stream Amazon Kinesis Analytics: Window Aggregation Amazon Kinesis Streams Producer S3: Aggregated Output CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM" SELECT STREAM "device_id", FLOOR("SOURCE_SQL_STREAM_001".ROWTIME TO MINUTE) as "round_ts", SUM("measurement") as "sample_sum", COUNT(*) AS "sample_count" FROM "SOURCE_SQL_STREAM_001" GROUP BY "device_id", FLOOR("SOURCE_SQL_STREAM_001".ROWTIME TO MINUTE); Aggregation Time Window
- 30. Cost comparison - assumptions • Variable message rate over 6 hours • Costs extrapolated over 30 days 20,000 10,000 20,000 50,000 20,000 10,000 1 2 3 4 5 6 MESSAGES/SEC HOURS
- 31. Serverless • Amazon Kinesis Stream with 5 shards Cost comparison Server-based on EC2 • Kafka cluster (3 x m3.large) • Zookeeper cluster (3 x m3.large) • Consumer (1 x c4.xlarge) Service Monthly Cost Amazon Kinesis Streams $ 58.04 AWS Lambda $259.85 Amazon S3 (Intermediate Files) $ 84.40 Amazon CloudWatch $ 4.72 Total $407.01 Service Monthly Cost EC2 Kafka Cluster $292.08 EC2 Zookeeper Cluster $292.08 EC2 Consumer $152.99 Total On-Demand $737.15 1-year All Upfront RI $452.42
- 32. Compare related services Amazon Kinesis Streams Amazon SQS Amazon SNS Message Durability Up to retention period Up to retention period Retry delivery (depends on destination type) Maximum Retention Period 7 days 14 days Up to retry delivery limit Message Ordering Strict within shard Standard - Best effort FIFO – Strict within Message Group None Delivery semantics Multiple consumers per shard Multiple readers per queue (but one message is only handled by one reader at a time) Multiple subscribers per topic Scaling By throughput using Shards Automatic Automatic Iterate over messages Shard iterators No No Delivery Destination Types Kinesis Consumers SQS Readers HTTP/S, Mobile Push, SMS, Email, SQS, Lambda
- 33. Lambda architecture Data Sources Serving Layer Speed Layer AWS Lambda: Splitter Amazon S3 Object Amazon DynamoDB: Mapper Results Amazon S3 AWS Lambda: Mappers …. …. AWS Lambda: Reducer Amazon S3 Results Batch Layer Sensors Amazon Kinesis: Stream Lambda: Stream Processor S3: Final Aggregated Output Lambda: Periodic Dump to S3 CloudWatch Events: Trigger every 5 minutes S3: Intermediate Aggregated Data Lambda: Scheduled Dispatcher KPL: Producer
- 35. Automation characteristics • Respond to alarms or events • Periodic jobs • Auditing and Notification • Extend AWS functionality …All while being Highly Available and Scalable
- 36. Automation: dynamic DNS for EC2 instances AWS Lambda: Update Route53 Amazon CloudWatch Events: Rule Triggered Amazon EC2 Instance State Changes Amazon DynamoDB: EC2 Instance Properties Amazon Route53: Private Hosted Zone Tag: CNAME = ‘xyz.example.com’ xyz.example.com A 10.2.0.134
- 37. Automation: image thumbnail creation from S3 AWS Lambda: Resize Images Users upload photos S3: Source Bucket S3: Destination Bucket Triggered on PUTs
- 38. CapitalOne Cloud Custodian AWS Lambda: Policy & Compliance Rules Amazon CloudWatch Events: Rules Triggered AWS CloudTrail: Events Amazon SNS: Alert Notifications Amazon CloudWatch Logs: Logs Read more here: http://www.capitalone.io/cloud-custodian/docs/index.html
- 39. Best practices • Document how to disable event triggers for your automation when troubleshooting • Gracefully handle API throttling by retrying with an exponential back- off algorithm (AWS SDKs do this for you) • Publish custom metrics from your Lambda function that are meaningful for operations (e.g. number of EBS volumes snapshotted)
- 40. Go build something. Daniel Geske <[email protected]>