5 Years Of Building SaaS On AWS
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Christian's part of the AWS re:Invent 2015 talk shared with Sajee Mathew - ARC304 - Designing for SaaS: Next Generation Software Delivery Models on AWS. Full video of the 60 minute presentation: https://www.youtube.com/watch?v=d16aUztH9hk&list=PLhr1KZpdzukdRxs_pGJm-qSy5LayL6W_Y
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5 Years Of Building SaaS On AWS
- 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Christian Beedgen October 2015 5 Years of Building SaaS on AWS A Story by Sumo Logic
- 2. $ whoami Co-Founder & CTO, Sumo Logic Cloud-based Machine Data Analytics Service Applications, Operations, Security Chief Architect, ArcSight Major SIEM player in the enterprise space Log Management for security and compliance
- 3. From Data to Decisions DEVOPS Streamline continuous delivery Monitor KPI’s and Metrics Accelerate Troubleshooting IT INFRASTRUCTURE AND OPERATIONS Monitor all workloads Troubleshoot and increase uptime Simplify, Modernize, and save costs COMPLIANCE AND SECURITY COMPLIANCE AND SECURITY Automate and demonstrate compliance Audit all systems Think beyond rules Cloud Analytics Platform DEVOPS IT INFRASTRUCTURE AND OPERATIONS COMPLIANCE AND SECURITY
- 4. Cloud Analytics Platform From Data to Decisions DEVOPS IT INFRASTRUCTURE AND OPERATIONS COMPLIANCE AND SECURITY Customer A Cloud COLLECTOR COLLECTOR Customer A Data Center Customer B Data Center COLLECTOR Customer B Cloud COLLECTOR
- 5. Why SaaS? Because enterprise software sucks™
- 6. Why SaaS? Because enterprise software sucks™ Too much pain for the customer Time spent running the system is not spent using the system Expensive when done adding hardware and people
- 7. Why SaaS? Because enterprise software sucks™ Too much pain for the customer Time spent running the system, not spent using the system Expensive when done adding hardware and people Disastrous for the vendor No control over the runtime, hard to diagnose problems Kills innovation because each release lives forever
- 8. Why AWS? We are developers, not data center people AWS has turned the data center into an API As developers, we understand reuse (libraries, OSs, …) Today’s systems require reuse on a higher level Do you really want to care for 4,000 machines? HA? DR?
- 9. Anti-monolithic In previous gigs, we dealt with monolithic systems With Sumo, we knew what we needed to build, no MVP required Get data into the system, index it, provide query function So we had a logical breakdown immediately And we knew it had to scale… …not just to the biggest customer, but to all customers!
- 10. Ingestion Path Receiver Bus Index Raw CQ S3
- 12. Scale Today 50 TB of new incoming data per day Double-digit PB of data under management >2,000,000 queries/day Thousands of instances in 4 regions globally
- 13. Divide & Conquer
- 14. Divide & Conquer 3 to 1000s of instances!
- 15. Divide & Conquer Each box in the previous images is an application
- 16. Divide & Conquer Blast radius, bulk-heading, concern separation
- 17. Divide & Conquer Not everything will break all the time – repair engines, not plane
- 18. Divide & Conquer Not everybody will need to work on everything all the time
- 19. What We Actually Did Compose applications from layers of modules Whole system is Scala on top of the JVM One Maven POM per module, one main() per application Initially one GitHub repository per module, today just one project Right size AWS instance for each application cluster Each application exposes a façade Avro over HTTP, or Avro over HornetQ, or Avro over Kafka
- 21. How I Actually Visualize Microservices
- 22. Deployment wide services Ingest Search Internal tools receiver hornetq- forge forge cqsplitter search cloud collector service api con- cierge stream katta glass, ganglia bill mix meta config zoo- keeper appvault org raw hornetq- inbound cocoa bloom filter analyticscsi cqmerger rework view autoview depman hornetq- internal hornetq- metadata nrt 2 to the power of 5 services (“32”), 170+ modules Don’t even ask about the # of dependencies At least 3 of each – everything is a separately scalable cluster
- 23. Service Discovery Loose coupling in the large… A deployment is made up of many things Some of these things need to talk to each other Some of these things come and go Don’t pass in a huge list of static dependencies Start each application with one parameter $ bin/receiver prod.service-registry.sumologic.com
- 24. Anti-singletenant Multi-dimensional scaling predicates multitenancy This is a data processing platform – cost matters! Autoscaling single tenants is too fine-grained for us Also, efficiency… one code line “master” in deployment Customers aren’t pets, they are cattle
- 25. Anti-singletenant Multi-dimensional scaling predicates multitenancy This is a data processing platform – cost matters! Autoscaling single tenants is too fine-grained for us Also, efficiency… one code line “master” in deployment Customers aren’t pets, they are cattle
- 26. Anti-singletenant Multi-dimensional scaling predicates multitenancy This is a data processing platform – cost matters! Autoscaling single tenants is too fine-grained for us Also, efficiency… one code line “master” in deployment Customers aren’t pets, they are cattle Yum yum yum… FEATURE FLAGS!!!
- 27. Just one typical Sumo Logic customer - 8x Variance!
- 28. Just one typical Sumo Logic customer - 8x Variance! Money flushed down the toilet
- 29. Just one typical Sumo Logic customer - 8x Variance! Money flushed down the toilet Load per tenant fluctuates wildly, but aggregated system load just goes up slowly
- 30. Anti-manual We use Jenkins, of course We still build system versions as cross-cuts and QA them We are busy moving toward true continuous delivery Application Groups for things that evolve together… …and that can be deployed together
- 32. ProdLongStagNite
- 33. dsh: Another AWS Deployment Tool Model-driven, describe desired state, run to make it so High performance due to parallelization Covers all layers of the stack – AWS, OS, Sumo Logic Easy to use and extend, scriptable CLI Developer-friendly, Scala-based, high-level APIs
- 34. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management EC2 EC2 Route53 S3 Glacier CloudFront DynamoDB RDSElastiCache DynamoDB DynamoDB RedShift WorkSpaces CloudWatch CloudTrail IAM CodeDeploy BeanstalkCloudFormationOpsWorksSWF SWF EMR EMR Kinesis SNS Mobile Analytics Kinesis SNS CognitoDirectory Service CloudSearch AppStream SES SQS SWF XCode Data Pipeline
- 35. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management 3 ELBs (service, api, receiver)
- 36. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management EC2, obviously RIs, dabbling with Spot
- 37. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management SES for alert emails to our customers
- 38. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management SQS for user registration from corporate website
- 39. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management Petabytes of S3
- 40. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management ElastiCache Memcache for client object caches
- 41. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management DynamoDB for feature flags and configuration
- 42. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management RDS MySQL for configuration and content objects
- 43. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management SimpleDB for deployment location
- 44. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management Sumo Logic
- 45. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management Sumo Logic CloudWatch, CloudTrail
- 46. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management Sumo Logic!
- 47. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management Zuora for billing
- 48. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management Jenkins, GitHub
- 49. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management Our own automation framework – “dsh”
- 50. Data Access Layer Delivery Authentication & Authorization MeteringMonitoring Ordering Provisioning Billing Analytics Resource Management SaaS Application(s) Business Services Core Platform Services Interaction Application AdditionalApplications Application Lifecycle Management CloudFormation for Mesos cluster setup
- 51. Integrations Generic S3 Collection Amazon S3 Audit Elastic Load Balancing Amazon CloudFront AWS CloudTrail Amazon VPC Flow Logs AWS Config
- 52. What Does the Future Hold? Super happy to see Amazon EFS introduced Borderline unnaturally excited about AWS KMS Planning on using AWS Lambda as a “plugin system” Implementing Mesos for new services Very excited about Docker to enable better utilization
Notes de l'éditeur
- Our 3rd generation analytics platform helps customers gain instant insights into this growing pool of machine data within their complex environments. Proven machine learning analytics help customers gain deep visibility across DevOps, IT ops and compliance and security environments. For DevOps – our service empowers DevOps teams with a simple and scalable solution for monitoring KPI's and metrics across the entire stack to deliver quality software. With pattern recognition and transaction analytics teams spend less time troubleshooting and more time developing code and real-time dashboards allow them to quickly collaborate for root cause analysis of bugs and fix performance issues before they impact customers. For IT Ops – Sumo Logic helps transforms IT data into better customer experience and business decisions by extracting valuable information such as latencies, performance metrics, trends and critical events tied with core systems and services. IT can monitor complex workloads and migrations for errors, warnings, performance and availability across cloud and on-premises infrastructure stacks and modernize their management stack with a SaaS solution designed for elastic scale with lower TCO. Compliance and Security – Our service helps organizations simplify and automate compliance and security monitoring across their entire stack with predictive analytics, pre-built searches, real-time dashboards, and pre-defined reports.
- Our 3rd generation analytics platform helps customers gain instant insights into this growing pool of machine data within their complex environments. Proven machine learning analytics help customers gain deep visibility across DevOps, IT ops and compliance and security environments. For DevOps – our service empowers DevOps teams with a simple and scalable solution for monitoring KPI's and metrics across the entire stack to deliver quality software. With pattern recognition and transaction analytics teams spend less time troubleshooting and more time developing code and real-time dashboards allow them to quickly collaborate for root cause analysis of bugs and fix performance issues before they impact customers. For IT Ops – Sumo Logic helps transforms IT data into better customer experience and business decisions by extracting valuable information such as latencies, performance metrics, trends and critical events tied with core systems and services. IT can monitor complex workloads and migrations for errors, warnings, performance and availability across cloud and on-premises infrastructure stacks and modernize their management stack with a SaaS solution designed for elastic scale with lower TCO. Compliance and Security – Our service helps organizations simplify and automate compliance and security monitoring across their entire stack with predictive analytics, pre-built searches, real-time dashboards, and pre-defined reports.
- This is my personal experience from the last decade It sucks for the customer and it sucks for the vendor
- This is our experience from the ArcSight days The system can’t just run on some gameboy sitting in the corner There’s big servers required, and in our case even a big Oracle database We just gave the customer an “installation guide” and hoped for the best
- Not having control over the execution environment puts the developer into a severe disadvantage “Works on my machine” is the daily reality but how do you debug something you can’t touch? Too many degrees of freedom for the customer to make the wrong decision: OS choice vs available funds, storage setup and RAID levels, … Everything you do becomes instant legacy Every release you push to customers will slow down your future velocity You will spend all your time back porting fixes to old versions Because your big customers refuse to take the time, money and pain to upgrade The result is that you become a maintenance organizations Why would you do this voluntarily?
- http://microservices.io/articles/scalecube.html I wish we had actually read that back then in detail But our intuition got us pretty close
- http://microservices.io/articles/scalecube.html This actually worked out We called it an “Internal SOA” We forgot one thing tho, but more about that later
- http://microservices.io/articles/scalecube.html This was extremely hotly debated internally
- http://microservices.io/articles/scalecube.html If one thing fails, the rest might continue to function High cohesion, low coupling
- http://microservices.io/articles/scalecube.html Every order of magnitude of scale something will break You will not be able to predict what You need to be able to just fix that, in a running system
- http://microservices.io/articles/scalecube.html Every order of magnitude of scale something will break You will not be able to predict what You need to be able to just fix that, in a running system
- With about 200 modules, code review is really hard when not in a single repo We also use messaging heavily in the ingestion path
- http://www.slideshare.net/alvarosanchezmariscal/stateless-authentication-for-microservices
- http://static3.jadedpixel.com/s/files/1/0010/2052/files/Puppy_dogs.jpg
- How they actually look like So this is our version of the Microservices death star
- Because scaling is hard and has latency Why make it harder than it has to be? Have you ever implemented a closed loop controller? The system itself as a whole scales slowly Our customers behave in unforeseen ways But they never do so at the same time Customers are balanced within the system all the time In the majority of cases we don’t have to spike-scale Our system is not batch-based so latency really matters http://s133.photobucket.com/user/Lurkerlake/media/cow3.png.htmlcow3.png
- Because scaling is hard and has latency Why make it harder than it has to be? Have you ever implemented a closed loop controller? The system itself as a whole scales slowly Our customers behave in unforeseen ways But they never do so at the same time Customers are balanced within the system all the time In the majority of cases we don’t have to spike-scale Our system is not batch-based so latency really matters http://s133.photobucket.com/user/Lurkerlake/media/cow3.png.htmlcow3.png
- Because scaling is hard and has latency Why make it harder than it has to be? Have you ever implemented a closed loop controller? The system itself as a whole scales slowly Our customers behave in unforeseen ways But they never do so at the same time Customers are balanced within the system all the time In the majority of cases we don’t have to spike-scale Our system is not batch-based so latency really matters http://s133.photobucket.com/user/Lurkerlake/media/cow3.png.htmlcow3.png
- Assume for a second that we had to provision for this customer at the peak… Most of the time, there would be too many resources, driving up the cost of providing the service and hence the price And it wouldn’t even be able to absorb the spike
- Assume for a second that we had to provision for this customer at the peak… Most of the time, there would be too many resources, driving up the cost of providing the service and hence the price And it wouldn’t even be able to absorb the spike
- Assume for a second that we had to provision for this customer at the peak… Most of the time, there would be too many resources, driving up the cost of providing the service and hence the price And it wouldn’t even be able to absorb the spike
- First level job builds “system”, deploys to NITE which has the latest cross cut If that crosscut passes, push to STAG, where QA will tear into it Ultimately, push to LONG where the rest of the company gets to see the latest that survives If nothing bad happens in LONG, it goes to PROD, usually once per week
- *Need to make sure this is list is accurate*
- *Need to make sure this is list is accurate*
- EFS – we thought for a long time that it would help to further decouple data from processing but it looks too expensive right now ($0.30/GB/month) Being able to allow customers to manage the encryption keys is a big deal for us We managed to get PCI certified based on what we have built but in an ideal future we would have customers control over the keys There’s points in our product where we would like customers to add functionality. Charting, query operators – looking into Lambda to enable this safely With all the microservices being their own clusters we are wasting resources that we pay for