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From the Monolith to Microservices - CraftConf 2015

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Most large-scale web companies have evolved their system architecture from a monolithic application and monolithic database to a set of loosely coupled microservices. Using examples from Google, eBay, and other large-scale sites, this talk outlines the pros and cons of these different stages of evolution, and makes practical suggestions about when and how other organizations should consider migrating to microservices. It continues with some more advanced implications of a microservices architecture, including SLAs, cost-allocation, and vendor-customer relationships within the organization. It concludes by exploring a set of common service anti-patterns.

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From the Monolith to Microservices - CraftConf 2015

  1. 1. From the Monolith to Microservices Lessons from Google and eBay Randy Shoup @randyshoup linkedin.com/in/randyshoup
  2. 2. Architecture Evolution • eBay • 5th generation today • Monolithic Perl  Monolithic C++  Java  microservices • Twitter • 3rd generation today • Monolithic Rails  JS / Rails / Scala  microservices • Amazon • Nth generation today • Monolithic C++  Java / Scala  microservices
  3. 3. Architecture Evolution • The Monolith • Ecosystem of Services • Building and Operating a Service • Service Anti-Patterns
  4. 4. Architecture Evolution • The Monolith • Ecosystem of Services • Building and Operating a Service • Service Anti-Patterns
  5. 5. The Monolithic Architecture 2-3 monolithic tiers • {JS, iOS, Android} • {PHP, Ruby, Python} • {MySQL, Mongo} Presentation Application Database
  6. 6. The Monolithic Application Simple at first In-process latencies Single codebase, deploy unit Resource-efficient at small scale Pros Coordination overhead as team grows Poor enforcement of modularity Poor scaling (vertical only) All-or-nothing deploy (downtime, failures) Long build times Cons
  7. 7. The Monolithic Database Simple at first Join queries are easy Single schema, deployment Resource-efficient at small scale Pros Coupling over time Poor scaling and redundancy (all-or- nothing, vertical only) Difficult to tune properly All-or-nothing schema management Cons
  8. 8. “If you don’t end up regretting your early technology decisions, you probably over-engineered” -- me
  9. 9. Architecture Evolution • The Monolith • Ecosystem of Services • Building and Operating a Service • Service Anti-Patterns
  10. 10. Microservices • Single-purpose • Simple, well-defined interface • Modular and independent • Fullest expression of encapsulation and modularity • Isolated persistence (!) A C D E B
  11. 11. Microservices Each unit is simple Independent scaling and performance Independent testing and deployment Can optimally tune performance (caching, replication, etc.) Pros Many cooperating units Many small repos Requires more sophisticated tooling and dependency management Network latencies Cons
  12. 12. Ecosystem of Services • Hundreds to thousands of independent services • Many layers of dependencies, no strict tiers • Graph of relationships, not a hierarchy C B A E F G D
  13. 13. Google Service Layering • Cloud Datastore: NoSQL service o Highly scalable and resilient o Strong transactional consistency o SQL-like rich query capabilities • Megastore: geo-scale structured database o Multi-row transactions o Synchronous cross-datacenter replication • Bigtable: cluster-level structured storage o (row, column, timestamp) -> cell contents • Colossus: next-generation clustered file system o Block distribution and replication • Borg: cluster management infrastructure o Task scheduling, machine assignment Cloud Datastore Megastore Bigtable Colossus Borg
  14. 14. Evolution, not Intelligent Design • No centralized, top-down design of the system • Variation and Natural selection o Create / extract new services when needed to solve a problem o Deprecate services when no longer used o Services justify their existence through usage • Appearance of clean layering is an emergent property
  15. 15. Architecture without an Architect? • No “Architect” title / role • (+) No central approval for technology decisions o Most technology decisions made locally instead of globally o Better decisions in the field
  16. 16. Standardization • Standardized communication o Network protocols o Data formats o Interface schema / specification • Standardized infrastructure o Source control o Configuration management o Cluster management o Monitoring, alerting, diagnosing, etc.
  17. 17. Standards become standards by being better than the alternatives!
  18. 18. Service Independence • No standardization of service internals o Programming languages o Frameworks o Persistence mechanisms
  19. 19. In a mature ecosystem of services, we standardize the arcs of the graph, not the nodes!
  20. 20. Creating New Services • Spinning out a new service o Almost always built for particular use-case first o If successful and appropriate, form a team and generalize for multiple use-cases • Pragmatism wins • Examples o Google File System o Bigtable o Megastore o Google App Engine o Gmail
  21. 21. Deprecating Old Services • What if a service is a failure? o Repurpose technologies for other uses o Redeploy people to other teams • Examples o Google Wave -> Google Apps o Multiple generations of core services
  22. 22. “Every service at Google is either deprecated or not ready yet.” -- Google engineering proverb
  23. 23. Architecture Evolution • The Monolith • Ecosystem of Services • Building and Operating a Service • Service Anti-Patterns
  24. 24. Goals of a Service Owner • Meet the needs of my clients … • Functionality • Quality • Performance • Stability and reliability • Constant improvement over time • … at minimum cost and effort • Leverage common tools and infrastructure • Leverage other services • Automate building, deploying, and operating my service • Optimize for efficient use of resources
  25. 25. Responsibilities of a Service Owner • End-to-end Ownership o Team owns service from design to deployment to retirement o No separate maintenance or sustaining engineering team o DevOps philosophy of “You build it, you run it” • Autonomy and Accountability o Freedom to choose technology, methodology, working environment o Responsibility for the results of those choices
  26. 26. Service-Service Relationships • Vendor – Customer Relationship o Friendly and cooperative, but structured o Clear ownership and division of responsibility o Customer can choose to use service or not (!) • Service-Level Agreement (SLA) o Promise of service levels by the provider o Customer needs to be able to rely on the service, like a utility
  27. 27. Service-Service Relationships • Charging and Cost Allocation o Charge customers for *usage* of the service o Aligns economic incentives of customer and provider o Motivates both sides to optimize for efficiency o (+) Pre- / post-allocation at Google
  28. 28. Architecture Evolution • The Monolith • Ecosystem of Services • Building and Operating a Service • Service Anti-Patterns
  29. 29. Service Anti-Patterns • The “Mega-Service” o Overbroad area of responsibility is difficult to reason about, change o Leads to more upstream / downstream dependencies • Shared persistence o Breaks encapsulation, encourages “backdoor” interface violations o Unhealthy and near-invisible coupling of services o (-) Initial eBay SOA efforts
  30. 30. Service Anti-Patterns • “Leaky Abstraction” Service o Interface reflects provider’s model of the interaction, not the consumer’s model o Consumer’s model is more aligned with the domain, simpler, more abstract o Leaking provider’s model in the interface constrains evolution of the implementation
  31. 31. Thank You! • @randyshoup • linkedin.com/in/randyshoup • Slides will be at slideshare.net/randyshoup

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