Scalable Service Architectures
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In the past years at Ustream we built several systems which had to be scalable and resilient by themselves. This included automatic monitoring and scaling, protection against overloads, and the like. We learned a lot of valuable lessons on the way, and I would like to share those experiences, with practical examples and open source tools.
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Scalable Service Architectures
- 1. Scalable Service Architectures Lessons learned Zoltán Németh Engineering Manager, Core Systems
- 2. Agenda Our scalability experience What is Scalability? Requirements in detail Tips and tools Extras, Closing remarks
- 6. Defining scalability Scalability is the ability to handle increased workload by repeatedly applying a costeffective strategy for extending a system’s capacity. (CMU paper, 2006) How well a solution to some problem will work when the size of the problem increases. When the size decreases, the solution must fit. (dictionary.com and Theo Schlossnagle, 2006)
- 7. Self-contained service Explicitly declare and isolate dependencies Isolation from the outside system Static linking Do not rely on system packages
- 8. Disposability Maximize robustness with fast startup and graceful shutdown Disposable processes Graceful shutdown on SIGTERM Handling sudden death: robust queue backend
- 9. Startup and Shutdown Automate all the things Chef Docker Gold image based deployment Immutable Handling tasks before shutdown
- 10. Backing Services Treat backing services as attached resources No distinction between local and third party services Easily swap out resources Export services via port binding Become the backing service for another app
- 11. Processes, concurrency Stateless processes (not even sticky sessions) Process types by work type We <3 linux process Shared-nothing adding concurrency is safe Process distribution spanning machines
- 12. Statelessness Store everything in a datastore Aggregate data Chandra Scalable datastores Redis Cassandra Aerospike Handling user sessions
- 13. Monitoring Application state and metrics Dashboards Alerting Health Remove failing nodes Capacity Act on trends
- 14. Monitoring Metrics collecting Graphite, New Relic Self-aware checks Cluster state Zookeeper, Consul Scaling decision types Capacity amount Graph derivative App requests
- 16. Load Balance and Resource Allocation Load Balance: distribute tasks Utilize machines efficiently VM compatible apps Flexibility Adapting to available resources
- 17. Load Balance DNS or API App level balance Uniform entry point or proxy Balance decisions Load Zookeeper state Resource policies
- 18. Service Separation Failure is inevitable Protect from failing components Cascading failure Fail fast Decoupling Asynchronous operations Message queues
- 19. Service Separation Rate limiting Circuit Breaker pattern Stop cascading failure, allow recovery Hystrix Fail fast, fail silent Service decoupling
- 20. Extras Debugging features Logs Clojure / JS consoles Runtime configuration via env Scaling API Integrating several cloud providers Automatic start / stop
- 21. Reading Scalable Internet Architectures by Theo Schlossnagle The 12-factor App: http://12factor.net/ Carnegie Mellon Paper: http://www.sei.cmu.edu/reports/06tn012.pdf Circuit Breaker: http://martinfowler.com/bliki/CircuitBreaker.html Release It! by Michael T. Nygard
Notas del editor
- A bit of Ustream intro
- Definition Requirements coming from 12-factor, and some added by us Some more detail and tools on selected requirements
- 30 day viewer graph. Clear peaks -> need for scaling
- Quick description of the streaming stack, roles of components, how they require scaling - Transcontroller/transcoder scaling - UMS scaling
- Quick description of the streaming stack, roles of components, how they require scaling - Transcontroller/transcoder scaling - UMS scaling
- Carnegie Mellon University paper by Charles B. Weinstock, John B. Goodenough: On System Scalability LINFO: The Linux Information Project http://www.linfo.org/
- Example: calling imagemagick or curl from code – they might be there or might not be Bundle everything into the app instead
- Disposable process: they can be started or stopped at a moment’s notice For a web process, graceful shutdown is achieved by ceasing to listen on the service port (thereby refusing any new requests), allowing any current requests to finish, and then exiting. Implicit in this model is that HTTP requests are short (no more than a few seconds), or in the case of long polling, the client should seamlessly attempt to reconnect when the connection is lost. For a worker process, graceful shutdown is achieved by returning the current job to the work queue.
- Docker: build images from dockerfile, deploy from repository Tasks before shutdown: moving jobs, log collection, sleep
- A backing service is any service the app consumes over the network as part of its normal operation. Examples include datastores (such as MySQL or CouchDB), messaging/queueing systems (such as RabbitMQ or Beanstalkd), SMTP services for outbound email (such as Postfix), and caching systems (such as Memcached). Put a resource locator in the config only – environment variables Example: Easily swap out a local mysql to a remote service The app does not rely on runtime injection of a webserver into the execution environment to create a web-facing service. The web app exports HTTP as a service by binding to a port, and listening to requests coming in on that port. One app can become the backing service for another app, by providing the URL to the backing app as a resource handle in the config for the consuming app
- Handle diverse workloads by assigning each type of work to a process type. For example, HTTP requests may be handled by a web process, and long-running background tasks handled by a worker process An individual VM can only grow so large (vertical scale), so the application must also be able to span multiple processes running on multiple physical machines.
- Aggregate everything within the app and write it out in bulk – careful about write frequency, must not lose too many data on a crash Redis: scales reads, write problematic Cassandra: quick scaling questionable Aerospike: scales reads and writes, working together with their eng team User sessions: persistent connection, NIO+
- Alerting -> openduty Two important groups: Health vs capacity
- Report everything to graphite, constantly check graph trends automatically Apps are self-aware, they know their health App instances report into Zookeeper and thus know about each other Central logic can request resource based on capacity or graph, app can request based on self-check or zookeeper Zookeeper, Consul: miért, mik az előnyei
- load balancing distributes workloads across multiple computing resources Flexibility: can increase or decrease its own size, example: Threadpools Adapting to CPU, RAM, disk, network
- App level: transcontroller selects transcoder App level balance with proxy can be SPOF, careful Resource policies: even distribution, keep large chunks free for possible large tasks (transcoder use case), group requests together on some attribute (pro, etc)
- Failure inevitable because: large numbers, hw issues, independent network Decoupling: serving one request should not wait on others
- Hystrix by Netflix 2011/12 Circuit Breaker: Martin Fowler post from 2014 Service decoupling example: inserting layers between DB and UMS -> RGW. Then another layer between RGW and UMS -> Queue
- Logs: logs as stream / stdout (factor #9), collect / transport / process Scaling API: Other considerations: price, network line to the cloud provider, instance type (spot vs normal)