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Similar a Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
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Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Kafka
- 2. Intro & Agenda Crawler Intro & Problem Statements Crawler Architecture Infrastructure: Akka Streams, Kafka, etc. The Goodies
- 4. Requirements Ever-expanding # of URLs Can’t crawl all URLs at once Control over concurrent web GETs Efficient resource usage Resilient under high burst Scales horizontally & vertically
- 5. Sizing the Crawl Job Let: i = Number of crawl URLs in a job n = Average number of links per page d = The crawl depth (how many layers to follow links) u = The max number of URLs to process Then: u = ind 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04 1.00E+05 1.00E+06 1.00E+07 0 2 4 6 8 10 12 totalURLs vs depth depth (initialURLs = 1, outLinks = 5) 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04 1.00E+05 1.00E+06 1.00E+07 1.00E+08 1.00E+09 1.00E+10 1.00E+11 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 1.E+07 totalURLs vs initialURLs initialURLs (depth = 5, outLinks = 5)
- 6. The Reactive Manifesto Responsive Message Driven Elastic Resilient
- 7. Why Does it Matter? Respond in a deterministic, timely manner Stays responsive in the face of failure – even cascading failures Stays responsive under workload spikes Basic building block for responsive, resilient, and elastic systems Responsive Resilient Elastic Message Driven
- 8. The Right Ingredients • Kafka • Huge persistent buffer for the bursts • Load distribution to very large number of processing nodes • Enable horizontal scalability • Akka streams • High performance, highly efficient processing pipeline • Resilient with end-to-end back-pressure • Fully asynchronous – utilizes mapAsyncUnordered with Async HTTP client • Async HTTP client • Non-blocking and consumes no threads in waiting • Integrates with Akka Streams for a high parallelism, low resource solution Efficient Resilient Scale Akka Stream Async HTTP Reactive Kafka
- 9. Crawl Jobs Job DB Validate URL Cache Downloa d Process URLs URLs Timestamps Adding Kafka & Akka Streams URLs Akka Streams
- 10. Akka Streams, what??? High performance, pure async, stream processing Conforms to reactive streams Simple, yet powerful GraphDSL allows clear stream topology declaration Central point to understand processing pipeline
- 11. Crawl Stream Actual Stream Declaration in Code prioritizeSource ~> crawlerFlow ~> bCast0 ~> result ~> bCast ~> outLinksFlow ~> outLinksSink bCast ~> dataSinkFlow ~> kafkaDataSink bCast ~> hdfsDataSink bCast ~> graphFlow ~> merge ~> graphSink bCast0 ~> maxPage ~> merge bCast0 ~> retry ~> bCastRetry ~> retryFailed ~> merge bCastRetry ~> errorSink Prioritized Source Crawl Result MaxPageReached Retry OutLinks Data Graph CheckFail CheckErr OutLinks Sink Kafka Data Sink HDFS Data Sink Graph Sink Error Sink
- 12. Resulting Characteristics Efficient • Low thread count, controlled by Akka and pure non-blocking async HTTP • High latency URLs do not block low latency URLs using MapAsyncUnordered • Well-controlled download concurrency using MapAsyncUnordered • Thread per concurrent crawl job Resilient • Processes only what can be processed – no resource overload • Kafka as short-term, persistent queue Scale • Kafka feeds next batch of URLs to available node cluster • Pull model – only processes that have capacity will get the load • Kafka distributes work to large number of processing nodes in cluster
- 13. Back-Pressure 0 20000 40000 60000 80000 100000 120000 0 100 200 300 400 500 600 700 Queue Size Time (seconds) 0 200 400 URLs/sec Time (seconds) initialURLs : 100 parallelism : 1000 processTime : 1 – 5 s outLinks : 0 - 10 depth : 5 totalCrawled : 312500
- 14. Challenges Training • Developers not used to E2E stream definitions • More familiar with deeply nested function calls Maturity of Infrastructure • Kafka 0.9 use fetch as heartbeat • Slow nodes cause timeout & rebalance • Solved in 0.10
- 15. What it would have been… Bloated, ineffective concurrency control Lack of well-thought-out and visible processing pipeline Clumsy code, hard to manage & understand Low training cost, high project TCO Dev / Support / Maintenance
- 16. Bottom Line
- 18. Efficiency & Resilience meets Standardization • Monitoring • Need to collect metrics, consistently • Logging • Correlation across services • Uniformity in logs • Security • Need to apply standard security configuration • Environment Resolution • Staging, production, etc. Consistency in the face of Heterogeneity
- 19. squbs is not… A framework by its own A programming model – use Akka Take all or none – Components/patterns can mostly be used independently
- 20. squbs Akka for large scale deployments Bootstrap Lifecycle management Loosely-coupled module system Integration hooks for logging, monitoring, ops integration
- 21. squbs Akka for large scale deployments JSON console HttpClient with pluggable resolver and monitoring/logging hooks Test tools and interfaces Goodies: - Activators for Scala & Java - Programming patterns and helpers for Akka and Akka Stream Use cases…, and growing
- 22. PerpetualStream • Provides a convenience trait to help write streams controlled by system lifecycle • Minimal/no message losses • Register PerpetualStream to make stream start/stop • Provides customization hooks – especially for how to stop the stream • Provides killSwitch (from Akka) to be embedded into stream • Implementers - just provide your stream! A non-stop stream; starts and stops with the system class MyStream extends PerpetualStream[Future[Int]] { def generator = Iterator.iterate(0) { p => if (p == Int.MaxValue) 0 else p + 1 } val source = Source.fromIterator(generator _) val ignoreSink = Sink.ignore[Int] override def streamGraph = RunnableGraph.fromGraph( GraphDSL.create(ignoreSink) { implicit builder => sink => import GraphDSL.Implicits._ source ~> killSwitch.flow[Int] ~> sink ClosedShape }) }
- 23. PersistentBuffer/BroadcastBuffer • Data & indexes in rotating memory-mapped files • Off-heap rotating file buffer – very large buffers • Restarts gracefully with no or minimal message loss • Not as durable as a remote data store, but much faster • Does not back-pressure upstream beyond data/index writes • Similar usage to Buffer and Broadcast • BroadcastBuffer – a FanOutShape decouples each output port making each downstream independent • Useful if downstream stage blocked or unavailable • Kafka is unavailable/rebalancing but system cannot backpressure/deny incoming traffic • Optional commit stage for at-least-once delivery semantics • Implementation based on Chronicle Queue A buffer of virtually unlimited size
- 24. Summary • Kafka + Akka Streams + Async I/O = Ideal Architecture for High Bursts & High Efficiency • Akka Streams • Clear view of stream topology • Back-pressure & Kafka allows buffering load bursts • Standardization • Walk like a duck, quack like a duck, and manage it like a duck • squbs: Have the cake, and eat it too, with goodies like • PerpetualStream • PersistentBuffer • BroadcastBuffer
- 25. Q&A – Feedback Appreciated