Drinking from the Firehose - Real-time Metrics
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To understand an application’s performance, first you have to know what to measure. That’s the easy part. How do you take those measurements? Store them? Analyze them? Get them to the people who need them? Well, that’s where things get complicated, especially in the high-traffic distributed systems of the modern web! Like careful scientists, we must observe our subjects without altering them, and we must report our findings quickly so that we have the data necessary to make smart choices about the health and growth of the system. Let’s explore the lessons learned by engineers at one of the world’s top web companies in their quest to find meaning at 5 MB/s. We’ll discuss the tools and techniques that enable the collection, indexing, and analysis of billions or more datapoints each hour, and learn how these same approaches can empower your applications and your business, no matter the scale.
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Drinking from the Firehose - Real-time Metrics
- 1. Drinking from the Firehose Real-Time Metrics Samantha Quiñones
- 4. “How would you let editors test how well different headlines perform for the same piece of content?”
- 5. Measuring User Behavior • Application path • Use patterns • Mouse & attention tracking
- 7. Multivariate Testing • Sort all users in to groups • 1 control group receives unaltered content • 1 or more groups receive altered content • Measure behavioral statistics (CTR, abandon rate, time on page, scroll depth) for each group
- 8. State Monitoring • Debugging • Load Monitoring
- 9. And then…?
- 10. • Augmented intelligence for content creators • Quality prediction
- 11. What if content could change itself based on the weather?
- 13. How big is big?
- 18. 25 megabytes / second At a rate up to
- 21. Hadoop • Framework for distributed storage and processing of data • Designed to make managing very large datasets simple with… • Well-documented, open-source, common libraries • Optimizing for commodity hardware
- 22. Hadoop Distributed File System • Modeled after Google File System • Stores logical files across multiple systems • Rack-aware • No read-write concurrency
- 23. MapReduce • Framework for massively parallel data processing tasks
- 24. Map <?php $document = "I'm a little teapot short and stout here is my handle here is my spout"; /** * Outputs: [0,0,0,0,0,0,0,0,1,0,0,0,1,0,0] */ function map($target_word, $document) { return array_map( function ($word) use ($target_word) { if ($word === $target_word) { return 1; } return 0; }, preg_split('/s+/', $document) ); } echo json_encode(map("is", $document)) . PHP_EOL;
- 25. Reduce <?php $data = [0,0,0,0,0,0,0,0,1,0,0,0,1,0,0]; /** * Outputs: 2 */ function reduce($data) { return array_reduce( $data, function ($count, $value) { return $count + $value; } ); } echo reduce($data) . PHP_EOL;
- 26. Hadoop Limitations • Hadoop jobs are batched and take significant time to run • Data may not be available for 1+ hours after collection
- 27. “How would you let editors test how well different headlines perform for the same piece of content?”
- 28. Consider Shelf-life • Most articles are relevant for < 24 hours • Interest peaks < 3 hours
- 33. Version 1 (PoC) CollectorCollectorCollectorStreamer CollectorCollectorCollectorReceiver CollectorCollectorCollectorStatsD Cluster ElasticSearch
- 35. this.visit = function(record) { if (record.userAgent) { var parser = new UAParser(); parser.setUA(record.userAgent); var user_agent = parser.getResult(); return { user_agent: user_agent } } return {}; };
- 37. Findings • Max throughput per collector: 300 events/second • ~70 receivers needed for prod • StatsD key format creates data redundancy and reduced data richness
- 38. Version 1 (PoC) CollectorCollectorCollectorStreamer CollectorCollectorCollectorReceiver CollectorCollectorCollectorStatsD Cluster ElasticSearch
- 39. Transits & Terminals • Transits - Short-term, in-memory, volatile storage for data with a life-span up to a few seconds • Terminals - Destinations for data that either store, abandon, or transmit
- 40. An efficient real-time data pathway consists of a network of transits and terminals, where no single node acts as both a transit and a terminal at the same time.
- 41. StatsD • Acts as a transit, taking data and passing it along… • BUT • Acts as a terminal, aggregating keys in memory and becoming a transit after a time or buffer threshold.
- 42. Version 2 CollectorCollectorCollectorStreamer CollectorCollectorCollectorReceiver ElasticSearchRabbitMQ
- 44. RabbitMQ • Lightweight message broker • Allows complex message routing without application-level logic • Can buffer 90-120 seconds of traffic
- 45. Version 2 • Eliminated eventing and improved performance • Replaced StatsD with RabbitMQ • Data records are kept together • No longer works with Kibana (sadface)
- 46. while (buffer.length > 0) { var char = buffer.shift(); if ('n' === char) { queue.push(new Buffer(outbuf.join(''))); continue; } outbuf.push(char); } var i = 0; var tBuf = buffer.slice(); while (i < buffer.length) { var char = tBuf[i++]; if ('n' === char) { queue.push(new Buffer(outbuf.join(''))); } outbuf.push(char); }
- 47. Findings • Max throughput per collector: 600 events/second • ~35 receivers needed for prod • Micro-optimized code became increasingly brittle and hard to maintain as custom logic was needed for every edge case
- 48. Version 2 CollectorCollectorCollectorStreamer CollectorCollectorCollectorReceiver ElasticSearchRabbitMQ
- 49. Need to Get Serious • Very high throughput • Multi-threaded worker pool with large memory buffers • Static & dynamic optimization • Efficient memory management for extremely volatile in-memory data • Eliminate any processing overhead. Receiver must be a Transit
- 50. And also… • Not GoLang (because no one on the team is familiar with it) • Not Rust (because no one on the team wants to be familiar with it) • Not C (because C)
- 51. mfw java :(
- 53. Why Java? • Solid static & dynamic analysis and optimizations in the S2BC & JIT compilers • Clients for the stuff I needed to talk to • Well-supported within AOL & within my team
- 56. public class StreamReader { private static final Logger logger = Logger.getLogger(StreamReader.class.getName()); private StreamerQueue queue = new StreamerQueue(); private StreamProcessor processor; private List<StreamReader.BeaconWorkerThread> workerThreads = new ArrayList(); private RtStreamerClient client; public StreamReader(String streamerURI, AmqpClient amqpClient, String appID, String tpcFltrs, String rfFltrs, String bt) { ArrayList queueList = new ArrayList(); this.processor = new StreamProcessor(amqpClient); byte numThreads = 8; for(int i = 0; i < numThreads; ++i) { StreamReader.BeaconWorkerThread worker = new StreamReader.BeaconWorkerThread(); this.workerThreads.add(worker); worker.start(); } queueList.add(this.queue); this.client = new RtStreamerClient(streamerURI, appID, tpcFltrs, rfFltrs, bt, queueList); } }
- 57. public class StreamProcessor { private static final Logger logger = Logger.getLogger(StreamProcessor.class.getName()); private AmqpClient amqpClient; public StreamProcessor(AmqpClient amqpClient) { this.amqpClient = amqpClient; } public void send(String data) throws Exception { this.amqpClient.send(data.getBytes()); logger.debug("Sent event " + data + " to AMQP"); } }
- 58. Queue Queue Queue Queue Queue Queue Queue Queue Queue Queue Queue Network Input Network Output Linked List Queues
- 59. Findings • Max throughput per collector: 2600 events/second • ~10 receivers needed for prod
- 61. Why ElasticSearch • Open-source Lucene search engine • Highly-distributed storage engine • Clusters nicely • Built-in aggregations like whoa
- 62. Aggregations • Geographic Boxing & Radius Grouping • Time-Series • Histograms • Min/Max/Avg Statistical Evaluation • MapReduce (coming soon!)
- 63. • How many users viewed my post on an android tablet in portrait mode within 10 miles of Denton, TX? • What is the average time from start of page-load to first click for readers on linux desktops between 3am and 5am? • Given two sets of link texts, which has the higher CTR for a randomized sample of readers on tablet devices?
- 64. Browser to Browser in < 5 seconds
- 65. But wait… Is that “real-time”?
- 67. Real-Time for Real • Live analysis of data as it is collected • Active visualization of very short-term trends in data
- 68. Potential Problems • Small sample sizes for new datasets / small analysis windows • Data volumes too high for end-user comprehension • Data volumes too high for end-user hardware/network connections
- 71. D3JS • Open-source data visualization library written in JavaScript
- 72. function plot(point) { var points = svg.selectAll("circle") .data([point], function(d) { return d.id; }); points.enter() .append("circle") .attr("cx", function (d) { return projection([parseInt(d.location.geopoint.lon), parseInt(d.location.geopoint.lat)])[0] }) .attr("cy", function (d) { return projection([parseInt(d.location.geopoint.lon), parseInt(d.location.geopoint.lat)])[1] }) .attr("r", function (d) { return 1; }) .style('fill', 'red') .style('fill-opacity', 1) .style('stroke', 'red') .style('stroke-width', '0.5px') .style('stroke-opacity', 1) .transition() .duration(10000) .style('fill-opacity', 0) .style('stroke-opacity', 0) .attr('r', '32px').remove(); } var buffer = []; var socket = io(); socket.on('geopoint', function(point) { if (point.location.geopoint) { plot(point); } });
- 75. By the way… xn = x + (r * COS(2π * n / v)) yn = y + (r * COS(2π * n / v)) where n = ordinal of vertex and where v = number of vertices and x,y = center of the polygon
- 76. var views = 0; var socket = io(); socket.on('pageview', function(point) { views++; }); function tick() { data.push(views); views = 0; path .attr("d", line) .attr("transform", null) .transition() .duration(500) .ease("linear") .attr("transform", "translate(" + x(0) + ",0)") .each("end", tick); data.shift(); } tick();
- 80. Receiver Layer Receiver Buffer/Transit Processing & Routing Layer Processing & Routing Transit Storage Engine End-User Consumable Queues Layers are • Geographically decoupled • Capable of independent scaling • Fully encapsulated with no cross- layer dependencies
- 81. Interfaces Input Stream (Java) Routing (node.js) Filtering (node.js) Aggregation (PHP) Visualization (D3JS) MV Testing (PHP)
- 82. Languages & Tools Rabbit MQ Hadoop Elastic Search PHP JS (node) JS (D3) Java MySQL
- 83. Where are we Now? • It took 6 months to build a rock-solid data pipeline • Entry points from: • User data collectors • Application code
- 84. That was the easy part.
- 85. What’s next?
- 86. • Live debugging & runtime profiling • Embeddable visualizations • On-demand stream filters • Predictive performance analysis • Real-time sentiment analysis
- 87. ???