Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Flink Forward San Francisco 2019: Streaming your Lyft Ride Prices - Thomas Weise & Akshay Balwally

89 visualizaciones

Publicado el

At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). To accomplish this, our system consumes a massive amount of events from different sources.

The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python/Tensorflow and Apache Flink as the streaming engine. Enablement of data science tools for machine learning and a process that allows for faster deployment is of growing importance for the business. Topics covered in this talk include:

* Examples for dynamic pricing based on real-time event streams, including location of driver, ride requests, user session event and based on machines learning models

* Comparison of legacy system and new streaming platform for dynamic pricing

* Processing live events in realtime to generate features for machine learning models

* Overview of streaming platform architecture and technology stack

* Apache Beam portability framework as bridge to distributed execution without code rewrite for JVM based streaming engine

* Lessons learned

Publicado en: Tecnología
  • Sé el primero en comentar

Flink Forward San Francisco 2019: Streaming your Lyft Ride Prices - Thomas Weise & Akshay Balwally

  1. 1. Streaming your Lyft Ride Prices Flink Forward, San Francisco, April 2nd 2019 Akshay Balwally | Engineer, Pricing Thomas Weise | @thweise | Engineer, Streaming Platform go.lyft.com/streaming-prices-ffsf-2019
  2. 2. Agenda 2 ● Introduction to dynamic pricing ● Legacy pricing infrastructure ● Streaming based infrastructure ● Beam & multiple languages ● Beam Flink runner ● Lessons learned
  3. 3. 3 Dynamic Pricing Supply/Demand curve ETA Pricing Notifications Detect Delays Coupons User Delight Fraud Behaviour Fingerprinting Monetary Impact Imperative to act fast Top Destinations Core Experience
  4. 4. Introduction to Dynamic Pricing 4
  5. 5. 5 ● Dynamic Pricing- price changes minutely at each location bucket ● Why? ○ At face value, dynamic pricing is strange ○ But Lyft’s marketplace changes quickly Dynamic Pricing- What and Why?
  6. 6. The Marketplace affects Prices ● An Imbalanced Market is Inefficient ○ Too many available drivers: bad ○ Too few available drivers: bad ○ Solution: Price lever controls passenger request rate, which maintains healthy supply levels ● Result: increase price if demand >> supply 6
  7. 7. What is PrimeTime? ● Belief: There exists some set of optimal price multipliers per location/time bucket ● PrimeTime- Lyft product that sets a multiplier for each gh6 each minute ● Example: In ‘9q8yyv’, at 5:01pm PST, PrimeTime = 2.0 ● Scale: Roughly 3 million geohashes prices every minute 7
  8. 8. Why is PrimeTime Hard? ● 1. Need low-latency information about supply and demand ● 2. Pricing is an unsupervised problem- correct answer is never observed ● Solution: break the problem into multiple models that form a DAG, where intermediate models are solving supervised problems ○ Example: f (available_supply) -> pickup_times 8
  9. 9. Legacy Pricing Infrastructure 9
  10. 10. Legacy architecture: A series of cron jobs ● Ingest high volume of client app events (Kinesis, KCL) ● Compute features (e.g. demand, conversation rate, supply) from events ● Run ML models on features to compute primetime for all regions (per min, per gh6) SFO, calendar_min_1: {gh6: 1.0, gh6: 2.0, ...} NYC: calendar_min_1: {gh6, 2.0, gh6: 1.0, ...} 10
  11. 11. Problems 1. Latency 2. Code complexity (LOC) 3. Hard to add new features involving windowing/join (i.e. arbitrary demand windows, subregional computation) 4. No dynamic / smart triggers 11
  12. 12. Can we use Flink? 12
  13. 13. 13 Streaming Stack 13 Streaming Application (SQL, Java) Stream / Schema Registry Deployment Tooling Metrics & Dashboards Alerts Logging Amazon EC2 Amazon S3 Wavefront Salt (Config / Orca) Docker Source Sink
  14. 14. 14 Streaming and Python ● Flink and many other big data ecosystem projects are Java / JVM based ○ Team wants to adopt streaming, but doesn’t have the Java skills ○ Jython != Python ● Use cases for different language environments ○ Python primary option for Machine Learning ● Cost of many API styles and runtime environments
  15. 15. 15 Python via Beam Streaming Application (Python/Beam) Source Sink
  16. 16. Streaming based Pricing Infrastructure 16
  17. 17. 17 Pipeline (conceptual outline) kinesis events (source) aggregate and window filter events run models to generate features (culminating in PT) internal services redis ride_requested, app_open, ... unique_users_per_min, unique_requests_per_5_ min, ... conversion learner, eta learner, ... Lyft apps (phones) valid sessions, dedupe, ...
  18. 18. Details of implementation 1. Filtering (with internal service calls) 2. Aggregation with Beam windowing: 1min, 5min (by event time) 3. Triggers: watermark, data-driven (stateful processing) 4. Join multiple streams: CoGroup or stateful processing 5. Machine learning models invoked within Beam transforms 6. Final gh6:pt output from pipeline stored to Redis 18
  19. 19. Gains • Latency: 3 minutes -> 30s ‒ Latency now dominated by model execution • Reuse of model code • 10K => 4K LOC • 300 => 120 AWS instances 19
  20. 20. Beam and multiple languages 20
  21. 21. 21 The Beam Vision 1. End users: who want to write pipelines in a language that’s familiar. 2. SDK writers: who want to make Beam concepts available in new languages. Includes IOs: connectors to data stores. 3. Runner writers: who have a distributed processing environment and want to support Beam pipelines Beam Model: Fn Runners Apache Flink Apache Spark Beam Model: Pipeline Construction Other LanguagesBeam Java Beam Python Execution Execution Cloud Dataflow Execution https://s.apache.org/apache-beam-project-overview
  22. 22. 22 Multi-Language Support ● Initially Java SDK and Java Runners ● 2016: Start of cross-language support effort ● 2017: Python SDK on Dataflow ● 2018: Go SDK (for portable runners) ● 2018: Python on Flink MVP ● Next: Cross-language pipelines, more portable runners
  23. 23. 23 Python Example p = beam.Pipeline(runner=runner, options=pipeline_options) (p | ReadFromText("/path/to/text*") | Map(lambda line: ...) | WindowInto(FixedWindows(120) trigger=AfterWatermark( early=AfterProcessingTime(60), late=AfterCount(1)) accumulation_mode=ACCUMULATING) | CombinePerKey(sum)) | WriteToText("/path/to/outputs") ) result = p.run() ( What, Where, When, How )
  24. 24. 24 ⋮ input | Sum.PerKey() Python input.apply( Sum.integersPerKey()) Java SELECT key, SUM(value) FROM input GROUP BY key SQL (via Java) ⋮ Cloud Dataflow Apache Spark Apache Flink Apache Apex Gearpump Apache Samza Apache Nemo (incubating) IBM Streams Sum Per Key Java objects Sum Per Key Dataflow JSON API Portability (originally) https://s.apache.org/state-of-beam-sfo-2018
  25. 25. 25 ⋮ input | Sum.PerKey() Python stats.Sum(s, input) Go SELECT key, SUM(value) FROM input GROUP BY key SQL (via Java) ⋮ input.apply( Sum.integersPerKey()) Java Apache Spark Apache Flink Apache Apex Gearpump Cloud Dataflow Apache Samza Apache Nemo (incubating) IBM Streams Sum Per Key Java objects Sum Per Key Portable protos Portability (current) https://s.apache.org/state-of-beam-sfo-2018
  26. 26. Beam Flink Runner 26
  27. 27. 27 Portable Flink Runner SDK (Python) Job Service Artifact Staging Job Manager Fn Services (Beam Flink Task) Task Manager Executor / Fn API Provision Control Data Artifact Retrieval State Logging gRPC Pipeline (protobuf) ClusterRunner Dependencies (optional) python -m apache_beam.examples.wordcount --input=/etc/profile --output=/tmp/py-wordcount-direct --runner=PortableRunner --job_endpoint=localhost:8099 --streaming Staging Location (DFS, S3, …) SDK Worker (UDFs) SDK Worker (UDFs) SDK Worker (Python) Flink Job
  28. 28. 28 Lyft Flink Runner Customizations ● Translator extension for streaming sources ○ Kinesis, Kafka consumers that we also use in Java Flink jobs ○ Message decoding, watermarks ● Python execution environment for SDK workers ○ Tailored to internal deployment tooling ○ Docker-free, frozen virtual envs ● https://github.com/lyft/beam/tree/release-2.11.0-lyft
  29. 29. Robert Bradshaw, Beam Summit London, 2018 Workers have Fn Runner Runner worker launches services. Control Service Data Service State Service Logging Service
  30. 30. 30 Fn API How slow is this ? ● Fn API Overhead 15% ? ● Fused stages ● Bundle size ● Parallel SDK workers ● TODO: Cython ● protobuf C++ bindings decode, …, window count (messages | 'reshuffle' >> beam.Reshuffle() | 'decode' >> beam.Map(lambda x: (__import__('random').randint(0, 511), 1)) | 'noop1' >> beam.Map(lambda x : x) | 'noop2' >> beam.Map(lambda x : x) | 'noop3' >> beam.Map(lambda x : x) | 'window' >> beam.WindowInto(window.GlobalWindows(), trigger=Repeatedly(AfterProcessingTime(5 * 1000)), accumulation_mode= AccumulationMode.DISCARDING) | 'group' >> beam.GroupByKey() | 'count' >> beam.Map(count) )
  31. 31. 31 Fast enough for real Python work ! ● c5.4xlarge machines (16 vCPU, 32 GB) ● 16 SDK workers / machine ● 1000 ms or 1000 records / bundle ● 280,000 transforms / second / machine (~ 17,500 per worker) ● Python user code will be gating factor
  32. 32. 32 Beam Portability Recap ● Pipelines written in non-JVM languages on JVM runners ○ Python, Go on Flink (and others) ● Full isolation of user code ○ Native CPython execution w/o library restrictions ● Flexible SDK worker execution ○ Docker, Process, Embedded, ... ● Multiple languages in a single pipeline (WIP) ○ Use Java Beam IO with Python ○ Use TFX with Java ○ <your use case here>
  33. 33. 33 Feature Support Matrix (Beam 2.11.0) https://s.apache.org/apache-beam-portability-support-table
  34. 34. Lessons Learned 34
  35. 35. Lessons Learned • Python Beam SDK and portable Flink runner evolving • Keep pipeline simple - Flink tasks / shuffles are not free • Stateful processing is essential for complex logic • Model execution latency matters • Instrument everything for monitoring • Think about pipeline restart and upgrade • Mind your dependencies - rate limit API calls • Long running Python processes may expose memory leaks 35
  36. 36. We’re Hiring! Apply at www.lyft.com/careers or email data-recruiting@lyft.com Data Engineering Engineering Manager San Francisco Software Engineer San Francisco, Seattle, & New York City Data Infrastructure Engineering Manager San Francisco Software Engineer San Francisco & Seattle Experimentation Software Engineer San Francisco Streaming Software Engineer San Francisco Observability Software Engineer San Francisco
  37. 37. Please ask questions! This presentation: http://go.lyft.com/streaming-prices-ffsf-2019

×