Flink Forward 2019--实战相关(2)--网约车公司Lyft整合Beam和Flink

Streaming your Lyft Ride Prices

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).

在Lyft，我们通过各种数据源、机器学习模型和流式基础设施的组合动态为我们的短途旅程定价，以实现低延迟、可靠性和可扩展性。动态定价使我们能够快速适应现实世界的变化，公平对待驾驶员（比如在需求量很大的时候提高利率），公平对待乘客（比如说提供10分钟后以更便宜的价格返回）。

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:

为了实现这一点，我们的系统将消耗来自不同来源的大量事件。流媒体平台通过使用ApacheBeam、Python/TensorFlow中的ML算法和作为流媒体引擎的ApacheFlink将两个世界中最好的算法结合在一起，从而提高定价能力。支持用于机器学习的数据科学工具和允许更快部署的流程对业务越来越重要。本次讲座的主题包括：

* 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

*基于实时事件流的动态定价示例，包括驾驶员位置、乘坐请求、用户会话事件和基于机器学习模型

*传统系统与新型流媒体动态定价平台的比较

*实时处理实时事件以生成机器学习模型的功能

*流平台架构和技术堆栈概述

*Apache Beam可移植性框架是基于JVM的流引擎实现无需代码重写的分布式执行的桥梁

*经验教训