Li Gao

Tech Lead, Lyft Inc.

Li Gao is the tech lead in the cloud native data compute team at Lyft that powers many of Lyft’s critical data and ML compute pipelines. Prior to Lyft, Li worked at Salesforce, Fitbit, and a few startups etc. on various technical leadership positions on cloud native and hybrid cloud data platforms at scale. Besides Spark, Li has scaled and productionized other open source projects, such as Presto, Apache HBase, Apache Phoenix, Apache Airflow, Apache Hive. Spark Summit 2019 – Scaling Spark on K8s

Past sessions

Summit 2020 Cloud-Native Apache Spark Scheduling with YuniKorn Scheduler

June 25, 2020 05:00 PM PT

Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads. We embrace Apache Spark for data engineering and machine learning, and by running Spark on Kubernetes, we are able to exploit compute power promisingly under such highly elastic, scalable and decoupled architecture. We made a lot of effort on enhancing the core resource scheduling, in order to bring high performance, efficient-sharing and multi-tenancy oriented capabilities to Spark jobs. In this talk, we will focus on revealing the architecture of the cloud-native infrastructure; How we leverage YuniKorn - an open-source resource scheduler to redefine the resource scheduling on Cloud. We will introduce how YuniKorn manages quotas, resource sharing, and auto-scaling, and ultimately how to schedule large scale Spark jobs efficiently on Kubernetes in the cloud.

Summit 2019 Scaling Apache Spark on Kubernetes at Lyft

April 23, 2019 05:00 PM PT

Lyft is on the mission to improve people's lives with the world's best transportation. As part of this mission Lyft invests heavily in open source infrastructure and tooling. At Lyft Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark at Lyft has evolved to solve both Machine Learning and large scale ETL workloads. By combining the flexibility of Kubernetes with the data processing power of Apache Spark, Lyft is able to drive ETL data processing to a different level. In this talk, Li Gao and Rohit Menon will talk about challenges the Lyft team faced and solutions they developed to support Apache Spark on Kubernetes in production and at scale.

Topics Include: - Key traits of Apache Spark on Kubernetes. - Deep dive into Lyft's multi-cluster setup and operationality to handle petabytes of production data. - How Lyft extends and enhances Apache Spark to support capabilities such as Spark pod life cycle metrics and state management, resource prioritization, and queuing and throttling. - Dynamic job scale estimation and runtime dynamic job configuration. - How Lyft powers internal Data Scientists, Business Analysts, and Data Engineers via a multi-cluster setup.