Xingbo Jiang - Databricks

Xingbo Jiang

Software Engineer, Databricks

Xingbo Jiang is a software engineer at Databricks, where he investigates the use cases on Spark Core and Spark SQL. Xingbo is an active contributor to Apache Spark. His areas of interest include distributed system, database, and data warehouse.

UPCOMING SESSIONS

Updates from Project Hydrogen: Unifying State-of-the-Art AI and Big Data in Apache SparkSummit Europe 2019

Updates from Project Hydrogen: Unifying State-of-the-Art AI and Big Data in Apache Spark Project Hydrogen is a major Apache Spark initiative to bring state-of-the-art AI and Big Data solutions together. It contains three major projects: 1) barrier execution mode 2) optimized data exchange and 3) accelerator-aware scheduling. A basic implementation of barrier execution mode was merged into Apache Spark 2.4.0, and the community is working on the latter two. In this talk, we will present progress updates to Project Hydrogen and discuss the next steps. First, we will review the barrier execution mode implementation from Spark 2.4.0. It enables developers to embed distributed training jobs properly on a Spark cluster. We will demonstrate distributed AI integrations built on top it, e.g., Horovod and Distributed TensorFlow. We will also discuss the technical challenges to implement those integrations and future work. Second, we will give updates on accelerator-aware scheduling and how it shall help accelerate your Spark training jobs. We will also outline on-going work for optimized data exchange.

PAST SESSIONS

A Deep Dive into Query Execution Engine of Spark SQLSummit 2019

Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.

A Deep Dive into Query Execution Engine of Spark SQL (continues)Summit 2019

Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.

Apache Spark SchedulerSummit 2018

As a core component of data processing platform, scheduler is responsible for schedule tasks on compute units. Built on a Directed Acyclic Graph (DAG) compute model, Spark Scheduler works together with Block Manager and Cluster Backend to efficiently utilize cluster resources for high performance of various workloads. This talk dives into the technical details of the full lifecycle of a typical Spark workload to be scheduled and executed, and also discusses how to tune Spark scheduler for better performance.

Deep Dive into the Apache Spark SchedulerSummit 2018

As a core component of data processing platform, scheduler is responsible for schedule tasks on compute units. Built on a Directed Acyclic Graph (DAG) compute model, Spark Scheduler works together with Block Manager and Cluster Backend to efficiently utilize cluster resources for high performance of various workloads. This talk dives into the technical details of the full lifecycle of a typical Spark workload to be scheduled and executed, and also discusses how to tune Spark scheduler for better performance. Session hashtag: #Dev9SAIS