As your enterprise starts deploying more and more Spark jobs, you will discover many common issues: deploying job jars; managing job lifecycles and progress; serializing and processing job results; keeping track of failures, job statuses, and jars; managing Spark contexts for fast interactive jobs. Also, every Job is an application with its own interface and parameters. Submitting and running jobs Hadoop-style just doesn’t work. Our open-source Spark Job Server offers a RESTful API for managing Spark jobs, jars, and contexts, turning Spark into an easy-to-use service, and offering a uniform API for all jobs. We will talk about our job server, its APIs, current and upcoming features in much greater detail.
Learn how the Spark Job Server can turn Spark into a easy to use service for your organization. As a developer, learn how the job server can let you focus on the job algorithm instead of on nitty gritty infrastructure details.
Evan loves to design, build, and improve bleeding edge distributed data and backend systems using the latest in open source technologies. He has led the design and implementation of multiple big data platforms based on Storm, Spark, Kafka, Cassandra, and Scala/Akka, including a columnar real-time distributed query engine. He is an active contributor to the Apache Spark project, a Datastax Cassandra MVP, and co-creator and maintainer of the open-source Spark Job Server. He is a big believer in GitHub, open source, and meetups, and have given talks at various conferences including Spark Summit, Cassandra Summit, FOSS4G, and Scala Days.
Kelvin is a founding member of the Hadoop team at Uber. He is creating tools and services on top of Spark to support multi-tenancy and large scale computation-intensive applications. He is creator and lead engineer of Spark Uber Development Kit, Paricon and SparkPlug services which are main initiatives of Spark Compute at Uber. At Ooyala, he was co-creator of Spark Job Server which was an open source RESTful server for submitting, running, and managing Spark jobs, jars and contexts. He implemented real-time video analytics engines on top of it by datacube materializations via RDD.