Prasanth Kothuri is currently working as Sr Big Data Engineer for CERN in defining and architecting the next generation of Data Analytics platform based on Hadoop and Spark. He’s working with various user communities at CERN in building data analytics solutions around Apache Hadoop, Apache Spark, and Apache Kudu for the past 3 years. Before this, he was an Oracle Database specialist for a decade, covering all areas from performance tuning to upgrading databases and disaster recovery to securing databases.
The physicists at CERN are increasingly turning to Spark to process large physics datasets in a distributed fashion with the aim of reducing time-to-physics with increased interactivity. The physics data itself is stored in CERN's mass storage system: EOS and CERN's IT department runs on-premise private cloud based on OpenStack as a way to provide on-demand compute resources to physicists. This provides both opportunity and challenges to Big Data team at CERN to provide elastic, scalable, reliable spark-as-a-service on OpenStack. The talk focuses on the design choices made and challenges faced while developing spark-as-a-service over kubernetes on openstack to simplify provisioning, automate management, and minimize the operating burden of managing Spark Clusters. In addition, the service tooling simplifies submitting applications on the behalf of the users, mounting user-specified ConfigMaps, copying application logs to s3 buckets for troubleshooting, performance analysis and accounting of spark applications and support for stateful spark streaming applications. We will also share results from running large scale sustained workloads over terabytes of physics data. Session hashtag: #SAISEco11
At CERN, the biggest physics laboratory in the world, large volumes of data are generated every hour, it implies serious challenges to store and process all this data. An important part of this responsibility comes to the database group which not only provides services for RDBMS but also scalable systems as Hadoop, Spark and HBase. Since databases are critical, they need to be monitored, for that we have built a highly scalable, secure and central repository that stores consolidated audit data and listener, alert and OS log events generated by the databases. This central platform is used for reporting, alerting and security policy management. The database group want to further exploit the information available in this central repository to build intrusion detection system to enhance the security of the database infrastructure. In addition, build pattern detection models to flush out anomalies using the monitoring and performance metrics available in the central repository. Finally, this platform also helps us for capacity planning of the database deployment. The audience would get first-hand experience of how to build real time Apache Spark application that is deployed in production. They would hear the challenges faced and decisions taken while developing the application and troubleshooting Apache Spark and Spark streaming application in production. Session hashtag: #EUde13