Distributed Data Collection for the Next Generation ATLAS EventIndex Project

Abstract

The ATLAS EventIndex currently runs in production in order to build a complete catalogue of events for experiments with large amounts of data. The current approach is to index all final produced data files at CERN Tier0, and at hundreds of grid sites, with a distributed data collection architecture using Object Stores to temporarily maintain the conveyed information, with references to them sent with a Messaging System. The final backend of all the indexed data is a central Hadoop infrastructure at CERN; an Oracle relational database is used for faster access to a subset of this information. In the future of ATLAS, instead of files, the event should be the atomic information unit for metadata, in order to accommodate future data processing and storage technologies. Files will no longer be static quantities, possibly dynamically aggregating data, and also allowing event-level granularity processing in heavily parallel computing environments. It also simplifies the handling of loss and or extension of data. In this sense the EventIndex may evolve towards a generalized whiteboard, with the ability to build collections and virtual datasets for end users. This proceedings describes the current Distributed Data Collection Architecture of the ATLAS EventIndex project, with details of the Producer, Consumer and Supervisor entities, and the protocol and information temporarily stored in the ObjectStore. It also shows the data flow rates and performance achieved since the new Object Store as temporary store approach was put in production in July 2017. We review the challenges imposed by the expected increasing rates that will reach 35 billion new real events per year in Run 3, and 100 billion new real events per year in Run 4. For simulated events the numbers are even higher, with 100 billion events/year in run 3, and 300 billion events/year in run 4. We also outline the challenges we face in order to accommodate future use cases in the EventIndex.