Too big to eat: Boosting analytics data ingestion from object stores with scoop

Moatti, Yosef; Rom, Eran; Gracia-Tinedo, Raúl; Naor, Dalit; Chen, Doron; Sampé, Josep; Sánchez-Artigas, Marc; García-López, Pedro; Gluszak, Filip; Deschodt, Eric; Pace, Francesco; Venzano, Daniele; Michiardi, Pietro
ICDE 2017, IEEE International Conference on Data Engineering, April 19-22, 2017, San Diego, USA

Extracting value from data stored in object stores,such as OpenStack Swift and Amazon S3, can be problematicin common scenarios where analytics frameworks and objectstores run in physically disaggregated clusters. One of the mainproblems is that analytics frameworks must ingest large amountsof data from the object store prior to the actual computation;this incurs a significant resources and performance overhead. Toovercome this problem, we present Scoop. Scoop enables analyticsframeworks to benefit from the computational resources of objectstores to optimize the execution of analytics jobs. Scoop achievesthis by enabling the addition of ETL-type actions to the dataupload path and by offloading querying functions to the objectstore through a rich and extensible active object storage layer. Asa proof-of-concept, Scoop enables Apache Spark SQL selectionsand projections to be executed close to the data in OpenStackSwift for accelerating analytics workloads of a smart energy gridcompany (GridPocket). Our experiments in a 63-machine clusterwith real IoT data and SQL queries from GridPocket show thatScoop exhibits query execution times up to 30x faster than thetraditional “ingest-then-compute” approach.

Poster / Demo
San Diego
Data Science
Eurecom Ref:
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