Modern data analytics infrastructures are composed of physically disaggregated compute and storage clusters. Thus, dataflow analytics engines, such as Apache Spark or Flink, are left with no choice but to transfer datasets to the compute cluster prior to their actual processing. For large data volumes, this becomes problematic, since it involves massive data transfers that exhaust network bandwidth, that waste compute cluster memory, and that may become a performance barrier. To overcome this problem, we present λFlow: a framework for automatically pushing dataflow operators (e.g., map, flatMap, filter, etc.) down onto the storage layer. The novelty of λFlow is that it manages the pushdown granularity at the operator level, which makes it a unique problem. To wit, it requires addressing several challenges, such as how to encapsulate dataflow operators and execute them on the storage cluster, and how to keep track of dependencies such that operators can be pushed down safely onto the storage layer. Our evaluation reports significant reductions in resource usage for a large variety of IO-bound jobs. For instance, λFlow was able to reduce both network bandwidth and memory requirements by 90% in Spark. Our Flink experiments also prove the extensibility of λFlow to other engines.

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.