Name
Abstract | ||
|---|---|---|
In this paper, we present a new C++ API with a fluent interface called PiCo (Pipeline Composition). PiCo’s programming model aims at making easier the programming of data analytics applications while preserving or enhancing their performance. This is attained through three key design choices: (1) unifying batch and stream data access models, (2) decoupling processing from data layout, and (3) exploiting a stream-oriented, scalable, efficient C++11 runtime system. PiCo proposes a programming model based on pipelines and operators that are polymorphic with respect to data types in the sense that it is possible to re-use the same algorithms and pipelines on different data models (e.g., streams, lists, sets, etc.). Preliminary results show that PiCo can attain better performances in terms of execution times and hugely improve memory utilization when compared to Spark and Flink in both batch and stream processing. |
Year | Venue | Field |
|---|---|---|
2017 | Euro-Par Workshops | Data modeling,Spark (mathematics),Data analysis,Programming paradigm,Computer science,Data type,Stream processing,Analytics,Distributed computing,Runtime system |
DocType | Citations | PageRank |
Conference | 1 | 0.40 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudia Misale | 1 | 23 | 5.44 |
| Maurizio Drocco | 2 | 88 | 12.09 |
| Guy Tremblay | 3 | 79 | 9.49 |
| Marco Aldinucci | 4 | 638 | 59.87 |