S4: Distributed Stream Computing Platform

S4 is a general-purpose, distributed, scalable, partially fault-tolerant, pluggable platform that allows programmers to easily develop applications for processing continuous unbounded streams of data. Keyed data events are routed with affinity to Processing Elements (PEs), which consume the events a...

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Bibliographic Details
Published in:2010 IEEE International Conference on Data Mining Workshops pp. 170 - 177
Main Authors: Neumeyer, L, Robbins, B, Nair, A, Kesari, A
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2010
Subjects:
actors programming model
complex event processing
Computational modeling
Computer architecture
concurrent programming
Data mining
data processing
distributed programming
Engines
map-reduce
middleware
parallel programming
Programming
Real time systems
real-time search
Servers
software design
stream computing
ISBN:9781424492442, 1424492440
ISSN:2375-9232
Online Access:Get full text
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Summary:S4 is a general-purpose, distributed, scalable, partially fault-tolerant, pluggable platform that allows programmers to easily develop applications for processing continuous unbounded streams of data. Keyed data events are routed with affinity to Processing Elements (PEs), which consume the events and do one or both of the following: (1) emit one or more events which may be consumed by other PEs, (2) publish results. The architecture resembles the Actors model, providing semantics of encapsulation and location transparency, thus allowing applications to be massively concurrent while exposing a simple programming interface to application developers. In this paper, we outline the S4 architecture in detail, describe various applications, including real-life deployments. Our design is primarily driven by large scale applications for data mining and machine learning in a production environment. We show that the S4 design is surprisingly flexible and lends itself to run in large clusters built with commodity hardware.
ISBN:9781424492442
1424492440
ISSN:2375-9232
DOI:10.1109/ICDMW.2010.172