MapReduce has emerged as a popular method to process big data. In the past few years, however, not just big data, but fast data has also exploded in volume and availability. Examples of such data include sensor data streams, the Twitter Firehose, and Facebook updates. Numerous applications must process fast data. Can we provide a MapReduce-style framework so that developers can quickly write such applications and execute them over a cluster of machines, to achieve low latency and high scalability? In this paper we report on our investigation of this question, as carried out at Kosmix and WalmartLabs. We describe MapUpdate, a framework like MapReduce, but specifically developed for fast data. We describe Muppet, our implementation of MapUpdate. Throughout the description we highlight the key challenges, argue why MapReduce is not well suited to address them, and briefly describe our current solutions. Finally, we describe our experience and lessons learned with Muppet, which has been used extensively at Kosmix and WalmartLabs to power a broad range of applications in social media and e-commerce.
In time when not only big data but also fast data are exploded in volume and availability, authors Wang L. et. al. in their paper REF , address the key challenges that MapReduce is not well suited for and provide solutions with MapUpdate use which is a framework like MapReduce and specifically developed for fast data.
Muppet: MapReduce-Style Processing of Fast Data
Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 12, pp. 1814-1825 (2012)
Computer Science