Resilient Distributed Datasets
Found 6 free book(s)
Prerequisite - Tutorialspoint
www.tutorialspoint.comResilient Distributed Datasets Resilient Distributed Datasets (RDD) is a fundamental data structure of Spark. It is an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of …
Spark: Cluster Computing with Working Sets
www.usenix.orgabstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time. 1 ...
AI and Cybersecurity: Opportunities and Challenges
www.nitrd.govstipulations below, it may be distributed and copied with acknowledgment to OSTP. Requests to use any images must ... corpus including systems, models and datasets for education, research, and validation. ... secure and resilient techniques and best practices are vitally important.
Resilient Distributed Datasets: A Fault-Tolerant ...
www.usenix.orgResilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion Stoica University of California, Berkeley Abstract We present Resilient Distributed Datasets (RDDs), a dis-
Apache Spark - Home | UCSD DSE MAS
mas-dse.github.iorEsiLiEnt distriBUtEd datasEt The core concept in apache spark is the resilient distributed ataset (RDD). It is an immutable distributed collection of data, which is partitioned across machines in a cluster. It facilitates two types of operations: transformation and action. A transformation is an operation
Machine Learning with Adversaries: Byzantine Tolerant ...
proceedings.neurips.ccStochastic Gradient Descent (SGD). So far, distributed machine learning frame-works have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system.