AdBench: A Complete Benchmark for Modern Data Pipelines

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• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10080
• 期：1
• 页码：107-120
• 丛书名：Performance Evaluation and Benchmarking. Traditional - Big Data - Interest of Things
• ISBN：978-3-319-54334-5
• 卷排序：10080

文摘

Since the introduction of Apache YARN, which modularly separated resource management and scheduling from the distributed programming frameworks, a multitude of YARN-native computation frameworks have been developed. These frameworks specialize in specific analytics variants. In addition to traditional batch-oriented computations (e.g. MapReduce, Apache Hive [14] and Apache Pig [18]), the Apache Hadoop ecosystem now contains streaming analytics frameworks (e.g. Apache Apex [8]), MPP SQL engines (e.g. Apache Trafodion [20], Apache Impala [15], and Apache HAWQ [12]), OLAP cubing frameworks (e.g. Apache Kylin [17]), frameworks suitable for iterative machine learning (e.g. Apache Spark [19] and Apache Flink [10]), and graph processing (e.g. GraphX). With emergence of Hadoop Distributed File System and its various implementations as preferred method of constructing a data lake, end-to-end data pipelines are increasingly being built on the Hadoop-based data lake platform.