SUMMARY: This article presents MarDRe, a de novo cloud-ready duplicate and near-duplicate removal tool that can process single- and paired-end reads from FASTQ/FASTA datasets. MarDRe takes advantage of the widely adopted MapReduce programming model to fully exploit Big Data technologies on cloud-based infrastructures. Written in Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for scalable Big Data processing. On a 16-node cluster deployed on the Amazon EC2 cloud platform, MarDRe is up to 8.52 times faster than a representative state-of-the-art tool. AVAILABILITY AND IMPLEMENTATION: Source code in Java and Hadoop as well as a user's guide are freely available under the GNU GPLv3 license at http://mardre.des.udc.es . CONTACT: [email protected].

The increasing adoption of Big Data analytics has led to a high demand for efficient technologies in order to manage and process large datasets. Popular MapReduce frameworks such as Hadoop are being replaced by emerging ones like Spark or Flink, which improve both the programming APIs and performance. However, few works have focused on comparing these frameworks. This paper addresses this issue by performing a comparative evaluation of Hadoop, Spark and Flink using representative Big Data workloads and considering factors like performance and scalability. Moreover, the behavior of these frameworks has been characterized by modifying some of the main parameters of the workloads such as HDFS block size, input data size, interconnect network or thread configuration. The analysis of the results has shown that replacing Hadoop with Spark or Flink can lead to a reduction in execution times by 77% and 70% on average, respectively, for non-sort benchmarks.