Scientific facilities such as the Advanced Light Source (ALS) and Joint Genome Institute and projects such as the Materials Project have an increasing need to capture, store, and analyze dynamic semi-structured data and metadata. A similar growth of semi-structured data within large Internet service providers has led to the creation of NoSQL data stores for scalable indexing and MapReduce for scalable parallel analysis. MapReduce and NoSQL stores have been applied to scientific data. Hadoop, the most popular open source implementation of MapReduce, has been evaluated, utilized and modified for addressing the needs of different scientific analysis problems. ALS and the Materials Project are using MongoDB, a document oriented NoSQL store. However, there is a limited understanding of the performance trade-offs of using these two technologies together. In this paper we evaluate the performance, scalability and fault-tolerance of using MongoDB with Hadoop, towards the goal of identifying the right software environment for scientific data analysis.

Abstract-MapReduce is increasingly becoming a popular framework, and a potent programming model. The most popular open source implementation of MapReduce, Hadoop, is based on the Hadoop Distributed File System (HDFS). However, as HDFS is not POSIX compliant, it cannot be fully leveraged by applications running on a majority of existing HPC environments such as Teragrid and NERSC. These HPC environments typically support globally shared file systems such as NFS and GPFS. On such resourceful HPC infrastructures, the use of Hadoop not only creates compatibility issues, but also affects overall performance due to the added overhead of the HDFS. This paper not only presents a MapReduce implementation directly suitable for HPC environments, but also exposes the design choices for better performance gains in those settings. By leveraging inherent distributed file systems' functions, and abstracting them away from its MapReduce framework, MARIANE (MApReduce Implementation Adapted for HPC Environments) not only allows for the use of the model in an expanding number of HPC environments, but also allows for better performance in such settings. This paper shows the applicability and high performance of the MapReduce paradigm through MARIANE, an implementation designed for clustered and shared-disk file systems and as such not dedicated to a specific MapReduce solution. The paper identifies the components and trade-offs necessary for this model, and quantifies the performance gains exhibited by our approach in distributed environments over Apache Hadoop in a data intensive setting, on the Magellan testbed at

Abstract-In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a key focus area for "Big Data" research. New generation data can prove costly and unpractical to administer with SQL databases due to lack of structure, high scalability, and elasticity needs. NoSQL data stores such as MongoDB and Cassandra provide a desirable platform for fast and efficient data queries. This leads to increased importance in areas such as cloud applications, e-commerce, social media, bio-informatics, and materials science. In an effort to combine the querying capabilities of conventional database systems and the processing power of the MapReduce model, this paper presents a thorough evaluation of the Cassandra NoSQL database when used in conjunction with the Hadoop MapReduce engine. We characterize the performance for a wide range of representative use cases, and then compare, contrast, and evaluate so that application developers can make informed decisions based upon data size, cluster size, replication factor, and partitioning strategy to meet their performance needs.

Abstract-MapReduce has since its inception been steadily gaining ground in various scientific disciplines ranging from space exploration to protein folding. The model poses a challenge for a wide range of current and legacy scientific applications for addressing their "Big Data" challenges. For example: MapReduce's best known implementation, Apache Hadoop, only offers native support for Java applications. While Hadoop streaming supports applications compiled in a variety of languages such as C, C++, Python and FORTRAN, streaming has shown to be a less efficient MapReduce alternative in terms of performance, and effectiveness. Additionally, Hadoop streaming offers lesser options than its native counterpart, and as such offers less flexibility along with a limited array of features for scientific software. The Hadoop File System (HDFS), a central pillar of Apache Hadoop is not a POSIX compliant file system. In this paper, we present an alternative framework to Hadoop streaming to address the needs of scientific applications: MARISSA (MApReduce Implementation for Streaming Science Applications). We describe MARISSA's design and explain how it expands the scientific applications that can benefit from the MapReduce model. We also compare and explain the performance gains of MARISSA over Hadoop streaming.

Abstract-The MapReduce paradigm provides a scalable model for large scale data-intensive computing and associated fault-tolerance. With data production increasing daily due to ever growing application needs, scientific endeavors, and consumption, the MapReduce model and its implementations need to be further evaluated, improved, and strengthened. Several MapReduce frameworks with various degrees of conformance to the key tenets of the model are available today, each, optimized for specific features. HPC application and middleware developers must thus understand the complex dependencies between MapReduce features and their application. We present a standard benchmark suite for quantifying, comparing, and contrasting the performance of MapReduce platforms under a wide range of representative use cases. We report the performance of three different MapReduce implementations on the benchmarks, and draw conclusions about their current performance characteristics. The three platforms we chose for evaluation are the widely used Apache Hadoop implementation, Twister, which has been discussed in the literature, and LEMO-MR, our own implementation. The performance analysis we perform also throws light on the available design decisions for future implementations, and allows Grid researchers to choose the MapReduce implementation that best suits their application's needs.