Hadoop Vs Apache Spark Introduction Comparative Analysis: Hadoop Vs Apache Spark
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Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions
00 Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions MUTAZ BARIKA, University of Tasmania SAURABH GARG, University of Tasmania ALBERT Y. ZOMAYA, University of Sydney LIZHE WANG, China University of Geoscience (Wuhan) AAD VAN MOORSEL, Newcastle University RAJIV RANJAN, Chinese University of Geoscienes and Newcastle University Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies and research outcomes. This has led to advancement in communication, programming and processing technologies, including Cloud computing services and technologies such as Hadoop, Spark and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These Big Data Workflows are vastly different in nature from traditional workflows. Researchers arecurrently facing the challenge of how to orchestrate and manage the execution of such workflows. In this paper, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We alsosurvey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Data analytics; • Computer systems organization → Cloud computing; Additional Key Words and Phrases: Big Data, Cloud Computing, Workflow Orchestration, Requirements, Approaches ACM Reference format: Mutaz Barika, Saurabh Garg, Albert Y. Zomaya, Lizhe Wang, Aad van Moorsel, and Rajiv Ranjan. 2018. Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions. -
Red Hat Data Analytics Infrastructure Solution
TECHNOLOGY DETAIL RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION TABLE OF CONTENTS 1 INTRODUCTION ................................................................................................................ 2 2 RED HAT DATA ANALYTICS INFRASTRUCTURE SOLUTION ..................................... 2 2.1 The evolution of analytics infrastructure ....................................................................................... 3 Give data scientists and data 2.2 Benefits of a shared data repository on Red Hat Ceph Storage .............................................. 3 analytics teams access to their own clusters without the unnec- 2.3 Solution components ...........................................................................................................................4 essary cost and complexity of 3 TESTING ENVIRONMENT OVERVIEW ............................................................................ 4 duplicating Hadoop Distributed File System (HDFS) datasets. 4 RELATIVE COST AND PERFORMANCE COMPARISON ................................................ 6 4.1 Findings summary ................................................................................................................................. 6 Rapidly deploy and decom- 4.2 Workload details .................................................................................................................................... 7 mission analytics clusters on 4.3 24-hour ingest ........................................................................................................................................8 -
Building Machine Learning Inference Pipelines at Scale
Building Machine Learning inference pipelines at scale Julien Simon Global Evangelist, AI & Machine Learning @julsimon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Problem statement • Real-life Machine Learning applications require more than a single model. • Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. • Predictions may need post-processing: filtering, sorting, combining, etc. Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker) © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Apache Spark https://spark.apache.org/ • Open-source, distributed processing system • In-memory caching and optimized execution for fast performance (typically 100x faster than Hadoop) • Batch processing, streaming analytics, machine learning, graph databases and ad hoc queries • API for Java, Scala, Python, R, and SQL • Available in Amazon EMR and AWS Glue © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. MLlib – Machine learning library https://spark.apache.org/docs/latest/ml-guide.html • Algorithms: classification, regression, clustering, collaborative filtering. • Featurization: feature extraction, transformation, dimensionality reduction. • Tools for constructing, evaluating and tuning pipelines • Transformer – a transform function that maps a DataFrame into a new -
Unlock Bigdata Analytic Efficiency with Ceph Data Lake
Unlock Bigdata Analytic Efficiency With Ceph Data Lake Jian Zhang, Yong Fu, March, 2018 Agenda . Background & Motivations . The Workloads, Reference Architecture Evolution and Performance Optimization . Performance Comparison with Remote HDFS . Summary & Next Step 3 Challenges of scaling Hadoop* Storage BOUNDED Storage and Compute resources on Hadoop Nodes brings challenges Data Capacity Silos Costs Performance & efficiency Typical Challenges Data/Capacity Multiple Storage Silos Space, Spent, Power, Utilization Upgrade Cost Inadequate Performance Provisioning And Configuration Source: 451 Research, Voice of the Enterprise: Storage Q4 2015 *Other names and brands may be claimed as the property of others. 4 Options To Address The Challenges Compute and Large Cluster More Clusters Storage Disaggregation • Lacks isolation - • Cost of • Isolation of high- noisy neighbors duplicating priority workloads hinder SLAs datasets across • Shared big • Lacks elasticity - clusters datasets rigid cluster size • Lacks on-demand • On-demand • Can’t scale provisioning provisioning compute/storage • Can’t scale • compute/storage costs separately compute/storage costs scale costs separately separately Compute and Storage disaggregation provides Simplicity, Elasticity, Isolation 5 Unified Hadoop* File System and API for cloud storage Hadoop Compatible File System abstraction layer: Unified storage API interface Hadoop fs –ls s3a://job/ adl:// oss:// s3n:// gs:// s3:// s3a:// wasb:// 2006 2008 2014 2015 2016 6 Proposal: Apache Hadoop* with disagreed Object Storage SQL …… Hadoop Services • Virtual Machine • Container • Bare Metal HCFS Compute 1 Compute 2 Compute 3 … Compute N Object Storage Services Object Object Object Object • Co-located with gateway Storage 1 Storage 2 Storage 3 … Storage N • Dynamic DNS or load balancer • Data protection via storage replication or erasure code Disaggregated Object Storage Cluster • Storage tiering *Other names and brands may be claimed as the property of others. -
Evaluation of SPARQL Queries on Apache Flink
applied sciences Article SPARQL2Flink: Evaluation of SPARQL Queries on Apache Flink Oscar Ceballos 1 , Carlos Alberto Ramírez Restrepo 2 , María Constanza Pabón 2 , Andres M. Castillo 1,* and Oscar Corcho 3 1 Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Ciudad Universitaria Meléndez Calle 13 No. 100-00, Cali 760032, Colombia; [email protected] 2 Departamento de Electrónica y Ciencias de la Computación, Pontificia Universidad Javeriana Cali, Calle 18 No. 118-250, Cali 760031, Colombia; [email protected] (C.A.R.R.); [email protected] (M.C.P.) 3 Ontology Engineering Group, Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, 28660 Madrid, Spain; ocorcho@fi.upm.es * Correspondence: [email protected] Abstract: Existing SPARQL query engines and triple stores are continuously improved to handle more massive datasets. Several approaches have been developed in this context proposing the storage and querying of RDF data in a distributed fashion, mainly using the MapReduce Programming Model and Hadoop-based ecosystems. New trends in Big Data technologies have also emerged (e.g., Apache Spark, Apache Flink); they use distributed in-memory processing and promise to deliver higher data processing performance. In this paper, we present a formal interpretation of some PACT transformations implemented in the Apache Flink DataSet API. We use this formalization to provide a mapping to translate a SPARQL query to a Flink program. The mapping was implemented in a prototype used to determine the correctness and performance of the solution. The source code of the Citation: Ceballos, O.; Ramírez project is available in Github under the MIT license. -
Key Exchange Authentication Protocol for Nfs Enabled Hdfs Client
Journal of Theoretical and Applied Information Technology 15 th April 2017. Vol.95. No 7 © 2005 – ongoing JATIT & LLS ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 KEY EXCHANGE AUTHENTICATION PROTOCOL FOR NFS ENABLED HDFS CLIENT 1NAWAB MUHAMMAD FASEEH QURESHI, 2*DONG RYEOL SHIN, 3ISMA FARAH SIDDIQUI 1,2 Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea 3 Department of Software Engineering, Mehran UET, Pakistan *Corresponding Author E-mail: [email protected], 2*[email protected], [email protected] ABSTRACT By virtue of its built-in processing capabilities for large datasets, Hadoop ecosystem has been utilized to solve many critical problems. The ecosystem consists of three components; client, Namenode and Datanode, where client is a user component that requests cluster operations through Namenode and processes data blocks at Datanode enabled with Hadoop Distributed File System (HDFS). Recently, HDFS has launched an add-on to connect a client through Network File System (NFS) that can upload and download set of data blocks over Hadoop cluster. In this way, a client is not considered as part of the HDFS and could perform read and write operations through a contrast file system. This HDFS NFS gateway has raised many security concerns, most particularly; no reliable authentication support of upload and download of data blocks, no local and remote client efficient connectivity, and HDFS mounting durability issues through untrusted connectivity. To stabilize the NFS gateway strategy, we present in this paper a Key Exchange Authentication Protocol (KEAP) between NFS enabled client and HDFS NFS gateway. The proposed approach provides cryptographic assurance of authentication between clients and gateway. -
Flare: Optimizing Apache Spark with Native Compilation
Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data Gregory Essertel, Ruby Tahboub, and James Decker, Purdue University; Kevin Brown and Kunle Olukotun, Stanford University; Tiark Rompf, Purdue University https://www.usenix.org/conference/osdi18/presentation/essertel This paper is included in the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18). October 8–10, 2018 • Carlsbad, CA, USA ISBN 978-1-939133-08-3 Open access to the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX. Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data Grégory M. Essertel1, Ruby Y. Tahboub1, James M. Decker1, Kevin J. Brown2, Kunle Olukotun2, Tiark Rompf1 1Purdue University, 2Stanford University {gesserte,rtahboub,decker31,tiark}@purdue.edu, {kjbrown,kunle}@stanford.edu Abstract cessing. Systems like Apache Spark [8] have gained enormous traction thanks to their intuitive APIs and abil- In recent years, Apache Spark has become the de facto ity to scale to very large data sizes, thereby commoditiz- standard for big data processing. Spark has enabled a ing petabyte-scale (PB) data processing for large num- wide audience of users to process petabyte-scale work- bers of users. But thanks to its attractive programming loads due to its flexibility and ease of use: users are able interface and tooling, people are also increasingly using to mix SQL-style relational queries with Scala or Python Spark for smaller workloads. Even for companies that code, and have the resultant programs distributed across also have PB-scale data, there is typically a long tail of an entire cluster, all without having to work with low- tasks of much smaller size, which make up a very impor- level parallelization or network primitives. -
HFAA: a Generic Socket API for Hadoop File Systems
HFAA: A Generic Socket API for Hadoop File Systems Adam Yee Jeffrey Shafer University of the Pacific University of the Pacific Stockton, CA Stockton, CA [email protected] jshafer@pacific.edu ABSTRACT vices: one central NameNode and many DataNodes. The Hadoop is an open-source implementation of the MapReduce NameNode is responsible for maintaining the HDFS direc- programming model for distributed computing. Hadoop na- tory tree. Clients contact the NameNode in order to perform tively integrates with the Hadoop Distributed File System common file system operations, such as open, close, rename, (HDFS), a user-level file system. In this paper, we intro- and delete. The NameNode does not store HDFS data itself, duce the Hadoop Filesystem Agnostic API (HFAA) to allow but rather maintains a mapping between HDFS file name, Hadoop to integrate with any distributed file system over a list of blocks in the file, and the DataNode(s) on which TCP sockets. With this API, HDFS can be replaced by dis- those blocks are stored. tributed file systems such as PVFS, Ceph, Lustre, or others, thereby allowing direct comparisons in terms of performance Although HDFS stores file data in a distributed fashion, and scalability. Unlike previous attempts at augmenting file metadata is stored in the centralized NameNode service. Hadoop with new file systems, the socket API presented here While sufficient for small-scale clusters, this design prevents eliminates the need to customize Hadoop’s Java implementa- Hadoop from scaling beyond the resources of a single Name- tion, and instead moves the implementation responsibilities Node. Prior analysis of CPU and memory requirements for to the file system itself. -
Large Scale Querying and Processing for Property Graphs Phd Symposium∗
Large Scale Querying and Processing for Property Graphs PhD Symposium∗ Mohamed Ragab Data Systems Group, University of Tartu Tartu, Estonia [email protected] ABSTRACT Recently, large scale graph data management, querying and pro- cessing have experienced a renaissance in several timely applica- tion domains (e.g., social networks, bibliographical networks and knowledge graphs). However, these applications still introduce new challenges with large-scale graph processing. Therefore, recently, we have witnessed a remarkable growth in the preva- lence of work on graph processing in both academia and industry. Querying and processing large graphs is an interesting and chal- lenging task. Recently, several centralized/distributed large-scale graph processing frameworks have been developed. However, they mainly focus on batch graph analytics. On the other hand, the state-of-the-art graph databases can’t sustain for distributed Figure 1: A simple example of a Property Graph efficient querying for large graphs with complex queries. Inpar- ticular, online large scale graph querying engines are still limited. In this paper, we present a research plan shipped with the state- graph data following the core principles of relational database systems [10]. Popular Graph databases include Neo4j1, Titan2, of-the-art techniques for large-scale property graph querying and 3 4 processing. We present our goals and initial results for querying ArangoDB and HyperGraphDB among many others. and processing large property graphs based on the emerging and In general, graphs can be represented in different data mod- promising Apache Spark framework, a defacto standard platform els [1]. In practice, the two most commonly-used graph data models are: Edge-Directed/Labelled graph (e.g. -
Decentralising Big Data Processing Scott Ross Brisbane
School of Computer Science and Engineering Faculty of Engineering The University of New South Wales Decentralising Big Data Processing by Scott Ross Brisbane Thesis submitted as a requirement for the degree of Bachelor of Engineering (Software) Submitted: October 2016 Student ID: z3459393 Supervisor: Dr. Xiwei Xu Topic ID: 3692 Decentralising Big Data Processing Scott Ross Brisbane Abstract Big data processing and analysis is becoming an increasingly important part of modern society as corporations and government organisations seek to draw insight from the vast amount of data they are storing. The traditional approach to such data processing is to use the popular Hadoop framework which uses HDFS (Hadoop Distributed File System) to store and stream data to analytics applications written in the MapReduce model. As organisations seek to share data and results with third parties, HDFS remains inadequate for such tasks in many ways. This work looks at replacing HDFS with a decentralised data store that is better suited to sharing data between organisations. The best fit for such a replacement is chosen to be the decentralised hypermedia distribution protocol IPFS (Interplanetary File System), that is built around the aim of connecting all peers in it's network with the same set of content addressed files. ii Scott Ross Brisbane Decentralising Big Data Processing Abbreviations API Application Programming Interface AWS Amazon Web Services CLI Command Line Interface DHT Distributed Hash Table DNS Domain Name System EC2 Elastic Compute Cloud FTP File Transfer Protocol HDFS Hadoop Distributed File System HPC High-Performance Computing IPFS InterPlanetary File System IPNS InterPlanetary Naming System SFTP Secure File Transfer Protocol UI User Interface iii Decentralising Big Data Processing Scott Ross Brisbane Contents 1 Introduction 1 2 Background 3 2.1 The Hadoop Distributed File System . -
Collective Communication on Hadoop
Harp: Collective Communication on Hadoop Bingjing Zhang, Yang Ruan, Judy Qiu Computer Science Department Indiana University Bloomington, IN, USA Abstract—Big data tools have evolved rapidly in recent years. MapReduce is very successful but not optimized for many important analytics; especially those involving iteration. In this regard, Iterative MapReduce frameworks improve performance of MapReduce job chains through caching. Further Pregel, Giraph and GraphLab abstract data as a graph and process it in iterations. However, all these tools are designed with fixed data abstraction and have limitations in communication support. In this paper, we introduce a collective communication layer which provides optimized communication operations on several important data abstractions such as arrays, key-values and graphs, and define a Map-Collective model which serves the diverse communication demands in different parallel applications. In addition, we design our enhancements as plug-ins to Hadoop so they can be used with the rich Apache Big Data Stack. Then for example, Hadoop can do in-memory communication between Map tasks without writing intermediate data to HDFS. With improved expressiveness and excellent performance on collective communication, we can simultaneously support various applications from HPC to Cloud systems together with a high performance Apache Big Data Stack. Fig. 1. Big Data Analysis Tools Keywords—Collective Communication; Big Data Processing; Hadoop Spark [5] also uses caching to accelerate iterative algorithms without restricting computation to a chain of MapReduce jobs. I. INTRODUCTION To process graph data, Google announced Pregel [6] and soon It is estimated that organizations with high-end computing open source versions Giraph [7] and Hama [8] emerged. -
Maximizing Hadoop Performance and Storage Capacity with Altrahdtm
Maximizing Hadoop Performance and Storage Capacity with AltraHDTM Executive Summary The explosion of internet data, driven in large part by the growth of more and more powerful mobile devices, has created not only a large volume of data, but a variety of data types, with new data being generated at an increasingly rapid rate. Data characterized by the “three Vs” – Volume, Variety, and Velocity – is commonly referred to as big data, and has put an enormous strain on organizations to store and analyze their data. Organizations are increasingly turning to Apache Hadoop to tackle this challenge. Hadoop is a set of open source applications and utilities that can be used to reliably store and process big data. What makes Hadoop so attractive? Hadoop runs on commodity off-the-shelf (COTS) hardware, making it relatively inexpensive to construct a large cluster. Hadoop supports unstructured data, which includes a wide range of data types and can perform analytics on the unstructured data without requiring a specific schema to describe the data. Hadoop is highly scalable, allowing companies to easily expand their existing cluster by adding more nodes without requiring extensive software modifications. Apache Hadoop is an open source platform. Hadoop runs on a wide range of Linux distributions. The Hadoop cluster is composed of a set of nodes or servers that consist of the following key components. Hadoop Distributed File System (HDFS) HDFS is a Java based file system which layers over the existing file systems in the cluster. The implementation allows the file system to span over all of the distributed data nodes in the Hadoop cluster to provide a scalable and reliable storage system.