DOCSLIB.ORG

Can High-Performance Interconnects Benefit Hadoop Distributed File

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Can High-Performance Interconnects Benefit Hadoop Distributed File Can High-Performance Interconnects Benefit Hadoop Distributed File System? Sayantan Sur, Hao Wang, Jian Huang, Xiangyong Ouyang and Dhabaleswar K. Panda Department of Computer Science and Engineering, The Ohio State University fsurs, wangh, huangjia, ouyangx, [email protected] Abstract of data distribution and fault tolerance. Most importantly, the model aims to be efficient on commodity clusters During the past several years, the MapReduce comput- connected with Gigabit Ethernet. This model can handle ing model has emerged as a scalable model that is capable both structured and unstructured data. Since the time when of processing petabytes of data. The Hadoop MapReduce the MapReduce paper was published, Doug Cutting et. framework, has enabled large scale Internet applications al. started developing Hadoop, which is an Open-source and has been adopted by many organizations. The Hadoop implementation of the MapReduce computing model. It Distributed File System (HDFS) lies at the heart of the is available from the Apache Software Foundation [27]. ecosystem of software. It was designed to operate and The Hadoop MapReduce software relies on the Hadoop scale on commodity hardware such as cheap Linux ma- Distributed File System (HDFS) as the underlying basis chines connected with Gigabit Ethernet. The field of High- for providing data distribution and fault tolerance. Over performance Computing (HPC) has been witnessing a time, HDFS has also become the underlying file system transition to commodity clusters. Increasingly, networks for the Hadoop database (HBase), that is an Open-source such as InfiniBand and 10Gigabit Ethernet have become implementation of Google’s BigTable [6]. The goal of commoditized and available on motherboards and as low- HBase is to provide random, real time read/write access cost PCI-Express devices. Software that drives InfiniBand to large quantities of data, in the order of billions of rows and 10Gigabit Ethernet works on mainline Linux kernels. and millions of columns. These interconnects provide high bandwidth along with The Hadoop project has gained widespread acceptance low CPU utilization for network intensive operations. As and is very widely used in many organizations around the the amounts of data processed by Internet applications world. As data gathering technologies (such as sensors) reaches hundreds of petabytes, it is expected that the witness an explosion, it is expected that in the future, network performance will be a key component towards massive quantities of data in hundreds or thousands of scaling data-centers. In this paper, we examine the impact petabytes will need to be processed to gain insight into pat- of high-performance interconnects on HDFS. Our findings terns and trends. In order to process these large quantities reveal that the impact is substantial. We observe up to of data, many more thousands of servers may be required. 11%, 30% and 100% performance improvement for the While the Hadoop framework has no fundamental scaling sort, random write and sequential write benchmarks using limitations, recently there has been some discussion about magnetic disks (HDD). We also find that with the emerging its efficiency. In particular, data-centers of the future cannot trend of Solid State Drives, having a faster interconnect expand at the rate at which data storage and gathering makes a larger impact as local I/O costs are reduced. We capabilities are expanding. This is due to power limitations. observe up to 48%, 59% and 219% improvement for the Improving efficiency of the HDFS will have a significant same benchmarks when SSD is used in combination with impact on the design of future data-centers. advanced interconnection networks and protocols. During the past decade, the field of High-performance Computing has been witnessing a transition to commodity clusters connected with modern interconnects such as In- finiBand and 10Gigabit Ethernet. Increasingly, InfiniBand I. Introduction has become commoditized and available on motherboards and as low-cost PCI-Express devices. Software that drives The MapReduce computing model has recently InfiniBand and 10Gigabit Ethernet also works on mainline emerged as a viable model for processing petabytes of Linux kernels. These interconnects provide not only high data. This model for processing data in large Internet bandwidth (up to 32Gbps), and low latency (1µs-2µs), but warehouses, was first proposed by Google Inc [7]. The also help server scalability by using very little CPU and MapReduce model enables developers to write highly reduced memory copies for network intensive operations. parallel codes without dealing with many intricate details The popularity of InfiniBand as such, can be measured by the fact that 42.6% of the compute clusters in the Top500 1) InfiniBand Architecture: The InfiniBand specifica- list [29] of most powerful supercomputers use InfiniBand. tion clearly demarcates the duties of hardware (such as These clusters are very high on the efficiency metric, Host Channel Adapters (HCAs)) and software. The inter- i.e. performance achieved compared to peak performance. action between software and HCAs is carried out by the Typical efficiencies of InfiniBand clusters range from 85%- verbs layer, which is described in the following section. 95%. The InfiniBand fabric can consist of multi-thousand nodes As the amount of data processed by Internet applica- with multiple adapters. Typically, InfiniBand networks are tions reaches hundreds of petabytes, it is expected that the deployed using the fat-tree topology, which provides con- network performance will be a key component towards stant bisection bandwidth. However, recently, some large scaling data-centers. In this paper, we examine the impact deployments have also adopted 3-D torus and hypercube of high-performance interconnects on HDFS. Our findings topologies. InfiniBand provides flexible static routing. The reveal that the impact is substantial. We observe up to routing tables at switches can be configured using the 11%, 30% and 100% performance improvement for the Subnet Manager. For more details on InfiniBand please sort, random write and sequential write benchmarks using refer to specification documents available from [11]. magnetic disks (HDD). We also find that with the emerging 2) InfiniBand Verbs Layer: Upper-level software uses trend of Solid State Drives, having a faster interconnect an interface called verbs to access the functionality pro- makes a larger impact as local I/O costs are reduced. We vided by HCAs and other network equipment (such as observe up to 48%, 59% and 219% improvement for the switches). This is illustrated in Figure 1(a) (to the ex- same benchmarks when SSD is used in combination with treme right). Verbs that are used to transfer data are advanced interconnection networks and protocols. completely OS-bypassed. The verbs interface is a low- The rest of the paper is organized as follows. In level communication interface that follows the Queue Pair Section II, we provide an overview of the topics dealt (or communication end-points) model. Queue pairs are with in this paper. In Section III, we show some of the required to establish a queue pair between themselves. benefits of modern interconnects. Experimental results and Each queue pair has a certain number of work queue discussions are presented in Section IV. We discuss related elements. Upper-level software places a work request on work in Section V. We conclude the paper in Section VI. the queue pair that is then processed by the HCA. When a work element is completed, it is placed in the completion II. Background queue. Upper level software can detect completion by polling the completion queue. In this Section, we provide a “bottom-up” overview of Additionally, there are different types of Queue Pairs networking and software components in a data-center that based on the type of transport used. There are Reliably is interconnected using High-performance networks. Connected (RC) queue pairs that provide reliable trans- mission (retransmissions after packet losses are performed A. InfiniBand Overview by the HCA). These RC queue pairs need to be established uniquely for each communicating pair. This implies an 2 InfiniBand [2] is an industry standard switched fabric O(n ) memory usage (for a system with N processes). that is designed for interconnecting nodes in HEC clusters. Another type of queue pair is the Unreliable Datagram It is a high-speed, general purpose I/O interconnect that is (UD). This queue pair type does not provide reliable trans- widely used by scientific computing centers world-wide. mission although it has a significant memory advantage – The recently released TOP500 rankings in November 2010 only one UD QP is capable of communicating with all reveal that more than 42% of the computing systems remote processes. Thus, the memory usage of UD QP is use InfiniBand as their primary interconnect. The yearly O(n) (for a system with N processes). growth rate of InfiniBand in the TOP500 systems is pegged 3) InfiniBand IP Layer: InfiniBand also provides a at 30%, indicating a strong momentum in adoption. One of driver for implementing the IP layer. This exposes the In- the main features of InfiniBand is Remote Direct Memory finiBand device as just another network interface available Access (RDMA). This feature allows software to remotely from the system with an IP address. Typically, Ethernet read memory contents of another remote process without interfaces are presented as eth0, eth1 etc. Similarly, any software involvement at the remote side. This feature IB devices are presented as ib0, ib1 and so on. This is very powerful and can be used to implement high- interface is presented in Figure 1(a) (second from the left, performance communication protocols. named IPoIB). It does not provide OS-bypass. This layer InfiniBand has started making inroads into the commer- is often called “IP-over-IB” or IPoIB in short. We will use cial domain with the recent convergence around RDMA this terminology in the paper.
Load more

Recommended publications
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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 .
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Overview of Mapreduce and Spark
    Overview of MapReduce and Spark Mirek Riedewald This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Key Learning Goals • How many tasks should be created for a job running on a cluster with w worker machines? • What is the main difference between Hadoop MapReduce and Spark? • For a given problem, how many Map tasks, Map function calls, Reduce tasks, and Reduce function calls does MapReduce create? • How can we tell if a MapReduce program aggregates data from different Map calls before transmitting it to the Reducers? • How can we tell if an aggregation function in Spark aggregates locally on an RDD partition before transmitting it to the next downstream operation? 2 Key Learning Goals • Why do input and output type have to be the same for a Combiner? • What data does a single Mapper receive when a file is the input to a MapReduce job? And what data does the Mapper receive when the file is added to the distributed file cache? • Does Spark use the equivalent of a shared- memory programming model? 3 Introduction • MapReduce was proposed by Google in a research paper. Hadoop MapReduce implements it as an open- source system. – Jeffrey Dean and Sanjay Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. OSDI'04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December 2004 • Spark originated in academia—at UC Berkeley—and was proposed as an improvement of MapReduce. – Matei Zaharia, Mosharaf Chowdhury, Michael J.
    [Show full text]
  • Design and Evolution of the Apache Hadoop File System(HDFS)
    Design and Evolution of the Apache Hadoop File System(HDFS) Dhruba Borthakur Engineer@Facebook Committer@Apache HDFS SDC, Sept 19 2011 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Outline Introduction Yet another file-system, why? Goals of Hadoop Distributed File System (HDFS) Architecture Overview Rational for Design Decisions 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Who Am I? Apache Hadoop FileSystem (HDFS) Committer and PMC Member Core contributor since Hadoop’s infancy Facebook (Hadoop, Hive, Scribe) Yahoo! (Hadoop in Yahoo Search) Veritas (San Point Direct, Veritas File System) IBM Transarc (Andrew File System) Univ of Wisconsin Computer Science Alumni (Condor Project) 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Hadoop, Why? Need to process Multi Petabyte Datasets Data may not have strict schema Expensive to build reliability in each application. Failure is expected, rather than exceptional. Elasticity, # of nodes in a cluster is never constant. Need common infrastructure Efficient, reliable, Open Source Apache License 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Goals of HDFS Very Large Distributed File System 10K nodes, 1 billion files, 100 PB Assumes Commodity Hardware Files are replicated to handle hardware failure Detect failures and recovers from them Optimized for Batch Processing Data locations exposed so that computations can move to where data resides Provides very high aggregate bandwidth User Space, runs on heterogeneous OS 2011 Storage Developer Conference. © Insert Your Company Name. All Rights Reserved. Commodity Hardware Typically in 2 level architecture – Nodes are commodity PCs – 20-40 nodes/rack – Uplink from rack is 4 gigabit – Rack-internal is 1 gigabit 2011 Storage Developer Conference.
    [Show full text]
  • Experiences with Lustre* and Hadoop*
    Experiences with Lustre* and Hadoop* Gabriele Paciucci (Intel) June, 2014 Intel* Some Condential name — Do Not Forwardand brands may be claimed as the property of others. Agenda • Overview • Intel Enterprise Edition for Lustre 2.0 • How to congure Cloudera Hadoop with Lustre using HAL • Benchmarks results • Conclusion Intel Enterprise Edition for Lustre v 2.0 Intel® Manager for Lustre CLI Hierarchical Storage Management REST API Tiered storage Management and Monitoring Services Hadoop Connectors Lustre File System Storage Plug-In Full distribution of open source Lustre software v2.5 Array Integration Global Technical Support from Intel open source Intel value-add Hadoop Connectors for Lustre (HAL and HAM) Intel® Manager for Lustre CLI Hierarchical Storage Management REST API Tiered storage Management and Monitoring Services Hadoop Connectors Lustre File System Storage Plug-In Full distribution of open source Lustre software v2.5 Array Integration Global Technical Support from Intel open source Intel value-add Hadoop Adapter for Lustre (HAL) org.apache.hadoop.fs • Replace HDFS with Lustre • Plugin for Apache Hadoop 2.3 and CDH 5.0.1 FileSystem • No changes to Lustre needed • Enable HPC environments to use MapReduce v2 with existing data in place RawLocalFileSystem • Allow Hadoop environments to migrate to a general purpose le system LustreFileSystem Hpc Adapter for Mapreduce (HAM) org.apache.hadoop.fs • Replace YARN with Slurm • Plugin for Apache Hadoop 2.3 and CDH 5.0.1 FileSystem • No changes to Lustre needed • Enable HPC environments
    [Show full text]
  • Apache Hadoop Goes Realtime at Facebook
    Apache Hadoop Goes Realtime at Facebook Dhruba Borthakur Joydeep Sen Sarma Jonathan Gray Kannan Muthukkaruppan Nicolas Spiegelberg Hairong Kuang Karthik Ranganathan Dmytro Molkov Aravind Menon Samuel Rash Rodrigo Schmidt Amitanand Aiyer Facebook {dhruba,jssarma,jgray,kannan, nicolas,hairong,kranganathan,dms, aravind.menon,rash,rodrigo, amitanand.s}@fb.com ABSTRACT 1. INTRODUCTION Facebook recently deployed Facebook Messages, its first ever Apache Hadoop [1] is a top-level Apache project that includes user-facing application built on the Apache Hadoop platform. open source implementations of a distributed file system [2] and Apache HBase is a database-like layer built on Hadoop designed MapReduce that were inspired by Googles GFS [5] and to support billions of messages per day. This paper describes the MapReduce [6] projects. The Hadoop ecosystem also includes reasons why Facebook chose Hadoop and HBase over other projects like Apache HBase [4] which is inspired by Googles systems such as Apache Cassandra and Voldemort and discusses BigTable, Apache Hive [3], a data warehouse built on top of the applications requirements for consistency, availability, Hadoop, and Apache ZooKeeper [8], a coordination service for partition tolerance, data model and scalability. We explore the distributed systems. enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the At Facebook, Hadoop has traditionally been used in conjunction system, and how this solution has significant advantages over the with Hive for storage and analysis of large data sets. Most of this sharded MySQL database scheme used in other applications at analysis occurs in offline batch jobs and the emphasis has been on Facebook and many other web-scale companies.
    [Show full text]
  • Hortonworks Data Platform Apache Ambari Troubleshooting (August 31, 2017)
    Hortonworks Data Platform Apache Ambari Troubleshooting (August 31, 2017) docs.cloudera.com Hortonworks Data Platform August 31, 2017 Hortonworks Data Platform: Apache Ambari Troubleshooting Copyright © 2012-2017 Hortonworks, Inc. All rights reserved. The Hortonworks Data Platform, powered by Apache Hadoop, is a massively scalable and 100% open source platform for storing, processing and analyzing large volumes of data. It is designed to deal with data from many sources and formats in a very quick, easy and cost-effective manner. The Hortonworks Data Platform consists of the essential set of Apache Hadoop projects including MapReduce, Hadoop Distributed File System (HDFS), HCatalog, Pig, Hive, HBase, ZooKeeper and Ambari. Hortonworks is the major contributor of code and patches to many of these projects. These projects have been integrated and tested as part of the Hortonworks Data Platform release process and installation and configuration tools have also been included. Unlike other providers of platforms built using Apache Hadoop, Hortonworks contributes 100% of our code back to the Apache Software Foundation. The Hortonworks Data Platform is Apache-licensed and completely open source. We sell only expert technical support, training and partner-enablement services. All of our technology is, and will remain free and open source. Please visit the Hortonworks Data Platform page for more information on Hortonworks technology. For more information on Hortonworks services, please visit either the Support or Training page. Feel free to Contact Us directly to discuss your specific needs. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
    [Show full text]