DOCSLIB.ORG

Hyracks: a Flexible and Extensible Foundation for Data-Intensive Computing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Hyracks: a Flexible and Extensible Foundation for Data-Intensive Computing Hyracks: A Flexible and Extensible Foundation for Data-Intensive Computing Vinayak Borkar, Michael Carey, Raman Grover, Nicola Onose, Rares Vernica Computer Science Department, University of California, Irvine Irvine, CA 92697 [email protected] Abstract—Hyracks is a new partitioned-parallel software plat- Statistics reported by companies such as Yahoo! and Face- form designed to run data-intensive computations on large book regarding the sources of jobs on their clusters indicate shared-nothing clusters of computers. Hyracks allows users to that these declarative languages are gaining popularity as express a computation as a DAG of data operators and connec- tors. Operators operate on partitions of input data and produce the interface of choice for large scale data processing. For partitions of output data, while connectors repartition operators’ example, Yahoo! recently reported that over 60 percent of outputs to make the newly produced partitions available at their production Hadoop jobs originate from Pig programs the consuming operators. We describe the Hyracks end user today, while Facebook reports that a remarkable 95 percent of model, for authors of dataflow jobs, and the extension model their production Hadoop jobs are now written in Hive rather for users who wish to augment Hyracks’ built-in library with new operator and/or connector types. We also describe our than in the lower-level MapReduce model. In light of the initial Hyracks implementation. Since Hyracks is in roughly the rapid movement to these higher-level languages, an obvious same space as the open source Hadoop platform, we compare question emerges for the data-intensive computing community: Hyracks with Hadoop experimentally for several different kinds If we had set out from the start to build a parallel platform of use cases. The initial results demonstrate that Hyracks has to serve as a target for compiling higher-level declarative significant promise as a next-generation platform for data- intensive applications. data-processing languages, what should that platform have looked like? It is our belief that the MapReduce model adds I. INTRODUCTION significant accidental complexity1 as well as inefficiencies to In recent years, the world has seen an explosion in the the translation from higher-level languages. amount of data owing to the growth of the Internet. In the In this paper, we present the design and implementation same time frame, the declining cost of hardware has made it of Hyracks, which is our response to the aforementioned possible for companies (even modest-sized companies) to set question. Hyracks is a flexible, extensible, partitioned-parallel up sizeable clusters of independent computers to store and pro- framework designed to support efficient data-intensive com- cess this growing sea of data. Based on their experiences with puting on clusters of commodity computers. Key contributions web-scale data processing, Google proposed MapReduce [1], of the work reported here include: a programming model and an implementation that provides a 1) The provision of a new platform that draws on time-tested simple interface for programmers to parallelize common data- contributions in parallel databases regarding efficient par- intensive tasks. Shortly thereafter, Hadoop [2], an open-source allel query processing, such as the use of operators with implementation of MapReduce, was developed and began to multiple inputs or the employment of pipelining as a gain followers. Similarly, Microsoft soon developed Dryad means to move data between operators. Hyracks includes [3] as a generalized execution engine to support their coarse- a built-in collection of operators that can be used to grained data-parallel applications. assemble data processing jobs without needing to write It has since been noted that, while MapReduce and Dryad processing logic akin to Map and Reduce code. are powerful programming models capable of expressing ar- 2) The provision of a rich API that enables Hyracks oper- bitrary data-intensive computations, it requires fairly sophis- ator implementors to describe operators’ behavioral and ticated skills to translate end-user problems into jobs with resource usage characteristics to the framework in order Map and Reduce primitives for MapReduce or into networks to enable better planning and runtime scheduling for jobs of channels and vertices for Dryad. As a result, higher-level that utilize their operators. declarative languages such as Sawzall [4] from Google, Pig 3) The inclusion of a Hadoop compatibility layer that en- [5] from Yahoo!, Jaql [6] from IBM, Hive [7] from Facebook, ables users to run existing Hadoop MapReduce jobs and DryadLINQ [8] and Scope [9] from Microsoft have been unchanged on Hyracks as an initial ”get acquainted” developed to make data-intensive computing accessible to strategy as well as a migration strategy for ”legacy” data- more programmers. The implementations of these languages intensive applications. translate declarative programs on partitioned data into DAGs of MapReduce jobs or into DAGs of Dryad vertices and 1Accidental complexity is complexity that arises in computer systems which channels. is non-essential to the problem being solved [10]. 4) Performance experiments comparing Hadoop against the (internal sub-steps or phases). At runtime, each activity of an Hyracks Hadoop compatibility layer and against the na- operator is realized as a set of (identical) tasks that are clones tive Hyracks model for several different types of jobs, of the activity and that operate on individual partitions of the thereby exploring the benefits of Hyracks over Hadoop data flowing through the activity. owing to implementation choices and the benefits of Let us examine a simple example based on a computation relaxing the MapReduce model as a means of job speci- over files containing CUSTOMER and ORDERS data drawn fication. from the TPC-H [11] dataset. In particular, let us examine a 5) An initial method for scheduling Hyracks tasks on a Hyracks job to compute the total number of orders placed by cluster that includes basic fault recovery (to guarantee customers in various market segments. The formats of the two job completion through restarts), and a brief performance input files for this Hyracks job are of the form: study of one class of job on Hyracks and Hadoop under CUSTOMER (C CUSTKEY, C MKTSEGMENT, . ) various failure rates to demonstrate the potential gains ORDERS (O ORDERKEY, O CUSTKEY, . ) offered by a less pessimistic approach to fault handling. where the dots stand for remaining attributes that are not 6) The provision of Hyracks as an available open source directly relevant to our computation. platform that can be utilized by others in the data- To be more precise about the intended computation, the intensive computing community as well. goal for the example job is to compute the equivalent of the The remainder of this paper is organized as follows. Section following SQL query: II provides a quick overview of Hyracks using a simple select C_MKTSEGMENT, count(O_ORDERKEY) example query. Section III provides a more detailed look from CUSTOMER join ORDERS on C_CUSTKEY = O_CUSTKEY group by C_MKTSEGMENT at the Hyracks programming model as seen by different classes of users, including end users, compilers for higher- {NC1: cust1.dat} level languages, and implementors of new operators for the {NC2: cust2.dat} Scanner E1[hash(C_CUSTKEY)] Hyracks platform. Section IV discusses the implementation of (CUSTOMER) HashJoin HashGroupby Hyracks, including its approaches to job control, scheduling, E4[1:1] C_CUSTKEY C_MKTSEGMENT Writer fault handling, and efficient data handling. Section V presents = O_CUSTKEY E3 Agg: count(O_ORDKEY) Scanner [hash a set of performance results comparing the initial implemen- (ORDERS) E2[hash(O_CUSTKEY)] (C_MKTSEGMENT)] tation of Hyracks to Hadoop for several disparate types of {NC3: ord1.dat, NC2: ord1.dat} jobs under both fault-free operation and in the presence of {NC1: ord2.dat, NC5: ord2.dat} failures. Section VI reviews key features of Hyracks and their Fig. 1: Example Hyracks job specification relationship to work in parallel databases and data-intensive Scanner computing. Finally, Section VII summarizes the paper and E1 discusses our plans for future work. (CUSTOMER) HashJoin HashGroupby E3 Hash Output E4 JoinBuild JoinProbe Aggregate Generator Writer II. HYRACKS OVERVIEW E2 Hyracks is a partitioned-parallel dataflow execution plat- Scanner form that runs on shared-nothing clusters of computers. Large (ORDERS) collections of data items are stored as local partitions dis- Fig. 2: Example Hyracks Activity Node graph tributed across the nodes of the cluster. A Hyracks job (the unit of work in Hyracks), submitted by a client, processes one A simple Hyracks job specification to perform this compu- or more collections of data to produce one or more output tation can be constructed as shown in Figure 1. For each data collections (also in the form of partitions). Hyracks provides source (CUSTOMER and ORDERS), a file scanner operator a programming model and an accompanying infrastructure is used to read the source data. A hash-based join operator to efficiently divide computations on large data collections receives the resulting streams of data (one with CUSTOMER (spanning multiple machines) into computations that work on instances and another with ORDERS instances) and pro- each partition of the data separately. In this section we utilize duces a stream of CUSTOMER-ORDERS pairs that match a small example to describe the steps involved in Hyracks on the specified condition (C CUSTKEY = O CUSTKEY). job execution. We also provide an introductory architectural The result of the join is then aggregated using a hash-based overview of the Hyracks software platform. group operator on the value of the C MKTSEGMENT field. This group operator is provided with a COUNT aggregation A. Example function to compute the count of O ORDERKEY occurrences As will be explained more completely in section III, within a group. Finally, the output of the aggregation is written a Hyracks job is a dataflow DAG composed of operators out to a file using a file writer operator.
Load more

Recommended publications
  • Towards an Analytics Query Engine *
    Towards an Analytics Query Engine ∗ Nantia Makrynioti Vasilis Vassalos Athens University of Economics and Business Athens University of Economics and Business Athens, Greece Athens, Greece [email protected] [email protected] ABSTRACT with black box libraries, evaluating various algorithms for a This vision paper presents new challenges and opportuni- task and tuning their parameters, in order to produce an ef- ties in the area of distributed data analytics, at the core of fective model, is a time-consuming process. Things become which are data mining and machine learning. At first, we even more complicated when we want to leverage paralleliza- provide an overview of the current state of the art in the area tion on clusters of independent computers for processing big and then analyse two aspects of data analytics systems, se- data. Details concerning load balancing, scheduling or fault mantics and optimization. We argue that these aspects will tolerance can be quite overwhelming even for an experienced emerge as important issues for the data management com- software engineer. munity in the next years and propose promising research Research in the data management domain recently started directions for solving them. tackling the above issues by developing systems for large- scale analytics that aim at providing higher-level primitives for building data mining and machine learning algorithms, Keywords as well as hiding low-level details of distributed execution. Data analytics, Declarative machine learning, Distributed MapReduce [12] and Dryad [16] were the first frameworks processing that paved the way. However, these initial efforts suffered from low usability, as they offered expressive but at the same 1.
    [Show full text]
  • Evaluation of Xpath Queries on XML Streams with Networks of Early Nested Word Automata Tom Sebastian
    Evaluation of XPath Queries on XML Streams with Networks of Early Nested Word Automata Tom Sebastian To cite this version: Tom Sebastian. Evaluation of XPath Queries on XML Streams with Networks of Early Nested Word Automata. Databases [cs.DB]. Universite Lille 1, 2016. English. tel-01342511 HAL Id: tel-01342511 https://hal.inria.fr/tel-01342511 Submitted on 6 Jul 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Universit´eLille 1 – Sciences et Technologies Innovimax Sarl Institut National de Recherche en Informatique et en Automatique Centre de Recherche en Informatique, Signal et Automatique de Lille These` pr´esent´ee en premi`ere version en vue d’obtenir le grade de Docteur par l’Universit´ede Lille 1 en sp´ecialit´eInformatique par Tom Sebastian Evaluation of XPath Queries on XML Streams with Networks of Early Nested Word Automata Th`ese soutenue le 17/06/2016 devant le jury compos´ede : Carlo Zaniolo University of California Rapporteur Anca Muscholl Universit´eBordeaux Rapporteur Kim Nguyen Universit´eParis-Sud Examinateur Remi Gilleron Universit´eLille 3 Pr´esident du Jury Joachim Niehren INRIA Directeur Abstract The eXtended Markup Language (Xml) provides a format for representing data trees, which is standardized by the W3C and largely used today for exchanging information between all kinds of computer programs.
    [Show full text]
  • QUERYING JSON and XML Performance Evaluation of Querying Tools for Offline-Enabled Web Applications
    QUERYING JSON AND XML Performance evaluation of querying tools for offline-enabled web applications Master Degree Project in Informatics One year Level 30 ECTS Spring term 2012 Adrian Hellström Supervisor: Henrik Gustavsson Examiner: Birgitta Lindström Querying JSON and XML Submitted by Adrian Hellström to the University of Skövde as a final year project towards the degree of M.Sc. in the School of Humanities and Informatics. The project has been supervised by Henrik Gustavsson. 2012-06-03 I hereby certify that all material in this final year project which is not my own work has been identified and that no work is included for which a degree has already been conferred on me. Signature: ___________________________________________ Abstract This article explores the viability of third-party JSON tools as an alternative to XML when an application requires querying and filtering of data, as well as how the application deviates between browsers. We examine and describe the querying alternatives as well as the technologies we worked with and used in the application. The application is built using HTML 5 features such as local storage and canvas, and is benchmarked in Internet Explorer, Chrome and Firefox. The application built is an animated infographical display that uses querying functions in JSON and XML to filter values from a dataset and then display them through the HTML5 canvas technology. The results were in favor of JSON and suggested that using third-party tools did not impact performance compared to native XML functions. In addition, the usage of JSON enabled easier development and cross-browser compatibility. Further research is proposed to examine document-based data filtering as well as investigating why performance deviated between toolsets.
    [Show full text]
  • Programming a Parallel Future
    Programming a Parallel Future Joseph M. Hellerstein Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2008-144 http://www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-144.html November 7, 2008 Copyright 2008, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Programming a Parallel Future Joe Hellerstein UC Berkeley Computer Science Things change fast in computer science, but odds are good that they will change especially fast in the next few years. Much of this change centers on the shift toward parallel computing. In the short term, parallelism will thrive in settings with massive datasets and analytics. Longer term, the shift to parallelism will impact all software. In this note, I’ll outline some key changes that have recently taken place in the computer industry, show why existing software systems are ill‐equipped to handle this new reality, and point toward some bright spots on the horizon. Technology Trends: A Divergence in Moore’s Law Like many changes in computer science, the rapid drive toward parallel computing is a function of technology trends in hardware. Most technology watchers are familiar with Moore's Law, and the general notion that computing performance per dollar has grown at an exponential rate — doubling about every 18‐24 months — over the last 50 years.
    [Show full text]
  • Declarative Data Analytics: a Survey
    Declarative Data Analytics: a Survey NANTIA MAKRYNIOTI, Athens University of Economics and Business, Greece VASILIS VASSALOS, Athens University of Economics and Business, Greece The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Data analytics; Data mining; • Computing methodologies → Machine learning approaches; • Software and its engineering → Domain specific languages; Data flow languages; Additional Key Words and Phrases: Declarative Programming, Data Science, Machine Learning, Large-scale Analytics ACM Reference Format: Nantia Makrynioti and Vasilis Vassalos. 2019. Declarative Data Analytics: a Survey. 1, 1 (February 2019), 36 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn 1 INTRODUCTION With the rapid growth of world wide web (WWW) and the development of social networks, the available amount of data has exploded. This availability has encouraged many companies and organizations in recent years to collect and analyse data, in order to extract information and gain valuable knowledge. At the same time hardware cost has decreased, so storage and processing of big data is not prohibitively expensive even for smaller companies.
    [Show full text]
  • Big Data Analytic Approaches Classification
    Big Data Analytic Approaches Classification Yudith Cardinale1 and Sonia Guehis2,3 and Marta Rukoz2,3 1Dept. de Computacion,´ Universidad Simon´ Bol´ıvar, Venezuela 2Universite´ Paris Nanterre, 92001 Nanterre, France 3Universite´ Paris Dauphine, PSL Research University, CNRS, UMR[7243], LAMSADE, 75016 Paris, France Keywords: Big Data Analytic, Analytic Models for Big Data, Analytical Data Management Applications. Abstract: Analytical data management applications, affected by the explosion of the amount of generated data in the context of Big Data, are shifting away their analytical databases towards a vast landscape of architectural solutions combining storage techniques, programming models, languages, and tools. To support users in the hard task of deciding which Big Data solution is the most appropriate according to their specific requirements, we propose a generic architecture to classify analytical approaches. We also establish a classification of the existing query languages, based on the facilities provided to access the Big Data architectures. Moreover, to evaluate different solutions, we propose a set of criteria of comparison, such as OLAP support, scalability, and fault tolerance support. We classify different existing Big Data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. We illustrate how our proposed generic architecture can be used to decide which Big Data analytic approach is suitable in the context of several use cases. 1 INTRODUCTION nally, despite these increases in scale and complexity, users still expect to be able to query data at interac- The term Big Data has been coined for represent- tive speeds. In this context, several enterprises such ing the challenge to support a continuous increase as Internet companies and Social Network associa- on the computational power that produces an over- tions have proposed their own analytical approaches, whelming flow of data (Kune et al., 2016).
    [Show full text]
  • A Dataflow Language for Large Scale Processing of RDF Data
    SYRql: A Dataflow Language for Large Scale Processing of RDF Data Fadi Maali1, Padmashree Ravindra2, Kemafor Anyanwu2, and Stefan Decker1 1 Insight Centre for Data Analytics, National University of Ireland Galway {fadi.maali,stefan.decker}@insight-centre.org 2 Department of Computer Science, North Carolina State University, Raleigh, NC {pravind2,kogan}@ncsu.edu Abstract. The recent big data movement resulted in a surge of activity on layering declarative languages on top of distributed computation plat- forms. In the Semantic Web realm, this surge of analytics languages was not reflected despite the significant growth in the available RDF data. Consequently, when analysing large RDF datasets, users are left with two main options: using SPARQL or using an existing non-RDF-specific big data language, both with its own limitations. The pure declarative nature of SPARQL and the high cost of evaluation can be limiting in some scenarios. On the other hand, existing big data languages are de- signed mainly for tabular data and, therefore, applying them to RDF data results in verbose, unreadable, and sometimes inefficient scripts. In this paper, we introduce SYRql, a dataflow language designed to process RDF data at a large scale. SYRql blends concepts from both SPARQL and existing big data languages. We formally define a closed algebra that underlies SYRql and discuss its properties and some unique optimisation opportunities this algebra provides. Furthermore, we describe an imple- mentation that translates SYRql scripts into a series of MapReduce jobs and compare the performance to other big data processing languages. 1 Introduction Declarative query languages have been a corner stone of data management since the early days of relational databases.
    [Show full text]
  • Extracting Data from Nosql Databases a Step Towards Interactive Visual Analysis of Nosql Data Master of Science Thesis
    Extracting Data from NoSQL Databases A Step towards Interactive Visual Analysis of NoSQL Data Master of Science Thesis PETTER NÄSHOLM Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering Göteborg, Sweden, January 2012 The Author grants to Chalmers University of Technology and University of Gothenburg the non-exclusive right to publish the Work electronically and in a non-commercial purpose make it accessible on the Internet. The Author warrants that he/she is the author to the Work, and warrants that the Work does not contain text, pictures or other material that violates copyright law. The Author shall, when transferring the rights of the Work to a third party (for example a publisher or a company), acknowledge the third party about this agreement. If the Author has signed a copyright agreement with a third party regarding the Work, the Author warrants hereby that he/she has obtained any necessary permission from this third party to let Chalmers University of Technology and University of Gothenburg store the Work electronically and make it accessible on the Internet. Extracting Data from NoSQL Databases A Step towards Interactive Visual Analysis of NoSQL Data PETTER NÄSHOLM © PETTER NÄSHOLM, January 2012. Examiner: GRAHAM KEMP Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering SE-412 96 Göteborg Sweden Telephone + 46 (0)31-772 1000 Department of Computer Science and Engineering Göteborg, Sweden January 2012 Abstract Businesses and organizations today generate increasing volumes of data. Being able to analyze and visualize this data to find trends that can be used as input when making business decisions is an important factor for competitive advan- tage.
    [Show full text]
  • IBM Infosphere Biginsights Version 2.1: Installation Guide Chapter 1
    IBM InfoSphere BigInsights Version 2.1 Installation Guide GC19-4100-00 IBM InfoSphere BigInsights Version 2.1 Installation Guide GC19-4100-00 Note Before using this information and the product that it supports, read the information in “Notices and trademarks” on page 89. © Copyright IBM Corporation 2013. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Chapter 1. Introduction to InfoSphere Installing the InfoSphere BigInsights Tools for BigInsights .............1 Eclipse ................58 InfoSphere BigInsights features and architecture . 1 Configuring access to the default task controller . 61 Hadoop Distributed File System (HDFS) ....3 Installing and configuring a Linux Task Controller 61 IBM General Parallel File System ......3 Adaptive MapReduce ..........6 Chapter 5. Upgrading InfoSphere Hadoop MapReduce...........7 BigInsights software .........63 Additional Hadoop technologies.......7 Preparing to upgrade software ........64 Text Analytics .............9 Upgrading InfoSphere BigInsights .......65 IBMBigSQL.............10 Upgrading the InfoSphere BigInsights Tools for InfoSphere BigInsights Console .......11 Eclipse ................68 InfoSphere BigInsights Tools for Eclipse ....11 Migrating from HDFS to GPFS ........68 Integration with other IBM products .....12 Chapter 6. Removing InfoSphere Chapter 2. Planning to install BigInsights software .........71 InfoSphere BigInsights ........15 Removing InfoSphere BigInsights by using scripts 71 Reviewing
    [Show full text]
  • HIL: a High-Level Scripting Language for Entity Integration
    HIL: A High-Level Scripting Language for Entity Integration Mauricio Hernández Georgia Koutrika Rajasekar Krishnamurthy IBM Research – Almaden HP Labs IBM Research – Almaden [email protected] [email protected] [email protected] Lucian Popa Ryan Wisnesky IBM Research – Almaden Harvard University [email protected] [email protected] ABSTRACT domain [5, 7], where the important entities and their relationships We introduce HIL, a high-level scripting language for entity res- are extracted and integrated from the underlying documents. We olution and integration. HIL aims at providing the core logic for refer to the process of extracting data from documents, integrating complex data processing flows that aggregate facts from large col- the information, and then building domain-specific entities, as en- lections of structured or unstructured data into clean, unified enti- tity integration.Enablingsuchintegrationinpracticeisachallenge, ties. Such flows typically include many stages of processing that and there is a great need for tools and languages that are high-level start from the outcome of information extraction and continue with but still expressive enough to facilitate the end-to-end development entity resolution, mapping and fusion. A HIL program captures the and maintenance of complex integration flows. overall integration flow through a combination of SQL-like rules There are several techniques that are relevant, at various levels, that link, map, fuse and aggregate entities. A salient feature of for entity integration: information extraction [9], schema match- HIL is the use of logical indexes in its data model to facilitate the ing [20], schema mapping [11], entity resolution [10], data fu- modular construction and aggregation of complex entities.
    [Show full text]
  • Schemas and Types for JSON Data
    Schemas and Types for JSON Data: from Theory to Practice Mohamed-Amine Baazizi Dario Colazzo Sorbonne Université, LIP6 UMR 7606 Université Paris-Dauphine, PSL Research University France France [email protected] [email protected] Giorgio Ghelli Carlo Sartiani Dipartimento di Informatica DIMIE Università di Pisa Università della Basilicata Pisa, Italy Potenza, Italy [email protected] [email protected] ABSTRACT CCS CONCEPTS The last few years have seen the fast and ubiquitous diffusion • Information systems → Semi-structured data; • The- of JSON as one of the most widely used formats for publish- ory of computation → Type theory. ing and interchanging data, as it combines the flexibility of semistructured data models with well-known data struc- KEYWORDS tures like records and arrays. The user willing to effectively JSON, schemas, schema inference, parsing, schema libraries manage JSON data collections can rely on several schema languages, like JSON Schema, JSound, and Joi, as well as on ACM Reference Format: the type abstractions offered by modern programming and Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli, and Carlo scripting languages like Swift or TypeScript. Sartiani. 2019. Schemas and Types for JSON Data: from Theory The main aim of this tutorial is to provide the audience to Practice. In 2019 International Conference on Management of (both researchers and practitioners) with the basic notions Data (SIGMOD ’19), June 30-July 5, 2019, Amsterdam, Netherlands. for enjoying all the benefits that schema and types can offer ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3299869. while processing and manipulating JSON data. This tuto- 3314032 rial focuses on four main aspects of the relation between JSON and schemas: (1) we survey existing schema language proposals and discuss their prominent features; (2) we ana- 1 INTRODUCTION lyze tools that can infer schemas from data, or that exploit The last two decades have seen a dramatic change in the schema information for improving data parsing and manage- data processing landscape.
    [Show full text]
  • Technology Overview
    Big Data Technology Overview Term Description See Also Big Data - the 5 Vs Everyone Must Volume, velocity and variety. And some expand the definition further to include veracity 3 Vs Know and value as well. 5 Vs of Big Data From Wikipedia, “Agile software development is a group of software development methods based on iterative and incremental development, where requirements and solutions evolve through collaboration between self-organizing, cross-functional teams. Agile The Agile Manifesto It promotes adaptive planning, evolutionary development and delivery, a time-boxed iterative approach, and encourages rapid and flexible response to change. It is a conceptual framework that promotes foreseen tight iterations throughout the development cycle.” A data serialization system. From Wikepedia, Avro Apache Avro “It is a remote procedure call and serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.” BigInsights Enterprise Edition provides a spreadsheet-like data analysis tool to help Big Insights IBM Infosphere Biginsights organizations store, manage, and analyze big data. A scalable multi-master database with no single points of failure. Cassandra Apache Cassandra It provides scalability and high availability without compromising performance. Cloudera Inc. is an American-based software company that provides Apache Hadoop- Cloudera Cloudera based software, support and services, and training to business customers. Wikipedia - Data Science Data science The study of the generalizable extraction of knowledge from data IBM - Data Scientist Coursera Big Data Technology Overview Term Description See Also Distributed system developed at Google for interactively querying large datasets. Dremel Dremel It empowers business analysts and makes it easy for business users to access the data Google Research rather than having to rely on data engineers.
    [Show full text]