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Msgpack Documentation Release 1.0
msgpack Documentation Release 1.0 Author 2021-03-18 Contents 1 API reference 3 2 Advanced usage 9 Python Module Index 11 Index 13 i ii msgpack Documentation, Release 1.0 MessagePack is a efficient format for inter language data exchange. Contents 1 msgpack Documentation, Release 1.0 2 Contents CHAPTER 1 API reference msgpack.pack(o, stream, **kwargs) Pack object o and write it to stream See Packer for options. dump() is alias for pack() msgpack.packb(o, **kwargs) Pack object o and return packed bytes See Packer for options. dumps() is alias for packb() msgpack.unpack(stream, **kwargs) Unpack an object from stream. Raises ExtraData when stream contains extra bytes. See Unpacker for options. load() is alias for unpack() msgpack.unpackb(packed, *, object_hook=None, list_hook=None, bool use_list=True, bool raw=False, int timestamp=0, bool strict_map_key=True, unicode_errors=None, object_pairs_hook=None, ext_hook=ExtType, Py_ssize_t max_str_len=-1, Py_ssize_t max_bin_len=-1, Py_ssize_t max_array_len=-1, Py_ssize_t max_map_len=-1, Py_ssize_t max_ext_len=-1) Unpack packed_bytes to object. Returns an unpacked object. Raises ExtraData when packed contains extra bytes. Raises ValueError when packed is incomplete. Raises FormatError when packed is not valid msgpack. Raises StackError when packed contains too nested. Other exceptions can be raised during unpacking. See Unpacker for options. max_xxx_len options are configured automatically from len(packed). loads() is alias for unpackb() 3 msgpack Documentation, Release 1.0 class msgpack.Packer(default=None, *, bool use_single_float=False, bool autoreset=True, bool use_bin_type=True, bool strict_types=False, bool datetime=False, uni- code_errors=None) MessagePack Packer Usage: packer= Packer() astream.write(packer.pack(a)) astream.write(packer.pack(b)) Packer’s constructor has some keyword arguments: Parameters • default (callable) – Convert user type to builtin type that Packer supports. -
Advanced JSON Handling in Go 19:40 05 Mar 2020 Jonathan Hall Devops Evangelist / Go Developer / Clean Coder / Salsa Dancer About Me
Advanced JSON handling in Go 19:40 05 Mar 2020 Jonathan Hall DevOps Evangelist / Go Developer / Clean Coder / Salsa Dancer About me Open Source contributor; CouchDB PMC, author of Kivik Core Tech Lead for Lana Former eCommerce Dev Manager at Bugaboo Former backend developer at Teamwork.com Former backend developer at Booking.com Former tech lead at eFolder/DoubleCheck 2 Show of hands Who has... ...used JSON in a Go program? ...been frustrated by Go's strict typing when dealing with JSON? ...felt limited by Go's standard JSON handling? What have been your biggest frustrations? 3 Today's Topics Very brief intro to JSON in Go Basic use of maps and structs Handling inputs of unknown type Handling data with some unknown fields 4 A brief intro to JSON JavaScript Object Notation, defined by RFC 8259 Human-readable, textual representation of arbitrary data Limted types: null, Number, String, Boolean, Array, Object Broad applications: Config files, data interchange, simple messaging 5 Alternatives to JSON YAML, TOML, INI BSON, MessagePack, CBOR, Smile XML ProtoBuf Custom/proprietary formats Many principles discussed in this presentation apply to any of the above formats. 6 Marshaling JSON Creating JSON from a Go object is (usually) very straight forward: func main() { x := map[string]string{ "foo": "bar", } data, _ := json.Marshal(x) fmt.Println(string(data)) } Run 7 Marshaling JSON, #2 Creating JSON from a Go object is (usually) very straight forward: func main() { type person struct { Name string `json:"name"` Age int `json:"age"` Description string `json:"descr,omitempty"` secret string // Unexported fields are never (un)marshaled } x := person{ Name: "Bob", Age: 32, secret: "Shhh!", } data, _ := json.Marshal(x) fmt.Println(string(data)) } Run 8 Unmarshaling JSON Unmarshaling JSON is often a bit trickier. -
Performance Comparison of Data Serialization Schemes for ETSI ITS Car-To-X Communication Systems
International Journal on Advances in Telecommunications, vol 8 no 1 & 2, year 2015, http://www.iariajournals.org/telecommunications/ 48 Performance Comparison of Data Serialization Schemes for ETSI ITS Car-to-X Communication Systems Sebastian Bittl, Arturo A. Gonzalez, Michael Spahn,¨ and Wolf A. Heidrich Fraunhofer ESK Munich, Germany Email: fsebastian.bittl, arturo.gonzalez, michael.spaehn, [email protected] Abstract—Wireless Car-to-X communication is about to enter sense collision avoidance mechanism (CSMA-CA) for multi- the mass market in upcoming years. The non-licensed, but user channel access, longer packets imply longer transmission reserved bandwidth for such communications is relatively narrow times and hence a higher channel busy ratio. This means that in terms of the few usable channels and the expected number nodes near a transmitter, which are willing to transmit as well, of communicating entities. Among other communication aspects, must wait longer until the channel is free to use. Moreover, data serialization schemes that allow compact encoding as well as the wireless channel has a limited capacity allowing only a fast encoding and decoding are required. Compact representation of data helps in keeping a minimal bandwidth overhead in limited number of transmitted bits per unit of time. The lower the wireless channel. Moreover, since delay is an important the number of transmitted messages, the higher the number of performance parameter in Car-to-X communication, the pro- users that could use the given frequency spectrum in parallel. cessing time of the serialization/deserialization schemes shall be kept to a minimum. So far, no detailed analysis of different The data serialization schemes also influence processing data serialization schemes for Car-to-X communication regarding power requirements in both ETSI Intelligent Transport Sys- important properties such as runtime, memory consumption tems (ITS) in Europe [2] and Wireless Access in Vehicular and encoded output length has been published. -
A Lightweight and Efficient Document Representation
docrep: A lightweight and efficient document representation framework Tim Dawborn and James R. Curran -lab,e School of Information Technologies University of Sydney NSW 2006, Australia tim.dawborn,james.r.curran @sydney.edu.au { } Abstract Modelling linguistic phenomena requires highly structured and complex data representations. Document representation frameworks (DRFs) provide an interface to store and retrieve multiple annotation layers over a document. Researchers face a difficult choice: using a heavy-weight DRF or implement a custom DRF. The cost is substantial, either learning a new complex system, or continually adding features to a home-grown system that risks overrunning its original scope. We introduce DOCREP, a lightweight and efficient DRF, and compare it against existing DRFs. We discuss our design goals and implementations in C++, Python, and Java. We transform the OntoNotes 5 corpus using DOCREP and UIMA, providing a quantitative comparison, as well as discussing modelling trade-offs. We conclude with qualitative feedback from researchers who have used DOCREP for their own projects. Ultimately, we hope DOCREP is useful for the busy researcher who wants the benefits of a DRF, but has better things to do than to write one. 1 Introduction Computational Linguistics (CL) is increasingly a data-driven research discipline with researchers us- ing diverse collections of large-scale corpora (Parker et al., 2011). Representing linguistic phenomena can require modelling intricate data structures, both flat and hierarchical, layered over the original text; e.g. tokens, sentences, parts-of-speech, named entities, coreference relations, and trees. The scale and complexity of the data demands efficient representations. A document representation framework (DRF) should support the creation, storage, and retrieval of different annotation layers over collections of hetero- geneous documents. -
Package 'Rcppmsgpack'
Package ‘RcppMsgPack’ November 18, 2018 Type Package Title 'MsgPack' C++ Header Files and Interface Functions for R Version 0.2.3 Date 2018-11-18 Author Travers Ching and Dirk Eddelbuettel; the authors and contributors of MsgPack Maintainer Dirk Eddelbuettel <[email protected]> Description 'MsgPack' header files are provided for use by R packages, along with the ability to access, create and alter 'MsgPack' objects directly from R. 'MsgPack' is an efficient binary serialization format. It lets you exchange data among multiple languages like 'JSON' but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the 'msgpack-c' implementation for C and C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of copyright holders and contributors to 'msgpack-c'. Copyright file inst/COPYRIGHTS License GPL (>= 2) Imports Rcpp LinkingTo Rcpp, BH BugReports https://github.com/eddelbuettel/rcppmsgpack/issues RoxygenNote 6.0.1 Suggests knitr, rmarkdown, microbenchmark VignetteBuilder knitr NeedsCompilation yes Repository CRAN Date/Publication 2018-11-18 21:10:03 UTC 1 2 RcppMsgPack-package R topics documented: RcppMsgPack-package . .2 arrayEx . .3 enumEx . .4 msgpack_format . .4 msgpack_map . .5 msgpack_pack . .6 msgpack_read . .6 msgpack_simplify . .7 msgpack_timestamp_decode . .8 msgpack_timestamp_encode . .8 msgpack_unpack . .9 msgpack_write . 10 Index 11 RcppMsgPack-package ’MsgPack’ C++ Header Files and Interface Functions for R Description ’MsgPack’ header files are provided for use by R packages, along with the ability to access, create and alter ’MsgPack’ objects directly from R. -
Enabling Digital Twins – a Comparative Study on Messaging Protocols and Serialization Formats for Digital Twins in Iov
Linköping University | Department of Computer and Information Science Master’s thesis, 30 ECTS | Datateknik 2019 | LIU-IDA/LITH-EX-A--19/066--SE Enabling Digital Twins – A comparative study on messaging protocols and serialization formats for Digital Twins in IoV Att möjliggöra digitala tvillingar Daniel Persson Proos Supervisor : Patrick Lambrix Examiner : Niklas Carlsson Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet - eller dess framtida ersättare - under 25 år från publicer- ingsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka ko- pior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervis- ning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säker- heten och tillgängligheten finns lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsman- nens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a period of 25 years starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to down- load, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose. -
Parsing Gigabytes of JSON Per Second
Parsing Gigabytes of JSON per Second Geoff Langdale · Daniel Lemire Abstract JavaScript Object Notation or JSON is a The JSON syntax can be viewed as a restricted form ubiquitous data exchange format on the Web. Ingesting of JavaScript, but it is used in many programming lan- JSON documents can become a performance bottleneck guages. JSON has four primitive types or atoms (string, due to the sheer volume of data. We are thus motivated number, Boolean, null) that can be embedded within to make JSON parsing as fast as possible. composed types (arrays and objects). An object takes Despite the maturity of the problem of JSON pars- the form of a series of key-value pairs between braces, ing, we show that substantial speedups are possible. where keys are strings (e.g., f"name":"Jack","age":22g). We present the first standard-compliant JSON parser An array is a list of comma-separated values between to process gigabytes of data per second on a single brackets (e.g., [1,"abc",null]). Composed types can core, using commodity processors. We can use a quar- contain primitive types or arbitrarily deeply nested com- ter or fewer instructions than a state-of-the-art refer- posed types as values. See Fig. 1 for an example. The ence parser like RapidJSON. Unlike other validating JSON specification defines six structural characters (`[', parsers, our software (simdjson) makes extensive use of `f', `]', `g', `:', `,'): they serve to delimit the locations Single Instruction, Multiple Data (SIMD) instructions. and structure of objects and arrays. To ensure reproducibility, simdjson is freely available as To access the data contained in a JSON document open-source software under a liberal license. -
JSON at Work.Pdf
JSON at Work PRACTICAL DATA INTEGRATION FOR THE WEB Tom Marrs JSON at Work Practical Data Integration for the Web Tom Marrs Beijing Boston Farnham Sebastopol Tokyo JSON at Work by Tom Marrs Copyright © 2017 Vertical Slice, Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/insti‐ tutional sales department: 800-998-9938 or [email protected]. Editor: Meg Foley Indexer: Ellen Troutman-Zaig Production Editor: Nicholas Adams Interior Designer: David Futato Copyeditor: Sharon Wilkey Cover Designer: Randy Comer Proofreader: Charles Roumeliotis Illustrator: Rebecca Demarest July 2017: First Edition Revision History for the First Edition 2017-06-16: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781449358327 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. JSON at Work, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. -
Keeping Properties with the Data CL-Metaheaders
Published in: Bański, Piotr/Kupietz, Marc/Lüngen, Harald/Rayson, Paul/Biber, Hanno/Breiteneder, Evelyn/Clematide, Simon/Mariani, John/stevenson, Mark/Sick, Theresa (eds.): Proceedings of the Workshop on Challenges in the Management of Large Corpora and Big Data and Natural Language Processing (CMLC-5+BigNLP) 2017 including the papers from the Web-as-Corpus (WAC-XI) guest section. Birmingham, 24 July 2017. - Mannheim: Institut für Deutsche Sprache, 2017. Pp. 35-41 Keeping Properties with the Data CL-MetaHeaders - An Open Specification John Vidler Stephen Wattam [email protected] [email protected] School of Computing and Communications Lancaster University Abstract On the one hand, as a discipline, difficulties arising from the existence of a broad tool suite are Corpus researchers, along with many an excellent problem to have; a multitude of other disciplines in science are being put solutions are present to handle any number of under continual pressure to show problems we may face. However, on the other accountability and reproducibility in their hand, having no standard interoperability level for work. This is unsurprisingly difficult these tools prevents some interactions across this when the researcher is faced with a wide space without a great deal of effort from the user. array of methods and tools through which Being aware of the file type is seldom enough to to do their work; simply tracking the correctly set up reader/writer operations in a tool, operations done can be problematic, especially if we consider character encoding especially when toolchains are often differences, and the overall engineering of the configured by the developers, but left tool has become very important in some cases. -