Building High Performance Microservices with Apache Thrift
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Deserialization Vulnerability by Abdelazim Mohammed(@Intx0x80)
Deserialization vulnerability By Abdelazim Mohammed(@intx0x80) Thanks to: Mazin Ahmed (@mazen160) Asim Jaweesh(@Jaw33sh) 1 | P a g e Table of Contents Serialization (marshaling): ............................................................................................................................ 4 Deserialization (unmarshaling): .................................................................................................................... 4 Programming language support serialization: ............................................................................................... 4 Risk for using serialization: .......................................................................................................................... 5 Serialization in Java ...................................................................................................................................... 6 Deserialization vulnerability in Java: ............................................................................................................ 6 Code flow work........................................................................................................................................... 11 Vulnerability Detection: .............................................................................................................................. 12 CVE: ........................................................................................................................................................... 17 Tools: ......................................................................................................................................................... -
Protolite: Highly Optimized Protocol Buffer Serializers
Package ‘protolite’ July 28, 2021 Type Package Title Highly Optimized Protocol Buffer Serializers Author Jeroen Ooms Maintainer Jeroen Ooms <[email protected]> Description Pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. Currently supports 'rexp.proto' for serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The 'RProtoBuf' package on the other hand uses the protobuf runtime library to provide a general- purpose toolkit for reading and writing arbitrary protocol-buffer data in R. Version 2.1.1 License MIT + file LICENSE URL https://github.com/jeroen/protolite BugReports https://github.com/jeroen/protolite/issues SystemRequirements libprotobuf and protobuf-compiler LinkingTo Rcpp Imports Rcpp (>= 0.12.12), jsonlite Suggests spelling, curl, testthat, RProtoBuf, sf RoxygenNote 6.1.99.9001 Encoding UTF-8 Language en-US NeedsCompilation yes Repository CRAN Date/Publication 2021-07-28 12:20:02 UTC R topics documented: geobuf . .2 mapbox . .2 serialize_pb . .3 1 2 mapbox Index 5 geobuf Geobuf Description The geobuf format is an optimized binary format for storing geojson data with protocol buffers. These functions are compatible with the geobuf2json and json2geobuf utilities from the geobuf npm package. Usage read_geobuf(x, as_data_frame = TRUE) geobuf2json(x, pretty = FALSE) json2geobuf(json, decimals = 6) Arguments x file path or raw vector with the serialized geobuf.proto message as_data_frame simplify geojson data into data frames pretty indent json, see jsonlite::toJSON json a text string with geojson data decimals how many decimals (digits behind the dot) to store for numbers mapbox Mapbox Vector Tiles Description Read Mapbox vector-tile (mvt) files and returns the list of layers. -
Tencentdb for Tcaplusdb Getting Started
TencentDB for TcaplusDB TencentDB for TcaplusDB Getting Started Product Documentation ©2013-2019 Tencent Cloud. All rights reserved. Page 1 of 32 TencentDB for TcaplusDB Copyright Notice ©2013-2019 Tencent Cloud. All rights reserved. Copyright in this document is exclusively owned by Tencent Cloud. You must not reproduce, modify, copy or distribute in any way, in whole or in part, the contents of this document without Tencent Cloud's the prior written consent. Trademark Notice All trademarks associated with Tencent Cloud and its services are owned by Tencent Cloud Computing (Beijing) Company Limited and its affiliated companies. Trademarks of third parties referred to in this document are owned by their respective proprietors. Service Statement This document is intended to provide users with general information about Tencent Cloud's products and services only and does not form part of Tencent Cloud's terms and conditions. Tencent Cloud's products or services are subject to change. Specific products and services and the standards applicable to them are exclusively provided for in Tencent Cloud's applicable terms and conditions. ©2013-2019 Tencent Cloud. All rights reserved. Page 2 of 32 TencentDB for TcaplusDB Contents Getting Started Basic Concepts Cluster Table Group Table Index Data Types Read/Write Capacity Mode Table Definition in ProtoBuf Table Definition in TDR Creating Cluster Creating Table Group Creating Table Getting Access Point Information Access TcaplusDB ©2013-2019 Tencent Cloud. All rights reserved. Page 3 of 32 TencentDB for TcaplusDB Getting Started Basic Concepts Cluster Last updated:2020-07-31 11:15:59 Cluster Overview A cluster is the basic TcaplusDB management unit, which provides independent TcaplusDB service for the business. -
Software License Agreement (EULA)
Third-party Computer Software AutoVu™ ALPR cameras • angular-animate (https://docs.angularjs.org/api/ngAnimate) licensed under the terms of the MIT License (https://github.com/angular/angular.js/blob/master/LICENSE). © 2010-2016 Google, Inc. http://angularjs.org • angular-base64 (https://github.com/ninjatronic/angular-base64) licensed under the terms of the MIT License (https://github.com/ninjatronic/angular-base64/blob/master/LICENSE). © 2010 Nick Galbreath © 2013 Pete Martin • angular-translate (https://github.com/angular-translate/angular-translate) licensed under the terms of the MIT License (https://github.com/angular-translate/angular-translate/blob/master/LICENSE). © 2014 [email protected] • angular-translate-handler-log (https://github.com/angular-translate/bower-angular-translate-handler-log) licensed under the terms of the MIT License (https://github.com/angular-translate/angular-translate/blob/master/LICENSE). © 2014 [email protected] • angular-translate-loader-static-files (https://github.com/angular-translate/bower-angular-translate-loader-static-files) licensed under the terms of the MIT License (https://github.com/angular-translate/angular-translate/blob/master/LICENSE). © 2014 [email protected] • Angular Google Maps (http://angular-ui.github.io/angular-google-maps/#!/) licensed under the terms of the MIT License (https://opensource.org/licenses/MIT). © 2013-2016 angular-google-maps • AngularJS (http://angularjs.org/) licensed under the terms of the MIT License (https://github.com/angular/angular.js/blob/master/LICENSE). © 2010-2016 Google, Inc. http://angularjs.org • AngularUI Bootstrap (http://angular-ui.github.io/bootstrap/) licensed under the terms of the MIT License (https://github.com/angular- ui/bootstrap/blob/master/LICENSE). -
Unravel Data Systems Version 4.5
UNRAVEL DATA SYSTEMS VERSION 4.5 Component name Component version name License names jQuery 1.8.2 MIT License Apache Tomcat 5.5.23 Apache License 2.0 Tachyon Project POM 0.8.2 Apache License 2.0 Apache Directory LDAP API Model 1.0.0-M20 Apache License 2.0 apache/incubator-heron 0.16.5.1 Apache License 2.0 Maven Plugin API 3.0.4 Apache License 2.0 ApacheDS Authentication Interceptor 2.0.0-M15 Apache License 2.0 Apache Directory LDAP API Extras ACI 1.0.0-M20 Apache License 2.0 Apache HttpComponents Core 4.3.3 Apache License 2.0 Spark Project Tags 2.0.0-preview Apache License 2.0 Curator Testing 3.3.0 Apache License 2.0 Apache HttpComponents Core 4.4.5 Apache License 2.0 Apache Commons Daemon 1.0.15 Apache License 2.0 classworlds 2.4 Apache License 2.0 abego TreeLayout Core 1.0.1 BSD 3-clause "New" or "Revised" License jackson-core 2.8.6 Apache License 2.0 Lucene Join 6.6.1 Apache License 2.0 Apache Commons CLI 1.3-cloudera-pre-r1439998 Apache License 2.0 hive-apache 0.5 Apache License 2.0 scala-parser-combinators 1.0.4 BSD 3-clause "New" or "Revised" License com.springsource.javax.xml.bind 2.1.7 Common Development and Distribution License 1.0 SnakeYAML 1.15 Apache License 2.0 JUnit 4.12 Common Public License 1.0 ApacheDS Protocol Kerberos 2.0.0-M12 Apache License 2.0 Apache Groovy 2.4.6 Apache License 2.0 JGraphT - Core 1.2.0 (GNU Lesser General Public License v2.1 or later AND Eclipse Public License 1.0) chill-java 0.5.0 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 OpenCensus 0.12.3 Apache License 2.0 ApacheDS Protocol -
Serializing C Intermediate Representations for Efficient And
Serializing C intermediate representations for efficient and portable parsing Jeffrey A. Meister1, Jeffrey S. Foster2,∗, and Michael Hicks2 1 Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA 2 Department of Computer Science, University of Maryland, College Park, MD 20742, USA SUMMARY C static analysis tools often use intermediate representations (IRs) that organize program data in a simple, well-structured manner. However, the C parsers that create IRs are slow, and because they are difficult to write, only a few implementations exist, limiting the languages in which a C static analysis can be written. To solve these problems, we investigate two language-independent, on-disk representations of C IRs: one using XML, and the other using an Internet standard binary encoding called XDR. We benchmark the parsing speeds of both options, finding the XML to be about a factor of two slower than parsing C and the XDR over six times faster. Furthermore, we show that the XML files are far too large at 19 times the size of C source code, while XDR is only 2.2 times the C size. We also demonstrate the portability of our XDR system by presenting a C source code querying tool in Ruby. Our solution and the insights we gained from building it will be useful to analysis authors and other clients of C IRs. We have made our software freely available for download at http://www.cs.umd.edu/projects/PL/scil/. key words: C, static analysis, intermediate representations, parsing, XML, XDR 1. Introduction There is significant interest in writing static analysis tools for C programs. -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Datacenter Tax Cuts: Improving WSC Efficiency Through Protocol Buffer Acceleration
Datacenter Tax Cuts: Improving WSC Efficiency Through Protocol Buffer Acceleration Dinesh Parimi William J. Zhao Jerry Zhao University of California, Berkeley University of California, Berkeley University of California, Berkeley [email protected] william [email protected] [email protected] Abstract—According to recent literature, at least 25% of cycles will serialize some set of objects stored in memory to a in a modern warehouse-scale computer are spent on common portable format defined by the protobuf compiler (protoc). “building blocks” [1]. Referred to by Kanev et al. as a “datacenter The serialized objects can be sent to some remote device, tax”, these tasks are prime candidates for hardware acceleration, as even modest improvements here can generate immense cost which will then deserialize the object into its own memory and power savings given the scale of such systems. to execute on. Thus protobuf is widely used to implement One of these tasks is protocol buffer serialization and parsing. remote procedure calls (RPC), since it facilitates the transport Protocol buffers are an open-source mechanism for representing of arguments across a network. Protobuf remains the most structured data developed by Google, and used extensively in popular serialization framework due to its maturity, backwards their datacenters for communication and remote procedure calls. While the source code for protobufs is highly optimized, certain compatibility, and extensibility. Specifically, the encoding tasks - such as the compression/decompression of integer fields scheme enables future changes to the protobuf schema to and the encoding of variable-length strings - bottleneck the remain backwards compatible. throughput of serializing or parsing a protobuf. -
The Programmer's Guide to Apache Thrift MEAP
MEAP Edition Manning Early Access Program The Programmer’s Guide to Apache Thrift Version 5 Copyright 2013 Manning Publications For more information on this and other Manning titles go to www.manning.com ©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders. http://www.manning-sandbox.com/forum.jspa?forumID=873 Licensed to Daniel Gavrila <[email protected]> Welcome Hello and welcome to the third MEAP update for The Programmer’s Guide to Apache Thrift. This update adds Chapter 7, Designing and Serializing User Defined Types. This latest chapter is the first of the application layer chapters in Part 2. Chapters 3, 4 and 5 cover transports, error handling and protocols respectively. These chapters describe the foundational elements of Apache Thrift. Chapter 6 describes Apache Thrift IDL in depth, introducing the tools which enable us to describe data types and services in IDL. Chapters 7 through 9 bring these concepts into action, covering the three key applications areas of Apache Thrift in turn: User Defined Types (UDTs), Services and Servers. Chapter 7 introduces Apache Thrift IDL UDTs and provides insight into the critical role played by interface evolution in quality type design. Using IDL to effectively describe cross language types greatly simplifies the transmission of common data structures over messaging systems and other generic communications interfaces. Chapter 7 demonstrates the process of serializing types for use with external interfaces, disk I/O and in combination with Apache Thrift transport layer compression. -
Getting Started with Apache Avro
Getting Started with Apache Avro By Reeshu Patel Getting Started with Apache Avro 1 Introduction Apache Avro Apache Avro is a remote procedure call and serialization framework developed with Apache's Hadoop project. This is uses JSON for defining data types and protocols, and tend to serializes data in a compact binary format. In other words, Apache Avro is a data serialization system. Its frist native use is in Apache Hadoop, where it's provide both a serialization format for persistent data, and a correct format for communication between Hadoop nodes, and from client programs to the apache Hadoop services. Avro is a data serialization system.It'sprovides: Rich data structures. A compact, fast, binary data format. A container file, to store persistent data. Remote procedure call . It's easily integration with dynamic languages. Code generation is not mendetory to read or write data files nor to use or implement Remote procedure call protocols. Code generation is as an optional optimization, only worth implementing for statically typewritten languages. Schemas of Apache Avro When Apache avro data is read, the schema use when writing it's always present. This permits every datum to be written in no per-value overheads, creating serialization both fast and small. It also facilitates used dynamic, scripting languages, and data, together with it's schema, is fully itself-describing. 2 Getting Started with Apache Avro When Apache avro data is storein a file, it's schema is store with it, so that files may be processe later by any program. If the program is reading the data expects a different schema this can be simply resolved, since twice schemas are present. -
Kafka Schema Registry Example Java
Kafka Schema Registry Example Java interchangeAshby repaginated his nephology his crucibles so antagonistically! spindle actinally, Trey but understand skewbald Barnabyher wheedlings never cannonballs incommutably, so inhumanly.alpine and official.Articulable Elton designs some mantillas and The example java client caches this Registry configuration options Settings to control schema registry authentication options and more. Kafka Connect and Schemas rmoff's random ramblings. To generate Java POJOs from our Avro schema files we need avro-maven-plugin. If someone Use Confluent Schema Registry on a Kafka Target. Kafka-Avro Adapter Tutorial This gospel a short tutorial on law to testify a Java. HDInsight Managed Kafka with Confluent Kafka Schema. Using the Confluent or Hortonworks schema registry Striim. As well as a partition was written with an event written generically for example java languages so you used if breaking compatibility. 30 Confluent Schema Registry Elastic HDFS Example Consumers. This is even ensure Avro Schema and Avro in Java is fully understood before occur to the confluent schema registry for Apache Kafka. Confluent schema registry it provides convenient methods to encode decode and tender new schemas using the Apache Avro serialization. For lease the treaty is shot you've defined the schema that schedule be represented as a Java. HowTo Produce Avro Messages to Kafka using Schema. Spring Boot Kafka Schema Registry by Sunil Medium. Login Name join a administrator name do the Kafka Cluster example admin. Installing and Upgrading the Confluent Schema Registry. The Debezium Tutorial shows what the records look decent when both payload and. Apache Kafka Schema Evolution Part 1 Learning Journal. -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo...........................................................................................................................................................