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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 -
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........................................................................................................................................................... -
Kyuubi Release 1.3.0 Kent
Kyuubi Release 1.3.0 Kent Yao Sep 30, 2021 USAGE GUIDE 1 Multi-tenancy 3 2 Ease of Use 5 3 Run Anywhere 7 4 High Performance 9 5 Authentication & Authorization 11 6 High Availability 13 6.1 Quick Start................................................ 13 6.2 Deploying Kyuubi............................................ 47 6.3 Kyuubi Security Overview........................................ 76 6.4 Client Documentation.......................................... 80 6.5 Integrations................................................ 82 6.6 Monitoring................................................ 87 6.7 SQL References............................................. 94 6.8 Tools................................................... 98 6.9 Overview................................................. 101 6.10 Develop Tools.............................................. 113 6.11 Community................................................ 120 6.12 Appendixes................................................ 128 i ii Kyuubi, Release 1.3.0 Kyuubi™ is a unified multi-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown in the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi provides the following features: USAGE GUIDE 1 Kyuubi, Release 1.3.0 2 USAGE GUIDE CHAPTER ONE MULTI-TENANCY Kyuubi supports the end-to-end multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server already exists. 1. Supports multi-client concurrency and authentication 2. Supports one Spark application per account(SPA). 3. Supports QUEUE/NAMESPACE Access Control Lists (ACL) 4. -
Implementing Replication for Predictability Within Apache Thrift Jianwei Tu the Ohio State University [email protected]
Implementing Replication for Predictability within Apache Thrift Jianwei Tu The Ohio State University [email protected] ABSTRACT have a large number of packets. A study indicated that about Interactive applications, such as search, social networking and 0.02% of all flows contributed more than 59.3% of the total retail, hosted in cloud data center generate large quantities of traffic volume [1]. TCP is the dominating transport protocol small workloads that require extremely low median and tail used in data center. However, the performance for short flows latency in order to provide soft real-time performance to users. in TCP is very poor: although in theory they can be finished These small workloads are known as short TCP flows. in 10-20 microseconds with 1G or 10G interconnects, the However, these short TCP flows experience long latencies actual flow completion time (FCT) is as high as tens of due in part to large workloads consuming most available milliseconds [2]. This is due in part to long flows consuming buffer in the switches. Imperfect routing algorithm such as some or all of the available buffers in the switches [3]. ECMP makes the matter even worse. We propose a transport Imperfect routing algorithms such as ECMP makes the matter mechanism using replication for predictability to achieve low even worse. State of the art forwarding in enterprise and data flow completion time (FCT) for short TCP flows. We center environment uses ECMP to statically direct flows implement replication for predictability within Apache Thrift across available paths using flow hashing. It doesn’t account transport layer that replicates each short TCP flow and sends for either current network utilization or flow size, and may out identical packets for both flows, then utilizes the first flow direct many long flows to the same path causing flash that finishes the transfer. -
Full-Graph-Limited-Mvn-Deps.Pdf
org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version} -
An Easy-To-Use, Scalable and Robust Messaging Solution for Smart Grid
285 An Easy-to-use, Scalable and Robust Messaging Solution for Smart Grid Research Ferdinand von Tüllenburg, Jia Lei Du, Georg Panholzer Salzburg Research Forschungsgesellschaft mbH, Salzburg, AUSTRIA, email: {ferdinand.tuellenburg, jia.du, georg.panholzer}@salzburgresearch.at Abstract: Smart Grids are characterized by tight issues regarding security, performance, scalability, reliability coupling and intertwining between the electrical system and robustness of sending and receiving messages. and information and communication technology. Due to The paper shows the application of the messaging solution this, application layer messaging systems are regularly in context of an agent-based flexibility trading application. required for many Smart Grid applications. Especially in ELATED ORK research messaging solutions are setup from scratch. In II. R W this paper we propose a generic and easy to setup message In context of messaging systems for Smart Grid oriented middleware (MOM) solution providing robust application especially solutions based on XMPP are often and scalable messaging. used [2]. Although, XMPP is a flexible solution also Keywords: Smart Grid, Messaging API, Middleware following a MOM approach, it has weaknesses with respect to ease of deployment and configuration as well as NTRODUCTION I. I implementation especially with respect to required aspects Future electrical power systems will be characterized by a such as reliability. One example here is OpenADR[3]. new control paradigm: Decentralized controllable power Recently, with FIWARE, an open source platform is available sources such as batteries, wind generators, and PV systems which provides a large set of application programming on production side and controllable loads on consumption interfaces (APIs) for a large variety of applications also side will be constantly monitored and operated depending on providing a messaging solution for Smart Grids. -
Pentaho EMR46 SHIM 7.1.0.0 Open Source Software Packages
Pentaho EMR46 SHIM 7.1.0.0 Open Source Software Packages Contact Information: Project Manager Pentaho EMR46 SHIM Hitachi Vantara Corporation 2535 Augustine Drive Santa Clara, California 95054 Name of Product/Product Version License Component An open source Java toolkit for 0.9.0 Apache License Version 2.0 Amazon S3 AOP Alliance (Java/J2EE AOP 1.0 Public Domain standard) Apache Commons BeanUtils 1.9.3 Apache License Version 2.0 Apache Commons CLI 1.2 Apache License Version 2.0 Apache Commons Daemon 1.0.13 Apache License Version 2.0 Apache Commons Exec 1.2 Apache License Version 2.0 Apache Commons Lang 2.6 Apache License Version 2.0 Apache Directory API ASN.1 API 1.0.0-M20 Apache License Version 2.0 Apache Directory LDAP API Utilities 1.0.0-M20 Apache License Version 2.0 Apache Hadoop Amazon Web 2.7.2 Apache License Version 2.0 Services support Apache Hadoop Annotations 2.7.2 Apache License Version 2.0 Name of Product/Product Version License Component Apache Hadoop Auth 2.7.2 Apache License Version 2.0 Apache Hadoop Common - 2.7.2 Apache License Version 2.0 org.apache.hadoop:hadoop-common Apache Hadoop HDFS 2.7.2 Apache License Version 2.0 Apache HBase - Client 1.2.0 Apache License Version 2.0 Apache HBase - Common 1.2.0 Apache License Version 2.0 Apache HBase - Hadoop 1.2.0 Apache License Version 2.0 Compatibility Apache HBase - Protocol 1.2.0 Apache License Version 2.0 Apache HBase - Server 1.2.0 Apache License Version 2.0 Apache HBase - Thrift - 1.2.0 Apache License Version 2.0 org.apache.hbase:hbase-thrift Apache HttpComponents Core -
Plugin Tapestry
PlugIn Tapestry Autor @picodotdev https://picodotdev.github.io/blog-bitix/ 2019 1.4.2 5.4 A tod@s l@s programador@s que en su trabajo no pueden usar el framework, librería o lenguaje que quisieran. Y a las que se divierten programando y aprendiendo hasta altas horas de la madrugada. Non gogoa, han zangoa Hecho con un esfuerzo en tiempo considerable con una buena cantidad de software libre y más ilusión en una región llamada Euskadi. PlugIn Tapestry: Desarrollo de aplicaciones y páginas web con Apache Tapestry @picodotdev 2014 - 2019 2 Prefacio Empecé El blog de pico.dev y unos años más tarde Blog Bitix con el objetivo de poder aprender y compartir el conocimiento de muchas cosas que me interesaban desde la programación y el software libre hasta análisis de los productos tecnológicos que caen en mis manos. Las del ámbito de la programación creo que usándolas pueden resolver en muchos casos los problemas típicos de las aplicaciones web y que encuentro en el día a día en mi trabajo como desarrollador. Sin embargo, por distintas circunstancias ya sean propias del cliente, la empresa o las personas es habitual que solo me sirvan meramente como satisfacción de adquirir conocimientos. Hasta el día de hoy una de ellas es el tema del que trata este libro, Apache Tapestry. Para escribir en el blog solo dependo de mí y de ninguna otra circunstancia salvo mi tiempo personal, es com- pletamente mío con lo que puedo hacer lo que quiera con él y no tengo ninguna limitación para escribir y usar cualquier herramienta, aunque en un principio solo sea para hacer un ejemplo muy sencillo, en el momento que llegue la oportunidad quizá me sirva para aplicarlo a un proyecto real. -
Apache Flume™
™ Apache Flume™ Flume 1.7.0 User Guide Introduction Overview Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. The use of Apache Flume is not only restricted to log data aggregation. Since data sources are customizable, Flume can be used to transport massive quantities of event data including but not limited to network traffic data, social-media-generated data, email messages and pretty much any data source possible. Apache Flume is a top level project at the Apache Software Foundation. There are currently two release code lines available, versions 0.9.x and 1.x. Documentation for the 0.9.x track is available at the Flume 0.9.x User Guide. This documentation applies to the 1.4.x track. New and existing users are encouraged to use the 1.x releases so as to leverage the performance improvements and configuration flexibilities available in the latest architecture. System Requirements 1. Java Runtime Environment - Java 1.7 or later 2. Memory - Sufficient memory for configurations used by sources, channels or sinks 3. Disk Space - Sufficient disk space for configurations used by channels or sinks 4. Directory Permissions - Read/Write permissions for directories used by agent Architecture Data flow model A Flume event is defined as a unit of data flow having a byte payload and an optional set of string attributes. A Flume agent is a (JVM) process that hosts the components through which events flow from an external source to the next destination (hop).