Apache Cassandra
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Reference Guide
Apache Syncope - Reference Guide Version 2.1.9 Table of Contents 1. Introduction. 2 1.1. Identity Technologies. 2 1.1.1. Identity Stores . 2 1.1.2. Provisioning Engines . 4 1.1.3. Access Managers . 5 1.1.4. The Complete Picture . 5 2. Architecture. 7 2.1. Core . 7 2.1.1. REST . 7 2.1.2. Logic . 8 2.1.3. Provisioning . 8 2.1.4. Workflow. 9 2.1.5. Persistence . 9 2.1.6. Security . 9 2.2. Admin UI. 10 2.2.1. Accessibility . 10 2.3. End-user UI. 12 2.3.1. Password Reset . 12 2.3.2. Accessibility . 13 2.4. CLI . 15 2.5. Third Party Applications. 15 2.5.1. Eclipse IDE Plugin . 15 2.5.2. Netbeans IDE Plugin. 15 3. Concepts . 16 3.1. Users, Groups and Any Objects . 16 3.2. Type Management . 17 3.2.1. Schema . 17 Plain . 17 Derived . 18 Virtual . 18 3.2.2. AnyTypeClass . 19 3.2.3. AnyType . 19 3.2.4. RelationshipType . 21 3.2.5. Type Extensions . 22 3.3. External Resources. 23 3.3.1. Connector Bundles . 24 3.3.2. Connector Instance details . 24 3.3.3. External Resource details . 25 3.3.4. Mapping . 26 3.3.5. Linked Accounts . 29 3.4. Realms . 29 3.4.1. Realm Provisioning . 30 3.4.2. LogicActions . 31 3.5. Entitlements. 31 3.6. Privileges . 31 3.7. Roles. 31 3.7.1. Delegated Administration . 32 3.8. Provisioning. 33 3.8.1. Overview. 33 3.8.2. -
Tracking Known Security Vulnerabilities in Third-Party Components
Tracking known security vulnerabilities in third-party components Master’s Thesis Mircea Cadariu Tracking known security vulnerabilities in third-party components THESIS submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in COMPUTER SCIENCE by Mircea Cadariu born in Brasov, Romania Software Engineering Research Group Software Improvement Group Department of Software Technology Rembrandt Tower, 15th floor Faculty EEMCS, Delft University of Technology Amstelplein 1 - 1096HA Delft, the Netherlands Amsterdam, the Netherlands www.ewi.tudelft.nl www.sig.eu c 2014 Mircea Cadariu. All rights reserved. Tracking known security vulnerabilities in third-party components Author: Mircea Cadariu Student id: 4252373 Email: [email protected] Abstract Known security vulnerabilities are introduced in software systems as a result of de- pending on third-party components. These documented software weaknesses are hiding in plain sight and represent the lowest hanging fruit for attackers. Despite the risk they introduce for software systems, it has been shown that developers consistently download vulnerable components from public repositories. We show that these downloads indeed find their way in many industrial and open-source software systems. In order to improve the status quo, we introduce the Vulnerability Alert Service, a tool-based process to track known vulnerabilities in software projects throughout the development process. Its usefulness has been empirically validated in the context of the external software product quality monitoring service offered by the Software Improvement Group, a software consultancy company based in Amsterdam, the Netherlands. Thesis Committee: Chair: Prof. Dr. A. van Deursen, Faculty EEMCS, TU Delft University supervisor: Prof. Dr. A. -
Apache Cassandra on AWS Whitepaper
Apache Cassandra on AWS Guidelines and Best Practices January 2016 Amazon Web Services – Apache Cassandra on AWS January 2016 © 2016, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Page 2 of 52 Amazon Web Services – Apache Cassandra on AWS January 2016 Notices 2 Abstract 4 Introduction 4 NoSQL on AWS 5 Cassandra: A Brief Introduction 6 Cassandra: Key Terms and Concepts 6 Write Request Flow 8 Compaction 11 Read Request Flow 11 Cassandra: Resource Requirements 14 Storage and IO Requirements 14 Network Requirements 15 Memory Requirements 15 CPU Requirements 15 Planning Cassandra Clusters on AWS 16 Planning Regions and Availability Zones 16 Planning an Amazon Virtual Private Cloud 18 Planning Elastic Network Interfaces 19 Planning -
Apache Cassandra and Apache Spark Integration a Detailed Implementation
Apache Cassandra and Apache Spark Integration A detailed implementation Integrated Media Systems Center USC Spring 2015 Supervisor Dr. Cyrus Shahabi Student Name Stripelis Dimitrios 1 Contents 1. Introduction 2. Apache Cassandra Overview 3. Apache Cassandra Production Development 4. Apache Cassandra Running Requirements 5. Apache Cassandra Read/Write Requests using the Python API 6. Types of Cassandra Queries 7. Apache Spark Overview 8. Building the Spark Project 9. Spark Nodes Configuration 10. Building the Spark Cassandra Integration 11. Running the Spark-Cassandra Shell 12. Summary 2 1. Introduction This paper can be used as a reference guide for a detailed technical implementation of Apache Spark v. 1.2.1 and Apache Cassandra v. 2.0.13. The integration of both systems was deployed on Google Cloud servers using the RHEL operating system. The same guidelines can be easily applied to other operating systems (Linux based) as well with insignificant changes. Cluster Requirements: Software Java 1.7+ installed Python 2.7+ installed Ports A number of at least 7 ports in each node of the cluster must be constantly opened. For Apache Cassandra the following ports are the default ones and must be opened securely: 9042 - Cassandra native transport for clients 9160 - Cassandra Port for listening for clients 7000 - Cassandra TCP port for commands and data 7199 - JMX Port Cassandra For Apache Spark any 4 random ports should be also opened and secured, excluding ports 8080 and 4040 which are used by default from apache Spark for creating the Web UI of each application. It is highly advisable that one of the four random ports should be the port 7077, because it is the default port used by the Spark Master listening service. -
Return of Organization Exempt from Income
OMB No. 1545-0047 Return of Organization Exempt From Income Tax Form 990 Under section 501(c), 527, or 4947(a)(1) of the Internal Revenue Code (except black lung benefit trust or private foundation) Open to Public Department of the Treasury Internal Revenue Service The organization may have to use a copy of this return to satisfy state reporting requirements. Inspection A For the 2011 calendar year, or tax year beginning 5/1/2011 , and ending 4/30/2012 B Check if applicable: C Name of organization The Apache Software Foundation D Employer identification number Address change Doing Business As 47-0825376 Name change Number and street (or P.O. box if mail is not delivered to street address) Room/suite E Telephone number Initial return 1901 Munsey Drive (909) 374-9776 Terminated City or town, state or country, and ZIP + 4 Amended return Forest Hill MD 21050-2747 G Gross receipts $ 554,439 Application pending F Name and address of principal officer: H(a) Is this a group return for affiliates? Yes X No Jim Jagielski 1901 Munsey Drive, Forest Hill, MD 21050-2747 H(b) Are all affiliates included? Yes No I Tax-exempt status: X 501(c)(3) 501(c) ( ) (insert no.) 4947(a)(1) or 527 If "No," attach a list. (see instructions) J Website: http://www.apache.org/ H(c) Group exemption number K Form of organization: X Corporation Trust Association Other L Year of formation: 1999 M State of legal domicile: MD Part I Summary 1 Briefly describe the organization's mission or most significant activities: to provide open source software to the public that we sponsor free of charge 2 Check this box if the organization discontinued its operations or disposed of more than 25% of its net assets. -
IBM Websphere Application Server Community Edition V3.0 Helps Streamline the Creation of Osgi and Java Enterprise Edition 6 Applications
IBM United States Software Announcement 211-083, dated September 27, 2011 IBM WebSphere Application Server Community Edition V3.0 helps streamline the creation of OSGi and Java Enterprise Edition 6 applications Table of contents 1 Overview 6 Technical information 2 Key prerequisites 8 Ordering information 2 Planned availability date 9 Services 3 Description 9 Order now 6 Product positioning At a glance With WebSphere® Application Server Community Edition V3.0: • Developers can select just the components they need for optimum productivity (using OSGi and a component assembly model). • Developers can get JavaTM Enterprise Edition (Java EE) 6 applications started quickly for no charge. • System administrators are given more deployment and management options. • Organizations can take advantage of world-class, IBM® support options under a socket-based pricing model that can help reduce the cost burden in larger configurations. • You have access to a comprehensive and proven portfolio of middleware products from the WebSphere family. Overview WebSphere Application Server Community Edition V3.0 is the IBM open source- based application server that provides: • Java Enterprise Edition (Java EE) 6 support • An enterprise OSGi application programming model • Java Standard Edition (Java SE) 6 support Version 3 is built on Apache Geronimo and integrated with best-of-breed, open- source technology such as Apache Tomcat, Eclipse Equinox OSGi Framework, Apache Aries, Apache OpenEJB, Apache OpenJPA, Apache OpenWebBeans, and Apache MyFaces. Eclipse-based -
Ultrasparc T2
© 2014 IJIRT | Volume 1 Issue 6 | ISSN : 2349-6002 MyEclipse-A Rapid Application Development Tool Abstract- MyEclipse 2014 could be a commercially on the • Connectors for databases of Oracle information, market Java EE and Ajax IDE created and maintained by MySQL, Microsoft SQL Server, Sybase the corporate Genuitec, a beginning member of the Eclipse • HTML five mobile tools Foundation. MyEclipse is made upon the Eclipse platform, • REST API cloud tools and integrates each proprietary and open ASCII text file Not gift within the Spring or Bling editions into the event surroundings. Some of the options enclosed (except within the Spring or I. INTRODUCTION Bling editions) are: MyEclipse has 2 primary versions (apart from the "Blue • HTML5 Visual Designer Edition and "MyEclipse Spring Edition cited below): • PhoneGap (Apache Cordova) support knowledgeable and a regular edition. the quality edition • etc. adds information tools, a visible net designer, persistence Present in each editions tools, Spring tools, Struts and JSF tooling, and variety of Some of the options enclosed altogether editions are: different options to the fundamental Eclipse Java • Ajax Tools Developer profile. It competes with the online Tools • Java Persistence Tools: Hibernate, TopLink, Project, that could be a a part of Eclipse itself, however Apache OpenJPA MyEclipse could be a separate project entirely and offers • Spring Framework Tools a distinct feature set. • Apache Struts Designer MyEclipse 2015 Continuous Integration Stream version • JavaServer Faces Designer five (September twenty five, 2014) is currently on the • Application server Connectors market to developers WHO need the newest versions of • JavaServer Pages Development MyEclipse designed with regular updates. -
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} -
Chapter 2 Introduction to Big Data Technology
Chapter 2 Introduction to Big data Technology Bilal Abu-Salih1, Pornpit Wongthongtham2 Dengya Zhu3 , Kit Yan Chan3 , Amit Rudra3 1The University of Jordan 2 The University of Western Australia 3 Curtin University Abstract: Big data is no more “all just hype” but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. from the 3V’s to up 10V’s of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units and components. Finally, we provide some big data online resources for reference. Keywords Big data, 3V of Big data, Cloud Computing, Data Lake, Enterprise Data Centre, PaaS, IaaS, SaaS, Hadoop, Spark, HBase, Information retrieval, Solr 2.1 Introduction The ability to exploit the ever-growing amounts of business-related data will al- low to comprehend what is emerging in the world. In this context, Big Data is one of the current major buzzwords [1]. Big Data (BD) is the technical term used in reference to the vast quantity of heterogeneous datasets which are created and spread rapidly, and for which the conventional techniques used to process, analyse, retrieve, store and visualise such massive sets of data are now unsuitable and inad- equate. This can be seen in many areas such as sensor-generated data, social media, uploading and downloading of digital media. -
Why Migrate from Mysql to Cassandra?
Why Migrate from MySQL to Cassandra? 1 Table of Contents Abstract ....................................................................................................................................................................................... 3 Introduction ............................................................................................................................................................................... 3 Why Stay with MySQL ........................................................................................................................................................... 3 Why Migrate from MySQL ................................................................................................................................................... 4 Architectural Limitations ........................................................................................................................................... 5 Data Model Limitations ............................................................................................................................................... 5 Scalability and Performance Limitations ............................................................................................................ 5 Why Migrate from MySQL ................................................................................................................................................... 6 A Technical Overview of Cassandra ..................................................................................................................... -
Apache Cassandra™ Architecture Inside Datastax Distribution of Apache Cassandra™
Apache Cassandra™ Architecture Inside DataStax Distribution of Apache Cassandra™ Inside DataStax Distribution of Apache Cassandra TABLE OF CONTENTS TABLE OF CONTENTS ......................................................................................................... 2 INTRODUCTION .................................................................................................................. 3 MOTIVATIONS FOR CASSANDRA ........................................................................................ 3 Dramatic changes in data management ....................................................................... 3 NoSQL databases ...................................................................................................... 3 About Cassandra ....................................................................................................... 4 WHERE CASSANDRA EXCELS ............................................................................................. 4 ARCHITECTURAL OVERVIEW .............................................................................................. 5 Highlights .................................................................................................................. 5 Cluster topology ......................................................................................................... 5 Logical ring structure .................................................................................................. 6 Queries, cluster-level replication ................................................................................