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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. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
Architecting Cloud-Native NET Apps for Azure (2020).Pdf
EDITION v.1.0 PUBLISHED BY Microsoft Developer Division, .NET, and Visual Studio product teams A division of Microsoft Corporation One Microsoft Way Redmond, Washington 98052-6399 Copyright © 2020 by Microsoft Corporation All rights reserved. No part of the contents of this book may be reproduced or transmitted in any form or by any means without the written permission of the publisher. This book is provided “as-is” and expresses the author’s views and opinions. The views, opinions, and information expressed in this book, including URL and other Internet website references, may change without notice. Some examples depicted herein are provided for illustration only and are fictitious. No real association or connection is intended or should be inferred. Microsoft and the trademarks listed at https://www.microsoft.com on the “Trademarks” webpage are trademarks of the Microsoft group of companies. Mac and macOS are trademarks of Apple Inc. The Docker whale logo is a registered trademark of Docker, Inc. Used by permission. All other marks and logos are property of their respective owners. Authors: Rob Vettor, Principal Cloud System Architect/IP Architect - thinkingincloudnative.com, Microsoft Steve “ardalis” Smith, Software Architect and Trainer - Ardalis.com Participants and Reviewers: Cesar De la Torre, Principal Program Manager, .NET team, Microsoft Nish Anil, Senior Program Manager, .NET team, Microsoft Jeremy Likness, Senior Program Manager, .NET team, Microsoft Cecil Phillip, Senior Cloud Advocate, Microsoft Editors: Maira Wenzel, Program Manager, .NET team, Microsoft Version This guide has been written to cover .NET Core 3.1 version along with many additional updates related to the same “wave” of technologies (that is, Azure and additional third-party technologies) coinciding in time with the .NET Core 3.1 release. -
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. -
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 ................................................................................ -
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. -
Using Stored Procedures Effectively in a Distributed Postgresql Database
Using stored procedures effectively in a distributed PostgreSQL database Bryn Llewellyn Developer Advocate, Yugabyte Inc. © 2019 All rights reserved. 1 What is YugabyteDB? © 2019 All rights reserved. 2 YugaByte DB Distributed SQL PostgreSQL Compatible, 100% Open Source (Apache 2.0) Massive Scale Millions of IOPS in Throughput, TBs per Node High Performance Low Latency Queries Cloud Native Fault Tolerant, Multi-Cloud & Kubernetes Ready © 2019 All rights reserved. 3 Functional Architecture YugaByte SQL (YSQL) PostgreSQL-Compatible Distributed SQL API DOCDB Spanner-Inspired Distributed Document Store Cloud Neutral: No Specialized Hardware Needed © 2019 All rights reserved. 4 Questions? Download download.yugabyte.com Join Slack Discussions yugabyte.com/slack Star on GitHub github.com/YugaByte/yugabyte-db © 2019 All rights reserved. 5 Q: Why use stored procedures? © 2019 All rights reserved. 6 • Large software systems must be built from modules • Hide implementation detail behind API • Software engineering’s most famous principle • The RDBMS is a module • Tables and SQLs that manipulate them are the implementation details • Stored procedures express the API • Result: happiness • Developers and end-users of applications built this way are happy with their correctness, maintainability, security, and performance © 2019 All rights reserved. 7 A: Use stored procedures to encapsulate the RDBMS’s functionality behind an impenetrable hard shell © 2019 All rights reserved. 8 Hard Shell Schematic © 2019 All rights reserved. 9 Public APP DATABASE © 2019 All rights reserved. 10 Public APP DATABASE © 2019 All rights reserved. 11 APP DATABASE © 2019 All rights reserved. 12 Data APP DATABASE © 2019 All rights reserved. 13 Data Code . APP DATABASE © 2019 All rights reserved. -
Hbase Or Cassandra? a Comparative Study of Nosql Database Performance
International Journal of Scientific and Research Publications, Volume 10, Issue 3, March 2020 808 ISSN 2250-3153 HBase or Cassandra? A Comparative study of NoSQL Database Performance Prashanth Jakkula* * National College of Ireland DOI: 10.29322/IJSRP.10.03.2020.p9999 http://dx.doi.org/10.29322/IJSRP.10.03.2020.p9999 Abstract- A significant growth in data has been observed with the growth in technology and population. This data is non-relational and unstructured and often referred to as NoSQL data. It is growing in complexity for the traditional database management systems to manage such vast databases. Present day cloud services are offering numerous NoSQL databases to manage such non-relational databases ad- dressing different user specific requirements such as performance, availability, security etc. Hence there is a need to evaluate and find the behavior of different NoSQL databases in virtual environments. This study aims to evaluate two popular NoSQL databases and in support to the study, a benchmarking tool is used to compare the performance difference between HBase and Cassandra on a virtual instance deployed on OpenStack. Index Terms- NoSQL Databases, Performance Analysis, Cassandra, HBase, YCSB I. INTRODUCTION ata is growing in complexity with the rise in data. Large amount of data is being generated every day from different sources and D corners of the internet. This exponential data growth is represented by big data. It is serving different use cases in the present day data driven environment and there is a need to manage it with respect to velocity, volume and variety. The traditional way of managing the databases using relational database management systems could not handle because of the volume and they are capable of storing the data which is schema based and only in certain predefined formats. -
Building a Scalable Distributed Data Platform Using Lambda Architecture
Building a scalable distributed data platform using lambda architecture by DHANANJAY MEHTA B.Tech., Graphic Era University, India, 2012 A REPORT submitted in partial fulfillment of the requirements for the degree MASTER OF SCIENCE Department Of Computer Science College Of Engineering KANSAS STATE UNIVERSITY Manhattan, Kansas 2017 Approved by: Major Professor Dr. William H. Hsu Copyright Dhananjay Mehta 2017 Abstract Data is generated all the time over Internet, systems, sensors and mobile devices around us this data is often referred to as 'big data'. Tapping this data is a challenge to organiza- tions because of the nature of data i.e. velocity, volume and variety. What make handling this data a challenge? This is because traditional data platforms have been built around relational database management systems coupled with enterprise data warehouses. Legacy infrastructure is either technically incapable to scale to big data or financially infeasible. Now the question arises, how to build a system to handle the challenges of big data and cater needs of an organization? The answer is Lambda Architecture. Lambda Architecture (LA) is a generic term that is used for a scalable and fault-tolerant data processing architecture that ensure real-time processing with low latency. LA provides a general strategy to knit together all necessary tools for building a data pipeline for real- time processing of big data. LA builds a big data platform as a series of layers that combine batch and real time processing. LA comprise of three layers - Batch Layer, responsible for bulk data processing; Speed Layer, responsible for real-time processing of data streams and Serving Layer, responsible for serving queries from end users.