This document compares the total cost of ownership (TCO) of MongoDB and Oracle for two example projects - a smaller enterprise project and a larger enterprise project. It analyzes the upfront and ongoing costs associated with initial developer effort, initial administrative effort, software licenses, server hardware, storage hardware, ongoing developer effort, ongoing administrative effort, and software maintenance and support. For both example projects, the analysis finds that the costs of initial developer effort, initial administrative effort, server hardware, and ongoing administrative and developer effort are lower for MongoDB compared to Oracle, while software license costs are significantly higher for Oracle.
This document provides an overview and introduction to MongoDB, an open-source, high-performance NoSQL database. It outlines MongoDB's features like document-oriented storage, replication, sharding, and CRUD operations. It also discusses MongoDB's data model, comparisons to relational databases, and common use cases. The document concludes that MongoDB is well-suited for applications like content management, inventory management, game development, social media storage, and sensor data databases due to its flexible schema, distributed deployment, and low latency.
MongoDB is an open-source, document-oriented database that provides high performance and horizontal scalability. It uses a document-model where data is organized in flexible, JSON-like documents rather than rigidly defined rows and tables. Documents can contain multiple types of nested objects and arrays. MongoDB is best suited for applications that need to store large amounts of unstructured or semi-structured data and benefit from horizontal scalability and high performance.
React is a JavaScript library for building user interfaces. It uses a component-based approach where UI is broken into independent, reusable pieces called components. Components are like functions that return markup describing part of a view. React uses a virtual DOM to efficiently update the real DOM by only making necessary changes. This improves performance by avoiding expensive DOM operations and minimizing DOM access. Components receive data and callbacks through properties and local state is updated using setState(), triggering a re-render of changed parts of the UI.
MongoDB is an open-source, document-oriented database that provides flexible schemas, horizontal scaling, and high performance. It stores data as JSON-like documents with dynamic schemas, making the integration of data easier for developers. MongoDB can be scaled horizontally and supports replication and load balancing for high availability.
The document provides an introduction to React, a JavaScript library for building user interfaces. It discusses key React concepts like components, properties, state, one-way data flow, and JSX syntax. It also covers setting up a development environment with Create React App and shows how to create a basic React component with state. The target audience appears to be people new to React who want to learn the fundamentals.
This is an introduction to NodeJS which is an open-source, cross-platform run-time environment for developing server-side Web Applications. It also discusses the implications of NodeJS in Internet of Things (IoT).
This document provides an overview and introduction to MongoDB, an open-source, high-performance NoSQL database. It outlines MongoDB's features like document-oriented storage, replication, sharding, and CRUD operations. It also discusses MongoDB's data model, comparisons to relational databases, and common use cases. The document concludes that MongoDB is well-suited for applications like content management, inventory management, game development, social media storage, and sensor data databases due to its flexible schema, distributed deployment, and low latency.
MongoDB is an open-source, document-oriented database that provides high performance and horizontal scalability. It uses a document-model where data is organized in flexible, JSON-like documents rather than rigidly defined rows and tables. Documents can contain multiple types of nested objects and arrays. MongoDB is best suited for applications that need to store large amounts of unstructured or semi-structured data and benefit from horizontal scalability and high performance.
React is a JavaScript library for building user interfaces. It uses a component-based approach where UI is broken into independent, reusable pieces called components. Components are like functions that return markup describing part of a view. React uses a virtual DOM to efficiently update the real DOM by only making necessary changes. This improves performance by avoiding expensive DOM operations and minimizing DOM access. Components receive data and callbacks through properties and local state is updated using setState(), triggering a re-render of changed parts of the UI.
MongoDB is an open-source, document-oriented database that provides flexible schemas, horizontal scaling, and high performance. It stores data as JSON-like documents with dynamic schemas, making the integration of data easier for developers. MongoDB can be scaled horizontally and supports replication and load balancing for high availability.
The document provides an introduction to React, a JavaScript library for building user interfaces. It discusses key React concepts like components, properties, state, one-way data flow, and JSX syntax. It also covers setting up a development environment with Create React App and shows how to create a basic React component with state. The target audience appears to be people new to React who want to learn the fundamentals.
This is an introduction to NodeJS which is an open-source, cross-platform run-time environment for developing server-side Web Applications. It also discusses the implications of NodeJS in Internet of Things (IoT).
Next.js vs React | what to choose for frontend development_ForceBolt
Vercel built the Next.js framework. It's free and open-source, built on Node.js and Babel, and it works with React to create single-page apps. This simplifies server-side rendering.
State management in react applications (Statecharts)Tomáš Drenčák
This document discusses state management in React applications and introduces statecharts as an alternative approach. It covers the limitations of approaches like Flux and Redux, such as global action handling registries and initialization/cleanup issues. Statecharts provide hierarchical and parallel states that can help solve these problems. The document demonstrates how statecharts can be used to structure stores, handle actions polymorphically, and implement common patterns like pages and lists in a more organized way.
Introduction to React JS for beginners | Namespace ITnamespaceit
React is a JavaScript library for building user interfaces using reusable components. It is used to create single page applications that dynamically update the current page with new data from the server. React uses a component-based approach and one-way data binding to build interfaces simply and allow for easy testing. Key concepts in React include components, props, state, lifecycles, hooks, JSX, and the virtual DOM. Major companies using React include Facebook, Netflix, Instagram, and WhatsApp.
React is an open source JavaScript library for building user interfaces. It was created by Jordan Walke at Facebook in 2011 and is now maintained by Facebook, Instagram, and a community of developers. Major companies like Facebook, Netflix, Instagram, Khan Academy, and PayPal use React to build their interfaces. React uses a virtual DOM for faster rendering and makes components that manage their own state. It uses JSX syntax and a one-way data flow that is declarative and composable.
This document provides an overview of NoSQL databases and summarizes key information about several NoSQL databases, including HBase, Redis, Cassandra, MongoDB, and Memcached. It discusses concepts like horizontal scalability, the CAP theorem, eventual consistency, and data models used by different NoSQL databases like key-value, document, columnar, and graph structures.
This document provides an overview of React including:
- React is a JavaScript library created by Facebook for building user interfaces
- It uses virtual DOM to efficiently re-render components on updates rather than entire page
- React supports ES6 features and uses classes, arrow functions, and other syntax
- Popular tools for React include Create React App for setting up projects and React Dev Tools for debugging
The Functional Programming Triad of Map, Filter and FoldPhilip Schwarz
This slide deck is my homage to SICP, the book which first introduced me to the Functional Programming triad of map, filter and fold.
It was during my Computer Science degree that a fellow student gave me a copy of the first edition, not long after the book came out.
I have not yet come across a better introduction to these three functions.
The upcoming slides are closely based on the second edition of the book, a free online copy of which can be found here:
https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book.html.
Download for original image quality.
Errata:
slide 20: the Clojure map function is in fact the Scheme one repeated - see code below for correction.
Scheme code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-scheme
Clojure code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-clojure
This presentation shortly describes key features of Apache Cassandra. It was held at the Apache Cassandra Meetup in Vienna in January 2014. You can access the meetup here: http://www.meetup.com/Vienna-Cassandra-Users/
O documento resume o que é React, suas principais vantagens como ser fácil de aprender, reaproveitamento de código e comunidade ativa. Explica que React é rápido devido ao Virtual DOM e que utiliza componentes como base para construção de interfaces.
Resilient Distributed DataSets - Apache SPARKTaposh Roy
RDDs (Resilient Distributed Datasets) provide a fault-tolerant abstraction for data reuse across jobs in distributed applications. They allow data to be persisted in memory and manipulated using transformations like map and filter. This enables efficient processing of iterative algorithms. RDDs achieve fault tolerance by logging the transformations used to build a dataset rather than the actual data, enabling recovery of lost partitions through recomputation.
This document provides an overview of React including what it is, who uses it, its core concepts, and why one should use React. React is a library for building user interfaces that uses a component-based approach and virtual DOM for declarative programming. It allows components to be written once and used on the web, mobile, and beyond. Core concepts include components, virtual DOM, and unidirectional data flow. Benefits of React include the ability to write code once and deploy anywhere, fast rendering with virtual DOM, and the growing demand for React skills.
React JS is a JavaScript library for building user interfaces. It uses virtual DOM and one-way data binding to render components efficiently. Everything in React is a component - they accept custom inputs called props and control the output display through rendering. Components can manage private state and update due to props or state changes. The lifecycle of a React component involves initialization, updating due to state/prop changes, and unmounting from the DOM. React promotes unidirectional data flow and single source of truth to make views more predictable and easier to debug.
MERN is one of several MEAN stack (MongoDB Express Angular Node) variants in which the traditional Angular.js frontend framework is replaced with React.js. MEVN (MongoDB, Express, Vue, Node) is another variant, and really any frontend JavaScript framework can work.Node.js is a popular and powerful JavaScript server platform, and Express.js is a server-side web framework. Regardless of which variant you choose, ME(RVA)N is the best way to work with JavaScript and JSON from start to finish.
DURATION:60 days
The document discusses MongoDB's use in the CMS experiment at CERN. MongoDB is used as the backend for CMS's Data Aggregation System (DAS), which acts as an intelligent cache to query distributed data services. DAS translates user queries, retrieves data from multiple services, aggregates the results, and returns consolidated responses. This architecture allows users to access different data without knowledge of the underlying services. MongoDB provides a flexible schema and fast I/O that make it suitable for caching distributed data and executing complex queries in DAS.
Synchronise your data between MySQL and MongoDBGiuseppe Maxia
The document discusses synchronizing data between MySQL and MongoDB using Tungsten Replicator. Tungsten Replicator allows data to be replicated from a MySQL database to MongoDB in near real-time. The document provides examples of data being inserted and updated in MySQL, and then appearing in MongoDB through the replication process. It also discusses security features and how to install Tungsten Replicator for basic master-slave replication between MySQL and MongoDB databases.
Next.js vs React | what to choose for frontend development_ForceBolt
Vercel built the Next.js framework. It's free and open-source, built on Node.js and Babel, and it works with React to create single-page apps. This simplifies server-side rendering.
State management in react applications (Statecharts)Tomáš Drenčák
This document discusses state management in React applications and introduces statecharts as an alternative approach. It covers the limitations of approaches like Flux and Redux, such as global action handling registries and initialization/cleanup issues. Statecharts provide hierarchical and parallel states that can help solve these problems. The document demonstrates how statecharts can be used to structure stores, handle actions polymorphically, and implement common patterns like pages and lists in a more organized way.
Introduction to React JS for beginners | Namespace ITnamespaceit
React is a JavaScript library for building user interfaces using reusable components. It is used to create single page applications that dynamically update the current page with new data from the server. React uses a component-based approach and one-way data binding to build interfaces simply and allow for easy testing. Key concepts in React include components, props, state, lifecycles, hooks, JSX, and the virtual DOM. Major companies using React include Facebook, Netflix, Instagram, and WhatsApp.
React is an open source JavaScript library for building user interfaces. It was created by Jordan Walke at Facebook in 2011 and is now maintained by Facebook, Instagram, and a community of developers. Major companies like Facebook, Netflix, Instagram, Khan Academy, and PayPal use React to build their interfaces. React uses a virtual DOM for faster rendering and makes components that manage their own state. It uses JSX syntax and a one-way data flow that is declarative and composable.
This document provides an overview of NoSQL databases and summarizes key information about several NoSQL databases, including HBase, Redis, Cassandra, MongoDB, and Memcached. It discusses concepts like horizontal scalability, the CAP theorem, eventual consistency, and data models used by different NoSQL databases like key-value, document, columnar, and graph structures.
This document provides an overview of React including:
- React is a JavaScript library created by Facebook for building user interfaces
- It uses virtual DOM to efficiently re-render components on updates rather than entire page
- React supports ES6 features and uses classes, arrow functions, and other syntax
- Popular tools for React include Create React App for setting up projects and React Dev Tools for debugging
The Functional Programming Triad of Map, Filter and FoldPhilip Schwarz
This slide deck is my homage to SICP, the book which first introduced me to the Functional Programming triad of map, filter and fold.
It was during my Computer Science degree that a fellow student gave me a copy of the first edition, not long after the book came out.
I have not yet come across a better introduction to these three functions.
The upcoming slides are closely based on the second edition of the book, a free online copy of which can be found here:
https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book.html.
Download for original image quality.
Errata:
slide 20: the Clojure map function is in fact the Scheme one repeated - see code below for correction.
Scheme code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-scheme
Clojure code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-clojure
This presentation shortly describes key features of Apache Cassandra. It was held at the Apache Cassandra Meetup in Vienna in January 2014. You can access the meetup here: http://www.meetup.com/Vienna-Cassandra-Users/
O documento resume o que é React, suas principais vantagens como ser fácil de aprender, reaproveitamento de código e comunidade ativa. Explica que React é rápido devido ao Virtual DOM e que utiliza componentes como base para construção de interfaces.
Resilient Distributed DataSets - Apache SPARKTaposh Roy
RDDs (Resilient Distributed Datasets) provide a fault-tolerant abstraction for data reuse across jobs in distributed applications. They allow data to be persisted in memory and manipulated using transformations like map and filter. This enables efficient processing of iterative algorithms. RDDs achieve fault tolerance by logging the transformations used to build a dataset rather than the actual data, enabling recovery of lost partitions through recomputation.
This document provides an overview of React including what it is, who uses it, its core concepts, and why one should use React. React is a library for building user interfaces that uses a component-based approach and virtual DOM for declarative programming. It allows components to be written once and used on the web, mobile, and beyond. Core concepts include components, virtual DOM, and unidirectional data flow. Benefits of React include the ability to write code once and deploy anywhere, fast rendering with virtual DOM, and the growing demand for React skills.
React JS is a JavaScript library for building user interfaces. It uses virtual DOM and one-way data binding to render components efficiently. Everything in React is a component - they accept custom inputs called props and control the output display through rendering. Components can manage private state and update due to props or state changes. The lifecycle of a React component involves initialization, updating due to state/prop changes, and unmounting from the DOM. React promotes unidirectional data flow and single source of truth to make views more predictable and easier to debug.
MERN is one of several MEAN stack (MongoDB Express Angular Node) variants in which the traditional Angular.js frontend framework is replaced with React.js. MEVN (MongoDB, Express, Vue, Node) is another variant, and really any frontend JavaScript framework can work.Node.js is a popular and powerful JavaScript server platform, and Express.js is a server-side web framework. Regardless of which variant you choose, ME(RVA)N is the best way to work with JavaScript and JSON from start to finish.
DURATION:60 days
The document discusses MongoDB's use in the CMS experiment at CERN. MongoDB is used as the backend for CMS's Data Aggregation System (DAS), which acts as an intelligent cache to query distributed data services. DAS translates user queries, retrieves data from multiple services, aggregates the results, and returns consolidated responses. This architecture allows users to access different data without knowledge of the underlying services. MongoDB provides a flexible schema and fast I/O that make it suitable for caching distributed data and executing complex queries in DAS.
Synchronise your data between MySQL and MongoDBGiuseppe Maxia
The document discusses synchronizing data between MySQL and MongoDB using Tungsten Replicator. Tungsten Replicator allows data to be replicated from a MySQL database to MongoDB in near real-time. The document provides examples of data being inserted and updated in MySQL, and then appearing in MongoDB through the replication process. It also discusses security features and how to install Tungsten Replicator for basic master-slave replication between MySQL and MongoDB databases.
NoSQL databases are currently used in several applications scenarios in contrast to Relations Databases. Several type of Databases there exist. In this presentation we compare Key Value, Column Oriented, Document Oriented and Graph Databases. Using a simple case study there are evaluated pros and cons of the NoSQL databases taken into account.
Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...Lucas Jellema
This presentation gives an brief overview of the history of relational databases, ACID and SQL and presents some of the key strentgths and potential weaknesses. It introduces the rise of NoSQL - why it arose, what is entails, when to use it. The presentation focuses on MongoDB as prime example of NoSQL document store and it shows how to interact with MongoDB from JavaScript (NodeJS) and Java.
Martin Tepper presented on using MongoDB as a queryable cache for Travel IQ, a meta search engine for flights and hotels. Travel IQ was experiencing slow API response times due to complex queries on its normalized SQL database. MongoDB was implemented as a caching layer to store denormalized offer data to allow for faster querying. This improved response times but also led to some headaches around data consistency and segmentation faults that were later addressed.
This document compares the total cost of ownership of MongoDB and Oracle databases. It outlines the various cost categories to consider, including upfront costs like software, hardware, development efforts, and ongoing costs like maintenance and support. The document then provides two example scenarios - a smaller and larger enterprise project - comparing the expected costs of building each using MongoDB versus Oracle. It finds that for these examples, using MongoDB is over 70% less expensive than using Oracle. Finally, it discusses how MongoDB's advantages in flexibility, ease of use and support for modern development can help reduce costs and speed development.
MongoDB BY VASUDEV PRAJAPATI, DOCUMENT BASED DATABASE PRESENTATION, NO SQL DATABASE, WHY MONGO DB IS USEFUL, USES OF MONGO DB, FEATURES OF MongoDB , WHO USE MongoDB , WHEN MongoDB IS USEFUL
1) The document discusses MongoDB, a NoSQL database, and how it can help telecommunications companies adapt to changing market conditions and develop new applications quickly.
2) MongoDB allows for flexible, rapid development and easy scaling to large volumes of data across multiple servers with no downtime. Its document-based data model is more intuitive for developers compared to relational databases.
3) The document argues that MongoDB enables telecom companies to act more agilely like startups, quickly developing and modifying applications in response to shifting customer needs and market disruptions.
La creación de una capa operacional con MongoDBMongoDB
The document discusses using MongoDB to modernize mainframe systems by reducing costs and increasing flexibility. It describes 5 phases of mainframe modernization with MongoDB, from initially offloading reads to using MongoDB as the primary system of record. Case studies are presented where MongoDB helped customers increase developer productivity by 5-10x, lower mainframe costs by 80%, and transform IT strategies by simplifying technology stacks.
how_can_businesses_address_storage_issues_using_mongodb.pptxsarah david
MongoDB enables seamless data storage and performance. Explore our blog to learn how MongoDB handles storage issues for startups and large-scale enterprises. Discover how to optimize MongoDB performance using open-source database storage.
how_can_businesses_address_storage_issues_using_mongodb.pdfsarah david
MongoDB is an open-source database that can help businesses address storage issues. It provides scalability, availability, and handles large amounts of data well. MongoDB uses a flexible document data model and has features like replication, sharding, and indexing that improve performance. While it has advantages like flexibility, simplicity, and speed, it also has drawbacks like limited transactions and joins compared to relational databases. Understanding both the benefits and limitations of MongoDB is important for businesses evaluating it for their data storage needs.
Introduction to MongoDB and its best practicesAshishRathore72
This document provides a summary of a presentation on MongoDB best practices. It discusses MongoDB concepts like data modeling, CRUD operations, querying, and aggregation. It also covers topics like MongoDB security, scaling options, real-world use cases, and best practices for hardware, schema design, indexing, and scalability. The presentation provides an overview of using MongoDB effectively.
This document discusses perspectives on big data applications for database engineers and IT students. It summarizes key concepts of big data and MongoDB, a popular NoSQL database for managing big data. It then demonstrates practical learning activities using MongoDB, such as installation, terminology, and basic syntax. The document concludes by emphasizing the importance of skills in big data and cloud computing for IT professionals and recommends further research on MongoDB security.
SQL vs NoSQL, an experiment with MongoDBMarco Segato
A simple experiment with MongoDB compared to Oracle classic RDBMS database: what are NoSQL databases, when to use them, why to choose MongoDB and how we can play with it.
A presentation on best practices for J2EE scalability from requirements gathering through to implementation, including design and architecture along the way.
Hands on Big Data Analysis with MongoDB - Cloud Expo Bootcamp NYCLaura Ventura
One of the most popular NoSQL databases, MongoDB is one of the building blocks for big data analysis. MongoDB can store unstructured data and makes it easy to analyze files by commonly available tools. This session will go over how big data analytics can improve sales outcomes in identifying users with a propensity to buy by processing information from social networks. All attendees will have a MongoDB instance on a public cloud, plus sample code to run Big Data Analytics.
Everything You Need to Know About MongoDB Development.pptx75waytechnologies
Today, organizations from different verticals want to harness the power of data to grab new business opportunities and touch new heights of success. Such an urge leads them to follow unique ways to use and handle data effectively. After all, the right use of data boosts the ability to make business decisions faster. But at the same time, working with data is not as easy as a walk in the garden. It sometimes turns out to be a long-standing problem for businesses that also affects their overall functioning.
Companies expect fast phase development and better data management in every scenario. Modern web-based applications development demands a quality working system that can be deployed faster, and the application is able to scale in the future as per the constantly changing environment.
Earlier, relational databases were used as a primary data store for web application development. But today, developers show a high interest in adopting alternative data stores for modern applications such as NoSQL (Not Only Structured Query Language) because of its incredible benefits. And if you ask us, one of the technologies that can do wonders in modern web-based application development is MongoDB.
MongoDB is the first name strike in our heads when developing scalable applications with evolving data schemas. Because MongoDB is a document database, it makes it easier for developers to store both structured and unstructured data. Stores and handles large amounts of data quickly, MongoDB is undoubtedly the smart move toward building scalable and data-driven applications. If you’re wondering what MongoDB is and how it can help your digital success, this blog is surely for you.
In this article, we compare MongoDB vs. Firebase. People learn about databases and their various varieties and a detailed comparison chart. We further learn about types of database, features, advantages, and disadvantages, when to use them, and when not to use them.
- The document discusses the differences between MongoDB Operations Manager, MongoDB Cloud Manager, and MongoDB Atlas for managing a MongoDB database server on a private multi-national cloud.
- It recommends using a sharded MongoDB topology to store each country's data locally due to data locality laws while allowing a single view of data across countries.
- It provides an overview of cloud computing, Platform as a Service (PaaS), and suggests using Cloud Foundry as a PaaS for automatically deploying, configuring, scaling, and managing applications on the cloud.
The document provides an introduction and overview of NoSQL databases. It discusses why NoSQL databases were created, the different categories of NoSQL databases including column stores, document stores, and key-value stores. It also provides an overview of Hadoop, describing it as a framework that allows distributed processing of large datasets across computer clusters.
This document provides an overview and introduction to MongoDB. It discusses how new types of applications, data, volumes, development methods and architectures necessitated new database technologies like NoSQL. It then defines MongoDB and describes its features, including using documents to store data, dynamic schemas, querying capabilities, indexing, auto-sharding for scalability, replication for availability, and using memory for performance. Use cases are presented for companies like Foursquare and Craigslist that have migrated large volumes of data and traffic to MongoDB to gain benefits like flexibility, scalability, availability and ease of use over traditional relational database systems.
Efficient and scalable multitenant placement approach for in memory database ...CSITiaesprime
Of late Multitenant model with In-Memory database has become prominent area for research. The paper has used advantages of multitenancy to reduce the cost for hardware, labor and make availability of storage by sharing database memory and file execution. The purpose of this paper is to give overview of proposed Supple architecture for implementing in-memory database backend and multitenancy, applicable in public and private cloud settings. Backend in memory database uses column-oriented approach with dictionary based compression technique. We used dedicated sample benchmark for the workload processing and also adopt the SLA penalty model. In particular, we present two approximation algorithms, multi-tenant placement (MTP) and best-fit greedy to show the quality of tenant placement. The experimental results show that MTP algorithm is scalable and efficient in comparison with best-fit greedy algorithm over proposed architecture.
This document discusses combining Apache Spark and MongoDB for real-time analytics. It describes how MongoDB provides rich analytics capabilities through queries, aggregations, and indexing. Apache Spark can further extend MongoDB's analytics by offering additional processing capabilities. Together, Spark and MongoDB enable organizations to perform real-time analytics directly on operational data without needing separate analytics infrastructure.
This document discusses different approaches to modeling messaging inboxes in MongoDB. It describes three main approaches: fan out on read, fan out on write, and bucketed fan out on write. Fan out on read involves storing a single document per message with all recipients, requiring a scatter-gather query to read an inbox. Fan out on write stores one document per recipient but still requires random reads. Bucketed fan out on write stores messages in bucketed inbox documents for each recipient, allowing an entire inbox to be read with one or two documents. In general, bucketed fan out on write is considered the best approach as it provides good performance for both sending messages and reading inboxes. Factors like data size, write vs read loads
This talk introduces the features of MongoDB by demonstrating how one can build a simple library application. The talk will cover the basics of MongoDB\'s document model, query language, and API.
Strategies For Backing Up Mongo Db 10.2012 CopyJeremy Taylor
This document discusses various strategies for backing up MongoDB deployments, including single node, replica sets, and sharded clusters. It covers using file system snapshots, mongodump, journaling, and stopping the balancer to get consistent backups. The key strategies are:
1. Using fsyncLock and mongodump to backup a single node deployment.
2. Backing up a hidden secondary in a replica set after killing mongod and using fsyncLock.
3. Using mongodump with the --oplog option and mongorestore with --oplogreplay to get a point-in-time backup of a replica set.
4. Stopping the balancer, backing up each shard and
MongoDB on Windows Azure provides two options for deploying the MongoDB database on Microsoft's cloud platform:
1) Windows Azure Virtual Machines allow more control over infrastructure but require more operational effort. Users can choose Windows or Linux and install software themselves.
2) Windows Azure Cloud Services decrease operational effort through automated management but provide less infrastructure control. Only Windows is supported and configurations are pre-defined.
Both options provide scalability and high availability through features like replication and sharding. Developers should evaluate the level of control and effort needed to determine the best deployment model for their application on the Windows Azure cloud.
Brig Lamoreaux\'s of Apollo Group worked with his colleagues to put together this WP detailing their evaluation of MongoDB. He also presented at Oracle Openworld 2012 on their use case with MongoDB.
Commands are special MongoDB operations that can be run from client libraries or the shell. This document provides a list and brief description of some of the most commonly used commands, including commands to get database and server information and statistics, manage collections and indexes, and configure replication and profiling.
The document provides an overview of running MongoDB on Amazon EC2, including basic tips, a basic installation process, architecture considerations, operations like backup and monitoring, and security recommendations. It discusses building MongoDB on EC2 instances for performance, durability, elasticity, and scalability. The installation instructions outline downloading and installing MongoDB, creating an EBS volume, and mounting it to store data on.
- Modern data workloads like big data, agile development, and cloud computing are driving new requirements for database management systems that relational databases can't meet.
- NoSQL databases like MongoDB were created to address these new requirements by providing horizontal scalability, flexible schemas, and compatibility with cloud environments.
- MongoDB scales across multiple servers, allows dynamic schema changes, and runs well on commodity hardware and virtual infrastructures, making it well-suited for modern applications.
1. A Total Cost of Ownership
Comparison of MongoDB & Oracle
A 10gen White Paper
2. A Total Cost of Ownership
Comparison of MongoDB & Oracle
Executive Summary
MongoDB is an open source, document-oriented database designed with both
scalability and developer agility in mind. MongoDB bridges the gap between
key-value stores – which are fast and scalable – and relational databases –
which have rich functionality. Instead of storing data in rows and columns as
one would with a relational database, MongoDB stores JSON documents with
dynamic schemas.
Customers should consider three primary factors when evaluating databases:
data model, robust feature set and high-performance, high-availability archi-
tecture make it suitable for a wide range of database use cases. Given that in
consider the relative costs of each solution when evaluating which database
to adopt.
It can be faster and cheaper to develop and deploy applications on MongoDB
and administrative costs – and topline advantages – it is easier and faster to
evolve applications to meet changing business and market conditions.
This white paper seeks to illustrate the business rationale for deploying
MongoDB over Oracle. We compare the total cost of ownership (TCO) of
MongoDB and Oracle, accounting for upfront and ongoing costs, including soft-
and one larger enterprise project – and a model for evaluating database TCO.
Customers can use this framework to assess the cost of undertaking projects
run than Oracle Database (Enterprise Edition deployed with Oracle Real Applica-
implications, as well.
2
3. Table 1: Summary of Costs Associated with Building and Running a Database
COST CATEGORIES DESCRIPTION
Initial Developer Effort - Personnel cost
- Developer coding required to get application and data store to work together
Initial Administrative Effort - Personnel cost
- Admin/s to install and configure software, cluster machines, set up sharding, etc.
UPFRONT COSTS
Software Licenses - All software related to the data store itself, as well as management tasks such as
clustering, replication and caching
Server Hardware - Servers required to run database (excludes storage)
- Driven primarily by the number and type of processors and RAM
- Lesser costs include enclosures, network connectivity, cabling and power supplies
Storage Hardware - Storage required to store the data
- Varies depending on whether internal or shared (SAN) storage is used, the amount
of storage and whether hard disk drives (HDDs) or solid state drives (SSDs) are
used
Ongoing Developer - Personnel
Effort - Coding needed to adapt data store to customer, market and business needs
Ongoing Administrative - Personnel
Effort
- Administrative effort required to keep data store healthy and running (e.g.,
planning and responding to downtime, upgrading software and hardware)
ONGOING COSTS
Software Maintenance and - Maintenance: upgrades and bug fixes to software
Support
- Support: on-call assistance for troubleshooting technical problems with software
Hardware Maintenance and - Maintenance: upgrades and bug fixes to firmware and any software that may come
Support with hardware
- Support: on-call assistance for troubleshooting technical problems with hardware
Miscellaneous Deployment - Other costs associated with keeping database up and running
Costs
- Includes cloud/hosting/colocation costs, bandwidth charges, electricity fees, etc.
- Generally correlated with the number of servers in use, but vary greatly depending
on a variety of factors
- Because the choice of MongoDB versus a relational database is not the major
driver of these costs, we don’t explore these costs in this paper
3
4. Cost Categories
To compare the economics of deploying MongoDB technology and current development practices – both of
and Oracle, we consider the total cost of ownership
the relational database industry 30 years ago.
TCO captures both the upfront and ongoing costs as-
sociated with building and running a database. It in-
cludes both personnel costs (e.g., developer salaries), can be summed up as follows:
as well as the cost of hardware, software and support.
» Ease of Use. MongoDB supports modern
Table 1 describes the cost categories we consider in development methodologies, such as the Agile
this analysis. Method, making it easy for developers to iterate
quickly and continually over the data model. By
contrast, Oracle imposes a strict set of constraints
of building and deploying one smaller and one larger on data model development.
enterprise application using MongoDB Subscriber Edi-
tion and Oracle Database (Enterprise Edition deployed » Data Model. With MongoDB, the developer only
with Oracle Real Application Clusters). Although there needs to create the data model in one place: the
are a number of potential deployment topologies, application. With Oracle, developers need to create
which will vary from application to application, the and maintain the data model in three places using
different interfaces: the application, the database
deployments described in this paper paint the picture
itself and the ORM layer.
of how the economics of these two databases typi-
cally stack up relative to one another. » Data Flexibility. Unlike Oracle, MongoDB allows
developers to easily store polymorphic data, as well
as structured, semi-structured and unstructured data
deployment and administration, simpler hardware all in a single data store.
requirements and open-source licensing can make
» JSON Support. Storing JSON – the basis of many
modern applications – is seamless and requires no
to build and run than Oracle. Moreover, customers translation in MongoDB. With Oracle, developers
to store it in relational tables, only to have to
-
ing MongoDB vs. Oracle are even greater than those the database.
depicted here.
» Cloud Architecture. MongoDB is well suited to
elastic cloud deployments given its scale-out design,
TCO for Example Projects whereas deploying Oracle in the cloud can be
challenging given the infrastructure requirements of
Upfront Costs relational databases.
INITIAL DEVELOPER EFFORT » Ease of Licensing. MongoDB licensing is simple;
Initial developer effort refers to the cost of developer subscriptions are priced on a per server, per year
time required to get an application and data store to
work together. it often necessitates that developers wait for
For relational databases, initial developer effort in- environments, which can take weeks or even months.
Given the above the TCO model assumes that Oracle
an object-relational mapping (ORM) layer and writing
requires 2x the initial developer effort of MongoDB. Thus,
the business logic for the application.
for the smaller project we assume baseline developer effort
MongoDB is designed to be easy to use for modern of 24 man-months for Oracle and 12 man-months for Mon-
developers. As a result, it is much more cost-effective goDB (a 50% reduction); for the larger project we assume
to develop with MongoDB than to develop with of 72 man-months for Oracle and 36 man-months for Mon-
relational databases. MongoDB derives this major goDB (a 50% reduction). Across both scenarios, we assume a
productivity advantage from its document-oriented fully-loaded developer salary of $120,000 per year.
design and dynamic schemas. The way it stores ap-
plication data matches both current development
4
5. INITIAL ADMINISTRATOR EFFORT
features, customers must buy Oracle Database Enter-
deployment, an administrator typically needs to Edition) plus Oracle Real Application Clusters (RAC), an
consider just one variable – the number of nodes in add-on application that enables horizontal scaling over
multiple servers.
settings to get up and running. By contrast, Oracle
-
tor effort can be an intense, multi-week process for
Oracle, as an administrator must consider tuning hun- Enterprise Edition ($47,500 per core) plus Oracle RAC
dreds of variables to get good performance out of the pricing ($23,000 per core), for a total of $70,500 per core.1
Discounts on Oracle pricing can range from 0% for small
deployments to 80% for large deployments. We assume a
consultants to do so. conservative 50% discount on the list price for the smaller
and larger projects. Additionally, we apply an additional
MongoDB administrators do not need to integrate 50% discount on top of that to Oracle’s core processor
caching layers or create custom sharding logic licensing factor.2 This amounts to $17,625 per core for
to direct queries to the right server node. Rather, both projects.
both caching and sharding are core capabilities of
SERVER HARDWARE
In contrast, enabling and scaling caching, sharding
and site-to-site replication often requires substantial is designed to use commodity hardware in scale-out
effort and custom code with Oracle. architectures. MongoDB deployments typically use
Based on the same logic applied to initial developer as $3,000; even a high-performance, low-power system
effort, the TCO model assumes that MongoDB requires
half the initial administrator effort required for Oracle.
We assume that Oracle requires 2 man-months of admin- By contrast, an Oracle deployment typically uses a large,
istrator time for the smaller project and 6 man-months single server to optimize performance based on its rela-
for the larger project, whereas MongoDB requires 1 tional architecture. 3 As an alternative to using propri-
man-month for the smaller project and 3 man-months etary scale-up hardware, Oracle does offer a clustering
for the larger project (a 50% reduction). We assume a add-on, Oracle Real Application Clusters (RAC), which
fully-loaded DBA salary of $120,000 per year across enables customers to deploy Oracle Database in a scale-
both scenarios. out fashion. As previously mentioned, in this paper we
-
SOFTWARE LICENSES -
MongoDB is an open-source database with a free
community version and a commercial subscriber edi-
tion; the latter includes support, software upgrades The TCO model assumes the same server hardware for
MongoDB and Oracle. For the smaller project, we assume
commercial edition of MongoDB is priced on an ongo- 3 servers, each with 8 cores and 32 GB RAM, at $4,000 per
ing basis versus a one-time basis (i.e., per server, per server. For the larger project, we assume 30 servers, each
year), we capture this cost under Software Support & with 8 cores and 32 GB RAM, at $4,000 per server.
Maintenance later in this paper.
STORAGE HARDWARE
Oracle licenses are priced on a per-core basis.
years have between 4 and 24 cores apiece, even -
- drives (SSDs).
base Standard Edition does not include a number
of central capabilities required for modern applica- Although Oracle can reduce its storage footprint using
tions, such as automated failover, memory caching, compression, Oracle deployments typically require much
auto-sharding and clustering. In order to get these
1 While one can reduce software costs by using only Oracle Database Enterprise Edition and forgoing Oracle RAC, this entails adopting a scale-up (vs. scale-
2 To account for differences in CPU core architecture, Oracle multiplies the number of cores by a core processor licensing factor. This ranges from 0.25 for
older Sun SPARC processors, to 0.5 for most AMD and Intel processors, to 1.0 for IBM Power and others.
3
cores apiece, and range from $25,000 to over $200,000 each.
6. typically requires a single storage model, like a Storage
Area Network (SAN), to ensure base levels of availabil-
ity and performance. SANs range from $25,000 to over translates to increased overhead. As data schemas and
$500,000, depending on their capabilities, and thus can
must evolve, too. Moreover, Oracle has thousands of
settings, and administering Oracle requires deep techni-
For MongoDB, the TCO model accounts for two 1 TB SSDs cal skills and training. Customers moving from Oracle
(1 TB mirrored) per server ($8,000), which translates to to MongoDB report that they can slash their adminis-
$24,000 in the smaller project and $240,000 in the larger
project. For Oracle, we assume a 3 TB SAN ($125,000 for 3 internal Oracle DBA and retained an outside consulting
TB usable) for the smaller project and a 30 TB SAN for the -
larger project ($500,000 for 30 TB usable). opers easily took on half-time administration of the
Ongoing Costs no longer needed.
ONGOING DEVELOPER EFFORT For the smaller project, we assume that Oracle demands
The dynamics of ongoing developer effort are similar 50% of one DBA’s time and MongoDB requires 25% of
to those of initial developer effort. With Oracle, the one DBA’s time (a 50% reduction). Similarly, for the larger
cost of making schema changes is even higher for an project, we assume that Oracle demands 1.5 full-time DBAs,
in-production database than for a database that has not while MongoDB requires half that (3/4 of a DBA’s time). We
yet been released. As a result, many companies strictly assume a fully-loaded DBA salary of $120,000 per year.
prohibit changes to databases or limit them to once or
twice a year. MAINTENANCE AND SUPPORT
MongoDB subscriptions are priced on a per server, per
With MongoDB, however, it is easy for developers to add
year basis. MongoDB Subscriber Edition costs $4,000 per
-
server per year (regardless of server size). This includes
access to MongoDB support, software upgrades and bug
applications as business demands evolve.
In the smaller scenario, we assume that MongoDB requires for-pay edition.4
50% less ongoing developer effort (6 man-months) than Or-
For Oracle, annual software maintenance and support in-
acle (12 man-months). In the larger scenario, we apply the
cludes customer support as well as upgrades to the soft-
same logic, assuming 18 man-months of ongoing developer
ware. It is typically 22% percent of the software license
effort for MongoDB and 36 man-months for Oracle. We as-
cost and thus is driven by the number of cores, not the
sume a fully-loaded developer salary of $120,000 per year.
number of CPUs or servers. As a result, even for small
ONGOING ADMINISTRATOR EFFORT
cost of MongoDB support, often by orders of magnitude.
Ongoing administrator effort includes activities that
keep the system healthy and running (e.g., upgrading
software and hardware, taking backups and recovering hardware purchase price. Given that Oracle typically
-
nance and support for Oracle deployments is higher than
-
it is for MongoDB.
ister MongoDB than it takes to administer Oracle.
Administering a MongoDB deployment mainly involves
For MongoDB, the TCO model assumes software mainte-
nance and support costs of $4,000 per server, per year for
are only a couple dozen MongoDB settings to under-
the smaller project, and $3,600 per server, per year for the
-
larger project (a 10% discount). The TCO model assumes
bility makes it easy to perform common administrative
22% of license costs for Oracle. The model also assumes
tasks like switching out failed hardware and upgrading
hardware maintenance and support costs of 10% of the
hardware purchase price for both MongoDB and Oracle.
system administrator groups have no trouble picking up
the task of managing MongoDB, because no special skills
are required.
4 The Subscriber Edition of MongoDB includes SNMP support and a commercial license.
6
7. Table 2: Database TCO Analysis Summary
SMALLER ENTERPRISE PROJECT LARGER ENTERPRISE PROJECT COMMENTS
MongoDB Oracle MongoDB Oracle
Configuration Software: MongoDB Software: Oracle Software: MongoDB Software: Oracle
Subscriber Edition Database Subscriber Subscriber Edition Database Subscriber
Description Edition & Oracle Edition & Oracle
Server Hardware: Server Hardware:
Real Application Real Application
3 servers (8 30 servers (8
Cluster (RAC) Cluster (RAC)
cores/server) cores/server)
Server Hardware: Server Hardware:
Storage Hardware: Storage Hardware:
3 servers (8 30 servers (8
3 TB SSD (mirrored) 30 TB SSD
cores/server) cores/server)
(mirrored)
Storage Hardware: Storage Hardware:
3 TB SAN (usable) 30 TB SAN (usable)
MongoDB: Assume that MongoDB's ease of use and
increased developer agility decreases development time
by 2x (see body of paper for more detailed explanation)
Initial Developer
$120,000 $240,000 $360,000 $720,000 Oracle: Assume baseline of 24 man-months of application
Effort development for smaller project and 72 man-months for
larger project
Assume fully-loaded developer salary of $120,000/yr.
MongoDB: Using same logic as above, assume MongoDB
Initial decreases admin. time by 2x vs. Oracle
Administrative $10,000 $20,000 $30,000 $60,000 Oracle: Assume baseline of 2-man months of admin. effort
for smaller project and 6 man-months for larger project
Effort
Assume fully-loaded DBA salary of $120,000/yr.
UPFRONT COSTS
MongoDB: Cost for MongoDB Subscriber Edition is captured
under Software Support & Maintenance (below)
Software Licenses $0 $423,000 $0 $4,230,000 Oracle: $70,500/RAC core ($47,500 for Oracle DB
Enterprise Edition + $23,000 for Oracle RAC), 0.5 Xeon Core
License Factor, 50% discount off list price
MongoDB and Oracle: 8-core servers with 32 GB RAM
Server Hardware $12,000 $12,000 $120,000 $120,000 ($4,000/server). 3 servers for smaller project; 30 servers
for larger project
MongoDB: 2, 1TB SSDs (mirrored) per server ($4,000/SSD).
6 SSDs for smaller project; 60 SSDs for larger project
Storage Hardware $24,000 $125,000 $240,000 $500,000
Oracle: 3 TB SAN (usable) for smaller project ($125,000);
30 TB SAN (usable) for larger project ($500,000)for larger
Total Upfront Costs $166,000 $820,000 $750,000 $5,630,000
MongoDB: Assume that MongoDB's ease of use and increased
developer agility decreases development time by 2x
Development Oracle: Assume baseline of 12 man-months of application
$60,000 $120,000 $180,000 $360,000 development for smaller project and 36 man-months for
Effort
larger project
Assume fully-loaded developer salary of $120,000/yr.
MongoDB: Using same logic as above, assume MongoDB
decreases admin. time by 2x
Administration
$30,000 $60,000 $90,000 $180,000 Oracle: Assume smaller project requires 50% of one DBA's
Effort time and larger project requires 1.5 full-time DBAs
ANNUAL ONGOING COSTS
Assume fully-loaded DBA salary of $120,000/yr.
Software MongoDB: MongoDB: $4,000/server/yr. for smaller project;
Maintenance $12,000 $93,060 $108,000 $930,600 $3,600/server/yr. for larger project (a 10% discount)
and Support Oracle: 22% of license fees
Server
Maintenance $1,200 $1,200 $12,000 $12,000 MongoDB and Oracle: 10% of hardware purchase price
and Support
Storage
Maintenance $2,400 $12,500 $24,000 $50,000 MongoDB and Oracle: 10% of hardware purchase price
and Support
Miscellaneous Varies Varies Varies Varies Not considered -- varies significantly and is assumed to be
Greatly Greatly Greatly Greatly comparable for MongoDB and Oracle
Deployment Costs
Total Ongoing
$105,600 $286,760 $414,000 $1,532,600
Costs
Total Ongoing
$316,800 $860,280 $1,242,000 $4,597,800
Costs over 3 Years
3-Year
$482,800 $1,680,280 $1,992,000 $10,227,800
Nominal TCO
Savings vs. Oracle 71% 81% MongoDB enables savings of over 70% vs. Oracle
8. SUMMARY Conclusion
Given the assumptions used in this TCO analysis, The TCO analysis presented in this paper attempts to
to build and run than Oracle (Enterprise Edition deployed by adopting MongoDB. Although cost disparities could
with Oracle RAC). be smaller or greater depending on several factors,
As previously mentioned, although we believe this
analysis is representative of the economics of MongoDB
vs. Oracle, applications, topologies and costs will vary
from use case to use case. The TCO analysis presented
-
-
plications could see even greater cost savings than those -
shown in this paper; in some cases, the cost disparities
may be smaller. We encourage those evaluating different
database solutions to use our framework as a starting faster time-to-market.
point for conducting this analysis for themselves.
evaluating TCO for whatever projects and databases
Topline Implications of Using they may be considering.
MongoDB To learn more about MongoDB and its TCO advantages,
or to speak to a sales representative, please email
info@10gen.com.
modify a rigid relational schema to change its applica-
but also suffers the opportunity cost of a slower time-
to-market.
-
cussed previously translate to increased time-to-value
-
ment method enable businesses to adapt their products
quickly if customers demand change. The ability to
deploy in elastic cloud environments means that busi-
nesses can scale technology in line with revenue and
customers.
they are far more subjective and situation-dependent
than any of the costs discussed in this paper. As such, we
-
what their businesses could achieve if database develop-
8
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