The document discusses decoupling application logic from data storage by implementing a plugin-like data layer. It describes how Pixable migrated their data storage from MySQL to MongoDB using this approach, allowing them to move data incrementally while maintaining service. The document outlines the benefits of this decoupled architecture, including easy integration of new data sources and balancing load across storage engines. It also notes some cons like increased queries and code complexity.
The document discusses new features and improvements in Grails 2.0, including enhanced plugin support, NoSQL integration, improved unit testing, static resource handling, GORM updates, and SQL database migration tools. It provides an overview of several new capabilities and summarizes key areas that received attention in the latest Grails release.
This document outlines the topics covered in an Edureka course on MongoDB. The course contains 8 modules that cover MongoDB fundamentals, CRUD operations, schema design, administration, scaling, indexing and aggregation, application integration, and additional concepts and case studies. Each module contains multiple topics that will be taught through online instructor-led classes, recordings, quizzes, assignments, and support.
First slide of Hadoop:
* Introduction to Big Data and Hadoop:
- Presenting and defining big data
- Introducing Hadoop and History
- Hadoop - how it works?
- HDFS
The document describes CUBRID Cluster, a project to enable CUBRID database to scale out across multiple servers. Key points:
- The goal is to provide dynamic scalability, location transparency, and increased volume/performance compared to a single CUBRID database.
- The design includes a global schema, distributed data partitioning across nodes, global transactions, and dynamic scalability through adding/removing nodes.
- Milestones included implementing the global schema, distributed partitioning, performance improvements, and dynamic scalability features.
- Demo and performance testing showed increased throughput from data and query distribution across multiple servers compared to a single CUBRID database.
Architecting virtualized infrastructure for big data presentationVlad Ponomarev
This document discusses architecting virtualized infrastructure for big data. It summarizes that big data is growing exponentially and new frameworks like Hadoop are enabling analysis of large, diverse data sets. Virtualization can simplify and optimize big data platforms by providing a unified analytics cloud that elastically provisions various data systems like Hadoop and SQL clusters on shared hardware infrastructure. This improves utilization and makes big data platforms faster and easier to deploy and manage.
MongoDB (from humongous) is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Released under a combination of the GNU Affero General Public License and the Apache License, MongoDB is free and open-source software.
This document discusses integrating Scala and MongoDB. It begins with an introduction to MongoDB, describing it as a document-oriented database that uses JSON-like documents and favors embedding related data over foreign keys. It supports various platforms and has a rich query interface. The document then outlines several Scala drivers for MongoDB, including mongo-scala-driver, lift-mongo, and casbah. It also discusses using STM with MongoDB via Akka. Finally, it notes that MongoDB provides native APIs for various languages to interact with it in a way adapted to each language.
The document discusses new features and improvements in Grails 2.0, including enhanced plugin support, NoSQL integration, improved unit testing, static resource handling, GORM updates, and SQL database migration tools. It provides an overview of several new capabilities and summarizes key areas that received attention in the latest Grails release.
This document outlines the topics covered in an Edureka course on MongoDB. The course contains 8 modules that cover MongoDB fundamentals, CRUD operations, schema design, administration, scaling, indexing and aggregation, application integration, and additional concepts and case studies. Each module contains multiple topics that will be taught through online instructor-led classes, recordings, quizzes, assignments, and support.
First slide of Hadoop:
* Introduction to Big Data and Hadoop:
- Presenting and defining big data
- Introducing Hadoop and History
- Hadoop - how it works?
- HDFS
The document describes CUBRID Cluster, a project to enable CUBRID database to scale out across multiple servers. Key points:
- The goal is to provide dynamic scalability, location transparency, and increased volume/performance compared to a single CUBRID database.
- The design includes a global schema, distributed data partitioning across nodes, global transactions, and dynamic scalability through adding/removing nodes.
- Milestones included implementing the global schema, distributed partitioning, performance improvements, and dynamic scalability features.
- Demo and performance testing showed increased throughput from data and query distribution across multiple servers compared to a single CUBRID database.
Architecting virtualized infrastructure for big data presentationVlad Ponomarev
This document discusses architecting virtualized infrastructure for big data. It summarizes that big data is growing exponentially and new frameworks like Hadoop are enabling analysis of large, diverse data sets. Virtualization can simplify and optimize big data platforms by providing a unified analytics cloud that elastically provisions various data systems like Hadoop and SQL clusters on shared hardware infrastructure. This improves utilization and makes big data platforms faster and easier to deploy and manage.
MongoDB (from humongous) is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Released under a combination of the GNU Affero General Public License and the Apache License, MongoDB is free and open-source software.
This document discusses integrating Scala and MongoDB. It begins with an introduction to MongoDB, describing it as a document-oriented database that uses JSON-like documents and favors embedding related data over foreign keys. It supports various platforms and has a rich query interface. The document then outlines several Scala drivers for MongoDB, including mongo-scala-driver, lift-mongo, and casbah. It also discusses using STM with MongoDB via Akka. Finally, it notes that MongoDB provides native APIs for various languages to interact with it in a way adapted to each language.
Introduction to Web Application Technologies
CGI Programs on the Web Server
What is servlet?
Jobs of servlet
Advantages over CGI
Why pages are build dynamically?
Servlet container
Installation & configuration
- Type 1: Integration of Tomcat server and eclipse
- Type 2: Java Servlet
Servlet Sample Example
Servlet Overview And Architecture
- Servlet Life cycle/Single Thread Model
- Interface Servlet
- HttpServlet Class
- HttpServletRequest, HttpServletResponse
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
This document provides an overview of integrating Scala and MongoDB. It begins with introductions to Scala and MongoDB individually, including their key features. It then discusses how Scala and MongoDB can be combined effectively, covering Scala driver libraries for MongoDB like lift-mongo and casbah. It also mentions how Scala's software transactional memory (STM) can be used with MongoDB via Akka. The document aims to illustrate how Scala and MongoDB can work well together.
NYC Amazon Web Services Meetup: How Glue uses AWSAlex Iskold
AdaptiveBlue presented on their social networking product called Glue. Glue is a contextual network that automatically connects people around common interests like books, music, movies using semantic technologies. It operates by distributing people's interactions around these things across Amazon Web Services like SimpleDB for data storage, S3 for object metadata, and EC2 for processing. The system scales by partitioning data and requests equally across load balanced EC2 instances behind round robin DNS.
The document introduces NoSQL databases and Couchbase. It notes that rigid schemas and inability to scale data drove many organizations to adopt NoSQL solutions. It then provides an overview of different NoSQL database models and focuses on document databases. The document explains that document databases store data as collections of JSON documents, each with a unique key but flexible structure. This allows embedding all information about an entity into a single document rather than requiring joins across tables as in a relational database. An example shows how user profile information can be stored in one document rather than across multiple tables.
Web Application Technologies,What is servlet?
Jobs of servlet
Advantages over CGI
Why pages are build dynamically?
Servlet container
Installation & configuration
- Type 1: Integration of Tomcat server and eclipse
- Type 2: Java Servlet
Servlet Sample Example
Servlet Overview And Architecture
- Servlet Life cycle/Single Thread Model
- Interface Servlet
- HttpServlet Class
- HttpServletRequest, HttpServletResponse
Handling client request :Http request
Generating Server Response : Http status code
Handling Session
- Cookies
- Session Tracking
- URL-re writing
- Hidden Form fields
Responsive & Responsible: Implementing Responsive Design at Scalescottjehl
Scott Jehl of Filament Group discussed building responsive and responsible websites. He advocated for a layered approach using progressive enhancement. This involves a basic mobile-first experience enhanced for newer browsers. Images and layout adapt to different screensizes using responsive design principles. Accessibility, performance, and usability were highlighted as key areas of responsibility.
The document provides a summary of a database administrator's skills and work experience. It summarizes over 4 years of experience as a DBA working with technologies including MS SQL Server, Oracle, MongoDB, Hadoop and others. Specific experiences are listed, including roles as a database engineer for Cathay Pacific Airways and Aviva Investors, where responsibilities involved installing, configuring and maintaining MongoDB databases in various environments. The summary highlights skills in SQL Server administration, database design, performance tuning, automation, and working on production issues for various projects.
This document summarizes the architecture and infrastructure of Douban.com. It describes how Douban uses Gentoo Linux, MySQL, Memcached, Quixote (Python web framework), and Lighttpd across multiple servers. It discusses strategies for scaling including replication, load balancing, and adding additional application servers. It also describes the evolution of Douban's storage systems from Memcache to DoubanFS to DoubanDB.
Navigating the Transition from relational to NoSQL - CloudCon Expo 2012Dipti Borkar
For more deep NoSQL content from Couchbase, check out http://www.couchbase.com/webinars
NoSQL databases have emerged as a better match than relational systems for modern interactive applications, offering cost-effective data management at “Big Data” scale. But there are significant differences between structured and schema-less database technology. What should architects and technical managers know as they explore NoSQL solutions for their teams?
In this workshop you will learn:
- How to evaluate NoSQL (both technical advantages and limitations) as a potential data management approach
- Critical differences between NoSQL and RDBMS for designing, building and running production applications
- Ideal use cases for NoSQL technology and sample reference architectures
Transition from relational to NoSQL Philly DAMA DayDipti Borkar
The document discusses the transition from relational databases to NoSQL databases. It notes that the two main drivers for adopting NoSQL databases are the lack of flexibility in relational schemas and the inability to scale out data. It provides examples of different types of NoSQL databases like key-value, document, columnar, and graph databases. The document specifically focuses on distributed document databases, explaining their structure where each record is a self-describing JSON or XML document that can have complex, nested data structures. It compares the relational and document data models, providing an example of how user profile data would be structured in each. Finally, it demonstrates how making changes to data is simpler with a document database by embedding all related information
The document discusses Java Database Connectivity (JDBC), which provides a standard interface for connecting to relational databases from Java applications. It describes the JDBC model and programming steps, which include loading a JDBC driver, connecting to a database, executing SQL statements via a Statement object, processing query results stored in a ResultSet, and closing connections. It also covers JDBC driver types, the roles of core classes like Connection and Statement, and transaction handling with JDBC.
JDBC (Java Database Connectivity) is an API that allows Java programs to connect to databases. It provides methods for querying and updating data in a database. The document discusses the history and types of JDBC drivers, how to connect to a database using JDBC, and provides sample code for executing queries and processing result sets.
This file tells about what is DAO and JDBC in the Spring framework, and the components present in Spring Framework.
This File is having Several Examples for Spring JDBC.
Integration and Batch Processing on Cloud FoundryJoshua Long
This talk explores the new possibilities for scale by using Spring Integration, Spring Batch and RabbitMQ on Cloud Foundry, the open source PaaS from VMWare.
Java servlets are small programs that run on a web server and dynamically generate web page content. They extend the functionality of web servers and allow for more complex interactions than CGI programs. Servlets support multithreading and sharing of resources, making them faster than CGI programs which require creating a new process for each request. Common ways to handle form data submitted to servlets include using the getParameter() method to retrieve parameter values by name. Sessions can be tracked across requests using cookies, which are small pieces of data stored in the user's browser by the web server. There are two main architectures for developing JSP applications - page-centric and servlet-centric, with servlet-centric following the MVC pattern and separating business
This document discusses the future of data storage and the rise of NoSQL databases. It notes that while SQL databases have dominated for decades, their suitability is cracking due to limitations in scaling and integration. NoSQL databases are designed to run on clusters across many machines, have flexible schemas, and are open source. They allow for embracing large scale and reducing development drag. However, relational databases are still relevant for some use cases. The future is one of "polyglot persistence" using the best data storage technology for each application's needs.
MongoDB is a scalable, high-performance, open-source NoSQL database that uses documents with dynamic schemas instead of tables. It supports embedded documents and arrays, replication, and sharding. MongoDB is commonly used for web applications, content management, real-time analytics, and caching due to its fast performance for typical web operations. Some key companies using MongoDB in production include eBay, Craigslist, Foursquare, and Sourceforge.
JavaOne 2016
JMS is pretty simple, right? Once you’ve mastered topics and queues, the rest can appear trivial, but that isn’t the case. The queuing system, whether ActiveMQ, OpenMQ, or WebLogic JMS, provides many more features and settings than appear in the Java EE documentation. This session looks at some of the important extended features and configuration settings. What would you need to optimize if your messages are large or you need to minimize prefetching? What is the best way to implement time-delayed messages? The presentation also looks at dangerous bugs that can be introduced via simple misconfigurations with pooled beans. The JMS APIs are deceptively simple, but getting an implementation into production and tuned correctly can be a bit trickier.
This document analyzes the performance of MongoDB and HBase databases. It describes the architectures and key characteristics of each database, including MongoDB's document model, auto-sharding, and replication features. It also covers HBase's use of HDFS for storage and Zookeeper for coordination. The document examines the security features of each database, such as authentication, authorization, and encryption. Finally, it discusses findings from literature that NoSQL databases sacrifice ACID properties for scalability and performance.
How to boost performance of your rails app using dynamo db and memcachedAndolasoft Inc
DynamoDB and Memcached is a powerful combination for your Rails app. If you're looking to improve the performance of your Rails application, this may be the solution for you.
Introduction to Web Application Technologies
CGI Programs on the Web Server
What is servlet?
Jobs of servlet
Advantages over CGI
Why pages are build dynamically?
Servlet container
Installation & configuration
- Type 1: Integration of Tomcat server and eclipse
- Type 2: Java Servlet
Servlet Sample Example
Servlet Overview And Architecture
- Servlet Life cycle/Single Thread Model
- Interface Servlet
- HttpServlet Class
- HttpServletRequest, HttpServletResponse
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
This document provides an overview of integrating Scala and MongoDB. It begins with introductions to Scala and MongoDB individually, including their key features. It then discusses how Scala and MongoDB can be combined effectively, covering Scala driver libraries for MongoDB like lift-mongo and casbah. It also mentions how Scala's software transactional memory (STM) can be used with MongoDB via Akka. The document aims to illustrate how Scala and MongoDB can work well together.
NYC Amazon Web Services Meetup: How Glue uses AWSAlex Iskold
AdaptiveBlue presented on their social networking product called Glue. Glue is a contextual network that automatically connects people around common interests like books, music, movies using semantic technologies. It operates by distributing people's interactions around these things across Amazon Web Services like SimpleDB for data storage, S3 for object metadata, and EC2 for processing. The system scales by partitioning data and requests equally across load balanced EC2 instances behind round robin DNS.
The document introduces NoSQL databases and Couchbase. It notes that rigid schemas and inability to scale data drove many organizations to adopt NoSQL solutions. It then provides an overview of different NoSQL database models and focuses on document databases. The document explains that document databases store data as collections of JSON documents, each with a unique key but flexible structure. This allows embedding all information about an entity into a single document rather than requiring joins across tables as in a relational database. An example shows how user profile information can be stored in one document rather than across multiple tables.
Web Application Technologies,What is servlet?
Jobs of servlet
Advantages over CGI
Why pages are build dynamically?
Servlet container
Installation & configuration
- Type 1: Integration of Tomcat server and eclipse
- Type 2: Java Servlet
Servlet Sample Example
Servlet Overview And Architecture
- Servlet Life cycle/Single Thread Model
- Interface Servlet
- HttpServlet Class
- HttpServletRequest, HttpServletResponse
Handling client request :Http request
Generating Server Response : Http status code
Handling Session
- Cookies
- Session Tracking
- URL-re writing
- Hidden Form fields
Responsive & Responsible: Implementing Responsive Design at Scalescottjehl
Scott Jehl of Filament Group discussed building responsive and responsible websites. He advocated for a layered approach using progressive enhancement. This involves a basic mobile-first experience enhanced for newer browsers. Images and layout adapt to different screensizes using responsive design principles. Accessibility, performance, and usability were highlighted as key areas of responsibility.
The document provides a summary of a database administrator's skills and work experience. It summarizes over 4 years of experience as a DBA working with technologies including MS SQL Server, Oracle, MongoDB, Hadoop and others. Specific experiences are listed, including roles as a database engineer for Cathay Pacific Airways and Aviva Investors, where responsibilities involved installing, configuring and maintaining MongoDB databases in various environments. The summary highlights skills in SQL Server administration, database design, performance tuning, automation, and working on production issues for various projects.
This document summarizes the architecture and infrastructure of Douban.com. It describes how Douban uses Gentoo Linux, MySQL, Memcached, Quixote (Python web framework), and Lighttpd across multiple servers. It discusses strategies for scaling including replication, load balancing, and adding additional application servers. It also describes the evolution of Douban's storage systems from Memcache to DoubanFS to DoubanDB.
Navigating the Transition from relational to NoSQL - CloudCon Expo 2012Dipti Borkar
For more deep NoSQL content from Couchbase, check out http://www.couchbase.com/webinars
NoSQL databases have emerged as a better match than relational systems for modern interactive applications, offering cost-effective data management at “Big Data” scale. But there are significant differences between structured and schema-less database technology. What should architects and technical managers know as they explore NoSQL solutions for their teams?
In this workshop you will learn:
- How to evaluate NoSQL (both technical advantages and limitations) as a potential data management approach
- Critical differences between NoSQL and RDBMS for designing, building and running production applications
- Ideal use cases for NoSQL technology and sample reference architectures
Transition from relational to NoSQL Philly DAMA DayDipti Borkar
The document discusses the transition from relational databases to NoSQL databases. It notes that the two main drivers for adopting NoSQL databases are the lack of flexibility in relational schemas and the inability to scale out data. It provides examples of different types of NoSQL databases like key-value, document, columnar, and graph databases. The document specifically focuses on distributed document databases, explaining their structure where each record is a self-describing JSON or XML document that can have complex, nested data structures. It compares the relational and document data models, providing an example of how user profile data would be structured in each. Finally, it demonstrates how making changes to data is simpler with a document database by embedding all related information
The document discusses Java Database Connectivity (JDBC), which provides a standard interface for connecting to relational databases from Java applications. It describes the JDBC model and programming steps, which include loading a JDBC driver, connecting to a database, executing SQL statements via a Statement object, processing query results stored in a ResultSet, and closing connections. It also covers JDBC driver types, the roles of core classes like Connection and Statement, and transaction handling with JDBC.
JDBC (Java Database Connectivity) is an API that allows Java programs to connect to databases. It provides methods for querying and updating data in a database. The document discusses the history and types of JDBC drivers, how to connect to a database using JDBC, and provides sample code for executing queries and processing result sets.
This file tells about what is DAO and JDBC in the Spring framework, and the components present in Spring Framework.
This File is having Several Examples for Spring JDBC.
Integration and Batch Processing on Cloud FoundryJoshua Long
This talk explores the new possibilities for scale by using Spring Integration, Spring Batch and RabbitMQ on Cloud Foundry, the open source PaaS from VMWare.
Java servlets are small programs that run on a web server and dynamically generate web page content. They extend the functionality of web servers and allow for more complex interactions than CGI programs. Servlets support multithreading and sharing of resources, making them faster than CGI programs which require creating a new process for each request. Common ways to handle form data submitted to servlets include using the getParameter() method to retrieve parameter values by name. Sessions can be tracked across requests using cookies, which are small pieces of data stored in the user's browser by the web server. There are two main architectures for developing JSP applications - page-centric and servlet-centric, with servlet-centric following the MVC pattern and separating business
This document discusses the future of data storage and the rise of NoSQL databases. It notes that while SQL databases have dominated for decades, their suitability is cracking due to limitations in scaling and integration. NoSQL databases are designed to run on clusters across many machines, have flexible schemas, and are open source. They allow for embracing large scale and reducing development drag. However, relational databases are still relevant for some use cases. The future is one of "polyglot persistence" using the best data storage technology for each application's needs.
MongoDB is a scalable, high-performance, open-source NoSQL database that uses documents with dynamic schemas instead of tables. It supports embedded documents and arrays, replication, and sharding. MongoDB is commonly used for web applications, content management, real-time analytics, and caching due to its fast performance for typical web operations. Some key companies using MongoDB in production include eBay, Craigslist, Foursquare, and Sourceforge.
JavaOne 2016
JMS is pretty simple, right? Once you’ve mastered topics and queues, the rest can appear trivial, but that isn’t the case. The queuing system, whether ActiveMQ, OpenMQ, or WebLogic JMS, provides many more features and settings than appear in the Java EE documentation. This session looks at some of the important extended features and configuration settings. What would you need to optimize if your messages are large or you need to minimize prefetching? What is the best way to implement time-delayed messages? The presentation also looks at dangerous bugs that can be introduced via simple misconfigurations with pooled beans. The JMS APIs are deceptively simple, but getting an implementation into production and tuned correctly can be a bit trickier.
This document analyzes the performance of MongoDB and HBase databases. It describes the architectures and key characteristics of each database, including MongoDB's document model, auto-sharding, and replication features. It also covers HBase's use of HDFS for storage and Zookeeper for coordination. The document examines the security features of each database, such as authentication, authorization, and encryption. Finally, it discusses findings from literature that NoSQL databases sacrifice ACID properties for scalability and performance.
How to boost performance of your rails app using dynamo db and memcachedAndolasoft Inc
DynamoDB and Memcached is a powerful combination for your Rails app. If you're looking to improve the performance of your Rails application, this may be the solution for you.
MongoDB is a cross-platform document-oriented database program that uses JSON-like documents with dynamic schemas, commonly referred to as a NoSQL database. It allows for embedding of documents and arrays within documents, hierarchical relationships between data, and indexing of data for efficient queries. MongoDB is developed by MongoDB Inc. and commonly used for big data and content management applications due to its scalability and ease of horizontal scaling.
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.
MongoDB World 2018: Bumps and Breezes: Our Journey from RDBMS to MongoDBMongoDB
The document summarizes the journey of migrating from an RDBMS to MongoDB. It describes the pre-MongoDB RDBMS environment, reasons for choosing MongoDB, and the evolution of the MongoDB environment over time. The evolution involved some bumps in configuring databases and applications, but also many breezes like improved performance, flexibility and scalability. Benchmarking showed MongoDB could handle more concurrent users. Future plans include using MongoDB 4.0 features and further optimizing sharding performance.
New generations of database technologies are allowing organizations to build applications never before possible, at a speed and scale that were previously unimaginable. MongoDB is the fastest growing database on the planet, and the new 3.2 release will bring the benefits of modern database architectures to an ever broader range of applications and users.
Spring Data provides a unified model for data access and management across different data access technologies such as relational, non-relational and cloud data stores. It includes utilities such as repository support, object mapping and templating to simplify data access layers. Spring Data MongoDB provides specific support for MongoDB including configuration, mapping, querying and integration with Spring MVC. It simplifies MongoDB access through MongoTemplate and provides a repository abstraction layer.
This document provides an overview of the MEAN stack and demonstrates how to build a sample application with it. It begins with defining each component of the MEAN stack: MongoDB as the database, Express as the web application framework, AngularJS for the frontend framework, and Node.js as the runtime environment. It then demonstrates setting up a basic Express app, integrating authentication with Passport, and interacting with MongoDB using Mongoose. The document also discusses key concepts like asynchronous I/O in Node.js and model-view-controller patterns in AngularJS. Overall, it serves as a high-level introduction to the technologies that make up the MEAN stack.
Node Js, AngularJs and Express Js TutorialPHP Support
This document provides an overview of the Node.js, Express.js, AngularJS, and MongoDB technologies and how they can be used together. It discusses what each technology is, its features and uses. Node.js is a JavaScript runtime built on Chrome's V8 engine for building fast network applications. Express.js is a web framework built on Node.js that simplifies building web apps. AngularJS is a JavaScript framework for building dynamic web apps. MongoDB is a popular open-source NoSQL database that stores data in JSON-like documents.
MEAN is a full-stack JavaScript solution that helps you build fast, robust, and maintainable production web applications.
MEAN stands for:
M – Mongo DB (database system)
E – Express (back-end framework)
A – Angular.js (front-end framework)
N – Node.js (back-end runtime environment)
This document summarizes strategies for scaling a Ruby on Rails application. It discusses starting with shared hosting and moving to dedicated servers, scaling the database horizontally using replication or clustering, scaling the web servers by adding more application servers behind a load balancer, implementing user clusters to shard user data, adding caching at various levels using solutions like Squid, Memcached, and fragment caching, and using elastic cloud architectures on services like Amazon EC2. The key steps are horizontal scaling of databases, vertical and horizontal scaling of application servers, implementing user sharding and caching to optimize performance, and using elastic cloud services for on-demand scaling.
MongoDB is a cross-platform, document-oriented database that is free, open source, and scalable. It stores data in flexible, JSON-like documents, allowing for easy addition of new document structures, and uses map-reduce functions for complex data processing and aggregation. Key features include horizontal scalability, replication for high availability, and the ability to handle structured, semi-structured, and unstructured data.
The document discusses how to scale a Ruby on Rails application from a single server setup to a more complex architecture using multiple application servers, load balancing, database replication and clustering, caching with Squid and Memcached, and splitting users and data across multiple databases and servers based on factors like location. It provides an overview of the stages of scaling and considerations at each step from basic hosting to a global deployment with millions of users.
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.
GoComet is a logistics company that uses data science and machine learning to maximize efficiency. The intern worked on their website using the MERN stack (MongoDB, Express, React, Node). Technologies used include MongoDB for data storage, Express for the backend API, React for the frontend UI, and Node.js as the runtime environment. The intern completed tasks like building forms with React Hook Form, implementing real-time functionality with Socket.io, integrating Redux for state management, and sending emails with Nodemailer.
MongoDB Developer's Notebook, March 2016 -- MongoDB Connector for Business In...Daniel M. Farrell
This document provides instructions for configuring MongoDB, the MongoDB Connector for BI, Eclipse, and Toad to allow running SQL queries against MongoDB from within Eclipse. It describes downloading and installing a Postgres JDBC driver, MongoDB, and the MongoDB Connector for BI. It also covers creating a sample MongoDB database and collection with documents, and configuring Eclipse and Toad to connect to MongoDB via the Connector using the JDBC driver. This will allow running SQL queries from within Eclipse to interact with MongoDB data.
MongoDb is a document oriented database and very flexible one as it gives horizontal scalability.
In this presentation basic study about mongodb with installation steps and basic commands are described.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
The document discusses guidelines for ordering fields in compound indexes to optimize query performance. It recommends the E-S-R approach: placing equality fields first, followed by sort fields, and range fields last. This allows indexes to leverage equality matches, provide non-blocking sorts, and minimize scanning. Examples show how indexes ordered by these guidelines can support queries more efficiently by narrowing the search bounds.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
The document describes a methodology for data modeling with MongoDB. It begins by recognizing the differences between document and tabular databases, then outlines a three step methodology: 1) describe the workload by listing queries, 2) identify and model relationships between entities, and 3) apply relevant patterns when modeling for MongoDB. The document uses examples around modeling a coffee shop franchise to illustrate modeling approaches and techniques.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
3. Pixable in numbers
~5 million users
9 billion photos (~5 Terabytes)
35 new million photos a day
80 million categories
16 million writes/hour (~40GB/hour)
30 million reads/hour (~120GB/hour)
Logging and profiling
- 15k inserts/sec
7. Presentation
In Pixable we have migrated from/to different data storage
solutions. To accomplish this, we've built a plugin-like data layer
to allow complete separation between application code and
data storage. In fact, our whole migration from MySQL to
MongoDB was performed over this layer, helping us to move
chunks of data little by little while learning how the system
behaved under the new configuration. During the process, we
managed to maintain duplicate copies in MySQL and Mongo for
a while until the transition was complete. All of this happened in
a way almost transparent to the application code, requiring very
little changes in the code.
During this talk, we are going to show how we built this
architecture and how easy is to integrate other data storages
(memcached, S3, etc) on it. We will also share some tips that
we've learned down the road and pros/cons of working under
this schema.
8. Initial infrastructure
LAMMP (Lynux-Apache-Memcache-MySQL-PHP)
Backend
Frontend MySQL
API User DB
class user {
$id;
$first_name;
$last_name;
public function getUser($id) {
$sql = ‘SELECT * FROM users WHERE id =’.$id;
$userRS = db->fetchArray($sql);
$user = $this->buildUser($userRS);
return $user;
}
}
9. Issues encountered
Limit on the DB connections to Master.
Not able to hit the DB hard without generating
lag on the slave servers.
Adding a field to an existing table with billions
of records would mean downtime of the App.
Adding new DB servers was slow (in some
cases required downtime of the app) and high
in server costs.
…so we needed a DB engine easy to
grow, schema-less and low in server cost.
10. Solution found
MongoDB
Has built-in sharding
ReplicaSet features automatic data
clone, synchronization and PRIMARY failover.
Our data fits perfectly in the MongoDB document
paradigm.
Schema-less.
Easier to have many small machines, failures or
maintenances are less traumatic.
Background index creation.
…now we needed a way to start migration
without having downtime and data loss.
11. Implementing solution
Migrating code from classes/functions with
SQL queries all around the project code to
the new Flexible Plugin-like data layer.
API Backend
Frontend MySQL
User DB
MySQL MySQL
User DS
DB
API Backend User
Frontend Data
User Source MongoDB Mongo
User DS
DB
12. Implementing solution – Step 1
User Data Source (Plugin manager)
• Gets the call from the backend for the user
data source.
• Evaluates the conditions defined by us to see
what Data Source to return and looks for the
User
user in the correct data source.
Data
• If the conditions for migrating are activated will
Source
migrate the user if its not migrated already to
the new DB Engine.
• Will return the Data Source defined by the
conditions above.
13. Implementing solution – Step 2
Building each DB engine plugin
User
Data
Source
MySQL MongoDB Memcached …
Plugin Plugin Plugin Plugin
Requirements:
• All plugins have to implement the same set of public
methods/functions.
• All have to reply in the exact same data structure and format.
• All plugins constructors may accept as a parameter another plugin
so we can chain them together if needed.
14. Implementing solution – Step 3
Moving all SQL queries from different classes
methods/functions to the new Data Source infrastructure:
Old Class code: New Class code:
class user { class user {
$id; $id;
$first_name; $first_name;
$last_name; $last_name;
public function getUser($id) { public function getUser($id) {
$sql = ‘SELECT * FROM users WHERE id =’.$id;
$userRS = db->fetchArray($sql);
$uDS = UserDataSource::getUserDS($id);
$user = $this->buildUser($userRS); $userRS = $uDS->getUser();
return $user; $user = $this->buildUser($userRS);
} return $user;
} }
}
15. Example 1
Condition:
• Read operation and found in Memcached
• Write operation, writing in MySQL and MongoDB.
MySQL
Plugin
User MongoDB
Backen Memcached
Data MySQL
d Plugin
Source DS
MongoDB
Plugin
Read operation
Write operation
16. Example 2
Condition:
• Read and write to MySQL but use MongoDB as backup.
MySQL
Plugin
User MongoDB
Backen Memcached
Data MySQL
d Plugin
Source DS
MongoDB
Plugin
Read operation
Write operation
17. Example 3
Condition:
• Only new users should be migrated but use MongoDB as
backup for all read operations from existing users.
MySQL
Plugin
User MongoDB
Backen Memcached
Data MySQL
d Plugin
Source DS
MongoDB
Plugin
Read operation
Write operation
18. Conclusion
Pros:
Separates your app’s code from the Data Storage engines
languages.
Adding new Data Engines easily.
Lets you balance the load generated to each Data Engine.
As the company grows, a team can be dedicated to the Data
Plugins development and optimization, while other team can
actually develop the application itself.
Cons:
Your App will generate more queries to the Data Engines.
You will have to write more lines of code when implementing
this plugins that when only using one Data Engine.
19. Final Recap
App
User
Data
Source
MySQL MongoDB Memcached …
Plugin Plugin Plugin Plugin
Now a days fast changing market/products force one to evolve during the process
Q: How many of you started with LAMP?Easy query, but usually queries are much more complicated than this. Tear them down once applied the new plugin structure.
For doing so we had to normalize all the objects to one same structure with default values and data types (dates to linux time format, etc.) as all Data Layers have to reply to the APP in the exact same format to guarantee compatibility if we decide to deactivate one of the layers.
Conditions for migrating can be from true to false to start migrating users as they come to the app, to more complex conditionals like comparing the last two digits of a user ID and comparing it to a percentage defined by us to see if it should be migrated or not to the new Data Source.Chain Data Sources
Plugins IMPLEMENT a interface class, for minimal public functions check.Data Format = reply with an instance of the object already.
Backend calls insertUser().User DS checks conditions and replies with the data source for the user.Memcached won’t find it so it will call the MongoMySQL DS.MongoMySQL DS will then run from the logic of migration and determined that because it’s a new user we will store it only on MongoDB.
Backend calls insertUser().User DS checks conditions and replies with the data source for the user.Memcached won’t find it so it will call the MongoMySQL DS.MongoMySQL DS will then run from the logic of migration and determined that because it’s a new user we will store it only on MongoDB.
Backend calls insertUser().User DS checks conditions and replies with the data source for the user.Memcached won’t find it so it will call the MongoMySQL DS.MongoMySQL DS will then run from the logic of migration and determined that because it’s a new user we will store it only on MongoDB.
----- Meeting Notes (10/23/12 18:59) -----More queries will only happen while in the migration process.
You can chain them together.You can have different DataSources in different geographic locations for testing a new DB Engine on a location where you have less amount of users.Went form LAMMP to LADP (D = Data = Multiple Data Storage)