MongoDB at community engine

Mar 18, 2012Download as PPT, PDF0 likes1,063 views

This talk will cover lessons learned at Community Engine regarding MongoDB, including: why we moved away from an Hybrid solution using SQL and MongoDB; an outline of the technologies and what we learned using MongoDB on Amazon Web Services; the MongoDB C# driver; MongoDB with SOLR for Full Text Search; how we do migration, deployment and more.

About me

Lead platform engineer
mathieu.kempe@communityengine.com
@mathieukempe

Agenda

Brain dump on our experience with MongoDB
• Why NoSQL
• Why we chose MongoDB
• Moving away from an hybrid solution
• MongoDB and Amazon
• SOLR and MongoDB
• Ease of development
• Zero downtime database deployment

About Community Engine
• Social network based on locality and
small business
• Launching in April
• Built on:
• ASP.NET MVC 3
• Amazon Web services
• MongoDB
• SOLR
• Mahout
• …

NoSQL?

• How to store the big amounts of data required
in social networking applications
• Data complexity, NoSQL handle hierarchical
and graph data structures better
• Change management is always difficult with
RDBMS
• Scaling

Why we chose MongoDB?

• Reviewed different products RavenDB, CouchDB...
• Selected MongoDB because we had the best
experience
– Very easy to install and get started
– Great developer experience
– Replication very easy to setup
– Good documentation
– Much of the convenience of SQL, Dynamic
Queries, Indexing

Why Hybrid?

• Team had a lot of experience with SQL Server
and Entity Framework
• Reporting
• Transaction

No more SQL Server
• Simplify our infrastructure
• Easier to Backup
• Better performance, not slowed down by SQL
Server, too many queries joined in the
application
• Development speed
• Lower cost

Transaction?

Transactions we could go around that using the atomic
document updates and a good schema design

MongoDB supports atomic operations on single
document.

When transactions across documents are needed
Two phase commits

Hosting with Amazon Web Service

• Elasticity and scalability
• Configure MongoDB using Amazon EC2 instance
bundled into an AMI.
• 64 bits EC2 instance
• Raid10 + EBS volumes
• Multi-datacenter 3-node replica set in different
availability zone
• Use secondaries for zero downtime backup
• We are not yet using sharded replica sets

What about durability?

Use journaling
Use replica sets

Critical writes

Verify that replication is working at write time

mongodb://host1,host2,host3/?safe=true;w=2;

•safe=true : Use safemode
•w=2: wmode, connect to a replica set waiting for
replication to succeed on the majority of nodes

Why we kept SOLR?

• Right tool for the right job
• Proven technology
• SOLR best solution for Full Text Indexing
• Faceted search, Spelling suggestions…
• Team already skilled with SOLR
• SOLR scales well

$Mongo Database Schema Using Type discriminator { "_id" : ObjectId("4f504e7acd3e1c190ce04198"), "_t" : "PhotoSpark", "Photo" : "MyMotorcycle.png " "DateCreated" : ISODate("2012-02-24T09:23:12.246Z") } { "_id" : ObjectId("4f504e7ccd3e1c190ce04199"), "_t" : "PostSpark", "Body" : "Hello World“ " DateCreated" : ISODate("2012-02-28T10:44:12.858Z") }$

Deployment of database with zero downtime

• We release every week
• We aim at zero downtime
• Our domain model change often

Deployment of database with zero downtime

Make sure that our code can handle both
"versions" of the data structure
When saving we updates to the new structure

Deployment of database with zero downtime

• Use a migration script

Deployment of database with zero downtime

Expansion Script Deploy new version Compression Script

t

Thank you!

mathieu.kempe@communityengine.com
@mathieukempe

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Unlike traditional product management, AI product leadership requires new mental models, collaborative approaches, and new measurement frameworks. This presentation breaks down how Product Managers can successfully lead AI Innovation in today's rapidly evolving technology landscape. Drawing from practical experience and industry best practices, I shared frameworks, approaches, and mindset shifts essential for product leaders navigating the unique challenges of AI product development. In this deck, you'll discover: - What AI leadership means for product managers - The fundamental paradigm shift required for AI product development. - A framework for identifying high-value AI opportunities for your products. - How to transition from user stories to AI learning loops and hypothesis-driven development. - The essential AI product management framework for defining, developing, and deploying intelligence. - Technical and business metrics that matter in AI product development. - Strategies for effective collaboration with data science and engineering teams. - Framework for handling AI's probabilistic nature and setting stakeholder expectations. - A real-world case study demonstrating these principles in action. - Practical next steps to begin your AI product leadership journey. This presentation is essential for Product Managers, aspiring PMs, product leaders, innovators, and anyone interested in understanding how to successfully build and manage AI-powered products from idea to impact. The key takeaway is that leading AI products is about creating capabilities (intelligence) that continuously improve and deliver increasing value over time.

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The MCP (Model Context Protocol) is a framework designed to manage context and interaction within complex systems. This SlideShare presentation will provide a detailed overview of the MCP Model, its applications, and how it plays a crucial role in improving communication and decision-making in distributed systems. We will explore the key concepts behind the protocol, including the importance of context, data management, and how this model enhances system adaptability and responsiveness. Ideal for software developers, system architects, and IT professionals, this presentation will offer valuable insights into how the MCP Model can streamline workflows, improve efficiency, and create more intuitive systems for a wide range of use cases.

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I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.

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At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including: -Artificial Intelligence Market Overview -Strategies for AI Adoption in 2025 -Anticipated drivers of AI adoption and transformative technologies -Benefits of AI and Big data for your business -Tips on how to prepare your business for innovation -AI and data privacy: Strategies for securing data privacy in AI models, etc. Download your free copy nowand implement the key findings to improve your business.

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