2012 mongo db_bangalore_roadmap_new

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This document summarizes MongoDB roadmaps and recent releases. It discusses features added in releases 2.2 and upcoming in 2.4, including concurrency improvements, the new aggregation framework, TTL collections, tag aware sharding, and read preferences. The document encourages participation in the MongoDB community through using the product, submitting feature requests, and joining local user groups.

#mongodbbangalore

Roadmap
Sridhar Nanjundeswaran, 10gen
sridhar@10gen.com, @snanjund
October 26, 2012

Recent Release History
1.8 2.0 2.2 2.4
March ‘11 Sept ‘11 Aug ‘12 winter ‘12

Journaling Index enhancements Aggregation
Sharding and to improve size and Framework
Replica set performance Multi-Data Center
enhancements Authentication with Deployments
Spherical geo sharded clusters Improved
search Replica Set Performance and
Enhancements Concurrency
Concurrency
improvements

2.2 Release August 2012
• Concurrency: yielding + db level locking

• New aggregation framework

• TTL Collections

• Improved free list implementation

• Tag aware sharding

• Read Preferences

• http://docs.mongodb.org/manual/release-notes/2.2/

Yielding and DB Locking
• Improved yielding on page fault
• Breaking down the global level lock
– Lock per Database in 2.2
– Lock per Collection post 2.2

Aggregation Framework
• Pipeline model (a bit like unix pipes)
– Like a "group by"
– Operators
• $project, $group, $match, $limit, $skip, $unwind, $sort
– Expressions
• Logical Expressions: $and, $not, $or, $cmp ...
• Math Expressions: $add, $divide, $mod ...
• String Expressions: $strcasecmp, $substr, $toLower ...
• Date/Time Expressions: $dayOfMonth, $hour...
• Multi-Expressions: $ifNull, $cond

• Use Cases: Real-time / inline analytics

Example – For each "tag", list
the authors
{
title : "my tech blog" ,
author : "bob" ,
tags : [ "fun" , "good" , "tech" ] ,
}

{
title : "cool tech" ,
author : "jim" ,
tags : [ "awesome" , "tech" ] ,
}

$Aggregate Command db.blogs.aggregate( { $project : { author : 1, tags : 1, } }, { $unwind : "$tags" }, { $group : { _id : { tags : "$tags" }, authors : { $addToSet : "$author" } }} );$

$Results { "result" : [ { "_id" : { "tags" : "tech" }, "authors" : [ "jim", "bob" ] },$

Time to Live (TTL)
• Auto expire data out of a collection
• Must be on a date datatype
• Single value is evaluated
• Use Cases: data retention, cache expiration

db.events.ensureIndex(
{ "timestamp": 1 },
{ expireAfterSeconds: 3600 } )

Tag Aware Sharding
• Distribute data based on a Tag
• Use Cases: Locality for Data by Data Center
sh.addShardTag("shard0000", "dc-emea")

sh.addTagRange("mydb.users",
{ country: "uk"}, { country: "ul"},
"dc-emea"
);

sh.addTagRange("mydb.users",
{ country: "by"},{ country: "bz"},
"dc-emea"
);

Read Preferences
• Mode
– PRIMARY, PRIMARY_PREFERRED
– SECONDARY, SECONDARY_PREFERRED
– NEAREST

• Tag Sets
– Uses Replica Set tags
– Passed Tag is used to find matching members

2.4 Highlights
• Security
– SASL, Kerberos, Additions to privileges and auditing

• Hash-based Sharding

• Geospatial Indexing: query intersecting polygons

• Aggregation framework: faster and more features

• V8, background secondary indexing, replica set flapping

• Distribute non-sharded collections throughout cluster

• MMS running in your own data center (separate)

Ongoing Work
• Collection / Extent level locking
• Field level authorization
• Auditing
• Full-text query

What can I do?
• MongoDB is a community driven product
– Use the product
– Log feature requests
– Vote for existing features

• Join your local meetup
– http://www.meetup.com/MongoDB-Bangalore-User-
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Michael Bright presented on using MongoDB and Python. Some key points: 1) MongoDB is a document-oriented NoSQL database that uses JSON-like documents with dynamic schemas, horizontal scaling, and high performance. It provides an alternative to relational databases for applications that need flexibility and scalability. 2) PyMongo is the main Python driver for working with MongoDB, but there are also frameworks and ORMs that provide higher-level APIs. Basic operations like inserting, finding, updating, and deleting documents can be done from the Python shell or code. 3) MongoDB supports indexing, sorting, projections and aggregation to optimize queries. The aggregation framework provides data processing pipelines to transform and analyze data in MongoDB.

Using MongoDB and PythonMike Bright

MongoDB can be used to store and query document-oriented data, and provides scalability through horizontal scaling. The document stores provide more flexibility than relational databases by allowing dynamic schemas with embedded documents. MongoDB combines the rich querying of relational databases with the flexibility and scalability of NoSQL databases. It uses indexes to improve query performance and supports features like aggregation, geospatial queries, and text search.

10gen Presents Schema Design and Data ModelingDATAVERSITY

This document provides an overview of schema design in MongoDB. It discusses topics such as: - The goals of schema design, which include avoiding anomalies, minimizing redesign, avoiding query bias, and making use of features. - Key terminology when comparing MongoDB to relational databases, such as using collections instead of tables and embedding/linking instead of joins. - Examples of basic collections, documents, indexing, and query operators. - Common schema patterns for MongoDB like embedding, normalization, inheritance, one-to-many, many-to-many, and trees. - Use cases like time series are also briefly covered.

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This document provides an agenda for a session on reporting and analytics options in MongoDB, including Map Reduce, the Aggregation Framework, and examples using geospatial and text search features. It discusses building reports in an application, tuning aggregation pipelines with explain plans, and computing aggregations on the fly or pre-computing and storing them. The next session will cover operational topics like scaling out, high availability, production preparation, and sizing.

How to use MongoDB with CakePHPichikaway

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Media owners are turning to MongoDB to drive social interaction with their published content. The way customers consume information has changed and passive communication is no longer enough. They want to comment, share and engage with publishers and their community through a range of media types and via multiple channels whenever and wherever they are. There are serious challenges with taking this semi-structured and unstructured data and making it work in a traditional relational database. This webinar looks at how MongoDB’s schemaless design and document orientation gives organisation’s like the Guardian the flexibility to aggregate social content and scale out.

Webinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & AggregationMongoDB

This document provides an agenda for a MongoDB basics session. It will cover reporting and aggregation options in MongoDB like MapReduce, the Aggregation Framework, and examples of using aggregation for common reports like popular tags, popular articles, and aggregating geospatial data. It also discusses using the aggregation framework pipeline and operators to build these reports and tuning performance with the explain plan.

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As your data grows, the need to establish proper indexes becomes critical to performance. MongoDB supports a wide range of indexing options to enable fast querying of your data, but what are the right strategies for your application? In this talk we’ll cover how indexing works, the various indexing options, and use cases where each can be useful. We'll dive into common pitfalls using real-world examples to ensure that you're ready for scale.

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In this talk we will focus on several of the reasons why developers have come to love the richness, flexibility, and ease of use that MongoDB provides. First we will give a brief introduction of MongoDB, comparing and contrasting it to the traditional relational database. Next, we’ll give an overview of the APIs and tools that are part of the MongoDB ecosystem. Then we’ll look at how MongoDB CRUD (Create, Read, Update, Delete) operations work, and also explore query, update, and projection operators. Finally, we will discuss MongoDB indexes and look at some examples of how indexes are used.

Green daoDroidcon Berlin

greenDAO is an ORM (object relational mapping) library that uses code generation to provide a higher level API for SQLite databases on Android. It generates Java classes from an entity model that allows reading and writing objects to the database instead of using raw SQL. This improves productivity by handling common database tasks like queries and transactions. The generated code avoids reflection for better performance compared to annotation-based ORMs. The library focuses on optimization through techniques like custom cursors, entity caching, and bulk operations to achieve high performance suitable for Android.

MongoDB using Grails plugin by puneet behlTO THE NEW | Technology

Introduction to MongoDB -Understanding collections, documents. -Advantages of MongoDB over relational database. Getting familiar with the MongoDB console. -Using selectors, inserts. -Updating document & update modifiers. -Upserts & Multiple Updates -Mastering Find Integrating MongoDB with Grails using plug-in. -Exploring plug-in features. -Using Geospatial queries. -Dynamic attributes. -Using Low Level API.

MongoDB - A Document NoSQL DatabaseRuben Inoto Soto

MongoDB is a document-oriented NoSQL database that uses a document-data model. It provides horizontal scaling with auto-sharding and replication. MongoDB can store documents in collections without a defined schema and support dynamic queries and indexing. RealNetworks uses MongoDB with Scala and other technologies for an Android app to send notifications to devices with installed RealNetworks applications at scale.

Getting Started with Geospatial Data in MongoDBMongoDB

MongoDB supports geospatial data and specialized indexes that make building location-aware applications easy and scalable. In this session, you will learn the fundamentals of working with geospatial data in MongoDB. We will explore how to store and index geospatial data and best practices for using geospatial query operators and methods. By the end of this session, you should be able to implement basic geolocation functionality in an application. In this webinar, you will learn: - Getting geospatial data into MongoDB and how to build geospatial indexes. - The fundamentals of MongoDB's geospatial query operators and how to design queries that meet the needs of your application. - Advanced geospatial capabilities with Java geospatial libraries and MongoDB.

NOSQL101, Or: How I Learned To Stop Worrying And Love The Mongo!Daniel Cousineau

MongoDB RoadmapMongoDB

Full metal mongoIsrael Gutiérrez

Mongodb introchristkv

MongoDB for Coder Training (Coding Serbia 2013)Uwe Printz

Starting with MongoDBDoThinger

2016 feb-23 pyugre-py_mongoMichael Bright

Using MongoDB and PythonMike Bright

10gen Presents Schema Design and Data ModelingDATAVERSITY

1403 app dev series - session 5 - analyticsMongoDB

How to use MongoDB with CakePHPichikaway

Managing Social Content with MongoDBMongoDB

Webinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & AggregationMongoDB

Fast querying indexing for performance (4)MongoDB

Webinar: General Technical Overview of MongoDB for Dev TeamsMongoDB

Green daoDroidcon Berlin

MongoDB using Grails plugin by puneet behlTO THE NEW | Technology

MongoDB - A Document NoSQL DatabaseRuben Inoto Soto

Getting Started with Geospatial Data in MongoDBMongoDB

NOSQL101, Or: How I Learned To Stop Worrying And Love The Mongo!Daniel Cousineau

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