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![Data Types
{
_id : ObjectId("4c4ba5e5e8aabf3"),
Data Types
employee_name: "Dunham, Justin",
department : "Marketing",
•Unstructured data
•Semi-structured data
title : "Product Manager, Web",
report_up: "Neray, Graham",
pay_band: “C",
benefits : [
{ type : "Health",
•Polymorphic data
plan : "PPO Plus" },
{ type : "Dental",
plan : "Standard" }
]
}
20](https://image.slidesharecdn.com/morningwithmongodbisrael-121217184113-phpapp02-130129172844-phpapp01/85/Morningwithmongodbisrael-121217184113-phpapp02-20-320.jpg)
Uploaded byAndrei Colta
Morningwithmongodbisrael 121217184113-phpapp02
The document summarizes 10gen, the company behind the MongoDB NoSQL database. 10gen has over 170 employees, 500+ customers, and has received $73M in funding from top investors. MongoDB is a leading document-oriented database that is scalable, high-performance, and open source. It supports flexible schemas, horizontal scaling, and replication for high availability. Many large organizations rely on MongoDB for its ability to handle high volumes of data, diverse data types including semi-structured data, and support for agile development processes.
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Morningwithmongodbisrael 121217184113-phpapp02
- 2. 10gen Overview 10gen is the company behind MongoDB – the leading NoSQL database 2
- 3. 10gen Overview 170+ employees 3
- 4. 10gen Overview 500+ customers 4
- 5. 10gen Overview $73M in funding from top investors 5
- 6. Leading Organizations Relyon MongoDB 6
- 7. Global MongoDB Community 41,000+ MonthlyUnique Downloads 24,000+ Online Education Registrants 12,000+ MongoDB User Group Members 10,000+ Annual MongoDB Days Attendees
- 8. mongoDB Adoption Resource User Data Management 8
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- 13. Organizations are becomingfrustrated using a RDBMS. Productivity decreases Productivity • Needed to add new software layers of ORM, Caching, Sharding, Message Queue • Polymorphic, semi-structured and unstructured data not well supported Costs Cost of database increases • Vertical, not horizontal, scaling • High cost of SAN
- 14. NoSQL Values, ForWhich Audience? What 14
- 15. Databases in theFuture Audience? What Values, For Which 15
- 16.
- 17. MongoDB is ascalable, high-performance NoSQL database. • Open source, written in C++ • Full featured indexes, query • Document-oriented Storage language – Based on JSON Documents • Replication & High Availability – Schema-less • Auto-sharding
- 18. Relational Database Challenges Data Types Agile Development •Unstructured data •Iterative •Semi-structured data •Short development cycles •Polymorphic data •New workloads Volume of Data New Architectures •Petabytes of data •Horizontal scaling •Trillions of records •Commodity servers •Tens of millions of queries per second •Cloud computing 18
- 19. Volume of Data Volume of Data •Petabytes of data •Trillions of records •Millions of queries per second 19
- 20. Data Types { _id : ObjectId("4c4ba5e5e8aabf3"), Data Types employee_name: "Dunham, Justin", department : "Marketing", •Unstructured data •Semi-structured data title : "Product Manager, Web", report_up: "Neray, Graham", pay_band: “C", benefits : [ { type : "Health", •Polymorphic data plan : "PPO Plus" }, { type : "Dental", plan : "Standard" } ] } 20
- 21. Agile Development Agile Development •Iterative •Short development cycles •New workloads 21
- 22. Problem Why MongoDB Impact A need to extract value from A need to extract value from Built around scalability, with Built around scalability, with Priority Moments project is Priority Moments project is existing semi-structured existing semi-structured auto-sharding features auto-sharding features a strong success a strong success data sources (social data sources (social mongoDB deployment mongoDB deployment Subsequent adoption of Subsequent adoption of networks etc.) networks etc.) architecture prevents any architecture prevents any mongoDB by O2 & mongoDB by O2 & A fast-growing customer- A fast-growing customer- single point of failure single point of failure Telefonica across a large Telefonica across a large base required any solution base required any solution Geospatial indexing out-of- Geospatial indexing out-of- number of projects number of projects to be easily scalable to be easily scalable the-box enables location- the-box enables location- based service delivery based service delivery “Selecting MongoDB as our database platform was a no brainer as the technology offered us the flexibility and scalability that we knew we’d need for Priority Moments.” Andrew Pattinson, Head of Online Delivery
- 23. Problem Why MongoDB Impact RDBMS architecture RDBMS architecture Flexible data model allows Flexible data model allows The Guardian has The Guardian has constrained their ability to constrained their ability to for heterogeneous structure for heterogeneous structure competitive advantage, competitive advantage, absorb upstream absorb upstream Rich query language Rich query language through enabling social through enabling social contributions from users contributions from users preserves functionality preserves functionality conversations through the conversations through the New features, competitions New features, competitions System updates with zero System updates with zero site site needed to log data into user needed to log data into user downtime downtime Interactive features can be Interactive features can be records, requiring schema records, requiring schema Ease of use, allowing a large Ease of use, allowing a large delivered more quickly, delivered more quickly, changes changes development team to adopt development team to adopt which translates to which translates to the technology quickly the technology quickly increased revenues increased revenues “Relational databases have a sound approach, but that doesn’t necessarily match the way we see our data. mongoDB gave us the flexibility to store data in the way that we understand it as opposed to somebody’s theoretical view.” Philip Wills, Software Architect
- 24. New Architectures New Architectures •Horizontal scaling •Commodity servers •Cloud computing 24
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- 26. Summary Solution MongoDB Document-Oriented Database Agile Scalable Best TCO
- 27. Best Total Costof Ownership (TCO) Developer and Ops Savings •Less code •More productive development •Easier to maintain Hardware Savings •Commodity servers •Internal storage (no SAN) •Scale out, not up Software and Support Savings •No upfront license – pay for value DB Alternative over time •Cost visibility for usage growth
- 28. Relational Database Challenges Data Types Agile Development •Unstructured data •Iterative •Semi-structured data •Short development cycles •Polymorphic data •New workloads Volume of Data New Architectures •Petabytes of data •Horizontal scaling •Trillions of records •Commodity servers •Tens of millions of queries per second •Cloud computing 28
- 29. Summary Solution MongoDB Document-Oriented Database Agile Scalable Best TCO
- 30.
Editor's Notes
- #5 Note: Growth refers to year-to-date revenue based on our fiscal years for 2011 and 2012, i.e., it compares Feb-Oct 2011 (calendar year) to Feb-Oct 2012 (calendar). These figures are unaudited and subject to change.
- #32 A highlight of some key features in 2.4. . . . We ’ll add more details and more items each month as we work towards a winter release. Security: SASL is a framework for authentication that helps decouple specific authentication mechanisms from client/server implementation. This framework will permit working with a variety of authentication mechanisms, initially we ’ll build in kerberos. We may add others over time, but SASL implementation will make it much easier for you to add your own without having to implement a new client. Kerberos is quite common, so we ’ll build that one in first. With additional authentication, we want to take a few steps to separate out activities authorized to various users. Separate read, read/write, security administration, database-specific (compact, validate, etc.), and server/cluster administration (fsync, log rotate, shutdown, create database, etc.). This is just an initial step in our authorization work. Hash-based sharding Apply a hash function to a selected key as the shard key. Evenly spread documents in a sharded cluster. Evenly spread the work associated with queries in a sharded cluster. Will minimize migrations (should only happen when growing a cluster). Note: this is something you can do now, but not automatic. Geospatial index resolution: Talk about challenge of specifying some polygon and finding overlap with another polygon in a document, this becomes interesting for location-aware applications, intelligence community. Replica set flapping: avoid electing a new primary due to a falsely detecting that the current primary went down. Adding mechanisms to reduce false detections. This is good for heavy load and network issues/blips in a data center.
- #34 Ok, so here are the presenters notes. Your first job is to add you name and other useful stuff so that your students can contact you afterwards. This is a good time to - introduce yourself - create a seating chart, get each student to say their name, company and what they want to learn... and write it on your seating chart
- #36 A highlight of some key features in 2.4. . . . We ’ll add more details and more items each month as we work towards a winter release. Security: SASL is a framework for authentication that helps decouple specific authentication mechanisms from client/server implementation. This framework will permit working with a variety of authentication mechanisms, initially we ’ll build in kerberos. We may add others over time, but SASL implementation will make it much easier for you to add your own without having to implement a new client. Kerberos is quite common, so we ’ll build that one in first. With additional authentication, we want to take a few steps to separate out activities authorized to various users. Separate read, read/write, security administration, database-specific (compact, validate, etc.), and server/cluster administration (fsync, log rotate, shutdown, create database, etc.). This is just an initial step in our authorization work. Hash-based sharding Apply a hash function to a selected key as the shard key. Evenly spread documents in a sharded cluster. Evenly spread the work associated with queries in a sharded cluster. Will minimize migrations (should only happen when growing a cluster). Note: this is something you can do now, but not automatic. Geospatial index resolution: Talk about challenge of specifying some polygon and finding overlap with another polygon in a document, this becomes interesting for location-aware applications, intelligence community. Replica set flapping: avoid electing a new primary due to a falsely detecting that the current primary went down. Adding mechanisms to reduce false detections. This is good for heavy load and network issues/blips in a data center.