Back to Basics Webinar 1: Introduction to NoSQL
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By Joe Drumgoole, Director of Developer Advocacy EMEA at MongoDB.
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Back to Basics Webinar 1: Introduction to NoSQL
- 2. Back to Basics 2017 : Webinar 1 Introduction to NoSQL Joe Drumgoole Director of Developer Advocacy, EMEA MongoDB @jdrumgoole V1.3
- 3. Welcome!
- 4. 4 Course Agenda Date Time Webinar 19-Jan-2017 15:00 GMT Introduction to NoSQL 26-Jan-2017 15:00 GMT Your First MongoDB Application 02-Feb-2017 11:00 GMT Introduction to Replica Sets 09-Feb-2017 11:00 GMT Introduction to Sharding
- 5. 5 Agenda for Today • Why NoSQL • The different types of NoSQL database • Detailed overview of MongoDB • Q&A
- 6. 6 Relational Expressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 7. 7 The World Has Changed Data Risk Time Cost
- 8. 8 NoSQL Scalability & Performance Always On, Global Deployments FlexibilityExpressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 9. 9 Nexus Architecture Scalability & Performance Always On, Global Deployments FlexibilityExpressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 10. 10 Types of NoSQL Database • Key/Value Stores • Column Stores • Graph Stores • Multi-model Databases • Document Stores
- 11. 11 Key Value Stores • An associative array • Single key lookup • Very fast single key lookup • Not so hot for “reverse lookups” Key Value 12345 4567.3456787 12346 { addr1 : “The Grange”, addr2: “Dublin” } 12347 “top secret password” 12358 “Shopping basket value : 24560” 12787 12345
- 12. 12 Revision : Row Stores (RDBMS) • Store data aligned by rows (traditional RDBMS, e.g MySQL) • Reads retrieve a complete row everytime • Reads requiring only one or two columns are wasteful ID Name Salary Start Date 1 Joe D $24000 1/Jun/1970 2 Peter J $28000 1/Feb/1972 3 Phil G $23000 1/Jan/1973 1 Joe D $24000 1/Jun/1970 2 Peter J $28000 1/Feb/1972 3 Phil G $23000 1/Jan/1973
- 13. 13 How a Column Store Does it 1 2 3 ID Name Salary Start Date 1 Joe D $24000 1/Jun/1970 2 Peter J $28000 1/Feb/1972 3 Phil G $23000 1/Jan/1973 Joe D Peter J Phil G $24000 $28000 $23000 1/Jun/1970 1/Feb/1972 1/Jan/1973
- 14. 14 Why is this Attractive? • A series of consecutive seeks can retrieve a column efficiently • Compressing similar data is super efficient • How do I align my rows? By order or by inserting a row ID • IF you just need a small number of columns you don’t need to read all the rows • But: – Updating and deleting by row is expensive • Append only is preferred • Better for OLAP than OLTP
- 15. 15 Graph Stores • Store graphs (edges and vertexes) • E.g. social networks • Designed to allow efficient traversal • Optimised for representing connections • Can be implemented as a key value stored with the ability to store links • MongoDB 3.4 supports graph queries
- 16. 16 Multi-Model Databases • Combine multiple storage/access models • Often Graph plus “something else” • Fixes the “polyglot persistence” issue of keeping multiple independent databases consistent • The “new new thing” in NoSQL Land • MongoDB is a "multi-modal" document store – Graph – Geo-Spatial – B-tree – Full Text
- 17. 17 Document Store • Not PDFs, Microsoft Word or HTML • Documents are nested structures created using Javascript Object Notation (JSON) { name : “Joe Drumgoole”, title : “Director of Developer Advocacy”, Address : { address1 : “Latin Hall”, address2 : “Golden Lane”, eircode : “D09 N623”, } expertise: [ “MongoDB”, “Python”, “Javascript” ], employee_number : 320, location : [ 53.34, -6.26 ] }
- 18. 18 MongoDB Documents are Typed { name : “Joe Drumgoole”, title : “Director of Developer Advocacy”, Address : { address1 : “Latin Hall”, address2 : “Golden Lane”, eircode : “D09 N623”, } expertise: [ “MongoDB”, “Python”, “Javascript” ], employee_number : 320, location : [ 53.34, -6.26 ] } Strings Nested Document Array Integer Geo-spatial Coordinates
- 19. 19 MongoDB Understands JSON Documents • From the very first version it was a native JSON database • Understands and can index the sub-structures • Stores JSON as a binary format called BSON • Efficient for encoding and decoding for network transmission • MongoDB can create indexes on any document field • (We will cover these areas in detail later on in the course)
- 20. 20 Why Documents? • Dynamic Schema • Elimination of Object/Relational Mapping Layer • Implicit denormalisation of the data for performance
- 21. 21 Why Documents? • Dynamic Schema • Elimination of Object/Relational Mapping Layer • Implicit denormalisation of the data for performance
- 23. 23 Pipeline Operators • $match Filter documents • $project Reshape documents • $group Summarize documents • $out Create new collections • $sort Order documents • $limit/$skip Paginate documents • $lookup Join two collections together • $unwind Expand an array
- 24. 24 Instant Start with Compass and Atlas Compass Atlas
- 25. 25 Next Webinar – Your First MongoDB Application • 26th Jan 2017 – 15:00 GMT. • Learn how to build your first MongoDB application – Create databases and collections – Look at queries – Build indexes – Start to understand performance • Register at https://www.mongodb.com/webinar/back-to-basics- webinar-series • Send feedback to back-to-basics@mongodb.com
- 26. 26 What's Next? • Sign up for an online course: https://university.mongodb.com/ • Join a MUG: https://www.meetup.com/pro/mongodb/
- 27. Q&A
Notas do Editor
- Who I am, how long have I been at MongoDB.
- Delighted to have you here. Hope you can make it to all the sessions. Sessions will be recorded so we can send them out afterwards so don’t worry if you miss one. If you have questions please pop them in the sidebar.
- A lot of people expect us to come in and bash relational database or say we don’t think they’re good. And that’s simply not true. Relational databases has laid the foundation for what you’d want out of a database, and we absolutely think there are capabilities that remain critical today Expressive query language & secondary Indexes. Users should be able to access and manipulate their data in sophisticated ways – and you need a query language that let’s you do all that out of the box. Indexes are a critical part of providing efficient access to data. We believe these are table stakes for a database. Strong consistency. Strong consistency has become second nature for how we think about building applications, and for good reason. The database should always provide access to the most up-to-date copy of the data. Strong consistency is the right way to design a database. Enterprise Management and Integrations. Finally, databases are just one piece of the puzzle, and they need to fit into the enterprise IT stack. Organizations need a database that can be secured, monitored, automated, and integrated with their existing IT infrastructure and staff, such as operations teams, DBAs, and data analysts.
- But of course the world has changed a lot since the 1980s when the relational database first came about. First of all, data and risk are significantly up. In terms of data 90% data created in last 2 years - think about that for a moment, of all the data ever created, 90% of it was in the last 2 years 80% of enterprise data is unstructured - this is data that doesn’t fit into the neat tables of a relational database Unstructured data is growing 2X rate of structured data At the same time, risks of running a database are higher than ever before. You are now faced with: More users - Apps have shifted from small internal departmental system with thousands of users to large external audiences with millions of users No downtime - It’s no longer the case that apps only need to be available during standard business hours. They must be up 24/7. All across the globe - your users are everywhere, and they are always connected On the other hand, time and costs are way down. There’s less time to build apps than ever before. You’re being asked to: Ship apps in a few months not years - Development methods have shifted from a waterfall process to an iterative process that ships new functionality in weeks and in some cases multiple times per day at companies like Facebook and Amazon. And costs are way down too. Companies want to: Pay for value over time - Companies have shifted to open-source business and SaaS models that allow them to pay for value over time Use cloud and commodity resources - to reduce the time to provision their infrastructure, and to lower their total cost of ownership
- Because the relational database was not designed for modern applications, starting about 10 years ago a number of companies began to build their own databases that are fundamentally different. The market calls these NoSQL. NoSQL databases were designed for this new world… Flexibility. All of them have some kind of flexible data model to allow for faster iteration and to accommodate the data we see dominating modern applications. While they all have different approaches, what they have in common is they want to be more flexible. Scalability + Performance. Similarly, they were all built with a focus on scalability, so they all include some form of sharding or partitioning. And they're all designed to deliver great performance. Some are better at reads, some are better at writes, but more or less they all strive to have better performance than a relational database. Always-On Global Deployments. Lastly, NoSQL databases are designed for highly available systems that provide a consistent, high quality experience for users all over the world. They are designed to run on many computers, and they include replication to automatically synchronize the data across servers, racks, and data centers. However, when you take a closer look at these NoSQL systems, it turns out they have thrown out the baby with the bathwater. They have sacrificed the core database capabilities you’ve come to expect and rely on in order to build fully functional apps, like rich querying and secondary indexes, strong consistency, and enterprise management.
- MongoDB was built to address the way the world has changed while preserving the core database capabilities required to build modern applications. Our vision is to leverage the work that Oracle and others have done over the last 40 years to make relational databases what they are today, and to take the reins from here. We pick up where they left off, incorporating the work that internet pioneers like Google and Amazon did to address the requirements of modern applications. MongoDB is the only database that harnesses the innovations of NoSQL and maintains the foundation of relational databases – and we call this our Nexus Architecture.
- Think redis, memcached or Couchbase.
- Column stores you know and love, HP Vertica, Cassandra.
- No copies of data for multi-modal