Introduction to MongoDB
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This document contains information about Justin Smestad and MongoDB. It includes Justin's contact information and background working as a software engineer with skills in Ruby, JavaScript, Clojure, and passion for DevOps. It also provides an overview of MongoDB, describing it as a scalable, high-performance, open source, schema-free, document-oriented database. Key features of MongoDB like indexing, master-slave replication, and horizontal scaling with replica sets and sharding are summarized.
![Use Cases
Audit Trails
Nested documents allow you to store audit trails in
context.
{
‘name’: ‘Justin Smestad’,
‘address’: ‘1441 Central St’,
‘history’: [
{
‘name’: ‘Justin Smestad’,
‘address’: ‘350 S Jackson St’
},....]
}](https://image.slidesharecdn.com/introtomongodbpearson-110414125727-phpapp01/85/Introduction-to-MongoDB-95-320.jpg)
Introduction to MongoDB
- 2. whoami Software Engineer (Ruby, Javascript, and Clojure) User Experience & Design on the side Passion for DevOps Chicagoland native
- 3. whoami Software Engineer (Ruby, Javascript, and Clojure) User Experience & Design on the side Passion for DevOps Chicagoland native
- 4. whoami Software Engineer (Ruby, Javascript, and Clojure) User Experience & Design on the side Passion for DevOps Chicagoland native
- 5. whoami Software Engineer (Ruby, Javascript, and Clojure) User Experience & Design on the side Passion for DevOps Chicagoland native
- 6. Background Java Engineer @ Orbitz Worldwide Brought Ruby and RSpec/Cucumber to replace Fitness Testing Application Developer @ Factory Design Labs Brand & Campaign sites for Audi, Oakley, and TNF Independent Contractor Development team for TechStars alumni
- 7. Background Java Engineer @ Orbitz Worldwide Brought Ruby and RSpec/Cucumber to replace Fitness Testing Application Developer @ Factory Design Labs Brand & Campaign sites for Audi, Oakley, and TNF Independent Contractor Development team for TechStars alumni
- 8. Background Java Engineer @ Orbitz Worldwide Brought Ruby and RSpec/Cucumber to replace Fitness Testing Application Developer @ Factory Design Labs Brand & Campaign sites for Audi, Oakley, and TNF Independent Contractor Development team for TechStars alumni
- 9. mongo machine hosted MongoDB wherever you need it
- 10. What’s mongo machine ?
- 11. mongo machine hosted MongoDB + data management http://mongomachine.com
- 12. mongo machine hosted MongoDB + data management Our Goals Educate and promote best practices Increase customer efficiency Promote transparency
- 13. Our Product
- 14. Hosted MongoDB Provide the best managed MongoDB experience, on any platform.
- 15. mongo machine hosted MongoDB + data management Managed MongoDB Infrastructure Our infrastructure on AWS or Rackspace Automated Deployments to your own infrastructure Management Console Instantly create new databases Track database trends Scale up & down on-demand
- 16. Management Console Analytics data about what your DB is doing.
- 17. Management Console Analytics data about what your DB is doing.
- 19. “NoSQL” hasn’t this been tried before? why should I care now?
- 20. So what is MongoDB? (10,000ft view)
- 21. MongoDB (from "humongous") is a scalable, high-performance, open source, schema-free, document-oriented database. -- mongodb.org
- 22. Who is using MongoDB?
- 23. Fortune 500 <=> Startups
- 24. Github Bug Tracking & Analytics
- 26. New York Times Photo Submissions
- 27. The list is large and growing fast http://www.mongodb.org/display/DOCS/Production+Deployments
- 29. Philosophy
- 30. Philosophy “One size DOESN’T fit”
- 31. Philosophy Non-relational (de-normalized) DBs are easier to scale, especially horizontally
- 32. Philosophy DBs should be an on-demand commodity (cloud-like)
- 33. Philosophy Focus on performance, flexibility and scalability (CA)
- 34. Philosophy Not concerned with transactional stuff or relational semantics
- 35. Philosophy Mongo aims for the performance of key- value stores while maintaining functionality of traditional RDBMS
- 37. Features
- 38. Features Standard database stuff Indexing Traditional master-slave Database References (no concept of JOINS)
- 39. Features Standard database stuff Indexing Traditional master-slave Database References (no concept of JOINS)
- 40. Features Standard database stuff Indexing Traditional master-slave Database References (no concept of JOINS)
- 41. Features Standard database stuff Indexing Traditional master-slave Database References (no concept of JOINS)
- 42. Features Speed in-memory dataset with fsync to disk
- 43. Features Durability solve with replication, or use journaling
- 45. Features: Document Storage Documents are stored in BSON (binary JSON)
- 46. Features: Document Storage BSON is a binary serialization of JSON-like objects
- 47. Features: Document Storage Large BSON documents are served in chunks (GridFS)
- 48. Features: Document Storage This is extremely powerful, b/c it means mongo understands JSON natively
- 49. Features: Document Storage Any valid JSON can be easily imported and queried
- 50. Features: Document Storage Documents can contain embedded documents (nested hashes) without losing any indexing capabilities.
- 51. Features Schema-less; very flexible no more blocking ALTER TABLE or NULL debates
- 52. Features Replica Sets and Sharding make horizontal scaling easy.
- 53. Features: Replica Set Traditional Master-Slave, but automatic failover Each server holds all data (CA) One is elected master at a given time Arbitration process detects failover New election within seconds
- 54. Features: Replica Set Traditional Master-Slave, but automatic failover Each server holds all data (CA) One is elected master at a given time Arbitration process detects failover New election within seconds
- 55. Features: Replica Set Traditional Master-Slave, but automatic failover Each server holds all data (CA) One is elected master at a given time Arbitration process detects failover New election within seconds
- 56. Features: Replica Set Traditional Master-Slave, but automatic failover Each server holds all data (CA) One is elected master at a given time Arbitration process detects failover New election within seconds
- 57. Features: Replica Set Traditional Master-Slave, but automatic failover Each server holds all data (CA) One is elected master at a given time Arbitration process detects failover New election within seconds
- 58. Features Map/Reduce Map Reduce operations are written in Javascript.
- 59. Features: Querying Rich, javascript-based query syntax Allows us to do deep, nested queries
- 60. Features: Querying Rich, javascript-based query syntax Allows us to do deep, nested queries
- 61. Features: Querying Rich, javascript-based query syntax Allows us to do deep, nested queries db.order.find( { shipping: { carrier: "usps" } } );
- 62. Features: Querying Rich, javascript-based query syntax Allows us to do deep, nested queries db.order.find( { shipping: { carrier: "usps" } } ); shipping is an embedded document (object)
- 63. Features atomic operations upserts, $set, $inc, $push, $pull, $pop, $addToSet, ...
- 64. Features Official Drivers .NET, Java, Javascript, Ruby, Node.js, PHP, Haskell, C/C++, Perl
- 65. Features Community & Market Growth MongoDB works alongside your existing technologies
- 67. Concepts
- 68. Concepts: Document-oriented Think of “documents” as objects / database records
- 69. Concepts: Document-oriented Think of “documents” as objects / database records Documents are basically just JSON in binary
- 70. Concept: Document-oriented Think of “documents” as objects / database records Documents are basically just JSON in binary Ability to store information all together
- 71. Concept Mapping RDBMS (mysql, postgres) MongoDB Tables Collections
- 72. Concept Mapping RDBMS (mysql, postgres) MongoDB Tables Collections Records/rows Documents/objects
- 73. Concept Mapping RDBMS (mysql, postgres) MongoDB Tables Collections Records/rows Documents/objects Queries return record(s) Queries return a cursor
- 74. Concept Mapping RDBMS (mysql, postgres) MongoDB Tables Collections Records/rows Documents/objects Queries return record(s) Queries return a cursor ???
- 75. Concepts: Cursors Queries return “cursors” instead of collections
- 76. Concepts: Cursors Queries return “cursors” instead of collections A cursor allows you to iterate through the result set
- 77. Concepts: Cursors Queries return “cursors” instead of collections A cursor allows you to iterate through the result set A big reason for this is performance
- 78. Concepts: Cursors Queries return “cursors” instead of collections A cursor allows you to iterate through the result set A big reason for this is performance Much more efficient than loading all objects into memory
- 79. Concepts: Cursors The find() function returns a cursor object
- 80. Concepts: Cursors The find() function returns a cursor object var cursor = db.logged_requests.find({ 'status_code' : 200 }) cursor.hasNext() // "true" cursor.forEach( function(item) { print(tojson(item)) } ); cursor.hasNext() // "false"
- 82. Cool Features
- 83. Cool Features Capped collections Fixed-sized, limited operation, auto-LRU age-out collections Fixed insertion order Extremely performant Ideal for logging and caching
- 84. Cool Features Capped collections Fixed-sized, limited operation, auto-LRU age-out collections Fixed insertion order Extremely performant Ideal for logging and caching
- 85. Cool Features Capped collections Fixed-sized, limited operation, auto-LRU age-out collections Fixed insertion order Extremely performant Ideal for logging and caching
- 86. Cool Features Capped collections Fixed-sized, limited operation, auto-LRU age-out collections Fixed insertion order Extremely performant Ideal for logging and caching
- 87. Cool Features Capped collections Fixed-sized, limited operation, auto-LRU age-out collections Fixed insertion order Extremely performant Ideal for logging and caching
- 89. Use Cases
- 90. Use Cases Data Warehouse Mongo understands JSON natively Very powerful map-reduce for analytics Nested hashes make roll-up (RRDtool) systems natural
- 91. Use Cases Data Warehouse Mongo understands JSON natively Very powerful map-reduce for analytics Nested hashes make roll-up (RRDtool) systems natural
- 92. Use Cases Data Warehouse Mongo understands JSON natively Very powerful map-reduce for analytics Nested hashes make roll-up (RRDtool) systems natural
- 93. Use Cases Data Warehouse Mongo understands JSON natively Very powerful map-reduce for analytics Nested hashes make roll-up (RRDtool) systems natural
- 94. Use Cases Audit Trails Nested documents allow you to store audit trails in context.
- 95. Use Cases Audit Trails Nested documents allow you to store audit trails in context. { ‘name’: ‘Justin Smestad’, ‘address’: ‘1441 Central St’, ‘history’: [ { ‘name’: ‘Justin Smestad’, ‘address’: ‘350 S Jackson St’ },....] }
- 97. Limitations
- 98. Limitations Transaction support Relational integrity (normalization) Partition balancing Map / Reduce is single threaded Data compaction
- 99. Limitations Transaction support Relational integrity (normalization) Partition balancing Map / Reduce is single threaded Data compaction
- 100. Limitations Transaction support Relational integrity (normalization) Partition balancing Map / Reduce is single threaded Data compaction
- 101. Limitations Transaction support Relational integrity (normalization) Partition balancing Map / Reduce is single threaded Data compaction
- 102. Limitations Transaction support Relational integrity (normalization) Partition balancing Map / Reduce is single threaded Data compaction
- 103. Why not `other db` ? CouchDB, Cassandra, Riak, Membase, Redis, ...
- 104. Why not `other db`? FLEXIBILITY MongoDB’s design choices empower developers and administrators with features that allow you to implement their own solutions. *