My Minnesota PHP Usergroup (mnphp.org) presentation where I give an overview on MongoDB and other non-relational databases and their ability to solve unique, complex problems.

1. A general overview of the non-relational database By Andrew Kandels

3. Personal Information

4. Access Control (Usernames & Passwords)

5. Order Processing

6. Logistics

7. Mailing Lists… or, any data that works more efficiently when normalized

9. Real-time Analytics

10. Caching

11. Logging and Archiving Events

12. Messaging

13. Job Queue

14. Social Networking

16. Consistency can be sacrificed for scale

17. Dynamic data models

18. Tables storing meta-data

19. BLOB tables storing serialized data!

20. Very high writes, reads, or both

21. Don’t have a DBA

22. Temporary & volatile dataCaching layers are a band aid that fix problems the RDMS was never meant to handle

23. Brewer’s CAP Theorem Consistency Service operates fully or not at all. You either clicked “Place Order” or you didn’t. Availability Service is always available with no need for scheduled downtime or maintenance windows. Partition Tolerance No set of failures less than total network failure is allowed to cause the system to respond incorrectly. Pick two.

25. HBase

27. Cassandra

28. Riak

29. VoldemortTrouble with partitions & scale. Deal with it through replication.

31. Document Databases

32. Graph Databases

34. Rate limiting

36. Replication

38. Replication out-of-the-box

40. Massive, engaged open source community (Twitter, Facebook)

43. In-memory with disk-backed persistence and fault tolerance (no single POF)

44. Very fast reads and writes (10-20k/second)

45. Drop-in storage layer (great for unit testing mock objects)

46. MVCC

48. Full, range and nested Indexes

49. Replication and distributed storage

50. Query language and Map/Reduce

51. GridFS file storage (NFS replacement)

52. BSON Serialization

55. Indexing

57. ETL optional

59. Very fast queries and joins

63. Native BSON Serialization

64. Full Index Support

65. Built-In Replication & Cluster Management

66. Distributed Storage (Sharding)

67. Easy to Query

68. Fast In-Place Updates

69. GridFS File Storage

70. Capped collectionsMongoDB in many ways “feels” like an RDMS. It’s easy to learn and quick to implement.

71. Semi-Structured Data MongoDB is NOT a key/value store. Store complex documents as arrays, hash tables, integers, objects and everything else supported by JSON:

72. Native BSON Serialization 100,000 serialize/de-serialize runs of bson_encode(), json_encode() and serialize() in the PHP: The PHP MongoDB extension serializes the data in C outside of the runtime leading to even better results.

73. Full Index Support

75. Fault tolerant (automatic failover AND recovery)

76. Consistency (wait-for-propagate or write-and-forget)

77. Distribute read load

78. Simplified maintenance

81. Append the new updated row

83. UUID replace / Password update

84. Session cookie

85. Counters (failed login attempts, visits)MongoDB can apply most updates over the existing row, keeping the index and data structure relatively untouched – and do so VERY FAST.

87. Pictures

88. Translations

91. Messaging

92. Job Queues

94. Can also query, delete and update out of FIFO order

95. FIFO reads/writes are nearly as fast as cat > file; tail –f /file

96. Tailable cursor stays open as reads rows as they are added

97. Persistent, fault-tolerant, distributed

99. Query using your existing class structures

100. Very easy migration path from an ORM

101. Rapid Application Development