The document introduces MongoDB as a high-performance, open-source, document database that is scalable and supports a variety of programming languages. It covers setting up MongoDB, modeling documents, basic application requirements, and operations like data insertion and aggregation. Additionally, it explains scaling options such as replica sets and sharding, along with guidance on using MongoDB drivers for application development.

1. #mongodb
Building your first app;
an introduction to MongoDB
Sandeep Parikh
Senior Solutions Architect, MongoDB

2. What is MongoDB?

3. MongoDB is a ___________
database
• Document
• Open source
• High performance
• Horizontally scalable
• Full featured

4. Document Database
• Not for .PDF & .DOC files
• A document is essentially an associative array
• Document = JSON object
• Document = PHP Array
• Document = Python Dict
• Document = Ruby Hash
• etc

5. Open Source
• MongoDB is an open source project
• On GitHub
• Licensed under the AGPL
• Started & sponsored by MongoDB Inc (formerly
10gen)
• Commercial licenses available
• Contributions welcome

6. High Performance
• Written in C++
• Extensive use of memory-mapped files
i.e. read-through write-through memory caching.
• Runs nearly everywhere
• Data serialized as BSON (fast parsing)
• Full support for primary & secondary indexes
• Document model = agile development

7. Database Landscape

8. Full Featured
• Flexible schema
• Rich ad-hoc queries
• Real time aggregation
• Strongly consistent
• Geospatial features
• Built-in automatic failover
• Horizontally scalable reads/writes
• Support for most programming languages

9. mongodb.org/downloads

10. Running MongoDB
$ tar zxf mongodb-osx-x86_64-2.6.4.tgz
$ cd mongodb-osx-x86_64-2.6.4/bin
$ mkdir –p /data/db
$ ./mongod

11. Mongo Shell
$ mongo
MongoDB shell version: 2.6.4
connecting to: test
> db.test.insert({text: 'Welcome to MongoDB'})
> db.test.find().pretty()
{
"_id" : ObjectId("51c34130fbd5d7261b4cdb55"),
"text" : "Welcome to MongoDB"
}

12. Building an App with
MongoDB

13. Terminology
RDBMS MongoDB
Table, View ➜ Collection
Row ➜ Document
Index ➜ Index
Join ➜ Embedded Document
Foreign Key ➜ Reference
Partition ➜ Shard

14. Let’s Build a Monitoring System

15. First step in any application is
Determine basic requirements

16. Basic Monitoring Requirements
• Data generated at per-second intervals
• Metrics to capture (ex. CPU, memory, IO)
• Document structure for fast writes and easy reads

17. In a relational base app
We would start by doing schema
design

18. Relational Schema Options
CPU Metrics
Memory Metrics
IO Metrics
Events
• Timestamp
• CPU metrics
• Memory metrics
• IO metrics
Or

19. In a MongoDB based app
We start with a generic model
and let the documents evolve

20. MongoDB Document Modeling
Events
Timestamp (min)
CPU []
• Second
• Metric
Memory []
• Second
• Metric
IO []
• Second
• Metric

21. Working With MongoDB

22. Start with the Mongo Shell
$ mongo
MongoDB shell version: 2.6.4
connecting to: test
>
Full Javascript shell

23. Switch to Your DB
> use monitoring
switching to db monitoring
DB files are created once you save a
document

24. Create an Empty Event
> var event = {
ts: ISODate(“2014-09-16T09:00:00”),
cpu: [ 0, 0, …, 0 ],
memory: [ 0, 0, …, 0 ],
io: [ 0, 0, …, 0 ],
}
Insert zeroed-out arrays to fill in later

25. Insert the Event
> db.events.insert(event)
No collection creation necessary

26. Find One Record
> db.events.findOne()
{
"_id" : ObjectId("50804d0bd94ccab2da652599"),
”ts" : ISODate(“2014-09-16T09:00:00”),
”cpu" : [ 0, 0, …, 0 ],
”memory" : [ 0, 0, …, 0 ],
“io” : [ 0, 0, …, 0 ]
}

27. How do you capture events?
Serve
r
Serve
r
Serve
r
MongoD
B
memory
interaction
interaction
interaction

28. Adding a New Event
> db.events.update(
{ ts: ISODate(“2014-09-16T09:00:00”) }
{ $set: {
“cpu.0” : 50,
“memory.0”: 1500,
“io.0”: 10 }
}
)
Use atomic in-place updates to add metric values

29. How do you plot charts?

30. Finding an Hour of Events
> db.events.find({
ts: {
$gte: ISODate(“2014-09-16T09:00:00”),
$lt: ISODate(“2014-09-16T10:00:00”)
}
}).sort({ts:1})
Find using a range and sort results
ascending

31. How do you roll up the data?
Hour Avg Cpu
0 50
1 65
2 75
3 40
4 45
5 60
6 25
… …
23 30

32. Aggregate Metrics
> db.events.aggregate([
{ $match: { ts: { $gte: date0, $lt: date1 } } },
{ $project: { _id: 0, ts: 1, cpu: 1 } },
{ $unwind: “$cpu” },
{ $group: {
_id: { $hour: “$ts” },
avg_cpu: { $avg: “$cpu” } } }
])
Use Aggregation Framework to roll up data

33. How do you scale this workload
• Replica Sets
– Add data redundancy
– Automatic failover
– Tunable durability, consistency
• Sharding
– Scale reads and writes
– Support dynamic data growth
– Automatically partitions workload
– Horizontally scalable

34. MongoDB Drivers

35. Real applications are not
built in the shell

36. MongoDB has native
bindings for over 12
languages

39. MongoDB Drivers
• Official Support for 12 languages
• Community drivers for tons more
• Drivers connect to MongoDB servers
• Drivers translate BSON into native types
• Shell is not a driver, but works like one in some ways
• Installed using typical means (npm, pecl, gem, pip)

40. docs.mongodb.org

41. Online Training at MongoDB
University

42. Questions?

43. #mongodb
Thank You
Sandeep Parikh
Senior Solutions Architect, MongoDB