The document discusses MongoDB, a document-oriented NoSQL database. It begins by providing an example of a JSON document that can be stored in MongoDB. It then shows how documents can contain nested objects and arrays. The document explains that MongoDB uses indexes to improve query performance and provides examples of indexing fields. It also demonstrates how to query the database using examples like finding documents by ID. Finally, it mentions several scaling and optimization techniques used in MongoDB as well as some deployment options.

1. MongoDB

2. What is a document?
• MS Word file stored in Sharepoint
• JSON or XML

3. What is a document?
{
“campaign_id”: 12,
“created_on”: ISODate("2013-04-08T18:10:48Z")
“name”: “Trade LG”,
“published”: true,
“status_id”: 2
}

4. What is a document?
{
“campaign_id”: 12,
“created_on”: ISODate("2013-04-08T18:10:48Z")
“name”: “Trade LG”,
“published”: true,
“status_id”: 2,
“keywords”: [ “trade lg”, “yahoo”, “test” ]
}

5. What is a document?
{
“campaign_id”: 12,
“created_on”: ISODate("2013-04-08T18:10:48Z")
“name”: “Trade LG [01JUL2013] [1308]”,
“published”: true,
“status_id”: 2,
“keywords”: [ “trade lg”, “yahoo”, “test” ],
“placements”: [
{
“placement_id”: 14,
“name”: “[3495] [01JUL2013] ROS 20minutes 300X250”
}
]
}

6. What is a document?
{
“campaign_id”: 12,
“created_on”: ISODate("2013-04-08T18:10:48Z")
“name”: “Trade LG [01JUL2013] [1308]”,
“published”: true,
“status_id”: 2,
“keywords”: [ “trade lg”, “yahoo”, “test” ],
“placements”: [
{
“placement_id”: 14,
“name”: “[3495] [01JUL2013] ROS 20minutes 300X250”,
“funnels”: [
{ “funnel_id”: 15, “name”: “funnel_1”, “published”: true },
{ “funnel_id”: 16 “name”: “funnel_2”, “published”: false },
{ “funnel_id”: 17, “name”: “funnel_3”, “published”: true }
]
}
]
}

7. Indexes
• ensureIndex( { “created_on”:1 } )
• ensureIndex( { “published”: 1, “status_id”: 1} )
• ensureIndex( { “keywords”: 1} )
• ensureIndex( { “placements.funnels.published”: 1 } )

8. Querying
• Query by example
• find({“campaign_id”: 12})
• find({
“published”: true,
“created_on”: {
$gt: ISODate("2013-04-01")
}
});

9. Scalability
• Horizontal
• Master/Slave replication
• Sharding

10. Read Optimization

11. Write Optimization

12. Backup Strategies
• Replication
• Files backup
• mongodump + mongorestore
• Journal backup

13. Deployment Options
• Hosted solutions
• Single node
• Replica set
• Replication across data centers

14. Who uses MongoDB?

15. Internal POC
• 24,000,000+ activities
• 11GB
• 14,500 inserts per second
• Fetch activities by user id:
44,438 results, ~60ms
• Fetch activities by type name:
3,585,101 results, ~6942ms
• Single server: M1-Large(300$)