An introduction to MongoDB. You can exercise the examples against 10gen's zips.json file.

1. MongoDB 101

2. Agenda
• Document Structures and Corrollaries
• Getting Started
• CRUD Operations
• Indexing

3. Agenda
• Aggregation
• Joins and Transactions
• Replica Sets
• Sharding

4. Document JSON Example
{
"_id" :
ObjectId("72f494c1c3df14726f1403b3"),
"city" : "Vienna",
"zipcode" : "22180",
"pop" : 20795
"surveyDate" : new Date()
}

5. Embedded Example
{
"name" : {
"first" : "Joe",
"last" : "Schmoe"
},
"address" : {
"street" : "123 Maple Avenue"
"city" : "Ashburn"
"state" : "VA"
"zipcode" : "20148"
}
"age" : 45
}

6. Another Example
{
"title" : "MongoDB 101",
"author" : "John Ragan",
"content" : "My thoughts on MongoDB",
"comments" : [
{ "name" : "Jake the Troll",
"comment" : "My trollish comments",
{ "name" : "Dwight Merriman",
"lastName" : ”Insightful comments from Dwight",
},
{ "name" : "Jake the Troll",
"comment" : "My even more trollish comments“,
],
"tags" : [ "mongodb", "101" ]
}

7. Documents
• Analogous to a database row
• Schema-free
• Keys and Values
– Strings
– Value types
• Special key: _id
– unique

8. MongoDB and Relational Corollaries
Relational Database MongoDB
Database instance Mongo instance
Database(s) Database(s)
Table(s) Collection(s)
Row(s) Document(s)
Values Keys and Values

9. Easy to Get Started

10. Insert
zip = {
"city" : "Ashburn",
"loc" : [ -77.480612, 39.039918],
"pop" : 19416,
"state" : "VA”,
"_id" : "20148"
}
db.census.insert(zip)

11. Insert
• JSON converted to BSON
• Must be less than 16 Mgs (in BSON)
• Adds _id unless already specified

12. Find
 find()
 findOne()
db.census.findOne(
{"_id": "22180"}
)

13. Projection
> db.census.find({}, {"city" : 1, "state" : 1})
{
"_id" : 22180,
"city" : "VIENNA",
"state" : "VA"
}
db.census.find({}, {"loc" : 0})

14. Query Conditionals
• $gt
• $gte
• $lt
• $lte
• $ne
• $not
db.census.find({"_id" : {"$gte" : "70300", "$lte" :
"70399"}})

15. In and Not In
• $in
• $nin
db.census.find({"_id" : {"$in" : ["22180",
"70301", "22030"]}})

16. OR Queries
db.census.find({
"$or" :
[
{"_id" : {"$in" : ["22180", "90210"]}},
{"city" : "ASHBURN"}
]
})

17. Regular Expressions
• Perl Compatible Regular Expression (PCRE)
db.census.find(
{"city" : /^ASHBU?/i}
)

18. Limits, Skips and Sorts
db.census.find(
{"city" : "CHICAGO"}
)
.skip(3)
.limit(4)
.sort("zipcode" : -1})

19. Update
update{ <criteria>, <new doc> }
db.census.update(
{_id : "22011"},
{city : "BROADLANDS"}
)

20. Update - $set and $unset
db.users.update({"name" : "joe"},
{"$set" : {"favorite book" : "harry potter"}})
db.users.update({"name" : "joe"},
{"$unset" : {"favorite book" : 1}})

21. Delete
db.census.remove( <criteria> )
db.census.remove({city : "NORTH POLE"})
db.census.remove()
db.drop_collection("census")

22. Indexes
db.census.ensureIndex({"city" : 1})
FAST:
db.census.find()
.sort("city" : 1})
SLOW:
db.census.find()
.sort({"pop" : 1, "city" : 1})
db.census.ensureIndex({"pop" : 1, "city" : 1})

23. Index Ordering
db.census.ensureIndex({"pop" : 1, "city" : 1})
Fast or Slow?
db.census.find()
.sort({"pop" : -1, "city" : 1})

24. Other Index Options
db.census.ensureIndex(
{"city" : 1},
{
"name" : "myIndex",
"unique" : true,
"dropDups", true
}
)

25. Explain
• explain will return information
– indexes used for the query (if any)
– stats about timing
– the number of documents scanned
db.census.find({city:"CHICAGO"}).explain()

26. Aggregation Framework
• Largest and smallest cities in Virginia,
California and Louisiana

27. MongoDB
• Relational Databases are Dead

28. MongoDB
• Relational Databases are Dead
– Of course that is not true!
– Right Tool for the Right Job

29. Why MongoDB?
• Schema flexibility
• Developer speed
• Horizontal scalability

30. Developer Flexibility
“An elephant should not always have to sit on
your data before you persist it”

31. Increasing Horizontal Scalability
• No joins
– Thus, no distributed
joins
• No transactions
– Thus, no distributed
transactions

32. Life Without Joins
• Already denormalized or Reference Id’s
• One to One relationships
• One to Many relationships
• Many to Many references

33. Life Without Transactions
• Document Level transaction boundaries
• Nesting within documents
• Two Phase commit

34. Update - $inc
{"url" : "www.example.com",
"pageviews" : 52}
db.analytics.update(
{"url" : "www.example.com"},
{"$inc" : {"pageviews" : 1}})
{"url" : "www.example.com",
"pageviews" : 53}

35. Replica Sets
• Primary-Secondary cluster
– Automatic failover
– Primary elected by cluster
• One Primary, many Secondary
– Others
• Fully automatic
– It handles voting, etc.
• 3 Node viable minimum

36. Demo Replica Set Failover

37. Sharding
• The process of splitting data up and storing
different portions of the data on different
machines
• Automatic vs. manual
• Chunks
– Shard Key

38. Mongod Mongod Mongod
Mongos
Client

39. Sharding
• Server types:
– Shard
• holds a subset of a collection’s data.
– Single mongod server
– Replica set
– Mongos
• router process and aggregates responses
• Does not store anything
– config server
• Stores cluster configuration: which data is on which shard.
• Start these in reverse

40. Summary
• Document Structures and Corrollaries
• Getting Started
• CRUD Operations
• Indexing

41. Summary
• Aggregation
• Joins and Transactions
• Replica Sets
• Sharding