Mongo db 101 dc group

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

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

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

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

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" ]
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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})

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

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

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()

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

MongoDB
• Relational Databases are Dead

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

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

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

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

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

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

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

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

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

Mongod Mongod Mongod
Mongos
Client

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

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

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

Mongo db 101 dc group

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