This document provides an overview of querying and aggregation on MongoDB. It discusses querying concepts like find(), indexes, and $where queries. It also covers aggregation methods like count, sum, distinct, and group to perform more complex aggregation by grouping documents. Examples are given for common queries and aggregations including counting documents, finding population sums by state, and finding largest/smallest cities by state.

1. Query & Aggregation on MongoDB
26 March 2012

2. Agenda
 About
 Remember: CRUD
 Querying
 Indexes
 Aggregation
 QA

3. About
 Who Am I?
{
"Name": "Bogdan Sabau",
“Position": "SQL Developer",
"Project": "lynda.com",
"Why": [
"Big Data",
"NoSQL",
"Open Source"
]
}

4. About
 Who Am I?
{
"Name": "Bogdan Sabau",
“Position": "SQL Developer",
"Project": "lynda.com",
"Why": [
"Big Data",
"NoSQL",
"Open Source"
]
}

5. Remember: CRUD
 CREATE new record:
 db.zips.insert({"city": "OINKTOWN", "loc": [-89.51557, 32.584132], "pop": 789, "state": "AL"})
 READ a record:
 db.zips.find({"city": "OINKTOWN"})
 UPDATE existing record:
 db.zips.update({"city": "OINKTOWN"}, {"city": "OINKTOWN", "loc": [-89.51557, 32.584132], "pop":
1000, "state": "AL"})
 db.zips.update({"_id": "999"}, { $set: {"pop":2000}})
 db.zips.update({"_id": "999"}, { $set: {"greencity":true}})
 DELETE a record:
 db.zips.remove({"_id": "999"})

6. Querying
 Adhoc queries using find() or findOne() functions
 You can query for ranges, set inclusion, inequalities, and more by using $
conditionals.
 For more complex queries you can use $where clause
 All queries return a cursor
 You can make operations on that specific cursor

7. Querying
 Simple examples:
 db.zips.find({"state": "AL"})
 db.zibs.find({"state": "AL"}, {"city":1, "pop":1})
 Using "$lt", "$lte", "$gt", "$gte“, "$in", "$or", "$eq", "$not":
 db.zips.find({"state": "AL", "pop": {"$lt": 500}}, {"loc":0})
 db.zips.find({"state": {"$in": ["AL", "NY"]}, "pop": {"$lt": 50}})
 db.zips.find({"$or": [{"pop": {"$lt": 1}}, {"city": "NEW YORK"}]}, {"city":1, "state":1, "pop":1})
 NULL match itself but also match a key that doesn’t exists
 We can use regular expressions: db.zips.find({"city": /new .*/i})
 We can query arrays and embedded documents

8. Querying
 $Where queries:
 Used when we are dealing with more complex queries
 Are using JavaScript
 Slower than regular queries because they convert every BSON object to a JavaScript object
 Example
 db.zips.insert({"_id":"2", "city":"Alba", "county":"Alba", "country":"Romania", "museums": ["Muzeul de
Istorie", "Muzeul Unirii"]})
 db.zips.find({$where: function() { return this.city == this.county }})
 Cursors
 Used same as on SQL; we can limit the results, fetch the results and apply JavaScript logic on them

9. Indexes
 MongoDB’s indexes work almost identically to typical relational database
indexes
 Using a query optimizer to find the best index
 We can create indexes on embedded documents
 Unique indexes are possible
 Compound unique indexes
 Geospatial indexes

10. Indexes
 db.collection.find().explain() – give us information about indexes
 db.collection.ensureIndex({“key”:<order>}) – create a new index
 db.system.indexes.find() – check all the indexes
 db.collection.dropIndex (“indexname”) – drop the index
 There is another type of query that is becoming increasingly common
(especially with the emergence of mobile devices): finding the nearest N
things to a current location. MongoDB provides a special type of index for
coordinate plane queries, called a geospatial index.

11. Aggregation
 Count: returns the number of documents from the collection
 Sum: returns the sum of all the values for a specified field in the grouped
document
 Distinct: find the distinctive values for a specific key
 Group allows you to perform more complex aggregation. You choose a key
to group by, and MongoDB divides the collection into separate groups for
each value of the chosen key. For each group, you can create a result
document by aggregating the documents that are members of that group.

12. Aggregation
 Count
 db.zips.count()
 db.zips.count({"state":"NY"})
 Distinct
 db.runCommand({"distinct" : "zips", "key" : "state"})
 Group: SQL equivlants:
 WHERE $match GROUP BY $group
 HAVING $match SELECT $project
 ORDER BY $sort LIMIT $limit
 SUM() $sum COUNT() $sum

13. Aggregation
 Group examples
 Population by State
db.zips.aggregate( { $group :
{ _id : "$state",
totalPop : { $sum : "$pop" } } })
 Population by State with condition
db.zips.aggregate( { $group :
{ _id : "$state",
totalPop : { $sum : "$pop" } } },
{ $match : {totalPop : { $gte : 10*1000*1000 } } }

14. Aggregation
 Group examples
 Largest and smallest cities by State
db.zips.aggregate( { $group:
{ _id: { state: "$state", city: "$city" },
pop: { $sum: "$pop" } } },
{ $sort: { pop: 1 } },
{ $group:
{ _id : "$_id.state",
biggestCity: { $last: "$_id.city" },
biggestPop: { $last: "$pop" },
smallestCity: { $first: "$_id.city" },
smallestPop: { $first: "$pop" } } })

15. QA