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  • A document-oriented database, which aims to ease scaling out
  • It automatically takes care of balancing data and load across a cluster, redistributing documents automatically and routing reads and writes to the correct machines.
  • By allowing embedded documents and arrays, the document-oriented approach makes it possible to represent complex hierarchical relationships with a single record.
  • There are also no predefined schemas: a document’s keys and values are not of fixed types or sizes so adding or removing fields as needed becomes easier.
  • It is type- and case-sensitive.
  • It can query for all documents where <bla_bla> is an element of the <bla_bla> array.
  • If there is a common query, you can even create an index on the <bla_bla> key to improve the query’s speed.
  • It allows atomic updates that modify the contents of arrays.
  • It understands the structure of embedded documents and can reach inside them to build indexes, perform queries, or make updates.

Basic concepts


  • MongoDB groups documents into collections.
  • Analog of a table in RDBMs
  • It has dynamic schemas.
    • A single collection can have any number of different shapes.
    • Defining schemas is good practice forced by
      • MongoDB’s document validation functionality
      • Object-document mapping libraries available for many PLs
  • It is identified by its name.
    • "" is not valid.
    • It must not contain \0 (the null character), which signifies the end of a key.
    • Not start with system..
    • $ have special properties and should only be used in certain circumstances.
  • Naming: <database_name>.<collection_name>

Why we should use more than one collection

  • If we’re querying for blog posts, it’s a hassle to weed out documents containing author data.
  • It’s much faster to get a list of collections than to extract a list of the types of documents in a collection.
  • Grouping documents of the same kind together in the same collection allows for data locality.
  • By putting only documents of a single type into the same collection, we can index our collections more efficiently.


  • <collection>.<subcollection> only for organizational purposes


  • MongoDB groups collections into databases.
  • A single instance of MongoDB can host multiple independent databases, each grouping together zero or more collections.
    • Storing all data for a single application in the same database is practical.
    • /, \, ., ", *, <, >, :, |, ?, $, , or \0 not be contained in naming.
    • Database names are case-insensitive, limited to a max of 64 bytes.
    • Reserved names
      • admin db plays a role in authentication and authorization.
      • local db stores data specific to a single server.
      • config db stores information about each shard.

show dbs use <database_name> <database_name>

Creating Users

db.createUser({ user : <user_name>, pwd : <password>, roles : ["readWrite", "dbAdmin"] });


  • The basic unit of data for MongoDB (~row in RDBMs)
  • An ordered set of keys with associated values
  • Its representation depends on the PL
  • Keys are strings.
    • It must not contain \0 (the null character), which signifies the end of a key.
    • . and $ have special properties and should only be used in certain circumstances.
    • It cannot contain replicated keys.

Mongo shell

  • Built-in support for administering MongoDB instances and manipulating data using the MongoDB query language.
  • mongo <code1>.js <code2>.js

Data types


  • Arrays can contain different data types as values.

    • {"x" : [ "pie", 3.14 ]}
  • Ordered operations

    • lists
    • stacks
    • queues
  • Unordered operations

    • sets
  • Arrays use 0-based indexing.

  • You can manipulate the values in array in two ways:

    • by position
    • by position operator $

Array operators

  • "$push" adds elements to the end of an array if the array exists. If not, it creates an array.
Modifiers with push
  • "$each" pushes multiple values in one operation.

    • i.e. db.stock.ticker.updateOne({"_id" : "GOOG"}, {"$push" : {"hourly" : {"$each" : [562.776, 562.790, 559.123]}}})
  • "$slice" prevents an array from growing beyond a certain size.

    • i.e. db.movies.updateOne({"genre" : "horror"}, {"$push" : {"top10" : {"$each" : ["Nightmare on Elm Street", "Saw"], "$slice" : -10}}}) limits the array to the last 10 elements pushed.
  • "$sort" sorts all of the objects in the array by <field>.

  • "$ne" adds values if they are not present.

    • i.e. db.papers.updateOne({"authors cited" : {"$ne" : "Richie"}}, {$push : {"authors cited" : "Richie"}})
  • "$addToSet" adds values if they are not present. It prevents duplications, which is useful where "$ne" does not work. "$addToSet"+"$each" can add multiple unique values, which cannot be done with "$ne"+"$push".

  • "$pop" treats an array like a queue or stack.

    • {"$pop" : {"key" : 1}} removes an element from the end of the array. {"$pop" : {"key" : -1}} removes it from the beginning.
  • "$pull" removes all elements that match the given criteria. ([1, 1, 2, 1] - pull 1 -> [2])

    db.lists.insertOne({"todo" : ["dishes", "laundry", "dry cleaning"]})
    db.lists.updateOne({}, {"$pull" : {"todo" : "laundry"}}) // todo: dishes, dry cleaning

Binary data

  • A string of arbitrary bytes


  • {"x" : true}


  • {"x" : function() { /* ... */ }} stores arbitrary JavaScript queries and documents.


  • Date() method which returns the current date as a string.

  • new Date() constructor which returns a Date object using the ISODate() wrapper.

  • ISODate() constructor which returns a Date object using the ISODate() wrapper.

Embedded documents

  • A document which is used as the value for a key
  • {"x" : {"foo" : "bar"}}
    "name"    : "John Doe",
    "address" : {
                  "street" : "123 Park Street",
                  "city"   : "Anytown",
                  "state"  : "NY"


  • {"x" : null}

Numeric Types

{ _id: 1, value: 1, expectedType: 'Int32' },
{ _id: 2, value: Long("1"), expectedType: 'Long' },
{ _id: 3, value: 1.01, expectedType: 'Double' },
{ _id: 4, value: Decimal128("1.01"), expectedType: 'Decimal128' },
{ _id: 5, value: 3200000001, expectedType: 'Double' }

Object ID

  • {"x" : ObjectId()} is a 12-byte ID for documents.


  • Default type for _id


  • A special key of each document within a collection.
  • It can be any type.


  • {"x" : /foobar/i}


  • {"title": "Hello, world!"}


Timestamp( { "t": <integer>, "i": <integer> } )

The features provided by MongoDB


  • It supports generic secondary indexes and provides unique, compound, geospatial, and full-text indexing.


  • It provides an aggregation framework based on the concept of data processing pipelines.
  • Aggregation pipelines allow you to build complex analytics engines by processing data through a series of relatively simple stages on the server side, taking full advantage of database optimizations.

Special collection and index types

  • It supports time-to-live (TTL) collections for data that should expire at a certain time, such as sessions and fixed-size (capped) collections, for holding recent data, such as logs.
  • It supports partial indexes limited to only those documents matching a criteria filter to increase efficiency and reduce the amount of storage space required.

File storage

  • It supports an easy-to-use protocol for storing large files and file metadata.


  • Query for ranges, set inclusion, inequalities, and more by using $ conditionals.


  • It performs queries in MongoDB.
  • It returns a subset of documents in a collection.
  • find(<query_criteria>, <specify_the_keys_you_want>)
    • i.e. db.users.find({}, {"username" : 1, "email" : 1}) returns only the username and email keys.
    • i.e. db.users.find({}, {"fatal_weakness" : 0}) returns except the fatal_weakness key.
  • {} matches everything in the collection.

Query conditionals

  • "$lt", "$lte", "$gt", "$gte"

    `db.users.find({"age" : {"$gte" : 18, "$lte" : 30}})` // 18 < age < 30
    // Dates
    start = new Date("01/01/2007")
    db.users.find("registered" : {"$lt" : start}) // before January 1, 2007
  • "$ne" := not equal to

    • i.e. db.users.find({"username" : {"$ne" : "joe"}})
  • "$in", "$nin" (not in), and "$or" queries for a variety of values for a single key.

  • While "$or" will always work, use "$in" whenever possible as the query optimizer handles it more efficiently.

db.raffle.find({"ticket_no" : {"$in" : [12345, "joe"]}})

db.raffle.find({"$or" : [{"ticket_no" : 725}, {"winner" : true}]})
db.raffle.find({"$or" : [{"ticket_no" : {"$in" : [725, 542, 390]}}, {"winner" : true}]}) // $or can contain other conditionals.
  • "$not" is a meta conditional so it can be applied on top of any other criteria.

Type-specific queries


  • It also matches “does not exist.” Thus, querying for a key with the value null will return all documents lacking that key.
  • If we only want to find keys whose value is null, we can check that the key is null and exists using the "$exists" conditional.
    • i.e. db.c.find({"z" : {"$eq" : null, "$exists" : true}})


  • i.e. db.users.find({"name" : {"$regex" : /joe/i }}) search for case-insensitive i.e. Joe and joe

Querying arrays{"fruit" : ["apple", "banana", "peach"]}){"fruit" : "banana"}) // will match
  • "$all" matches a list of elements. Order does not matter.{"_id" : 1, "fruit" : ["apple", "banana", "peach"]}){"_id" : 2, "fruit" : ["apple", "kumquat", "orange"]}){"_id" : 3, "fruit" : ["cherry", "banana", "apple"]}){"fruit" : {"$all" : ["apple", "banana"]}}) // ["cherry", "banana", "apple"], ["apple", "banana", "peach"]

// index @2{"fruit.2" : "peach"}) // ["apple", "banana", "peach"]
  • "$size" queries for arrays of a given size.

    • It cannot be combined with another conditional.
      • i.e.{"fruit" : {"$size" : 3}}) // returns all of the above
    • i.e., {"comments" : {"$slice" : 10}}) limits to the first 10 elements.
  • $ operator returns the matching element. It helps when you do not know the index of the element.

    • i.e.{"" : "bob"}, {"comments.$" : 1})
  • "$elemMatch" forces MongoDB to compare clauses with a single array element.

// For the following documents {"x" : 5}, {"x" : 15}, {"x" : 25}, {"x" : [5, 25]}

db.test.find({"x" : {"$gt" : 10, "$lt" : 20}}) // {"x" : 15}, {"x" : [5, 25]}
db.test.find({"x" : {"$elemMatch" : {"$gt" : 10, "$lt" : 20}}}) // {}
db.test.find({"x" : {"$gt" : 10, "$lt" : 20}}).min({"x" : 10}).max({"x" : 20}) // {"x" : 15}

Querying on embedded documents

  • There are two ways:

    1. query for the whole documents
    2. query for the individual key/value pairs
  • Query documents can contain dots, which means “reach into an embedded document”.

    • i.e. db.people.find({"name.first" : "Joe", "name.last" : "Schmoe"})

$where queries

  • It allows you to execute arbitrary JavaScript as part of your query.
  • It should not be used unless strictly necessary because it is much slower than a regular query.
  • For security, the use of “$where” clauses should be highly restricted or eliminated. End users should never be allowed to execute arbitrary “$where” clauses.


  • Queries return a database cursor, which lazily returns batches of documents as you need them.
  • The database returns results from find using a cursor.
  • Metaoperations on a cursor include
    • skipping a certain number of results,
    • limiting the number of results returned, and
    • sorting results by any combination of keys in any direction.

How to create a cursor

  1. Put some documents into a collection.
  2. Do a query on them.
  3. Assign the results to a local variable.
for(i=0; i<100; i++) {
  db.collection.insertOne({x : i});
var cursor = db.collection.find();

cursor.forEach(function(x) {

while (cursor.hasNext()) {
  obj =;
  // do stuff

Covered queries

  • When an index contains all the values requested by a query, the query is covered.
  • If your query is only looking for the fields that are included in the index, it does not need to fetch the document.

CRUD operations


  • Adding new documents to a collection

  • MongoDB will add _id key.

  • <database_name>.<collection_name>.insertOne(<document>), where <document> = { <key>: <value> }.

  • <database_name>.<collection_name>.insertMany([<document1>, <document2>]) passes an array of documents to the database.

    • default: true := ordered, but slow


  • <database_name>.<collection_name>.findOne();
  • <database_name>.<collection_name>.find(); displays up to 20 documents.
  • <database_name>.<collection_name>.find().pretty();, where pretty() as a helper function.


  • Updating existing documents

  • Updating a document is atomic: if two updates happen at the same time, whichever one reaches the server first will be applied, and then the next one will be applied.

  • <database_name>.<collection_name>.updateOne(<filter_document>, <modifier_document>)

  • <database_name>.<collection_name>.updateMany(<filter_document>, <modifier_document>)

  • <database_name>.<collection_name>.replaceOne(<filter_document>, <replace_document>) fully replaces a matching document with a new one.

  • i.e. db.customers.update({first_name:"Steven"}, {$set:{gender:"male"}}); adds gender: male to the document of which first_name is Steven.

  • Your update should always specify a unique document, perhaps by matching on a key like _id.

db.flightData.updateOne({<key>:<value>}, {$set:{<key>:<value>}})

Update operators

  • Special keys that can alter, add, or remove keys, and even manipulate arrays and embedded documents.

  • $inc increments the value of the field by the specified amount.

    • If the field does not yet exist, it will be created.
    • It can be used only on values of type integer, long, double, or decimal.
  • $currentDate sets the value of a field to the current date, either as a Date or a Timestamp.

  • $min only updates the field if the specified value is less than the existing field value.

  • $max only updates the field if the specified value is greater than the existing field value.

  • $mul multiplies the value of the field by the specified amount.

  • $rename renames a field.

  • $set sets the value of a field in a document.

    • If the field does not yet exist, it will be created.
  • $setOnInsert sets the value of a field if an update results in an insert of a document.

    • It does not affect update operations that modify existing documents.
  • $unset removes the specified field.


  • An upsert is a special type of update.
  • If no document is found that matches the filter, a new document will be created by combining the criteria and updated documents.
  • If a matching document is found, it will be updated normally.


  • Removing documents from a collection

  • <database_name>.<collection_name>.deleteOne(<filter_document>) deletes only a single match even if there are many.

  • <database_name>.<collection_name>.deleteMany(<filter_document>) deletes all the documents that match a filter.


  • <database_name>.<collection_name>.drop() removes all documents in a collection.

Security: Choosing the correct level of safety versus speed for all of these operations

  • .


  • explain command to see what MongoDB is doing when it executes the query.
    • i.e. db.users.find({"username": "user101"}).explain("executionStats")
    • executionStats mode helps us understand the effect of using an index to satisfy queries.
    • totalKeysExamined := the number of keys within the index MongoDB walked through to generate the result set.
    • totalDocsExamined := the number of documents MongoDB looked at while trying to satisfy the query.
    • executionTimeMillis := the number of milliseconds it took to execute the query.
    • nReturned := the number of results returned.
    • and more…

Creating an index

  • To choose which fields to create indexes for, look through your frequent queries and queries that need to be fast and try to find a common set of keys from those.
  • db.users.createIndex({"username" : 1})
  • If you need to sort on two (or more) criteria, you may need to have index keys go in different directions.
    • i.e. age by asc and name by desc order
  • The direction (asc or desc) only matters for multikey sorts.
  • If you have more than 32 MB of results MongoDB will just error out, refusing to sort that much data.
    • To avoid this, you must create an index supporting the sort operation or use sort in conjunction with limit to reduce the results to below 32 MB.

Compound indexes

  • db.users.createIndex({"age" : 1, "username" : 1}) makes an index on both age and username.
  • Below holds for most of the situations, when designing a compound index:
    • Keys for equality filters should appear first.
    • Keys used for sorting should appear before multivalue fields.
    • Keys for multivalue filters should appear last.
  • This allows the query to find an exact value for the first index key and then search within that for a second index range.

Implicit indexes

  • If an index has N keys, you get a “free” index on any prefix of those keys. For example, if we have an index that looks like {“a”: 1, “b”: 1, “c”: 1, …, “z”: 1}, we effectively have indexes on {“a”: 1}, {“a”: 1, “b” : 1}, {“a”: 1, “b”: 1, “c”: 1}, and so on.

How $ operators use indexes

Inefficient operators

  • In general, negation is inefficient.
    • "$ne" has to scan the entire index.

Or queries

  • Normally, MongoDB will use one of the indexes you created, not both. However, "$or" can use one index per “$or” clause, as “$or” performs two queries and then merges the results.

Indexing objects and arrays

  • MongoDB allows you to reach into your documents and create indexes on nested fields and arrays.

Indexing embedded docs

  • Indexes can be created on keys in embedded documents in the same way that they are created on normal keys.
    • i.e. db.users.createIndex({"<field>.<subfield>" : 1})

Indexing arrays

  • Indexing an array field indexes each element of the array, not the array itself.
  • Array indexes are more expensive than single-value ones. For a single insert, update, or remove, every array entry might have to be updated.

Multikey index implications

  • If any document has an array field for the indexed key, the index immediately is flagged as a multikey index, which may be a bit slower than non-multikey indexes.

Index cardinality

  • Cardinality is the number of distinct values for a field in a collection.
  • In general, the greater the cardinality of a field, the more helpful an index on that field can be.
    • gender := 2, name := N -> name is better.


  • A protocol for storing large files
  • It uses subcollections to store file metadata separately from content chunks.

Special index and collection types

Geospatial Indexes

1. 2d

  • It indexes points stored on a 2D plane.
  • 2d indexes support both flat geometries and distance-only calculations on spheres.

2. 2dsphere

  • It works with spherical geometries that model the surface of the earth based on the WGS84 datum.
  • It allows you to specify geometries for points, lines, and polygons in the GeoJSON format.
  • Queries using spherical geometries will be more performant and accurate with a 2dsphere index.
  • You can create a geospatial index using the “2dsphere” type with createIndex.
    • i.e. db.openStreetMap.createIndex({"loc" : "2dsphere"})
// A point is given by a two-element array, representing `[longitude, latitude]`.
  "name" : "New York City",
  "loc" : {
    "type" : "Point",
    "coordinates" : [50, 2]

// A line is given by an array of points.
  "name" : "Hudson River",
  "loc" : {
    "type" : "LineString",
    "coordinates" : [[0,1], [0,2], [1,2]]

// A polygon is given by an array of points, but with a different "type".
  "name" : "New England",
  "loc" : {
    "type" : "Polygon",
    "coordinates" : [[0,1], [0,2], [1,2]]

Types of geospatial queries: intersection, within, nearness

var eastVillage = {
  "type" : "Polygon",
  "coordinates" : [
      [ -73.9732566, 40.7187272 ],
      [ -73.9724573, 40.7217745 ],
      [ -73.9732566, 40.7187272 ]

db.openStreetMap.find({"loc" : {"$geoIntersects" : {"$geometry" : eastVillage}}}) // intersection operator
db.openStreetMap.find({"loc" : {"$geoWithin" : {"$geometry" : eastVillage}}}) // within operator
db.openStreetMap.find({"loc" : {"$near" : {"$geometry" : eastVillage}}}) // nearness operator

Using geospatial indexes

geometric_query1 geometric_query2