Slide perkenalan dengan dasar MongoDB-query

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NoSQL databases
Introduction to MongoDB

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MongoDB: Introduction
The leader in the NoSQL Document-based
databases
Full of features, beyond NoSQL
High performance
High availability
Native scalability
High flexibility
Open source

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Terminology – Approximate mapping
Relational database MongoDB
Table Collection
Record Document
Column Field

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MongoDB: Document Data Design
High-level, business-ready representation of the data
Records are stored into Documents
• field-value pairs
• similar to JSON objects
• may be nested

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MongoDB: Document Data Design
High-level, business-ready representation of the data
Flexible and rich syntax, adapting to most use cases
Mapping into developer-language objects
year, month, day, timestamp,
lists, sub-documents, etc.

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MongoDB: Main features
Rich query language
Documents can be created, read, updated and
deleted.
The SQL language is not supported
APIs available for many programming languages
JavaScript, PHP, Python, Java, C#, ..
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MongoDB
Querying data using operators

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MongoDB: query language
MySQL clause MongoDB operator
SELECT find()
SELECT *
FROM people
db.people.find()
Most of the operations available in SQL language
can be expressend in MongoDB language

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MongoDB: Read data from documents
Select documents
db.<collection name>.find( {<conditions>},
{<fields of interest>} );
E.g.,
db.people.find();
Returns all documents contained in the people
collection

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Select documents
Select the documents satisfying the specified
conditions and specifically only the fields
specified in fields of interest
<conditions> are optional
conditions take a document with the form:
{field1 : <value>, field2 : <value> ... }
Conditions may specify a value or a regular
expression

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Select documents
Select the documents satisfying the specified
conditions and specifically only the fields
specified in fields of interest
<fields of interest> are optional
projections take a document with the form:
{field1 : <value>, field2 : <value> ... }
1/true to include the field, 0/false to exclude the
field

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E.g.,
db.people.find().pretty();
No conditions and no fields of interest
Returns all documents contained in the people
collection
pretty()displays the results in an easy-to-read
format
db.people.find({age:55})
One condition on the value of age
Returns all documents having age equal to 55

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db.people.find({ }, { user_id: 1, status: 1 })
No conditions, but returns a specific set of fields
of interest
Returns only user_id and status of all documents contained in the
people collection
Default of fields is false, except for _id
db.people.find({ status: "A", age: 55})
status = “A” and age = 55
Returns all documents having status = “A” and age = 55

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MongoDB: find() operator
SELECT find()
SELECT id,
user_id,
status
FROM people
db.people.find(
{ },
{ user_id: 1,
status: 1
}
)

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SELECT find()
SELECT id,
user_id,
status
FROM people
db.people.find(
{ },
{ user_id: 1,
status: 1
}
)
Where Condition
Select fields

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SELECT find()
WHERE find({<WHERE CONDITIONS>})
SELECT *
FROM people
WHERE status = "A"
db.people.find(
{ status: "A" }
)
Where Condition

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SELECT user_id, status
FROM people
WHERE status = "A"
db.people.find(
{ status: "A" },
{ user_id: 1,
status: 1,
_id: 0
}
)
Where Condition
Selection fields
SELECT find()
By default, the _id field is shown.
To remove it from visualization use: _id: 0

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SELECT find()
db.people.find(
{"address.city":“Rome" }
)
nested document
{ _id: "A",
address: {
street: “Via Torino”,
number: “123/B”,
city: “Rome”,
code: “00184”
}
}

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db.people.find({ age: { $gt: 25, $lte: 50 } })
Age greater than 25 and less than or equal to 50
Returns all documents having age > 25 and age <= 50
db.people.find({$or:[{status: "A"},{age: 55}]})
Status = “A” or age = 55
Returns all documents having status=“A” or age=55
db.people.find({ status: {$in:["A", "B"]}})
Status = “A” or status = B
Returns all documents where the status field value is either
“A” or “B”

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Select a single document
db.<collection name>.findOne(
{<conditions>}, {<fields of interest>} );
Select one document that satisfies the specified
query criteria.
If multiple documents satisfy the query, it returns
the first one according to the natural order which
reflects the order of documents on the disk.

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MongoDB: (no) joins
There are other operators for selecting data from
MongoDB collections
However, no join operator exists (but $lookup)
You must write a program that
Selects the documents of the first collection you are
interested in
Iterates over the documents returned by the first
step, by using the loop statement provided by the
programming language you are using
Executes one query for each of them to retrieve the
corresponding document(s) in the other collection
https://docs.mongodb.com/manual/reference/operator/aggregation/lookup

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MongoDB: (no) joins
(no) joins
Relations among documents/records are provided by
Object(ID) reference, with no native join
DBRef, across collections and databases
https://docs.mongodb.com/manual/reference/database-references/

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MongoDB: comparison operators
In SQL language, comparison operators are
essential to express conditions on data.
In Mongo query language they are available with
a different syntax.
MySQL MongoDB Description
> $gt greater than
>= $gte greater equal then
< $lt less than
<= $lte less equal then
= $eq equal to
!= $neq not equal to

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MongoDB: Comparison query operators
Name Description
$eq or : Matches values that are equal to a specified value
$gt Matches values that are greater than a specified value
$gte Matches values that are greater than or equal to a
specified value
$in Matches any of the values specified in an array
$lt Matches values that are less than a specified value
$lte Matches values that are less than or equal to a specified
value
$ne Matches all values that are not equal to a specified value
$nin Matches none of the values specified in an array

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MongoDB: comparison operators (>)
> $gt greater than
SELECT *
FROM people
WHERE age > 25
db.people.find(
{ age: { $gt: 25 } }
)

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MongoDB: comparison operators (>=)
SELECT *
FROM people
WHERE age >= 25
db.people.find(
{ age: { $gte: 25 } }
)
> $gt greater than

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MongoDB: comparison operators (<)
SELECT *
FROM people
WHERE age < 25
db.people.find(
{ age: { $lt: 25 } }
)
> $gt greater than
< $lt less than

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MongoDB: comparison operators (<=)
SELECT *
FROM people
WHERE age <= 25
db.people.find(
{ age: { $lte: 25 } }
)
> $gt greater than
< $lt less than

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MongoDB: comparison operators (=)
> $gt greater than
< $lt less than
= $eq equal to
The $eq expression is
equivalent to
{ field: <value> }.
SELECT *
FROM people
WHERE age = 25
db.people.find(
{ age: { $eq: 25 } }
)

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MongoDB: comparison operators (!=)
> $gt greater than
< $lt less than
= $eq equal to
!= $neq Not equal to
SELECT *
FROM people
WHERE age != 25
db.people.find(
{ age: { $neq: 25 } }
)

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MongoDB: conditional operators
To specify multiple conditions, conditional
operators are used
MongoDB offers the same functionalities of
MySQL with a different syntax.
AND , Both verified
OR $or At least one verified

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MongoDB: conditional operators (AND)
AND , Both verified
SELECT *
FROM people
WHERE status = "A"
AND age = 50
db.people.find(
{ status: "A",
age: 50 }
)

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MongoDB: conditional operators (OR)
AND , Both verified
OR $or At least one verified
SELECT *
FROM people
WHERE status = "A"
OR age = 50
db.people.find(
{ $or:
[ { status: "A" } ,
{ age: 50 }
]
}
)

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MongoDB: Cursor
db.collection.find()gives back a cursor. It
can be used to iterate over the result or as input
for next operations.
E.g.,
cursor.sort()
cursor.count()
cursor.forEach() //shell method
cursor.limit()
cursor.max()
cursor.min()
cursor.pretty()

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MongoDB: Cursor
Cursor examples:
db.people.find({ status: "A"}).count()
Select documents with status=“A” and count them.
db.people.find({ status: "A"}).forEach(
function(myDoc) { print( "user: ”+myDoc.name );
})
forEach applies a JavaScript function to apply to
each document from the cursor.
Select documents with status=“A” and print the
document name.

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MongoDB: sorting data
Sort is a cursor method
Sort documents
sort( {<list of field:value pairs>} );
field specifies which filed is used to sort the
returned documents
value = -1 descending order
Value = 1 ascending order
Multiple field: value pairs can be specified
Documents are sort based on the first field
In case of ties, the second specified field is
considered

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E.g.,
db.people.find({ status: "A"}).sort({age:1})
Select documents with status=“A” and sort them
in ascending order based on the age value
Returns all documents having status=“A”. The result
is sorted in ascending age order

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SELECT *
FROM people
WHERE status = "A"
ORDER BY user_id ASC
db.people.find(
{ status: "A" }
).sort( { user_id: 1 } )
ORDER BY sort()
Sorting data with respect to a given field in
MongoDB: sort() operator

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SELECT *
FROM people
WHERE status = "A"
ORDER BY user_id ASC
db.people.find(
{ status: "A" }
).sort( { user_id: 1 } )
ORDER BY sort()
Sorting data with respect to a given field in
MongoDB: sort() operator
SELECT *
FROM people
WHERE status = "A"
ORDER BY user_id DESC
db.people.find(
{ status: "A" }
).sort( { user_id: -1 } )

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MongoDB: counting
SELECT COUNT(*)
FROM people
db.people.count()
or
db.people.find().count()
COUNT count()or find().count()

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MongoDB: counting
SELECT COUNT(*)
FROM people
WHERE age > 30
db.people.count(
{ age: { $gt: 30 } }
)
COUNT count()or find().count()
Similar to the find() operator, count() can embed
conditional statements.

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MongoDB
Introduction to data aggregation

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Aggregation in MongoDB
Aggregation operations process data records and
return computed results.
Documents enter a multi-stage pipeline that
transforms the documents into an aggregated
result.

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MongoDB: Aggregation Framework
SQL MongoDB
WHERE $match
GROUP BY $group
HAVING $match
SELECT $project
ORDER BY $sort
//LIMIT $limit
SUM $sum
COUNT $sum

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MongoDB: Aggregation
Aggregate functions can be applied to collections
to group documents
db.collection.aggregate({<set of stages>})
Common stages: $match, $group ..
The aggregate function allows applying
aggregating functions (e.g. sum, average, ..)
It can be combined with an initial definition of
groups based on the grouping fields

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db.people.aggregate( [
{ $group: { _id: null,
mytotal: { $sum: "$age" },
mycount: { $sum: 1 }
}
}
] )
Considers all documents of people and
sum the values of their age
sum a set of ones (one for each document)
The returned value is associated with a field
called “mytotal” and a field “mycount”

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myaverage: { $avg: "$age" },
mytotal: { $sum: "$age" }
}
}
] )
Considers all documents of people and computes
sum of age
average of age

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{ $match: {status: "A"} } ,
count: { $sum: 1 }
}
}
] )
Counts the number of documents in people with
status equal to “A”
Where conditions

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{ $group: { _id: "$status",
count: { $sum: 1 }
}
}
] )
Creates one group of documents for each value of
status and counts the number of documents per
group
Returns one value for each group containing the
value of the grouping field and an integer
representing the number of documents

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count: { $sum: 1 }
}
},
{ $match: { count: { $gte: 3 } } }
] )
Creates one group of documents for each value
of status and counts the number of documents
per group. Returns only the groups with at least
3 documents

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count: { $sum: 1 }
}
},
{ $match: { count: { $gte: 3 } } }
] )
Creates one group of documents for each value
of status and counts the number of documents
per group. Returns only the groups with at least
3 documents
Having condition

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MongoDB: Aggregation Framework
SQL MongoDB
WHERE $match
GROUP BY $group
HAVING $match
SELECT $project
ORDER BY $sort
LIMIT $limit
SUM $sum
COUNT $sum

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SELECT status,
AVG(age) AS total
FROM people
GROUP BY status
db.orders.aggregate( [
{
$group: {
_id: "$status",
total: { $avg: "$age" }
}
}
] )
Aggregation in MongoDB: Group By
GROUP BY aggregate($group)

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SELECT status,
SUM(age) AS total
FROM people
GROUP BY status
{
$group: {
_id: "$status",
total: { $sum: "$age" }
}
}
] )
Group field

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SELECT status,
SUM(age) AS total
FROM people
GROUP BY status
{
$group: {
_id: "$status",
}
}
] )
Group field
Aggregation function

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SELECT status,
SUM(age) AS total
FROM people
GROUP BY status
HAVING total > 1000
{
$group: {
_id: "$status",
}
},
{ $match: { total: { $gt: 1000 } } }
] )
HAVING aggregate($group, $match)

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SELECT status,
SUM(age) AS total
FROM people
GROUP BY status
HAVING total > 1000
{
$group: {
_id: "$status",
}
},
{ $match: { total: { $gt: 1000 } } }
] )
Group stage: Specify
the aggregation field
and the aggregation
function

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SELECT status,
SUM(age) AS total
FROM people
GROUP BY status
HAVING total > 1000
{
$group: {
_id: "$status",
}
},
{ $match: { total: { $gt: 1000 } } }
] )
Group stage: Specify
the aggregation field
and the aggregation
function
Match Stage: specify
the condition as in
HAVING

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MongoDB Compass
GUI for Mongo DB

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MongoDB Compass
Visually explore data.
Available on Linux, Mac, or Windows.
MongoDB Compass analyzes documents and
displays rich structures within collections.
Visualize, understand, and work with your
geospatial data.

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MongoDB Compass
Connect to local or remote instances of MongoDB.

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MongoDB Compass
Get an overview of the data in list or table format.

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MongoDB Compass
Analyze the documents and their fields.
Native support for geospatial coordinates.

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MongoDB Compass
Visually build the query conditioning on
analyzed fields.

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MongoDB Compass
Autocomplete enabled by default.
Construct the query step by step.

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MongoDB Compass
Analyze query performance and get hints to speed it up.

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MongoDB Compass
Specify contraints to validate data
Find unconsistent documents.

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MongoDB Compass: Aggregation
Build a pipeline
consisting of multiple
aggregation stages.
Define the filter and
aggregation attributes
for each operator.

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MongoDB Compass: Aggregation stages

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MongoDB Compass: Aggregation stages
The _id corresponds to
the GROUP BY
parameter in SQL
Other fields contain the
attributes required for
each group.
One group for each “vendor”.

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MongoDB Compass: Pipelines
1st stage: grouping by vendor
2nd stage: condition over fields created in
the previous stage (avg_fuel, total).

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MongoDB: Indexes
Indexes are data structures that store a small
portion of the collection’s data set in a form easy
to traverse.
They store ordered values of a specific field, or
set of fields, in order to efficiently support
equality matches, range-based queries and
sorting operations.

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MongoDB: Indexes
MongoDB provides different data-type indexes
Single field indexes
Compound field indexes
Multikey indexes
Geospatial indexes
Text indexes
Hashed indexes

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MongoDB: Create new indexes
Creating an index
db.collection.createIndex(<index keys>, <options>)
Before v. 3.0 use db.collection.ensureIndex()
Options include: name, unique (whether to accept
or not insertion of documents with duplicate index
keys), background, dropDups, ..

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MongoDB: Indexes
Single field indexes
Support user-defined ascending/descending
indexes on a single field of a document
E.g.,
db.orders.createIndex( {orderDate: 1} )
Compound field indexes
Support user-defined indexes on a set of fields
E.g.,
db.orders.createIndex( {orderDate: 1,
zipcode: -1} )

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MongoDB: Indexes
MongoDB supports efficient queries of geospatial
data
Geospatial data are stored as:
GeoJSON objects: embedded document { <type>,
<coordinate> }
E.g., location: {type: "Point", coordinates: [-
73.856, 40.848]}
Legacy coordinate pairs: array or embedded document
point: [-73.856, 40.848]

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MongoDB: Indexes
Geospatial indexes
Two type of geospatial indexes are provided: 2d
and 2dsphere
A 2dsphere index supports queries that
calculate geometries on an earth-like sphere
Use a 2d index for data stored as points on a
two-dimensional plane.
E.g.,
db.places.createIndex( {location: “2dsphere”} )
Geospatial query operators
$geoIntersects, $geoWithin, $near, $nearSphere

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MongoDB: Indexes
{
<location field>: {
$near: {
$geometry: {
type: "Point" ,
coordinates: [ <longitude> , <latitude> ]
},
$maxDistance: <distance in meters>,
$minDistance: <distance in meters>
}
}
}
$near syntax:

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MongoDB: Indexes
E.g.,
db.places.createIndex( {location: “2dsphere”} )
Geospatial query operators
$geoIntersects, $geoWithin, $near, $nearSphere
Geopatial aggregation stage
$near

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MongoDB: Indexes
E.g.,
db.places.find({location:
{$near:
{$geometry: {
type: "Point",
coordinates: [ -73.96, 40.78 ] },
$maxDistance: 5000}
}})
Find all the places within 5000 meters from the
specified GeoJSON point, sorted in order from
nearest to furthest

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MongoDB: Indexes
Text indexes
Support efficient searching for string content in a
collection
Text indexes store only root words (no language-
specific stop words or stem)
E.g.,
db.reviews.createIndex( {comment: “text”} )
Wildcard ($**) allows MongoDB to index every
field that contains string data
E.g.,
db.reviews.createIndex( {“$**”: “text”} )

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