# NoSQL Databases

## NoSQL databases

From the early 1970s to about 2000, SQL dominated the database market. For most practical discussions, “SQL” and “relational model” are considered synonymous, although relational theorists and purists point out that SQL is not a full and faithful implementation of the model.

More recently, alternative database models have become popular, too. There are models based on objects, documents, graphs, and other structures. Sometimes these models overlap or are difficult to distinguish cleanly. Hybrid approaches that draw on multiple non-relational models are common. The loose umbrella term NoSQL was originally used to refer to these post-relational approaches. Since hybrids of non-relational and relational models have also appeared, the term NoSQL is now frequently defined as “Not Only SQL”.

## Document databases

The second DBMS covered in this tutorial is classified as a document database.

To transition from relational model thinking to document model thinking, begin by considering the humble .CSV file.

"email","sport_name","gender"
"[email protected]","Golf","Men"
"[email protected]","Baseball","Men"
"[email protected]","Soccer","Men"
"[email protected]","Soccer","Women"
"[email protected]","Softball","Women"
"[email protected]","Baseball","Men"


Comma-separated values (.CSV) files might be called the “least common denominator” of data formats. They are plain ASCII text files, which means that they can be read by nearly any software on any computing platform. They capture data in a tabular format: the first line typically gives “column” names, separated by commas. Each remaining line is one table “row”, with commas separating the column values. Often, quotation marks will be used to enclose the values, which makes it safe for the fields to contain internal comma characters.

The explosion of the Web and its documents (or pages) built with Hypertext Markup Language (HTML) inspired lots of other work with markup languages. In particular, eXtensible Markup Language (XML) is a format that replaces CSV in many Web-oriented settings. XML fits the “Web way” of doing things, and provides some advantages over CSV.

<?xml version="1.0"?>

<resultset statement="select * from learning_center.student_sport" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<row>
<field name="email">[email protected]</field>
<field name="sport_name">Baseball</field>
<field name="gender">Men</field>
</row>

<row>
<field name="email">[email protected]</field>
<field name="sport_name">Baseball</field>
<field name="gender">Men</field>
</row>

<row>
<field name="email">[email protected]</field>
<field name="sport_name">Golf</field>
<field name="gender">Men</field>
</row>

<row>
<field name="email">[email protected]</field>
<field name="sport_name">Soccer</field>
<field name="gender">Men</field>
</row>

<row>
<field name="email">[email protected]</field>
<field name="sport_name">Soccer</field>
<field name="gender">Women</field>
</row>

<row>
<field name="email">[email protected]</field>
<field name="sport_name">Softball</field>
<field name="gender">Women</field>
</row>
</resultset>


XML is the basis for many document-oriented database tools, though not the one that you will study.

The Web also drove the popularity of the JavaScript programming language. Here’s some JavaScript code.

let n = 1;
let s = "hello";
let o = { name: "Alice", age: 20 };
let o2 = { name: "Bob", age: 19 };


Nearly every programming language supports something like the first two statements. In the first, the assigned value is a numeric literal. In the second, the assigned value is a string literal.

But the third statement’s assigned value is an object literal. In most object-oriented languages, this is impossible because you must first define a datatype (a “class”) and then use that class definition as a template or blueprint for creating objects. The class defines the structure or “shape” of its instances (objects).

The final statement assigns another object literal to another variable. Clearly the two objects have the same structure (the same two properties or “fields”), and you could write code to handle that structure. But there is no Person class that defines this structure, as there would be in most OO languages.

Since JavaScript objects don’t need to have a pre-defined structure, they can be flexible. For example, we could continue the code above with the following statement.

o.nickname = "Ally";


This modifies the “shape” of the object by adding a third field that did not exist before.

This is not a JavaScript tutorial, but these concepts are relevant to the document-oriented DBMS you will study. In that case, why is it called a document DBMS instead of an object DBMS?

Probably because of a data format called JSON. Pronounced “Jason”, it stands for JavaScript Object Notation. Just as HTML Web work inspired the XML data format, JavaScript Web work inspired the JSON data format.

[
{
"email": "[email protected]",
"gender": "Men",
"sport_name": "Baseball"},
{
"email": "[email protected]",
"gender": "Men",
"sport_name": "Baseball"
},
{
"email": "[email protected]",
"gender": "Men",
"sport_name": "Golf"
},
{
"email": "[email protected]",
"gender": "Men",
"sport_name": "Soccer"
},
{
"email": "[email protected]",
"gender": "Women",
"sport_name": "Soccer"
},
{
"email": "[email protected]",
"gender": "Women",
"sport_name": "Softball"
}
]


The text above is essentially valid JavaScript code. It is not a statement, because it does not “do” anything; it is an expression because it defines a value. If a JavaScript program read that text from a file, it would have one long text string. But JavaScript provides easy ways to “parse” that string so that it is transformed into real JavaScript objects with named properties and associated values. It is also easy to go the other direction: “serializing” runtime JavaScript objects into a JSON-format string that is just text– easy to write to a file or send across a network.

The JSON format is so useful that it has transcended JavaScript. Many popular programming languages now provide similar parse and serialize functions for the JSON format.

## Getting started with MongoDB

MongoDB is a popular document DBMS that uses JSON.

This tutorial assumes that you are already set up with:

1. a running instance of the MongoDB server (daemon),
2. an operating system where you can open a terminal (bash, PowerShell, etc.),
3. the command line client software, mongo

If that is not the case, contact your instructor for help with setting up a work environment.

### Starting the mongo shell

In your terminal, launch the mongo shell.

When you have successfully connected to the server, your operating system’s terminal prompt will be replaced with a > prompt. The shell is waiting for your command.

### Creating/selecting the initial database

In the mongo shell, execute the following command. (Note: the > symbol is the mongo shell’s prompt; you do not type it.)

> use learning_center


The learning_center database did not exist, so MongoDB created it. You are now working within that database.

With MongoDB, you will often see this kind of dynamic flexibility. SQL required you predefine table formats, which were tricky to change. This reflected the static, relatively strong typing found in many programming languages.

In contrast, MongoDB resembles JavaScript’s dynamic typing, allowing you to create elements of your database without predefined structure, and modify them flexibly.

Each time you sit down to work with MongoDB, you will need to repeat the preceding steps to:

1. start the mongo shell (client), and
2. select the database that you want to work on.

The remainder of this tutorial will leverage MongoDB’s flexibility to build a document database that meets the same data requirements as the relational database you studied earlier.

## Creating new documents with MongoDB

When you have open, unmatched grouping symbols (parenthesis, braces, brackets), the mongo shell knows that your command is not complete. Press Enter at the end of each line in the following command; the ... will automatically appear at the start of each continuation line. Like the prompt symbol, these ellipses are not actually part of the command syntax.

> db.students.insertOne({
...		_id: 1,
...     first_name: 'Gary',
...     last_name: 'Gatehouse',
...     email: 'ggate[email protected]',
...     academic_rank: 'Sophomore',
...     residential_status: 'On campus',
...     majors: ['Math', 'Computer Science'],
...     slp_instructor_first_name: 'Terry',
...     slp_instructor_last_name: 'Tutor'
... })


The response should be:

{ "acknowledged" : true, "insertedId" : 1 }


The preceding command inserted one document into the current database, in a collection named students.

A MongoDB collection is a named group of documents. Since the students collection did not already exist, it was created.

The inserted document is a JSON-format object literal that contains eight fields representing Gary’s student information, plus a field _id with value of 1. Every MongoDB document has an _id field, with a value that is unique to its collection. It acts as a primary key, and the value cannot be changed. If you do not provide an _id value when creating the document, MongoDB will automatically generate one. However, those ID values are hard to type and arbitrary (you would get different results than examples shown to you.) Often, simple MongoDB examples will just use sequential numbers.

Look at the information on Gary’s majors. Where all the other values are quoted strings, the majors value is an array of quoted strings. In JSON, square brackets [ ] are used to group an array of comma-separated values. This is one way that MongoDB can model a one-to-many relationship: one student has multiple majors.

The following command uses array syntax in a different way to insert multiple documents at one time. (To clearly show the syntax of elaborate MongoDB queries, examples like the following will use multi-level indentation. It is awkward to do this directly in the mongo shell. You might prefer to edit your queries in a text editor, the paste them into the mongo shell.)

> db.students.insertMany(
...     [
...         {
...	            _id: 2,
...             first_name: 'Charlie',
...             last_name: 'Cadillac',
...             email: '[email protected]',
...             academic_rank: 'Junior',
...             residential_status: 'Off campus',
...             majors: ['English'],
...             sports: [
...                 {
...                     name: 'Soccer',
...                     gender: 'Men'
...                 },
...                 {
...                     name: 'Baseball',
...                     gender: 'Men'
...                 }
...             ],
...             slp_instructor_first_name: 'Terry',
...             slp_instructor_last_name: 'Tutor'
...         },
...         {
...	            _id: 3,
...             first_name: 'Irving',
...             last_name: 'Icehouse',
...             email: '[email protected]',
...             academic_rank: 'Sophomore',
...             residential_status: 'On campus',
...             majors: ['Chemistry'],
...             slp_instructor_last_name: 'Sam',
...             slp_instructor_last_name: 'Studybuddy'
...         }
...     ]
... )


The response should be:

{ "acknowledged" : true, "insertedIds" : [ 2, 3 ] }


Where the insertOne() command processed a single object enclosed with { }, the insertMany() command processes an array enclosed with [ ]. The elements of the array are separated by commas; each is an object enclosed with { }.

Notice that one student may play many sports, so the sport field is an array (if it is present). Each array item is a (sub-)document with fields of its own. If a student plays no sports, then the sport field will not be present for that student document; this is the equivalent of a NULL in a relational database.

You can retrieve all the documents that you have inserted like this.

> db.students.find({})


The empty braces { } define an object that acts as a filter on the query. Since the filter object is empty, all documents will be returned.

{ "_id" : 1, "first_name" : "Gary", "last_name" : "Gatehouse", "email" : "[email protected]", "academic_rank" : "Sophomore", "residential_status" : "On campus", "majors" : [ "Math", "Computer Science" ], "slp_instructor_first_name" : "Terry", "slp_instructor_last_name" : "Tutor" }
{ "_id" : 2, "first_name" : "Charlie", "last_name" : "Cadillac", "email" : "[email protected]", "academic_rank" : "Junior", "residential_status" : "Off campus", "majors" : [ "English" ], "sports" : [ { "name" : "Soccer", "gender" : "Men" }, { "name" : "Baseball", "gender" : "Men" } ], "slp_instructor_first_name" : "Terry", "slp_instructor_last_name" : "Tutor" }
{ "_id" : 3, "first_name" : "Irving", "last_name" : "Icehouse", "email" : "[email protected]", "academic_rank" : "Sophomore", "residential_status" : "On campus", "majors" : [ "Chemistry" ], "slp_instructor_last_name" : "Studybuddy" }


You can count the number of documents in the collection as follows.

> db.students.count()
3


### Exercise set 20

MongoDB does not have a convenient way to selectively write shell contents to a file. You will need to copy and paste your work for the following exercises using a text editor. Be sure to copy both the MongoDB command(s) and the results that are returned. Use the filename exercise20-1.txt for the first exercise, and so on.

1. Insert documents into the students collection for each of the six remaining students. Use the insertOne() command at least once, and the insertMany() command at least once. Include information for student majors and (where applicable) student sports, following the examples given above. Assign _id values as follows.
• Alice: 4
• Bob: 5
• Debbie: 6
• Eric: 7
• Frank: 8
• Hannah: 9
2. Write a query that returns all nine student documents.

## Retrieving data with MongoDB

### All collection documents and fields

You have already seen the MongoDB equivalent of SELECT * FROM table_name;

db.collection_name.find({});


### Selected fields

To return only selected fields in your query, pass a second object to find(). Use field names with values of 1 to indicate which fields you want. The special _id field is included by default.

> db.students.find({}, { last_name: 1, email: 1 })
{ "_id" : 1, "last_name" : "Gatehouse", "email" : "[email protected]" }
{ "_id" : 2, "last_name" : "Cadillac", "email" : "[email protected]" }
{ "_id" : 3, "last_name" : "Icehouse", "email" : "[email protected]" }
{ "_id" : 4, "last_name" : "Albert", "email" : "[email protected]" }
{ "_id" : 5, "last_name" : "Booth", "email" : "[email protected]" }
{ "_id" : 6, "last_name" : "Davis", "email" : "[email protected]" }
{ "_id" : 7, "last_name" : "Elkins", "email" : "[email protected]" }
{ "_id" : 8, "last_name" : "Forest", "email" : "[email protected]" }
{ "_id" : 9, "last_name" : "Hermanson", "email" : "[email protected]" }


Alternatively, use zeroes to indicate which fields to exclude.

> db.students.find({}, { email: 0, first_name: 0, majors: 0, sports: 0 })
{ "_id" : 1, "last_name" : "Gatehouse", "academic_rank" : "Sophomore", "slp_instructor_first_name" : "Terry", "slp_instructor_last_name" : "Tutor", "residential_status" : "On campus" }
{ "_id" : 2, "last_name" : "Cadillac", "academic_rank" : "Junior", "residential_status" : "Off campus", "slp_instructor_first_name" : "Terry", "slp_instructor_last_name" : "Tutor" }
{ "_id" : 3, "last_name" : "Icehouse", "residential_status" : "On campus", "slp_instructor_last_name" : "Studybuddy", "academic_rank" : "Sophomore" }
{ "_id" : 4, "last_name" : "Albert", "academic_rank" : "Senior", "residential_status" : "On campus", "slp_instructor_first_name" : "Sam", "slp_instructor_last_name" : "Studybuddy" }
{ "_id" : 5, "last_name" : "Booth", "academic_rank" : "Junior", "residential_status" : "On campus" }
{ "_id" : 6, "last_name" : "Davis", "academic_rank" : "Sophomore", "residential_status" : "On campus" }
{ "_id" : 7, "last_name" : "Elkins", "academic_rank" : "Senior", "residential_status" : "Off campus" }
{ "_id" : 8, "last_name" : "Forest", "academic_rank" : "Sophomore", "residential_status" : "On campus" }
{ "_id" : 9, "last_name" : "Hermanson", "academic_rank" : "Senior", "residential_status" : "On campus" }


You cannot combine includes and excludes in the same statement. The _id field is an exception to that rule.

### Selected documents

As mentioned earlier, the first document passed to find() is a filter. It corresponds to an SQL WHERE clause.

> db.students.find({ email: '[email protected]' })
{ "_id" : 1, "first_name" : "Gary", "last_name" : "Gatehouse", "email" : "[email protected]", "academic_rank" : "Sophomore", "residential_status" : "On campus", "majors" : [ "Math", "Computer Science" ], "slp_instructor_first_name" : "Terry", "slp_instructor_last_name" : "Tutor" }


You can write the kind of compound Boolean expressions that your programming experience would lead you to expect. Here are several examples. (Each begins with a comment. The DBMS ignores anything following a double slash //.)

> // Example: AND
> db.students.find(
...     {
...         $and: [ ... { first_name: 'Gary'}, {last_name: 'Gatehouse'} ... ] ... } ... ) { "_id" : 1, "first_name" : "Gary", "last_name" : "Gatehouse", "email" : "[email protected]", "academic_rank" : "Sophomore", "residential_status" : "On campus", "majors" : [ "Math", "Computer Science" ], "slp_instructor_first_name" : "Terry", "slp_instructor_last_name" : "Tutor" } > db.students.find( ... { ...$or: [
...             {first_name: 'Gary'}, {first_name: 'Irving'}
...         ]
...     }
... )
{ "_id" : 1, "first_name" : "Gary", "last_name" : "Gatehouse", "email" : "[email protected]", "academic_rank" : "Sophomore", "residential_status" : "On campus", "majors" : [ "Math", "Computer Science" ], "slp_instructor_first_name" : "Terry", "slp_instructor_last_name" : "Tutor" }
{ "_id" : 3, "first_name" : "Irving", "last_name" : "Icehouse", "email" : "[email protected]", "academic_rank" : "Sophomore", "residential_status" : "On campus", "majors" : [ "Chemistry" ], "slp_instructor_last_name" : "Studybuddy" }

> // Example: "greater than" operator with character data
> db.students.find(
...     {
...         last_name: {
...             $gt: 'G' ... } ... }, ... { ... last_name: 1 ... } ... ) { "_id" : 1, "last_name" : "Gatehouse" } { "_id" : 3, "last_name" : "Icehouse" }  Other comparison operators are shown below. SQL MongoDB = $eq
<> $ne <= $lte
>= $gte IN $in
NOT IN $nin You may recall that special SQL syntax was needed to match NULL values. mysql> select email from student where slp_instructor_last_name IS NULL; +---------------------+ | email | +---------------------+ | bbooth@dewv.net | | ddavis@dewv.net | | eelkins@dewv.net | | fforest@dewv.net | | hhermanson@dewv.net | +---------------------+ 5 rows in set (0.00 sec)  Here is the MongoDB equivalent. > db.students.find( ... { ... slp_instructor_last_name: {$exists: false }
...     },
...     {
...         email: 1
...     }
... )
{ "_id" : 5, "email" : "[email protected]" }
{ "_id" : 6, "email" : "[email protected]" }
{ "_id" : 7, "email" : "[email protected]" }
{ "_id" : 8, "email" : "[email protected]" }
{ "_id" : 9, "email" : "[email protected]" }


### Sorting results

You can sort results by following the find() call with a call to sort(). Here is an ascending sort on last name.

> db.students.find(
...     {
...         last_name: {
...             $gt: 'G' ... } ... }, ... { ... last_name: 1 ... } ... ).sort({ last_name: 1}) { "_id" : 1, "last_name" : "Gatehouse" } { "_id" : 3, "last_name" : "Icehouse" }  Here is the same query with a descending sort. > db.students.find( ... { ... last_name: { ...$gt: 'G'
...         }
...     },
...     {
...         last_name: 1
...     }
... ).sort({ last_name: -1})
{ "_id" : 3, "last_name" : "Icehouse" }
{ "_id" : 1, "last_name" : "Gatehouse" }


Finally, here is a primary and secondary sort in different directions. It sorts first by residential status, in ascending order. Where records have the same residential status, they are sorted by SLP instructor last name, in descending order.

> db.students.find({}, { email: 1, residential_status: 1, slp_instructor_last_name
: 1 }).sort({residential_status: 1, slp_instructor_last_name: -1})
{ "_id" : 2, "email" : "[email protected]", "residential_status" : "Off campus", "slp_instructor_last_name" : "Tutor" }
{ "_id" : 1, "email" : "[email protected]", "residential_status" : "On campus", "slp_instructor_last_name" : "Tutor" }
{ "_id" : 3, "email" : "[email protected]", "residential_status" : "On campus", "slp_instructor_last_name" : "Studybuddy" }


### Exercise set 21

1. List the first and last names for all Seniors.
2. List the first and last names for all Freshmen and Sophomores.
3. List the first and last names for all students who are not Seniors.
4. List the first and last names of every student, and the sports they play, if any. Sort the results by last name.
5. List the first and last names of every athlete, and the sport(s) they play. Sort the results by last name.

## Updating data with MongoDB

Suppose that Sam Studybuddy wins the lottery and is no longer working as an SLP instructor. This will affect only one student.

> db.students.find(
... { $and: [ ... { slp_instructor_first_name: "Sam" }, ... { slp_instructor_last_name: "Studybuddy" } ... ] ... }, ... { email: 1 } ... ) { "_id" : 4, "email" : "[email protected]" }  The updateOne() function uses its first argument as a filter. In the example below, the second argument changes the values of SLP instructor name properties for the affected student. > db.students.updateOne( ... { _id: 4 }, ... {$set: { slp_instructor_first_name: "Terry", slp_instructor_last_name: "Tutor" }}
... )
{ "acknowledged" : true, "matchedCount" : 1, "modifiedCount" : 1 }


The response shows that one document matched the filter, and one document was modified.

Now suppose that a new college policy requires all students to live on campus. So, you want to update off campus students to be on campus. As with SQL, it is a good idea to first SELECT/find the proper data. This verifies that the filter is correct.

> db.students.find({ residential_status: "Off campus" }, { email: 1, residential_status: 1 })
{ "_id" : 2, "email" : "[email protected]", "residential_status" : "Off campus" }
{ "_id" : 7, "email" : "[email protected]", "residential_status" : "Off campus" }


To modify both documents at once, pass the same filter as the first argument to updateMany(). The second argument sets a new value for the residential status property.

> db.students.updateMany(
...     { residential_status: "Off campus" },
...     { $set: { residential_status: "On campus" } } ... ) { "acknowledged" : true, "matchedCount" : 2, "modifiedCount" : 2 }  The response shows that two documents matched the filter, and both were modified. The updateOne() function always modifies zero or one document. If more than one document matches the filter, only one will be modified. (At this point, we will assume that we can’t control which, and will take care to ensure that the filters passed to updateOne() match at most one document.) In contrast, updateMany() will process all matching documents. But “process” here does not necessarily mean changes are made. The modified count will exclude documents that match the filter but would not be modified by the second argument (because they already have those values). The examples above replace the values of existing properties. The $set operator can also define a value for a document property that did not previously exist.

> db.students.updateOne(
... { _id: 3 },
... { $set: { sports: [ { name: "Basketball", gender: "Men" } ] } } ... ) { "acknowledged" : true, "matchedCount" : 1, "modifiedCount" : 1 }  The $unset operator does the opposite. It removes the value for a property, like setting a NULL in SQL. The following example reverses the change just made.

> db.students.updateOne( { _id: 3 }, { $unset: { sports: undefined } } ) { "acknowledged" : true, "matchedCount" : 1, "modifiedCount" : 1 }  The value specified for properties that are being $unset is ignored. The example uses the JavaScript undefined value, but any value would have the same effect.

Because some documents already have sports data, the example that adds Irving to the Men’s Basketball team is analogous to replacing a NULL value in SQL. However, the $set operator is more powerful than this suggests. MongoDB is not constrained by pre-existing tabular formats as in a relational database. The following example alters the “shape” or format of all student documents, except the first, by setting a value for a new property that did not previously exist on any of the documents. > db.students.updateMany( ... { _id: {$ne: 1 } },
... { $set: { someFieldThatWasJustDreamedUp: "just because" } } ... ) { "acknowledged" : true, "matchedCount" : 8, "modifiedCount" : 8 }  MongoDB defines other field update operators in addition to $set and $unset. These include operators that: • modify a field to hold the current date, • increment a field’s value, • set a specified value only if it is less than the fields current value, and • modify the contents of array-valued fields without requiring you to re$set the entire array.

### Exercise set 22

1. Many lottery winners later end up broke– including Sam. Since Sam is back, update Albert’s record so that Sam is again his SLP instructor.
2. Use two updateOne() statements to reverse the changes from the college’s altered residence policy.
3. Use updateMany() to remove the dreamed up property from all documents.

## Deleting data with MongoDB

First, execute the following command.

db.students.aggregate([ { $match: {} }, {$out: 'temp_students' } ]);


You will learn more about the aggregate() function later. Here, it is used to make a copy of the students collection, named temp_students. You will use the temp_students collection to practice deleting data.

You might have guessed that MongoDB defines functions named deleteOne() and deleteMany(). They use the same filtering as other commands.

> // Don't forget to use the temporary collection!
> db.temp_students.deleteOne( { email: '[email protected]'})
{ "acknowledged" : true, "deletedCount" : 1 }

> db.temp_students.deleteMany( { academic_rank: 'Sophomore'})
{ "acknowledged" : true, "deletedCount" : 3 }

> db.temp_students.count({ academic_rank: 'Sophomore' }
0


### Exercise set 23

Be sure to use the temporary collection!

1. Use deleteOne() to delete any single document from the temporary collection.
2. Use deleteMany() to delete all the remaining documents from the temporary collection.

## Normalizing data with MongoDB references

You have seen one way to model one-to-many relationships in MongoDB: one student has many majors, and those majors can be modeled as an array embedded within the student document. This is a denormalized approach. The equivalent structure in a relational database would be a repeating group, which fails to meet first normal form.

MongoDB supports a second, more normalized approach to modeling one-to-many relationships.

Use this command to create a document in a new collection to represent visit data.

> db.visits.insertOne(
...   {
...     _id: 1,
...     students_id: 1,
...     check_in_time: "2016-08-30 14:35:55",
...     check_out_time: "2016-08-30 15:53:44",
...     location: "Albert Hall",
...     purpose: "study hall",
...     purpose_achieved: "Y",
...     comments: "New year, fresh start!",
...   }
... )
{ "acknowledged" : true, "insertedId" : 1 }


This creates a collection named visits and a document in that collection. The students_id field is a reference that indicates this visit is for the students document with _id: 1. In other words, your old friend Gary Gatehouse.

Gary’s visit has the earliest check in on record. Here are the next two.

> db.visits.insertMany([
...   {
...     _id: 2,
...     students_id: 2,
...     check_in_time: "2016-08-30 14:55:55",
...     check_out_time: "2016-08-30 16:53:44",
...     location: "Albert Hall",
...     purpose: "baseball meeting",
...     purpose_achieved: "?",
...   },
...   {
...     _id: 3,
...     students_id: 3,
...     check_in_time: "2016-08-30 15:56:56",
...     check_out_time: "2016-08-30 16:56:46",
...     location: "Albert Hall",
...     purpose: "Meet SLP instructor",
...     purpose_achieved: "Y",
...     comments: "Cubicle B computer is not working.",
...   },
... ]);
{ "acknowledged" : true, "insertedIds" : [ 2, 3 ] }


The students_id references can be used with the aggregate() function to “join” the two collections.

> db.students.aggregate([
... {
...    $lookup: ... { ... from: "visits", ... localField: "_id", ... foreignField: "students_id", ... as: "visits_array" ... } ... } ... ]) { "_id" : 1, "first_name" : "Gary", "last_name" : "Gatehouse", "email" : "[email protected]", ... "visits_array" : [ { "_id" : 1, "students_id" : 1, "check_in_time" : "2016-08-30 14:35:55", "check_out_time" : "2016-08-30 15:53:44", "location" : "Albert Hall", "purpose" : "study hall", "purpose_achieved" : "Y", "comments" : "New year, fresh start!" } ] } { "_id" : 2, "first_name" : "Charlie", "last_name" : "Cadillac", "email" : "[email protected]", ... "visits_array" : [ { "_id" : 2, "students_id" : 2, "check_in_time" : "2016-08-30 14:55:55", "check_out_time" : "2016-08-30 16:53:44", "location" : "Albert Hall", "purpose" : "baseball meeting", "purpose_achieved" : "?" } ] } ... { "_id" : 9, "first_name" : "Hannah", "last_name" : "Hermanson", "email" : "[email protected]", ... "visits_array" : [ ] }  To save space, only partial results are shown. The statement above processes all students documents; for each, it uses the _id field value to look up visits documents with the same value in students_id. The matching visits documents are inserted into the results as a field named visits_array. So, results for both Gary and Charlie have a visits_array field containing their visits documents. Notice that Hannah’s results have an empty visits_array. The actual results include all students documents (although the output shown has been condensed to save space). So the $lookup aggregator is like a left outer join.

### Exercise set 24

1. The $lookup query shown above is the equivalent of students LEFT OUTER JOIN visits in SQL. Write a $lookup query that is the equivalent of visits LEFT OUTER JOIN students. Before you run it, predict the number of result documents. Run the query, to see if you were correct, and explain the result count.
2. Insert documents into the visits collection for each of the eleven remaining visits. Use the data values from the MySQL table visit2nf, with the rows sorted by ascending check in time. Continue to use sequential _id values.
3. To verify results of the previous exercise, write a query that returns all fourteen visit documents.
4. Repeat the $lookup query shown above to produce full results of students “joined” with (the now expanded collection) visits. ## Aggregating data The MongoDB syntax for “joins” is the aggregate() function with a $lookup operator.

But as the name suggests, most aggregate() operators have nothing to do with joins.

There is a small number of “single purpose aggregation operations”, which aggregate data from a single collection. One example you have already seen is count().

> db.visits.count()
14


Another example is distinct().

> db.visits.distinct("location")
[ "Albert Hall", "Writing center" ]


More advanced aggregation can be performed using MongoDB’s “multi-stage aggregation pipeline.” Each stage or step in the pipeline transforms its input to an aggregate that can, optionally, become the input to another stage.

Suppose you want to know how many visits were made by each student.

> db.visits.aggregate(
...     [
...         {$group: {_id: '$students_id', visits: {$sum: 1}}} ... ] ... ) { "_id" : 8, "visits" : 2 } { "_id" : 9, "visits" : 1 } { "_id" : 5, "visits" : 1 } { "_id" : 6, "visits" : 3 } { "_id" : 7, "visits" : 1 } { "_id" : 1, "visits" : 2 } { "_id" : 2, "visits" : 2 } { "_id" : 3, "visits" : 1 } { "_id" : 4, "visits" : 1 }  The aggregate() function’s argument is an array whose elements are stages in the aggregation pipeline. Here, there is only one stage. The $group stage is similar to an SQL GROUP BY clause. It takes its inputs (here, all the documents in the visits collection) and outputs a document for each distinct students_id value. The output documents have two fields: one for a student’s id, and one for that student’s number of visits. The second field’s value is calculated as a sum of the constant expression 1 across all the documents.

To filter the documents going into the aggregation, add a $match stage to the pipeline, before the grouping. Here are the number of closed (checked out) visits for each student. > db.visits.aggregate( ... [ ... {$match: { check_out_time: { $exists: true}}}, ... {$group: {_id: '$students_id', visits: {$sum: 1}}}
...     ]
... )
{ "_id" : 8, "visits" : 2 }
{ "_id" : 9, "visits" : 1 }
{ "_id" : 5, "visits" : 1 }
{ "_id" : 6, "visits" : 2 }
{ "_id" : 7, "visits" : 1 }
{ "_id" : 1, "visits" : 1 }
{ "_id" : 2, "visits" : 2 }
{ "_id" : 3, "visits" : 1 }
{ "_id" : 4, "visits" : 1 }


The ordering of the pipeline steps is important. In the example above, the $match stage acts like an SQL WHERE clause. In the example below, the $match stage is used to filter aggregate results, so it is acting like an SQL HAVING clause. This query lists all students who have visited more than once.

> db.visits.aggregate(
...     [
...         { $group: {_id: '$students_id', visits: {$sum: 1}}}, ... {$match: {visits: { $gt: 1}}} ... ] ... ) { "_id" : 8, "visits" : 2 } { "_id" : 6, "visits" : 3 } { "_id" : 1, "visits" : 2 } { "_id" : 2, "visits" : 2 }  This computes the number of visits for each students_id, then displays only the documents with a sum greater than one. It is possible to apply both $match stages. This query lists students who have more than one closed visit, and the number of closed visits for each.

> db.visits.aggregate(
...     [
...         { $match: { check_out_time: {$exists: true}}},
...         { $group: {_id: '$students_id', visits: {$sum: 1}}}, ... {$match: {visits: { $gt: 1}}} ... ] ... ) { "_id" : 8, "visits" : 2 } { "_id" : 6, "visits" : 2 } { "_id" : 2, "visits" : 2 }  Incidentally, MongoDB defines views (virtual collections) in terms of an aggregation pipeline. The following command creates a view named athletes, based on documents in the students collection, as processed by a single-stage aggregation pipeline. > db.createView("athletes", "students", [ {$match: { sports: { $exists: true } } } ] ) { "ok" : 1 }  MongoDB views are read-only. > db.athletes.find( { }, { email: 1 } ) { "_id" : 2, "email" : "[email protected]" } { "_id" : 5, "email" : "[email protected]" } { "_id" : 6, "email" : "[email protected]" } { "_id" : 7, "email" : "[email protected]" } > db.athletes.deleteOne( { _id: 2 } ) 2020-10-28T23:35:04.128+0000 E QUERY [js] WriteError: Namespace learning_center.athletes is a view, not a collection  ### Exercise set 25 1. Create a new collection named computers and insert documents with the data from the MySQL table computer. Then write a single query to answer this question: how many computers have less than 8GB of memory? 2. Write a single query to list the number of visits at each location. 3. Write a single query to list the number of students in each academic rank. 4. Modify the preceding query to count only commuter students. 5. Write a single query to list students who have made more than one visit at the Albert Hall location, and the number of visits they made there. ## Indexes As with SQL, MongoDB indexes have two distinct purposes. First, indexes can improve the performance of data retrieval (at the cost of additional storage, plus time to update indexes when data changes). Second, indexes can enforce data constraints– particularly uniqueness constraints. MongoDB automatically creates a unique index on each collection’s built-in _id field. You can create others. > db.students.createIndex({ email: 1 }, { unique: true }) { "createdCollectionAutomatically" : false, "numIndexesBefore" : 1, "numIndexesAfter" : 2, "ok" : 1 }  There is now a unique index on the email field of the students collection; duplicate entries will not be permitted. The following command shows all indexes defined for the collection: the built-in _id index as well as the email index just created. > db.students.getIndexes() [ { "v" : 2, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "learning_center.students" }, { "v" : 2, "unique" : true, "key" : { "email" : 1 }, "name" : "email_1", "ns" : "learning_center.students" } ]  As with SQL, you can ask MongoDB to explain how it will process a query. The following command explains the strategy for processing a find() call on the visits collection, filtering on specified values for two fields. The details are beyond the scope of this tutorial, but note that the “winning plan” will use a collection scan (COLLSCAN)– the equivalent of an expensive table scan in a relational DBMS. > db.visits.explain().find( {$and: [ { students_id: 8 }, { check_in_time: "2016-08-31 11:19:15"} ] } )
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "learning_center.visits",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [ { "check_in_time" : { "$eq" : "2016-08-31 11:19:15"
}
},
{
"students_id" : {
"$eq" : 8 } } ] }, "winningPlan" : { "stage" : "COLLSCAN", "filter" : { "$and" : [
{
"check_in_time" : {
"$eq" : "2016-08-31 11:19:15" } }, { "students_id" : { "$eq" : 8
}
}
]
},
"direction" : "forward"
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "ws-ae5f4d7b-56a7-49ae-ac74-3e88d70ee3b5",
"port" : 27017,
"version" : "4.0.19",
"gitVersion" : "7e28f4296a04d858a2e3dd84a1e79c9ba59a9568"
},
"ok" : 1
}


You can improve the performance of that and similar queries by creating this “compound” index on the two fields used in the filter.

> db.visits.createIndex( { students_id: 1, check_in_time: 1 }, { unique: true } )
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}


Now an explain() on the same query shows that the index will be used.

> db.visits.explain().find( { $and: [ { students_id: 8 }, { check_in_time: "2016-08-31 11:19:15"} ] } ) { "queryPlanner" : { "plannerVersion" : 1, "namespace" : "learning_center.visits", "indexFilterSet" : false, "parsedQuery" : { "$and" : [
{
"check_in_time" : {
"$eq" : "2016-08-31 11:19:15" } }, { "students_id" : { "$eq" : 8
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"students_id" : 1,
"check_in_time" : 1
},
"indexName" : "students_id_1_check_in_time_1",
"isMultiKey" : false,
"multiKeyPaths" : {
"students_id" : [ ],
"check_in_time" : [ ]
},
"isUnique" : true,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 2,
"direction" : "forward",
"indexBounds" : {
"students_id" : [
"[8.0, 8.0]"
],
"check_in_time" : [
"[\"2016-08-31 11:19:15\", \"2016-08-31 11:19:15\"]"
]
}
}
},
"rejectedPlans" : [ ]
},
"serverInfo" : {
"host" : "ws-ae5f4d7b-56a7-49ae-ac74-3e88d70ee3b5",
"port" : 27017,
"version" : "4.0.19",
"gitVersion" : "7e28f4296a04d858a2e3dd84a1e79c9ba59a9568"
},
"ok" : 1
}


Recall that updateOne() and deleteOne() calls affect only the first document (if any) matching the provided filter. Generally, MongoDB designers create unique indexes for the field sets that will be used in such filters. This ensures that the operation affects only a clearly identified single document.

MongoDB indexes do not have to be unique. Simply omit the second argument to create a non-unique index.

> db.students.createIndex( { majors: 1 } )
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 2,
"numIndexesAfter" : 3,
"ok" : 1
}


Notice that the index just created is on the majors field, which is an embedded array within the students documents. The index can speed up the performance of queries like the following.

> db.students.find( { majors: "Computer Science" }, { email: 1, majors: 1 } )
{ "_id" : 1, "email" : "[email protected]", "majors" : [ "Math", "Computer Science" ] }
{ "_id" : 4, "email" : "[email protected]", "majors" : [ "Computer Science" ] }
{ "_id" : 5, "email" : "[email protected]", "majors" : [ "Computer Science", "Philosophy" ] }


### Exercise set 26

1. Create an index on the computers collection, corresponding to the primary key in the MySQL table.
2. Create an index on the students collection’s sports field.
3. Compare and contrast the (My)SQL and NoSQL(MongoDB) features that ensure entity integrity. Is either significantly better than the other?
4. Compare and contrast the (My)SQL and NoSQL(MongoDB) features that ensure referential integrity. Is either significantly better than the other?

## Data integrity

Like MySQL, MongoDB provides special utility programs to backup and restore the contents of a database while the underlying files are held open by the running DBMS server. These utilities are called mongodump and mongorestore.

MongoDB also matches SQL’s concept of transactions. However, MongoDB transaction processing is usually performed in applications programs that use an API to communicate with a MongoDB server. It is possible to manage transactions in the mongo shell, but it is both awkward and limited.