• In replica set Primary is the only member that receives write operations.
• In replica set secondary member is to log and apply the operations to their data.
• MongoDB applies write operations and records the operation on primary’s oplog.
• The primary member accepts all write operations in the following three-members replica set.
• The secondary member replicates the primary’s oplog to apply their data sets.
• In replica sets all members can accept to read captions.
• In replica sets, read preference describes how MongoDB clients route read operations to the members.
• An election determines the new primary, If the current primary becomes unavailable.
• Replica setsuse elections to determine which set member will become primary.
• The primary becomes unavailable, in the following 3-member replica set.
• This triggers an election which selects one of the remaining secondaries as the new primary.

Replica set Secondary Member:

• In replica sets secondary maintains a copy of the primary’s data set.
• A secondary applies operations from the primary’s oplog to its own data set in an asynchronous process, to replicate data.
• A replica set can have one or more secondaries.
• In replica sets, the following three-member has two secondary members.
• The secondaries replicate the primary’s oplog and apply the operations to their data sets.
• In replica set client cannot write data, but they can read data from secondary members.
• A secondary can become a primary.
• The replica set holds an election to choose which of the secondaries becomes the new primary, If the current primary becomes unavailable.

Various types of charts in MongoDB

Column and Bar Charts :

• Column and Bar chart provides high level overviews of data trends by using comparing values inside a specific category.

Line and Area Charts :

• Line and Area chart is connected by straight line segments and displays information as a series of data points.

Combo Charts

• Combo chart offer a line view and combined column.

Grid Charts

• Grid chart is also known as Heatmaps represent aggregated data in a tabular format as a range of colors.

Scatter Charts :

• Scatter chart is a plot data with individual data points and placed along with X axis and Y axis.

Circular charts :

1. Donut chart:

• • Donut chart is with larger segments representing larger data values and display data in a series of segments of a circle.
  • In this chart, each segment indicates the proportion of each category in the data to the total and the entire circle represents the sum of all data values.

    2.Gauge chart:

• • Gauge chart is with customizable minimum and maximum values and display data as a percentage of a semicircle.
  • Optionally you may also specify a Target value, which renders as a heavy line within the gauge’s range.

Text charts :

1. Data Tables:

• • Data tables are similar to a spreadsheet and represent data in a tabular view.

MMAPv1 :

• This type of storage engine uses memory-mapped files to store its data.
• A segment of virtual memory that has been assigned a direct byte-for-byte correlation with some portion of a file.
• This storage engine allows a great deal of performance for heavy read applications.
• Indexes and Data are mapped into virtual space.
• Data access is placed into RAM.
• It will swap out memory to disk to make space for the newly requested data if the OS runs out of RAM and an application requests for memory.
• MongoDB’s memory was managed by operating system’s virtual memory subsystem.

WiredTiger :

• From Mongo 3.0 WiredTiger is the default storage engine.
• This type storage engine uses document-level concurrency control for write operations. At the same time multiple clients can modify different documents of a collection.
• It uses only intent locks at the global, database and collection levels when the storage engine detects conflicts between two operations, one will incur a write conflict causing MongoDB to transparently retry that operation.
• MongoDB utilizes both the filesystem cache and WiredTiger internal cache. the wired tiger cache will use 50% of RAM minus 1 GB or 256 MB by default.
• It is efficient use of CPU cores and RAM.
• It allows more tuning storage engines than MMVPv1. 7 to 10X better write performance. 80% less storage with compression.
• In WiredTiger internal cache collection level data is uncompressed and uses a different representation from the on-disk format.

Encrypted :

• It is available in MongoDB enterprise only.
• During encryption master keys and database keys are used.
• Master key encrypts the database keys, data is encrypted with the database keys.
• Application-Level Encryption provides encryption on a per-field or per-document basis within the application layer.

In-memory :

• Available in the enterprise editions starting from version 3.2.6.
• It Handles ultra-high throughput with high availability and low latency.
• Low latency on less in-memory infrastructure and more predictable.
• Supports high level infrastructure based on zonal sharding.
• Indexing support and MongodB rich query capability.

Mongoimport :

• In MongoDB, it provides a special tool that is used to import data.

Syntax :
mongoimport-jsonarray-db database_name-collection collection_name-file file_location

Mongoexport :

• In MongoDB it allows you to export its data into other file formats like JSON or CSV.

mongoexport-db database_name-collection collection_name-outpath_or_name_of_the_file

Mongodump :

• In MongoDB it is used creating a binary export of the contents and export data from either mongod or mongos

Mongorestore :

• This program loads data from either a binary database dump created by mongodump or the standard input into a mongod or mongos instance.

1. 1. Shards
   2. Query Router
   3. Config server

Shards :

• It is a single MongoDB instance that holds a subset of the sharded data.
• It can be deployed as replica sets to provide redundancy and increase availability.
• The combination of multiple shards creates a complete data set.

Query router :

• Query router acts as a Mongos providing a stable interface between the application and the sharded cluster.
• In this MongoDB instance is responsible for routing the client requests to the correct shard.

Config Server :

• Config Server becomes to store the metadata (Data about the data) and the configuration settings for the whole cluster.

Developer UX :

• It is created in such a way that ensures that the developers have an excellent experience while creating apps.
• Programming languages like JavaScript , Java , Go , C, C# , Python , PHP , Swift, Scala, Rust, and Ruby on Rails were MongoDB used.
• In Enterprise, IT new features are being added for supporting operations of MongoDB as an increasing number of businesses are opting for this database.
• For MongoDB customers, excellent tech support is also available.
• Assigning clusters straight away and you can start coding with just a couple of clicks in the web UI.

Scalability & Transactionality :

• In Scalability, you can develop apps that are capable of handling traffic spikes smoothly. It is one of the biggest advantages of MongoDB.
• Massive volumes of the read and write operations are supported, Due to the many innovations by MongoDB.
• Information clusters can be stored in one place, though the information itself is stored on numerous computer clusters, Due to the manner of Sharding in MongoDB.
• This is in stark contrast to the relational database architecture which scales up in order to create computers they are more powerful and speeder, therefore limited.
• In MongoDB objects can be embedded within one another during data modeling.

Platform & Ecosystem maturity :

• MongoDB was launched in 2007, and till now thousands of apps have been used for various purposes.
• Ergo is one of the best platforms that has been updated to ensure that emerging demands can be fulfilled.
• There is a huge, vibrant community of MongoDB developers around the world in the open-source arena, consulting firms, academia, system integrators and so on.

Disadvantages of MongoDB :

Joins not supported :

• In MongoDB joins like relational database doesn’t support.
• It is manually, can use joins functionality by adding by coding.
• It may affect performance and slow execution.

High memory usage :

• In MongoDB stores key names for each value pair.
• There is data redundancy, due to no functionality of joins.
• This results in increasing unnecessary usage of memory.

Limited Data Size :

• Document size should not have more than 16MB.

Limited Nesting:

• The nesting documents cannot perform for more than 100 levels.

1. 1. Vertical Scaling
   2. Horizontal Scaling

Vertical Scaling

• In this existing system where the new resources are added to meet the expectation.
• Resources that comprise the existing system and consider as a rack of servers.
• It is based on the idea of adding more power (CPU, RAM) to existing systems, basically adding more resources.
• It becomes cheaper than Horizontal scaling but it is not easy to process.
• The size of the existing system become expands vertically.
• It may involve downtime and harder to upgrade.

Horizontal Scaling

• In this existing system where the new server racks are added to meet the higher expectation.
• Resources that comprise the existing system and consider as a rack of servers
• It is based on the idea of adding more machines into our pool of resources.
• It is costlier than Vertical Scaling and difficult.
• To upgrade it is easy.

1. 1. Name- It represents the name of the collection and it is of string type.
   2. Options–It is an optional parameter.

Syntax:

This syntax specifies the following fields:

1. Collection Name: It is used to create as capped collection.
2. Capped: It is a Boolean field. If it is true, we create a capped collection.
3. auto indexed: It is a Boolean flag and use for auto-indexing. Indexes will be created automatically, if the flag is true.
4. Size: The maximum number of documents in bytes is represented by size. In context of capped collection, this is required field.
5. Max: The highest number of documents that permit the collection was represented by max.

  It uses role-based access control. It uses privilege security-based model Performance becomes faster than MySQL. Performance becomes slower than MongoDB. Key features are Auto sharding, Comprehensive secondary indexes, In-memory speed, Native location, Embedded data models support. Key features are Triggers & SSL support, provides text searching and indexing, Query caching, Integrated replication support, Different storage engines. It supports built-in Replication, Sharding and auto elections. It supports Master-Slave Replication. Best fit for unconstructed data, works better for large datasets, High write loads, Data is location-based. Best fit for data with tables and rows, works better for small datasets, Frequent updates, Modify large volume of records. The community of MySQL is much better than MongoDB. It has a good active community.

For example,

                                   {
                                          "name" : "New York City",
                                          "loc" : {
                                                 "type" : "Point",
                                                 "coordinates" : [50, 2]
                                        }
                                  }

A line is given by an array of points:

For example,

{
                            "name" : "Hudson River",
                            "loc" : {
                                  "type" : "LineString",
                                  "coordinates" : [[0,1], [0,2], [1,2]]
                           }
                      }

Sum :

• This aggregation function is used to sum numeric values for the document in each group.

Count :

• This aggregation function is used to count total number of documents.

Average :

• This aggregation function is used to calculates the average of all given values from all documents.

Minimum :

• This aggregation function is used to get the minimum value from all the documents.

Maximum :

• This aggregation function is used to get the maximum value from all the documents.

First :

• This aggregation function is used to get the first document from the grouping.

Last :

• This aggregation function is used to get the last document from the grouping.

Null :

• It is used to store Null values.

 Syntax:  {“x”: null}     

Boolean :

• It is used to store whether true or false.

  Syntax: {“x”: true}

Integer :

• It is used to store an integer value.
• In two forms we can store integer data type 32 -bit signed integer and 64 – bit signed integer.

Syntax: {“x”: 4}

String :

• It is used to store data and the most commonly used data type in MongoDB, BSON strings are of UTF-8.
• In each programming language, the drivers convert from the string format of the language to UTF-8 while serializing and deserializing BSON.

Syntax: {“x”: “foobar”}

Date :

• It is used to store date, and it is a 64-bit integer that represents the number of milliseconds.
• It is signed and the BSON data type generally supports UTC datetime.
• If the value of the date data type is negative, then it represents the dates before 1970.
• In this type, there are various type:

1. 1. Date ()
   2. New Date ()
   3. New ISO Date ()

Syntax: {“x”: “new Date ()”}

Regular expression :

• It is used to store regular expressions.

Syntax: {“x”: /foobar/i ()”}

Array :

• It is the set of values and stores the same or different data types values in it.
• The array is created using square brackets ([]) in MongoDB.

Syntax: {“x”: [“a”, “b”, “c”]}

Object ID :

• MongoDB automatically creates a unique object ID Whenever we create a new document in the collection.
• In MongoDB, there is an _id field for each document.
• The data stored in Id is length is 12 bytes and it is in hexadecimal format.

Syntax: {“x”: objectId ()}

Binary Data :

• It is used to store binary data. It is a string of arbitrary bytes.

Code :

• The string data type is similar to this data.
• Mongo shell is generally not supported in this.
• If the shell gets a symbol from the database, then it converts this type into a string type.

Syntax: {“x”: function() { /* …*/ }}

1. 1. Key value NoSQL database
   2. Document NoSQL database
   3. Column family NoSQL database
   4. Graph base NoSQL database

Key value NoSQL database :

• In NoSQL database this is the simplest type.
• In this type, the data is stored in the form of key/value pairs.
• The value can be a JSON, BLOB (Binary Large Objects), string, etc.
• It Stores maintain data as pair consisting of an index key and a value.
• The value in a key-value store can be anything: a string, a number, but also an entirely new set of key-value pairs encapsulated in an object.

Document NoSQL database :

• In this, the values are stored in structured documents like XML or JSON, and it is an extension of the simplicity of Key-Value stores.
• In object-oriented software, Document stores make it easy to map Objects.
• It does not support relations and allows us to store complex data in document formats (JSON, XML etc.).
• There is no relational integrity, and Each document in the document store is independent.
• It can be used for all use cases of a KV store database, and also data in each document can be in a different format.

Column family NoSQL database :

• It is developed to store and process very large amounts of data distributed over many machines.
• The columns are arranged by column family, and they point to multiple columns.
• It becomes high performance on aggregation queries like SUM, COUNT, AVG, MIN, etc.

Graph base NoSQL database :

• It is the storage of graph–oriented data structures and is used for specific purposes.
• A graph database is any storage system that provides index-free adjacency.
• No index lookups are necessary, and every node contains a direct pointer to its adjacent element.
• There is no need to define the types of edges and vertices because it usually comes with a flexible data model.

Create Operations:

• This operation is used to insert or add new documents in collection.
• It will create a new collection in the database If a collection does not exist.
• In this operation, there are two types of methods provided by MongoDB:

1. 1. collection.insertOne()
   2. collection.insertMany()

Read Operations :

• It is used to query a collection for a document and to retrieve documents from the collection, or in other words.
• In this operation, there is single method provided by MongoDB:

1. 1. collection.find ()

Update Operations :

• It is used to modify or update the existing document in the collection.
• In this operation, there is three types of method provided by MongoDB:

1. 1. collection.updateOne()
   2. collection.updateMany()
   3. collection.replaceOne()

Delete Operations :

• It is used to remove or delete the existing document in the collection.
• In this operation, there are two types of method provided by MongoDB:
  • collection.deleteOne()
  • collection.deleteMany()

Index Creating :

•  createIndex() is a method that allows user to create an index.

Syntax :

For Example,

db.mycol.createIndex({“age”:1})
{
“createdCollectionAutomatically” : false,
“numIndexesBefore” : 1,
“numIndexesAfter” : 2,
“ok” : 1
} 

• In createIndex() method, there are five types of parameters:

1. 1. background (Boolean)
   2. unique (Boolean)
   3. name (string)
   4. sparse (Boolean)

Index for Drop :

• dropIndex() is a method that allows users to drop an index.
• This method can delete only one index at a time.
• MongoDB provides the dropIndexes() method that takes multiple indexes as its parameters.

Syntax :

Get description of all indexes :

• It will retrieve all the description of the indexes created within the collection.

Syntax :

For Example,

{_id:

name: “Venkat”,

Age: 36,

Address: {{street: “No-1, southern street”,

           city: “Chennai”,

           state: “Tamil Nadu”,

           zip: “600021”,

           country: “India”}}