![\"Why](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/01\/Reasons-to-learn-MongoDB-01.jpg\")
<\/a>Top Reasons to learn MongoDB<\/p><\/div>\n
i) Aggregation framework<\/h4>\n
We can use it in a very efficient manner by MongoDB. MapReduce<\/strong> can be used for batch processing of data and also for aggregation operations. MapReduce is nothing but a process, in which large datasets will process and generate results with the help of parallel and distributed algorithms on clusters.<\/p>\n It consists of two sets of operations in itself, they are: Map() and Reduce().<\/p>\n Aggregation Framework<\/p><\/div>\n It is JSON-like storage a format. BSON stands for Binary JSON<\/strong>. BSON is binary-encoded serialization of JSON like documents<\/strong> and MongoDB uses it, when to stores documents in collections. We can add data types like date and binary (JSON doesn’t support).<\/p>\n BSON format makes use of _id as a primary key over here. As stated that _id is being used as a primary key so it is having a unique value associated with itself called as ObjectId, which is either generated by application driver or MongoDB service.<\/p>\n Below is an example to understand BSON format in a more better way:<\/p>\n Example-<\/strong><\/p>\n Another advantage of using BSON format is that it enables to internally index and map document properties. As it is designed to be more efficient in size and speed, it increases the read\/write throughput of MongoDB.<\/p>\n The major problem with any web\/mobile application is scaling. To overcome this MongoDB has added sharding feature. It is a method in which, data is being distributed across multiple machines. Horizontal scalability is being provided with the sharding.<\/p>\n It is a complicated process and is done with the help of several shards. Each shard holds some part of data and functions as a separate database<\/strong>. Merging all the shards together forms a single logical database. Operations over here are being performed by query routers.<\/p>\n MongoDB supports range query, regular expression and many more types of searches. Queries include user-defined Javascript functions and it can also return specific fields from the documents. MongoDB can support ad hoc queries by using a unique query language or by indexing BSON documents.<\/p>\n Let\u2019s see the difference between SQL SELECT query and resembling query:<\/p>\n E.g. Fetching all records of student table with student name like ABC.<\/p>\n As it is a schema-less database(written in C++), it is much more flexible than the traditional database. Due to this, the data does not require much to set up for itself and reduced friction with OOP. If you want to save an object, then just serialize it to JSON and send it to MongoDB.<\/p>\n MongoDB supports capped collection, as it is having fixed size of collections<\/strong> in it. It maintains the insertion order. Once the limit is reached it starts behaving like a circular queue.<\/p>\n Example – Limiting our capped collection to 2MB<\/p>\n To improve the performance of searches indexes are being created<\/strong>. We can index any field in MongoDB document either primary or secondary.<\/p>\n Due to this reason, the database engine can efficiently resolve queries.<\/p>\n Indexing<\/p><\/div>\n MongoDB can also be used as a file storing system, which avoids load imbalance and also data replication. This function performed with the help of Grid File System<\/strong>, it is included in drivers which stores files.<\/p>\n File Storage in MongoDB<\/p><\/div>\n Replication is being provided by distributing data across different machines. It can have one primary node and more than one secondary nodes in it (replica set).<\/p>\n This set acts like a master-slave. Here, a master can perform read and write and a slave copies data from a master as a backup only for a read operation.<\/p>\n Replication<\/p><\/div>\n MongoDB has a very powerful feature of MMS, due to which we can track our databases or machines and if needed can backup our data. It also tracks hardware metrics for managing the deployment.<\/p>\n It provides a feature of custom alert, due to which we can discover issues before our MongoDB instance will affect.<\/p>\n MongoDB Management Service (MMS)<\/p><\/div>\n This is the second phase of Why MongoDB, advantages.<\/strong><\/p>\n If you are having a large set of data that you need to process then you can distribute the traffic amongst different machines with the help of load balancing.<\/p>\n It helps the user in a way that you can continue your work even if one of the nodes\/machines has stopped working due to some reason. The other nodes will keep the work in a continuation and your processing will not stop.<\/p>\n Load Balancing<\/p><\/div>\n To increase performance, scalability, and handle enormous datasets, MongoDB uses the data splitting technique known as sharding to horizontally spread data over numerous servers or nodes. Each MongoDB shard functions separately to perform read and write operations for its specific data range.<\/p>\n A shard is a distinct database instance that contains a part of the data. Data is divided throughout the sharding process based on a field in the document called a shard key, which is used to pinpoint the position of each shard. The workload may be dynamically distributed as the data increases, adding additional shards as needed to ensure effective data distribution and retrieval throughout the whole database cluster.<\/p>\n The key component of MongoDB’s design that enables it to manage large datasets and high-throughput applications is sharding.<\/p>\n Sharding in MongoDB<\/p><\/div>\n It does not require data structures, that are unified in nature across all the objects that are being used. This makes it easier to use MongoDB. With the help of dynamic schema, it is very easy to use MongoDB.<\/p>\n Flexibility<\/p><\/div>\n MongoDB can fast and easy process the data. But this is valid up to your data is in document format. We can say that it\u2019s speed automatically increases as it is handling a large amount of unstructured data within seconds which feels like magic.<\/p>\n Schema-less data models do not have a specified or strictly enforced data structure. These databases, like MongoDB, allow data to be kept in a flexible and dynamic fashion with each document or record having its own unique collection of fields and data types, in contrast to typical relational databases with preset schemas. It is simpler to repeat and modify the data model during development thanks to this flexibility, which enables developers to manage a variety of changing data without the requirement for a preset structure.<\/p>\n Schema-less data models allow quicker development and simpler scaling, making them particularly suitable for organisations working with unstructured data or those whose data requirements change often.<\/p>\n They do, however, also come with difficulties, such as preserving data integrity without a set schema and more difficult data analysis because of different data formats. The decision to utilise a schema-less data model should ultimately be based on the project’s particular requirements and the type of data being handled.<\/p>\n\n
<\/a>ii) BSON format<\/h4>\n
{\r\n\"_id\": ObjectId(\"12e6789f4b01d67d71da3211\"),\r\n\"title\": \"Key features Of MongoDB\",\r\n\"comments\": [\r\n...\r\n]\r\n}<\/pre>\niii. Sharding<\/h4>\n
iv. Ad hoc queries<\/h4>\n
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v. Schema-Less<\/h4>\n
vi. Capped Collections<\/h4>\n
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viii. Indexing<\/h4>\n
<\/a>viii. File Storage<\/h4>\n
<\/a>ix. Replication<\/h4>\n
<\/a>x. MongoDB Management Service (MMS)<\/h4>\n
<\/a>Benefits of MongoDB<\/h3>\n
i. Load Balancing<\/h4>\n
<\/a>ii. Sharding<\/h4>\n
<\/a>iii. Flexibility<\/h4>\n
<\/a>iv. Speed<\/h4>\n
v. Schema-less<\/h4>\n