MongoDB Fits in Modern Apps and Global Cloud Database Settings
MongoDB is a database software in which SQL is not required. In other words, it can be said that it is a NoSQL database, which can be used for storing a high volume of data. There are many features due to which the software is being used in a very large manner and these features are discussed below.
The aggregation features in this software can be used easily and efficiently. MapReduce is an implementation that can be used for processing big data sets. Batch processing of data can also be processed with the help of MapReduce. There are two procedures used in MapReduce which include Map () and Reduce (). This feature can be used to get the same results as users to get through the Group By clause in a relational database.
MongoDB uses BSON format, which is similar to JSON like storage format. The full form of BSON is Binary JSON. This helps in serializing the documents and MongoDB stores these documents in the collection. Date and binary are also a part of BSON, which are not available in JSON. BSON also helps MongoDB in indexing and mapping the document properties. These properties are used in reading and writing data at a fast pace.
Developers face problems in designing web applications in which scaling is necessary. Sharding feature has been introduced in MongoDB to sort out this problem. Sharding is a method with MongoDB uses in the distribution over multiple machines. Deployments, which contain large datasets, can be easily done. This also helps in high-speed operations of reading and writing data.
There are many shards in this process and each of them consists of a portion of data in the form of a separate database. The collection leads to the formation of one logical database. App servers and configuration servers have the property of deciding the touring of a particular operation to a particular shard.
Ad hoc queries
Feature of being schema-less
C++ is the language, which is used to create the software of the MongoDB database. The database does not consist of a schema. This has made the software more flexible in comparison to relational database tables. To save an object, it has to be serialized in JSON and then the object is sent to the MongoDB. This procedure also has eliminated the procedure of mapping.
Capped collections are fixed collections, which can be used to insert the data. After the insertion has reached its maximum capacity, the collections act as a circular queue.
Indexing in MongoDB
Indexing is a procedure which can be used to make data search easy and at a fast pace. The indexing of any field can be done in this database into primary and secondary indices. The database engine uses a pre-defined index, which helps in mapping the documents. This helps in letting the engine decide about the query and its relation to one or more documents.
The database can also be used in the form of a file system. This file system has load balancing and data replication feature so it can be used over multiple machines to store the file. There is a function called Grid File System () which is used to store files.
Replication is a feature, which the database provides by distributing data across various machines. In this process, there is one primary node and one or more secondary nodes. This creates a replica set in the form of master salve replication. The master has the right to reading and writing data while the slave has the data of copying data from the master.
The data copied by the slaves can only be used for reading and backup. In case, the primary node is not accessible within ten seconds, a secondary replica starts acting as a primary one. This process is called an automatic failover.
These are some of the features, which have made MongoDB very powerful and users can use it for fast read and write and also making replicas.