BSON (Binary Interchange and Structure Object Notation) elements are usually stored in database collection. The integration of collection and database is known as a namespace.

A replica set comprises of one primary and another secondary node. In between, all data replicates.

First, assess user requirement.

Combine or separate the document as per requirement.

The schema should be optimized if the cases occur frequently.

Aggregation in the schema to be performed.

When you insert a document, the command syntax is database.collection.insert (document)

These are special structures in MongoDB to store a small quantum of data in the value of a specific field or set of fields.

Yes, the object attribute is deleted. So you better eliminate the attribute and save the object again.

When the RAM finds it not able to accommodate an index due to the volume, MongoDB comes to rescue to read the index more speedily than the RAM.

All the MongoDB data are encrypted on the storage to make sure that protected data can be accessed by none other than authorized processes.

The aggregation tasks are performed within this structure. The pipeline is an abstract conduit to convert documents into aggregated results.

Both are free and open source database. But there are operational differences between them.

The language of MongoDB is C++. C extension is also used by some drivers for better performance.

It is virtually a structure to store and retrieve data which is designed except in the tabular arrangement and applied in relational databases like SQL, Oracle etc.

It can be divided into 4 basics;

• Document-centric
• Key value
• Graph
• Column-oriented

Not at all. MongoDB uses Collections to store data.

Obviously, the query will backlash with an error message provided you set a partial query. Mongos will wait for a response, shard being slow.

In sharding, data records are stored amongst multiple machines. Horizontal database or search engine is created. Each respective partition is termed as a shard.

A database profiler is inherent in MongoDB that reflects performance features of one and all activities against the database. With the help of profiler, queries and write operations that certainly are moderate than assumed. These statistics are utilized to ascertain the requirement of an index.

An extra memory mapped file is made agile in journaling. It will, in turn, delimit DB size of 32 bit builds. Presently, journaling is not specified on 32 bit systems.

Customary locking or for that matter composite tractions with rollback is rarest of rare in MongoDB. The reason is that it is depicted to be light in weight, brisk and inevitable in its presentation. If a system is used with many servers, the very performance is improved remarkably.

10 to 30 seconds duration is enough for other members to let down the primary and replaced by another primary. The cluster is obviously not able to perform the primary operations (write and read). Ultimately, compatible queries can be touch-based to secondaries at any given point of time within the time frame.

Yes, they are alternate terms. Master or Primary is a node or member, which is as of now a Primary and it helps process the writes for the replica set. Also, another member has the option to become Primary in the event of failover of a replica set.

Application of operations from the recent Primary is called a Secondary node or member. Tailing the replication oplog is the essence. Copying Primary to Secondary can be chronologically misplaced. Although, the proximity of the Secondary will be next to the current, which can be a few milliseconds on a LAN.

A hostile preallocation of the reserved area is enacted by MongoDB in view of fragmentation of file system.

The snapshot method can be implemented on a cursor to detach an operation for a particular case. The index is intervened by snapshot and ensures that each document will be returned by the query at least once.

It is a specification, leading to storage and recovery of files which is in excess of the size of the BSON document of 16 GB. Gridfs helps in defragmenting a file, thus far concentrated in one bond. Each fragment is stored as a separate document.

Reader-Write locks are used which enables concurrent readers to have access to a resource viz., database or collection and distinctive permission is given to a single write operation.