What is the difference between the data module of mongodb and the traditional database model

mogodb is a non-relational (NoSQL) database, it document database.

I have used mongodb to do a small project exercise, I briefly say (because I also do not understand y) it with the traditional database difference:

The most basic difference is the difference between the data model:

Traditional database from large to small for the database, table, row. And mongodb is: database, collection, document, BSON (json like binary data).

Traditional databases require a predefined table structure (once defined, it can not be changed), while mongodb does not, and the document structure can be varied (for example, used to relate the documents are placed in the same collection, but the same collection of documents do not necessarily have the same structure)

Should be more, can not think of.

Data models are different, corresponding to different query methods. Traditional database queries are all sql, while mongodb queries are nothing like sql.

There are other things too, such as schemes to improve reliability, levels of atomic operations, etc. that are different.

Some of the concepts of traditional databases do not exist in mongodb.

Designing a database is also different, as traditional databases are designed to be paradigmatically normalized, while mongodb databases are often designed to be anti-paradigmatic.

The following is from the Baidu encyclopedia:

There is no clear scope or definition of NoSQL, but they all generally have some of the following **** the same characteristics:

No need for predefined schema: There is no need to pre-define the data schema, predefined table structure. Each record in the data may have different attributes and formats. When inserting data, there is no need to predefine their schema.

No***** architecture: as opposed to an all***** architecture where all data is stored in a network of storage areas. noSQL tends to divide the data and store it on individual local servers. This improves system performance because reading data from local disks tends to perform better than reading data over network transfers.

Elastic and scalable: nodes can be dynamically added or removed while the system is running. No downtime is required for maintenance and data can be migrated automatically.

Partitioning: Compared to storing data in the same node, NoSQL databases need to partition the data and spread the records over multiple nodes. And usually the partitioning is done at the same time as replication. This improves parallel performance and ensures that there is no single point of failure.

Asynchronous replication: unlike RAID storage systems, replication in NoSQL, tends to be asynchronous and log-based. This allows data to be written to a node as quickly as possible without delays caused by network transmissions. The downside is that consistency is not always guaranteed, and such an approach can result in the loss of small amounts of data in the event of a failure.

BASE: NoSQL databases guarantee the BASE characteristic as opposed to the strict ACID characteristic of transactions.BASE is the ultimate consistency and soft transactions.

NoSQL databases do not have a unified architecture, the difference between the two NoSQL databases, even far more than the difference between the two relational databases. It can be said that NoSQL each has its own strengths, and successful NoSQL is bound to be particularly suitable for certain occasions or certain applications, where it will far outperform relational databases and other NoSQL.