It is typically executed using a very rich set of operators that are linked to each other using JSON. To build a query in JSON documents, you need to specify a document with properties you wish the results to match. MongoDB uses an unstructured query language. MySQL creates a strict schema-template and it is less prone to mistakes in this manner. MongoDB creates schemaless documents which can store any information you want though it may cause problems with data consistency. Clearly, there isn’t much space for flexibility in the manner of storing data in case that normalization is followed (which is definitely recommended). On the other hand, before you can store anything in MySQL, you need to clearly define tables and columns, and every row in the table should have the same column. The payoff is: due to the absence of joins and transactions you need to frequently optimize your schema based on how the application will be accessing the data. The only restriction with this is supported data structures. It is possible to just drop a couple of documents within a collection and there is no need to have relations between those documents. Great thing about MongoDB is that there are no restrictions on schema design. Let’s see the difference between SQL and NoSQL and a faceoff between two dominant solutions that are close in popularity: MySQL from the SQL and MongoDB from the NoSQL world. NoSQL databases raised and took a significant part of the market. During the time demand and requirements changed in the line of more diversity and scalability. There are some famous names in this field: MySQL, MsSQL, Oracle DB, Postgres… just to name some. Relational databases held the lead for quite a time. The main focuses of this story are SQL (Structured Query Language) and NoSQL (Not only Structured Query Language), their differences and advantages over each other. Even if the code fails, it is not such a big disaster compared to when the data fails (or misleads). And what code does – roughly said it transforms that data in some way or another, so that is more readable and understandable. We can claim this on a premise that in the end of the day you look at data, you use data, you make decisions based on the information from that data as a source. We can freely say that data is more important than code.
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