A 37

   package main

func main() {
	a,b,c := 1,2,3 // a,b,c have type int
}

public class Main {
  public static void main(String[] args) {
    Integer a = 1;
    Integer b = 2;
    Integer c = 3;
  }
}

Different models for data storage with dynamic schemas, such as "key-value", documents, graphs, wide columns, etc. Storage models are optimized for higher capacity and scalability.

This approach provides more flexibility for data upload.

Data is being normalized and transformed into tables, consisting of rows and columns. All elements, including indexes and table-relations, have a predefined schema.

Such a structure secures integrity of linked data and table relations.

{
  "First_name": "Helen",
  "Last_name": "Taylor",
  "Id_card": 123,
  "Status": "Married"
}

Data is stored as a record in a row

Object-oriented API allows developers to record or withdraw data structures. By using key sections of applications, users can implement search by "key-value" pairs, sets of columns or by partly structured documents, containing serial objects and application attributes.

MongoDb uses MQL(Mongo Query language) for data extraction.

Data selection:

db.find.personal_data()

Data insertion:

db.personal_data.insert(
{person_id:'3', 
 Last_name:'Taylor', 
 status:'Married'
})

Data update:

db.personal_data.update(
{person_id::{$eq:3}},
{$set{ id_card:'135'}},
{multi:true})

Requests for data record and selection are made using SQL. Those requests are analyzed and processed by RBD.

Data selection:

SELECT * FROM personal_data;

Data insertion:

INSERT
INTO personal_data 
(First_name,
  Last_name,
  id_card,Status) 
VALUES 
('Helen',
 'Taylor',
 '123',
 'Married');

Data update:

UPDATE personal_data 
SET id_card = '135' 
WHERE person_id =3;

Fits better for Horizontal scaling (scaling out). MongoDB is considered to be more scalable than conventional RDBMS. Most NoSQL systems, including MongoDB, are capable of storing your database on multiple machines at the same time while maintaining high-speed performance. This DB has in-built replication support and sharding(a method for distributing data across multiple machines).

MongoDB is a universal DB that ensures stable productivity, wherever you want to get it. At the same time, its requirements are relatively low, which allows it to run on powerful servers, virtual machines, and cloud services.

Implies Vertical scaling (scaling up). Relational databases typically scale by increasing the computational power of the hardware or adding separate copies for read workloads. Performance depends on the disk subsystem. Optimization of queries, indexes and table structure is usually required for maximum performance.

It centers around the CAP theorem (Consistency, Availability, and Partition). NoSQL databases often offer a trade-off by relaxing the strict ACID property requirements in favor of a more flexible data model that can scale out. This makes NoSQL a great choice for high throughput, low latency use cases that need to scale out beyond a single instance.

It centers around ACID properties (Atomicity, Consistency, Isolation, and Durability). Atomicity means that a transaction should be either fully completed or not completed at all. Consistency means that as soon as the transaction ends, the data must conform to the database schema. Isolation requires concurrent transactions to run separately from each other.Reliability refers to the ability to recover to the last saved state after an unexpected system failure or power outage

Mongo DB

RDB

Data Model

Different models for data storage with dynamic schemas, such as "key-value", documents, graphs, wide columns, etc. Storage models are optimized for higher capacity and scalability.

This approach provides more flexibility for data upload.

Data is being normalized and transformed into tables, consisting of rows and columns. All elements, including indexes and table-relations, have a predefined schema.

Such a structure secures integrity of linked data and table relations.

Data Representation

Supports JSON with key-value matches for each record:

{
  "First_name": "Helen",
  "Last_name": "Taylor",
  "Id_card": 123,
  "Status": "Married"
}

Data is stored as a record in a row

Data Accessibility/API

Object-oriented API allows developers to record or withdraw data structures. By using key sections of applications, users can implement search by "key-value" pairs, sets of columns or by partly structured documents, containing serial objects and application attributes.

MongoDb uses MQL(Mongo Query language) for data extraction.

Data selection:

db.find.personal_data()

Data insertion:

db.personal_data.insert({person_id:'3', Last_name:'Taylor', status:'Married'})

Data update:

db.personal_data.update({person_id::{$eq:3}},{$set{ id_card:'135'}},{multi:true})

Requests for data record and selection are made using SQL. Those requests are analyzed and processed by RBD.

Data selection:

SELECT * FROM personal_data;

Data insertion:

INSERT INTO personal_data (First_name, Last_name, id_card, Status) 
VALUES ('Helen', 'Taylor', '123', 'Married');

Data update:

UPDATE personal_data SET id_card = '135' WHERE person_id =3;

Scalability and replication

Fits better for Horizontal scaling (scaling out). MongoDB is considered to be more scalable than conventional RDBMS. Most NoSQL systems, including MongoDB, are capable of storing your database on multiple machines at the same time while maintaining high-speed performance. This DB has in-built replication support and sharding(a method for distributing data across multiple machines).

MongoDB is a universal DB that ensures stable productivity, wherever you want to get it. At the same time, its requirements are relatively low, which allows it to run on powerful servers, virtual machines, and cloud services.

Implies Vertical scaling (scaling up).

Relational databases typically scale by increasing the computational power of the hardware or adding separate copies for read workloads.

Performance depends on the disk subsystem. Optimization of queries, indexes and table structure is usually required for maximum performance.

ACID properties

It centers around the CAP theorem (Consistency, Availability, and Partition).

NoSQL databases often offer a trade-off by relaxing the strict ACID property requirements in favor of a more flexible data model that can scale out. This makes NoSQL a great choice for high throughput, low latency use cases that need to scale out beyond a single instance.

It centers around ACID properties (Atomicity, Consistency, Isolation, and Durability).

Atomicity means that a transaction should be either fully completed or not completed at all.

Consistency means that as soon as the transaction ends, the data must conform to the database schema.

Isolation requires concurrent transactions to run separately from each other.

Reliability refers to the ability to recover to the last saved state after an unexpected system failure or power outage.