How to choose the right database engine when learning big data technology? MySQL or Oracle?

How to choose a suitable database engine when learning big data technology? MySQL or Oracle?

In today's era of data explosion, big data technology has become an important part of enterprise development and decision-making. As the core of big data technology, the choice of database engine is even more crucial. Among many database engines, MySQL and Oracle are two database engines that have attracted much attention and use. This article will analyze and compare how to choose an appropriate database engine when learning big data technology, especially MySQL and Oracle, with code examples.

When it comes to choosing a database engine, the first thing we must consider is the needs. Different database engines have different characteristics and applicable scenarios. MySQL is an open source relational database management system with the characteristics of high performance, ease of use, and low cost. It is suitable for small applications and fast data storage. Oracle is a powerful and complete commercial relational database management system that supports massive data storage and complex data processing, and is suitable for large and medium-sized enterprises and high-performance application scenarios. Therefore, when learning big data technology, we must choose a database engine that suits us according to our own needs.

Next let's look at some code examples about MySQL and Oracle.

MySQL sample code:

//Connect to the database
import mysql.connector

mydb = mysql.connector.connect(
host="localhost" ,
user="yourusername",
passwd="yourpassword"
)

print(mydb)

// Create table
import mysql.connector

mydb = mysql.connector.connect(
host="localhost",
user="yourusername",
passwd="yourpassword",
database="mydatabase"
)

mycursor = mydb.cursor()

mycursor.execute("CREATE TABLE customers (name VARCHAR(255), address VARCHAR(255))")

//Insert data
import mysql.connector

mydb = mysql.connector.connect(
host="localhost",
user="yourusername",
passwd="yourpassword ",
database="mydatabase"
)

mycursor = mydb.cursor()

sql = "INSERT INTO customers (name, address) VALUES (%s, % s)"
val = ("John", "Highway 21")
mycursor.execute(sql, val)

mydb.commit()

print(mycursor. rowcount, "record inserted.")

Oracle's sample code:

//Connect to the database
import cx_Oracle

connection = cx_Oracle.connect("hr", "welcome", "localhost/XE")
print(connection.version)

// Create table
import cx_Oracle

connection = cx_Oracle.connect("hr", "welcome", "localhost/XE")
cursor = connection.cursor()
cursor.execute("""

CREATE TABLE employees ( employee_id NUMBER, first_name VARCHAR2(50), last_name VARCHAR2(50), email VARCHAR2(100), hire_date DATE, salary NUMBER, CONSTRAINT pk_employee PRIMARY KEY (employee_id) )

""")

// Insert Data
import cx_Oracle

connection = cx_Oracle.connect("hr", "welcome", "localhost/XE")

cursor = connection.cursor()

data = [

(1, 'John', 'Doe', 'john@example.com', '01-JAN-2020', 5000), (2, 'Jane', 'Smith', 'jane@example.com', '01-FEB-2020', 6000), (3, 'Tom', 'Hanks', 'tom@example.com', '01-MAR-2020', 7000),

]

cursor.executemany("""

INSERT INTO employees (employee_id, first_name, last_name, email, hire_date, salary) VALUES (:1, :2, :3, :4, :5, :6)

""", data)

connection.commit()

print(cursor.rowcount, "record inserted.")

Through the above code examples, we can see the differences between MySQL and Oracle in connecting to the database, creating tables, and inserting data.

In summary, when learning big data technology, choosing an appropriate database engine needs to be considered based on your own needs. If you are a beginner or have relatively simple database requirements, then MySQL is a good choice. It has the advantages of low cost and ease of use; if you are a large enterprise or need to process massive data and complex queries, then MySQL Oracle would be a more suitable choice, with its powerful functionality and performance.

Whether you choose MySQL or Oracle, in the process of learning big data technology, you should practice more and deepen your understanding and application by writing code examples. Only through in-depth practice can we better understand and master the characteristics and usage of the database engine, thereby providing better support for enterprise data storage and analysis.

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