Este artículo discutirá en detalle cómo conectarse a todo tipo de bases de datos en Python e implementar las operaciones CRUD (crear, leer, actualizar, eliminar) correspondientes. Analizaremos los métodos para conectar la base de datos MySQL, SQL Server, Oracle, PostgreSQL, MongoDB, SQLite, DB2, Redis, Cassandra, Microsoft Access, ElasticSearch, Neo4j, InfluxDB, Snowflake, Amazon DynamoDB, Microsoft Azure CosMos DB uno por uno, y demostrar las operaciones CRUD correspondientes.
mysql
Conectarse a la base de datos
Python puede usar la biblioteca mysql-connector-python para conectarse a la base de datos MySQL:
import mysql.connector
conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
print("Opened MySQL database successfully")
conn.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en MySQL.
Crear
conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("CREATE TABLE Employees (ID INT PRIMARY KEY NOT NULL, NAME TEXT NOT NULL, AGE INT, ADDRESS CHAR(50), SALARY REAL)")
print("Table created successfully")
conn.close()Leer (Recuperar)
conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
print("ID = ", row[0])
print("NAME = ", row[1])
print("ADDRESS = ", row[2])
print("SALARY = ", row[3])
conn.close()Actualizar
conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()Borrar
conn = mysql.connector.connect(user='username', password='password', host='127.0.0.1', database='my_database')
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()servidor SQL
Conectarse a la base de datos
Python puede usar la biblioteca pyodbc para conectarse a la base de datos de SQL Server:
import pyodbc
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
print("Opened SQL Server database successfully")
conn.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en SQL Server.
Crear
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("CREATE TABLE Employees (ID INT PRIMARY KEY NOT NULL, NAME VARCHAR(20) NOT NULL, AGE INT, ADDRESS CHAR(50), SALARY REAL)")
conn.commit()
print("Table created successfully")
conn.close()Leer (Recuperar)
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
print("ID = ", row[0])
print("NAME = ", row[1])
print("ADDRESS = ", row[2])
print("SALARY = ", row[3])
conn.close()Actualizar
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()Borrar
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=my_database;UID=username;PWD=password')
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()Oráculo
Conectarse a la base de datos
Python puede usar la biblioteca cx_Oracle para conectarse a la base de datos de Oracle:
import cx_Oracle
dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database')
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
print("Opened Oracle database successfully")
conn.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en Oracle.
Crear
dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database')
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("CREATE TABLE Employees (ID NUMBER(10) NOT NULL PRIMARY KEY, NAME VARCHAR2(20) NOT NULL, AGE NUMBER(3), ADDRESS CHAR(50), SALARY NUMBER(10, 2))")
conn.commit()
print("Table created successfully")
conn.close()Leer (Recuperar)
dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database')
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
print("ID = ", row[0])
print("NAME = ", row[1])
print("ADDRESS = ", row[2])
print("SALARY = ", row[3])
conn.close()Actualizar
dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database')
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()Borrar
dsn_tns = cx_Oracle.makedsn('localhost', '1521', service_name='my_database')
conn = cx_Oracle.connect(user='username', password='password', dsn=dsn_tns)
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()postgresql
Conectarse a la base de datos
Python puede usar la biblioteca psycopg2 para conectarse a la base de datos PostgreSQL:
import psycopg2
conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
print("Opened PostgreSQL database successfully")
conn.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en PostgreSQL.
Crear
conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute('''CREATE TABLE Employees
(ID INT PRIMARY KEY NOT NULL,
NAME TEXT NOT NULL,
AGE INT NOT NULL,
ADDRESS CHAR(50),
SALARY REAL);''')
conn.commit()
print("Table created successfully")
conn.close()Leer (Recuperar)
conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
print("ID = ", row[0])
print("NAME = ", row[1])
print("ADDRESS = ", row[2])
print("SALARY = ", row[3])
conn.close()Actualizar
conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()Borrar
conn = psycopg2.connect(database="my_database", user="username", password="password", host="127.0.0.1", port="5432")
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()MongoDB
Conectarse a la base de datos
Python puede usar la biblioteca pymongo para conectarse a la base de datos MongoDB:
from pymongo import MongoClient
client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
print("Opened MongoDB database successfully")
client.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en MongoDB.
Crear
En MongoDB, la creación de documentos generalmente se incluye en una operación de inserción:
client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
employee = {"id": "1", "name": "John", "age": "30", "address": "New York", "salary": "1000.00"}
employees.insert_one(employee)
print("Document inserted successfully")
client.close()Leer (Recuperar)
client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
cursor = employees.find()
for document in cursor:
print(document)
client.close()Actualizar
client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
query = { "id": "1" }
new_values = { "$set": { "salary": "25000.00" } }
employees.update_one(query, new_values)
print("Document updated successfully")
client.close()Borrar
client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
employees = db["Employees"]
query = { "id": "1" }
employees.delete_one(query)
print("Document deleted successfully")
client.close()SQLite
Conectarse a la base de datos
Python usa la biblioteca sqlite3 para conectarse a la base de datos SQLite:
import sqlite3
conn = sqlite3.connect('my_database.db')
print("Opened SQLite database successfully")
conn.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en SQLite.
Crear
conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute('''CREATE TABLE Employees
(ID INT PRIMARY KEY NOT NULL,
NAME TEXT NOT NULL,
AGE INT NOT NULL,
ADDRESS CHAR(50),
SALARY REAL);''')
conn.commit()
print("Table created successfully")
conn.close()Leer (Recuperar)
conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
print("ID = ", row[0])
print("NAME = ", row[1])
print("ADDRESS = ", row[2])
print("SALARY = ", row[3])
conn.close()Actualizar
conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()Borrar
conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()DB2
Conectarse a la base de datos
Python puede usar la biblioteca ibm_db para conectarse a la base de datos DB2:
import ibm_db
dsn = (
"DRIVER={
{IBM DB2 ODBC DRIVER}};"
"DATABASE=my_database;"
"HOSTNAME=127.0.0.1;"
"PORT=50000;"
"PROTOCOL=TCPIP;"
"UID=username;"
"PWD=password;"
)
conn = ibm_db.connect(dsn, "", "")
print("Opened DB2 database successfully")
ibm_db.close(conn)Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en DB2.
Crear
conn = ibm_db.connect(dsn, "", "")
sql = '''CREATE TABLE Employees
(ID INT PRIMARY KEY NOT NULL,
NAME VARCHAR(20) NOT NULL,
AGE INT NOT NULL,
ADDRESS CHAR(50),
SALARY DECIMAL(9, 2));'''
stmt = ibm_db.exec_immediate(conn, sql)
print("Table created successfully")
ibm_db.close(conn)Leer (Recuperar)
conn = ibm_db.connect(dsn, "", "")
sql = "SELECT id, name, address, salary from Employees"
stmt = ibm_db.exec_immediate(conn, sql)
while ibm_db.fetch_row(stmt):
print("ID = ", ibm_db.result(stmt, "ID"))
print("NAME = ", ibm_db.result(stmt, "NAME"))
print("ADDRESS = ", ibm_db.result(stmt, "ADDRESS"))
print("SALARY = ", ibm_db.result(stmt, "SALARY"))
ibm_db.close(conn)Actualizar
conn = ibm_db.connect(dsn, "", "")
sql = "UPDATE Employees set SALARY = 25000.00 where ID = 1"
stmt = ibm_db.exec_immediate(conn, sql)
ibm_db.commit(conn)
print("Total number of rows updated :", ibm_db.num_rows(stmt))
ibm_db.close(conn)Borrar
conn = ibm_db.connect(dsn, "", "")
sql = "DELETE from Employees where ID = 1"
stmt = ibm_db.exec_immediate(conn, sql)
ibm_db.commit(conn)
print("Total number of rows deleted :", ibm_db.num_rows(stmt))
ibm_db.close(conn)acceso Microsoft
Conectarse a la base de datos
Python puede usar la biblioteca pyodbc para conectarse a las bases de datos de Microsoft Access:
import pyodbc
conn_str = (
r'DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};'
r'DBQ=path_to_your_access_file.accdb;'
)
conn = pyodbc.connect(conn_str)
print("Opened Access database successfully")
conn.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en Access.
Crear
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute('''CREATE TABLE Employees
(ID INT PRIMARY KEY NOT NULL,
NAME TEXT NOT NULL,
AGE INT NOT NULL,
ADDRESS CHAR(50),
SALARY DECIMAL(9, 2));''')
conn.commit()
print("Table created successfully")
conn.close()Leer (Recuperar)
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute("SELECT id, name, address, salary from Employees")
rows = cursor.fetchall()
for row in rows:
print("ID = ", row[0])
print("NAME = ", row[1])
print("ADDRESS = ", row[2])
print("SALARY = ", row[3])
conn.close()Actualizar
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute("UPDATE Employees set SALARY = 25000.00 where ID = 1")
conn.commit()
print("Total number of rows updated :", cursor.rowcount)
conn.close()Borrar
conn = pyodbc.connect(conn_str)
cursor = conn.cursor()
cursor.execute("DELETE from Employees where ID = 1")
conn.commit()
print("Total number of rows deleted :", cursor.rowcount)
conn.close()casandra
Conectarse a la base de datos
Python puede usar la biblioteca de controladores de Cassandra para conectarse a la base de datos de Cassandra:
from cassandra.cluster import Cluster
cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
print("Opened Cassandra database successfully")
cluster.shutdown()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en Cassandra.
Crear
cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
session.execute("""
CREATE TABLE Employees (
id int PRIMARY KEY,
name text,
age int,
address text,
salary decimal
)
""")
print("Table created successfully")
cluster.shutdown()Leer (Recuperar)
cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
rows = session.execute('SELECT id, name, address, salary FROM Employees')
for row in rows:
print("ID = ", row.id)
print("NAME = ", row.name)
print("ADDRESS = ", row.address)
print("SALARY = ", row.salary)
cluster.shutdown()Actualizar
cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
session.execute("UPDATE Employees SET salary = 25000.00 WHERE id = 1")
print("Row updated successfully")
cluster.shutdown()Borrar
cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
session.execute("DELETE FROM Employees WHERE id = 1")
print("Row deleted successfully")
cluster.shutdown()redis
Conectarse a la base de datos
Python puede usar la biblioteca redis-py para conectarse a la base de datos de Redis:
import redis
r = redis.Redis(host='localhost', port=6379, db=0)
print("Opened Redis database successfully")Operaciones CRUD
A continuación, le mostraremos cómo realizar operaciones CRUD básicas en Redis.
Crear
r = redis.Redis(host='localhost', port=6379, db=0)
r.set('employee:1:name', 'John')
r.set('employee:1:age', '30')
r.set('employee:1:address', 'New York')
r.set('employee:1:salary', '1000.00')
print("Keys created successfully")Leer (Recuperar)
r = redis.Redis(host='localhost', port=6379, db=0)
print("NAME = ", r.get('employee:1:name').decode('utf-8'))
print("AGE = ", r.get('employee:1:age').decode('utf-8'))
print("ADDRESS = ", r.get('employee:1:address').decode('utf-8'))
print("SALARY = ", r.get('employee:1:salary').decode('utf-8'))Actualizar
r = redis.Redis(host='localhost', port=6379, db=0)
r.set('employee:1:salary', '25000.00')
print("Key updated successfully")Borrar
r = redis.Redis(host='localhost', port=6379, db=0)
r.delete('employee:1:name', 'employee:1:age', 'employee:1:address', 'employee:1:salary')
print("Keys deleted successfully")ElasticSearch
Conectarse a la base de datos
Python puede usar la biblioteca elasticsearch para conectarse a la base de datos ElasticSearch:
from elasticsearch import Elasticsearch
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
print("Opened ElasticSearch database successfully")Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en ElasticSearch.
Crear
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
employee = {
'name': 'John',
'age': 30,
'address': 'New York',
'salary': 1000.00
}
res = es.index(index='employees', doc_type='employee', id=1, body=employee)
print("Document created successfully")Leer (Recuperar)
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
res = es.get(index='employees', doc_type='employee', id=1)
print("Document details:")
for field, details in res['_source'].items():
print(f"{field.upper()} = ", details)Actualizar
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
res = es.update(index='employees', doc_type='employee', id=1, body={
'doc': {
'salary': 25000.00
}
})
print("Document updated successfully")Borrar
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
res = es.delete(index='employees', doc_type='employee', id=1)
print("Document deleted successfully")neo4j
Conectarse a la base de datos
Python puede usar la biblioteca neo4j para conectarse a la base de datos Neo4j:
from neo4j import GraphDatabase
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
print("Opened Neo4j database successfully")
driver.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en Neo4j.
Crear
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
with driver.session() as session:
session.run("CREATE (:Employee {id: 1, name: 'John', age: 30, address: 'New York', salary: 1000.00})")
print("Node created successfully")
driver.close()Leer (Recuperar)
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
with driver.session() as session:
result = session.run("MATCH (n:Employee) WHERE n.id = 1 RETURN n")
for record in result:
print("ID = ", record["n"]["id"])
print("NAME = ", record["n"]["name"])
print("ADDRESS = ", record["n"]["address"])
print("SALARY = ", record["n"]["salary"])
driver.close()Actualizar
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
with driver.session() as session:
session.run("MATCH (n:Employee) WHERE n.id = 1 SET n.salary = 25000.00")
print("Node updated successfully")
driver.close()Borrar
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
with driver.session() as session:
session.run("MATCH (n:Employee) WHERE n.id = 1 DETACH DELETE n")
print("Node deleted successfully")
driver.close()InflujoDB
Conectarse a la base de datos
Python puede usar la biblioteca InfluxDB-Python para conectarse a la base de datos InfluxDB:
from influxdb import InfluxDBClient
client = InfluxDBClient(host='localhost', port=8086)
print("Opened InfluxDB database successfully")
client.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en InfluxDB.
Crear
client = InfluxDBClient(host='localhost', port=8086)
json_body = [
{
"measurement": "employees",
"tags": {
"id": "1"
},
"fields": {
"name": "John",
"age": 30,
"address": "New York",
"salary": 1000.00
}
}
]
client.write_points(json_body)
print("Point created successfully")
client.close()Leer (Recuperar)
client = InfluxDBClient(host='localhost', port=8086)
result = client.query('SELECT "name", "age", "address", "salary" FROM "employees"')
for point in result.get_points():
print("ID = ", point['id'])
print("NAME = ", point['name'])
print("AGE = ", point['age'])
print("ADDRESS = ", point['address'])
print("SALARY = ", point['salary'])
client.close()Actualizar
El modelo de datos de InfluxDB es diferente a otras bases de datos, no tiene operaciones de actualización. Pero puede lograr algo como una operación de actualización escribiendo en el mismo punto de datos (es decir, con la misma marca de tiempo y etiqueta) y cambiando el valor del campo.
Borrar
Asimismo, InfluxDB no proporciona una operación para eliminar puntos de datos individuales. Sin embargo, puede eliminar una serie completa (es decir, una tabla) o eliminar datos durante un cierto período de tiempo.
client = InfluxDBClient(host='localhost', port=8086)
# 删除整个系列
client.query('DROP SERIES FROM "employees"')
# 删除某个时间段的数据
# client.query('DELETE FROM "employees" WHERE time < now() - 1d')
print("Series deleted successfully")
client.close()Copo de nieve
Conectarse a la base de datos
Python puede usar la biblioteca snowflake-connector-python para conectarse a la base de datos Snowflake:
from snowflake.connector import connect
con = connect(
user='username',
password='password',
account='account_url',
warehouse='warehouse',
database='database',
schema='schema'
)
print("Opened Snowflake database successfully")
con.close()Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en Snowflake.
Crear
con = connect(
user='username',
password='password',
account='account_url',
warehouse='warehouse',
database='database',
schema='schema'
)
cur = con.cursor()
cur.execute("""
CREATE TABLE EMPLOYEES (
ID INT,
NAME STRING,
AGE INT,
ADDRESS STRING,
SALARY FLOAT
)
""")
cur.execute("""
INSERT INTO EMPLOYEES (ID, NAME, AGE, ADDRESS, SALARY) VALUES
(1, 'John', 30, 'New York', 1000.00)
""")
print("Table created and row inserted successfully")
con.close()Leer (Recuperar)
con = connect(
user='username',
password='password',
account='account_url',
warehouse='warehouse',
database='database',
schema='schema'
)
cur = con.cursor()
cur.execute("SELECT * FROM EMPLOYEES WHERE ID = 1")
rows = cur.fetchall()
for row in rows:
print("ID = ", row[0])
print("NAME = ", row[1])
print("AGE = ", row[2])
print("ADDRESS = ", row[3])
print("SALARY = ", row[4])
con.close()Actualizar
con = connect(
user='username',
password='password',
account='account_url',
warehouse='warehouse',
database='database',
schema='schema'
)
cur = con.cursor()
cur.execute("UPDATE EMPLOYEES SET SALARY = 25000.00 WHERE ID = 1")
print("Row updated successfully")
con.close()Borrar
con = connect(
user='username',
password='password',
account='account_url',
warehouse='warehouse',
database='database',
schema='schema'
)
cur = con.cursor()
cur.execute("DELETE FROM EMPLOYEES WHERE ID = 1")
print("Row deleted successfully")
con.close()Amazon DynamoDB
Conectarse a la base de datos
Python puede usar la biblioteca boto3 para conectarse a Amazon DynamoDB:
import boto3
dynamodb = boto3.resource('dynamodb', region_name='us-west-2',
aws_access_key_id='Your AWS Access Key',
aws_secret_access_key='Your AWS Secret Key')
print("Opened DynamoDB successfully")Operaciones CRUD
A continuación, le mostraremos cómo realizar operaciones CRUD básicas en DynamoDB.
Crear
table = dynamodb.create_table(
TableName='Employees',
KeySchema=[
{
'AttributeName': 'id',
'KeyType': 'HASH'
},
],
AttributeDefinitions=[
{
'AttributeName': 'id',
'AttributeType': 'N'
},
],
ProvisionedThroughput={
'ReadCapacityUnits': 5,
'WriteCapacityUnits': 5
}
)
table.put_item(
Item={
'id': 1,
'name': 'John',
'age': 30,
'address': 'New York',
'salary': 1000.00
}
)
print("Table created and item inserted successfully")Leer (Recuperar)
table = dynamodb.Table('Employees')
response = table.get_item(
Key={
'id': 1,
}
)
item = response['Item']
print(item)Actualizar
table = dynamodb.Table('Employees')
table.update_item(
Key={
'id': 1,
},
UpdateExpression='SET salary = :val1',
ExpressionAttributeValues={
':val1': 25000.00
}
)
print("Item updated successfully")Borrar
table = dynamodb.Table('Employees')
table.delete_item(
Key={
'id': 1,
}
)
print("Item deleted successfully")Microsoft Azure Cosmos DB
Conectarse a la base de datos
Python puede usar la biblioteca azure-cosmos para conectarse a Microsoft Azure CosMos DB:
from azure.cosmos import CosmosClient, PartitionKey, exceptions
url = 'Cosmos DB Account URL'
key = 'Cosmos DB Account Key'
client = CosmosClient(url, credential=key)
database_name = 'testDB'
database = client.get_database_client(database_name)
container_name = 'Employees'
container = database.get_container_client(container_name)
print("Opened CosMos DB successfully")Operaciones CRUD
A continuación, mostraremos cómo realizar operaciones CRUD básicas en CosMos DB.
Crear
database = client.create_database_if_not_exists(id=database_name)
container = database.create_container_if_not_exists(
id=container_name,
partition_key=PartitionKey(path="/id"),
offer_throughput=400
)
container.upsert_item({
'id': '1',
'name': 'John',
'age': 30,
'address': 'New York',
'salary': 1000.00
})
print("Container created and item upserted successfully")Leer (Recuperar)
for item in container.read_all_items():
print(item)Actualizar
for item in container.read_all_items():
if item['id'] == '1':
item['salary'] = 25000.00
container.upsert_item(item)
print("Item updated successfully")Borrar
for item in container.read_all_items():
if item['id'] == '1':
container.delete_item(item, partition_key='1')
print("Item deleted successfully")Si es útil, preste más atención a la cuenta pública personal de WeChat: [Perspectiva completa de Python] TeahLead_KrisChang, más de 10 años de experiencia en la industria de Internet e inteligencia artificial, más de 10 años de experiencia en tecnología y gestión de equipos comerciales, Tongji Software Licenciado en Ingeniería, Máster en Gestión de Ingeniería de Fudan, Arquitecto sénior de servicios en la nube certificado por Aliyun, Jefe de negocio de productos de IA con cientos de millones de ingresos.