关于python:Python史上最全种类数据库操作方法你能想到的数据库类型都在里面甚至还有云数据库

本文将具体探讨如何在 Python 中连贯全品种数据库以及实现相应的 CRUD（创立，读取，更新，删除）操作。咱们将逐个解析连贯 MySQL，SQL Server，Oracle，PostgreSQL，MongoDB，SQLite，DB2，Redis，Cassandra，Microsoft Access，ElasticSearch，Neo4j，InfluxDB，Snowflake，Amazon DynamoDB，Microsoft Azure CosMos DB 数据库的办法，并演示相应的 CRUD 操作。

MySQL

连贯数据库

Python 能够应用 mysql-connector-python 库连贯 MySQL 数据库：

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()

CRUD 操作

接下来，咱们将展现在 MySQL 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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()

SQL Server

连贯数据库

Python 能够应用 pyodbc 库连贯 SQL Server 数据库：

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()

CRUD 操作

接下来，咱们将展现在 SQL Server 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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()

Oracle

连贯数据库

Python 能够应用 cx_Oracle 库连贯 Oracle 数据库：

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()

CRUD 操作

接下来，咱们将展现在 Oracle 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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

连贯数据库

Python 能够应用 psycopg2 库连贯 PostgreSQL 数据库：

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()

CRUD 操作

接下来，咱们将展现在 PostgreSQL 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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

连贯数据库

Python 能够应用 pymongo 库连贯 MongoDB 数据库：

from pymongo import MongoClient

client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]
print("Opened MongoDB database successfully")
client.close()

CRUD 操作

接下来，咱们将展现在 MongoDB 中如何进行根本的 CRUD 操作。

创立（Create）

在 MongoDB 中，文档的创立操作通常蕴含在插入操作中：

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()

读取（Retrieve）

client = MongoClient("mongodb://localhost:27017/")
db = client["my_database"]

employees = db["Employees"]
cursor = employees.find()
for document in cursor:
    print(document)

client.close()

更新（Update）

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()

删除（Delete）

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

连贯数据库

Python 应用 sqlite3 库连贯 SQLite 数据库：

import sqlite3

conn = sqlite3.connect('my_database.db')
print("Opened SQLite database successfully")
conn.close()

CRUD 操作

接下来，咱们将展现在 SQLite 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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

连贯数据库

Python 能够应用 ibm_db 库连贯 DB2 数据库：

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)

CRUD 操作

接下来，咱们将展现在 DB2 中如何进行根本的 CRUD 操作。

创立（Create）

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)

读取（Retrieve）

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)

更新（Update）

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)

删除（Delete）

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)

Microsoft Access

连贯数据库

Python 能够应用 pyodbc 库连贯 Microsoft Access 数据库：

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()

CRUD 操作

接下来，咱们将展现在 Access 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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()

Cassandra

连贯数据库

Python 能够应用 cassandra-driver 库连贯 Cassandra 数据库：

from cassandra.cluster import Cluster

cluster = Cluster(['127.0.0.1'])
session = cluster.connect('my_keyspace')
print("Opened Cassandra database successfully")
cluster.shutdown()

CRUD 操作

接下来，咱们将展现在 Cassandra 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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

连贯数据库

Python 能够应用 redis-py 库连贯 Redis 数据库：

import redis

r = redis.Redis(host='localhost', port=6379, db=0)
print("Opened Redis database successfully")

CRUD 操作

接下来，咱们将展现在 Redis 中如何进行根本的 CRUD 操作。

创立（Create）

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")

读取（Retrieve）

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'))

更新（Update）

r = redis.Redis(host='localhost', port=6379, db=0)

r.set('employee:1:salary', '25000.00')

print("Key updated successfully")

删除（Delete）

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

连贯数据库

Python 能够应用 elasticsearch 库连贯 ElasticSearch 数据库：

from elasticsearch import Elasticsearch

es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
print("Opened ElasticSearch database successfully")

CRUD 操作

接下来，咱们将展现在 ElasticSearch 中如何进行根本的 CRUD 操作。

创立（Create）

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")

读取（Retrieve）

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)

更新（Update）

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")

删除（Delete）

es = Elasticsearch([{'host': 'localhost', 'port': 9200}])

res = es.delete(index='employees', doc_type='employee', id=1)

print("Document deleted successfully")

Neo4j

连贯数据库

Python 能够应用 neo4j 库连贯 Neo4j 数据库：

from neo4j import GraphDatabase

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
print("Opened Neo4j database successfully")
driver.close()

CRUD 操作

接下来，咱们将展现在 Neo4j 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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()

InfluxDB

连贯数据库

Python 能够应用 InfluxDB-Python 库连贯 InfluxDB 数据库：

from influxdb import InfluxDBClient

client = InfluxDBClient(host='localhost', port=8086)
print("Opened InfluxDB database successfully")
client.close()

CRUD 操作

接下来，咱们将展现在 InfluxDB 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

InfluxDB 的数据模型和其余数据库不同，它没有更新操作。然而你能够通过写入一个雷同的数据点（即具备雷同的工夫戳和标签）并扭转字段值，实现相似更新操作的成果。

删除（Delete）

同样，InfluxDB 也没有提供删除单个数据点的操作。然而，你能够删除整个系列（即表）或者删除某个时间段的数据。

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()

Snowflake

连贯数据库

Python 能够应用 snowflake-connector-python 库连贯 Snowflake 数据库：

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()

CRUD 操作

接下来，咱们将展现在 Snowflake 中如何进行根本的 CRUD 操作。

创立（Create）

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()

读取（Retrieve）

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()

更新（Update）

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()

删除（Delete）

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

连贯数据库

Python 能够应用 boto3 库连贯 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")

CRUD 操作

接下来，咱们将展现在 DynamoDB 中如何进行根本的 CRUD 操作。

创立（Create）

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")

读取（Retrieve）

table = dynamodb.Table('Employees')

response = table.get_item(
   Key={'id': 1,}
)

item = response['Item']
print(item)

更新（Update）

table = dynamodb.Table('Employees')

table.update_item(
    Key={'id': 1,},
    UpdateExpression='SET salary = :val1',
    ExpressionAttributeValues={':val1': 25000.00}
)

print("Item updated successfully")

删除（Delete）

table = dynamodb.Table('Employees')

table.delete_item(
    Key={'id': 1,}
)

print("Item deleted successfully")

Microsoft Azure CosMos DB

连贯数据库

Python 能够应用 azure-cosmos 库连贯 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")

CRUD 操作

接下来，咱们将展现在 CosMos DB 中如何进行根本的 CRUD 操作。

创立（Create）

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")

读取（Retrieve）

for item in container.read_all_items():
    print(item)

更新（Update）

for item in container.read_all_items():
    if item['id'] == '1':
        item['salary'] = 25000.00
        container.upsert_item(item)
        
print("Item updated successfully")

删除（Delete）

for item in container.read_all_items():
    if item['id'] == '1':
        container.delete_item(item, partition_key='1')
        
print("Item deleted successfully")

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TeahLead_KrisChang，10+ 年的互联网和人工智能从业教训，10 年 + 技术和业务团队治理教训，同济软件工程本科，复旦工程治理硕士，阿里云认证云服务资深架构师，上亿营收 AI 产品业务负责人。