mssql-python驱动为执行查询、处理多结果集和高效内存管理提供光标对象。
光标基础知识
创建并使用光标
调用 conn.cursor() 创建光标，然后使用 execute() 和提取方法来运行查询并检索结果：
import mssql_python
conn = mssql_python.connect(
"Server=<server>.database.windows.net;"
"Database=<database>;"
"Authentication=ActiveDirectoryDefault;"
"Encrypt=yes"
)
# Create cursor
cursor = conn.cursor()
# Execute query
cursor.execute("SELECT TOP 10 Name, ListPrice FROM Production.Product")
# Process results
for row in cursor:
print(row.Name)
# Close cursor when done
cursor.close()
上下文管理器模式
使用该 with 语句实现上下文管理器以实现自动清理：
with mssql_python.connect(connection_string) as conn:
with conn.cursor() as cursor:
cursor.execute("SELECT TOP 10 Name, ListPrice FROM Production.Product")
products = cursor.fetchall()
# Cursor automatically closed on exit
# Connection automatically closed on exit
多个游标
Important
mssql-python驱动不支持多重主动结果集（MARS）。 你可以在单一连接上创建多个光标，但一次只能有一个光标有活跃查询。 在同一连接上对另一个光标执行操作之前，务必先取完该光标的所有结果。
conn = mssql_python.connect(connection_string)
# Multiple cursors on same connection
cursor1 = conn.cursor()
cursor2 = conn.cursor()
# Fetch results completely from cursor1 before using cursor2
cursor1.execute("SELECT TOP 5 ProductID, Name FROM Production.Product")
products = cursor1.fetchall()
cursor2.execute("SELECT TOP 5 ProductCategoryID, Name FROM Production.ProductCategory")
categories = cursor2.fetchall()
cursor1.close()
cursor2.close()
如果你需要同时运行查询，可以使用不同的连接：
conn1 = mssql_python.connect(connection_string)
conn2 = mssql_python.connect(connection_string)
cursor1 = conn1.cursor()
cursor2 = conn2.cursor()
cursor1.execute("SELECT TOP 5 ProductID, Name FROM Production.Product")
cursor2.execute("SELECT TOP 5 ProductCategoryID, Name FROM Production.ProductCategory")
products = cursor1.fetchall()
categories = cursor2.fetchall()
cursor1.close()
cursor2.close()
conn1.close()
conn2.close()
取球策略
全部获取与迭代获取
使用 fetchall() 可一次性将整个结果集加载到内存中，或者遍历游标，在不进行缓冲的情况下逐行处理。
# Fetch all at once - loads entire result into memory
cursor.execute("SELECT * FROM Production.Product")
all_products = cursor.fetchall()
print(f"Loaded {len(all_products)} products")
# Iterative fetch - memory efficient
cursor.execute("SELECT * FROM Production.Product")
count = 0
for row in cursor:
count += 1
print(f"Processed {count} products")
分批获取
使用 fetchmany() 批量大小处理大型结果集，无需将所有内容加载到内存中。
def process_batch(rows):
# Example: print each row. Replace with your own logic.
for row in rows:
print(row)
def fetch_in_batches(cursor, batch_size: int = 1000):
"""Fetch results in batches to manage memory."""
while True:
batch = cursor.fetchmany(batch_size)
if not batch:
break
yield batch
cursor.execute("SELECT * FROM LargeTable")
for batch in fetch_in_batches(cursor, batch_size=5000):
process_batch(batch)
print(f"Processed batch of {len(batch)} rows")
对单个值使用 fetchval
对返回单个值的标量查询使用 fetchval()。 它返回第一行的第一列。
# Efficient for scalar queries
cursor.execute("SELECT COUNT(*) FROM Production.Product")
count = cursor.fetchval() # Returns single value directly
cursor.execute("SELECT MAX(ListPrice) FROM Production.Product")
max_price = cursor.fetchval()
多个结果集
处理多个结果集
在取出前一个结果集中的所有行后，使用 nextset() 越过当前结果集并进入下一个结果集。
# Query returns multiple results
cursor.execute("""
SELECT TOP 3 CustomerID, AccountNumber FROM Sales.Customer;
SELECT TOP 3 SalesOrderID, OrderDate FROM Sales.SalesOrderHeader;
SELECT TOP 3 ProductID, Name FROM Production.Product;
""")
# First result set
print("Customers:")
customers = cursor.fetchall()
for c in customers:
print(f" {c.AccountNumber}")
# Move to second result set
if cursor.nextset():
print("Orders:")
orders = cursor.fetchall()
for o in orders:
print(f" Order #{o.SalesOrderID}")
# Move to third result set
if cursor.nextset():
print("Products:")
products = cursor.fetchall()
for p in products:
print(f" {p.Name}")
迭代所有结果集
循环直到 nextset() 返回 False ，消耗一次执行调用中的所有结果集：
def process_all_result_sets(cursor):
"""Process all result sets from a query."""
result_sets = []
while True:
# Fetch current result set
rows = cursor.fetchall()
result_sets.append(rows)
# Try to move to next result set
if not cursor.nextset():
break
return result_sets
cursor.execute("""
SELECT TOP 3 ProductID, Name FROM Production.Product ORDER BY ProductID;
SELECT TOP 3 SalesOrderID, TotalDue FROM Sales.SalesOrderHeader ORDER BY SalesOrderID;
""")
all_results = process_all_result_sets(cursor)
print(f"Retrieved {len(all_results)} result sets")
检查是否存在更多结果集
在循环中检查 nextset() 的返回值，以便在无需预先知道结果集数量的情况下处理完所有结果集：
cursor.execute("""
SELECT COUNT(*) AS ProductCount FROM Production.Product;
SELECT COUNT(*) AS PersonCount FROM Person.Person;
""")
result_num = 1
while True:
count = cursor.fetchval()
print(f"Result set {result_num}: {count}")
result_num += 1
if not cursor.nextset():
break
光标描述
获取列元数据
执行查询后，cursor.description 包含一系列 7 项元组，每列对应一个，分别包含名称、类型代码、显示大小、内部大小、精度、标度和可空性：
cursor.execute("SELECT ProductID, Name, ListPrice FROM Production.Product WHERE ProductID < 10")
# Get column information
for col in cursor.description:
print(f"Column: {col[0]}, Type: {col[1]}")
# description structure: (name, type_code, display_size, internal_size,
# precision, scale, null_ok)
构建动态结果处理程序
通过在运行时从 cursor.description 构建列列表，构建适用于任何查询的结果处理程序：
def query_to_dicts(cursor) -> list[dict]:
"""Convert query results to list of dictionaries."""
columns = [col[0] for col in cursor.description]
return [dict(zip(columns, row)) for row in cursor.fetchall()]
cursor.execute("SELECT ProductID, Name, ListPrice FROM Production.Product WHERE ProductID < 10")
products = query_to_dicts(cursor)
for p in products:
print(p["Name"])
处理无结果的查询
cursor.description 在 INSERT、UPDATE 和 DELETE 等非 SELECT 语句之后是 None。 在调用取取方法前请先检查一下：
cursor.execute("CREATE TABLE #UpdDemo (Name NVARCHAR(50), Price DECIMAL(10,2), CategoryID INT)")
cursor.execute("INSERT INTO #UpdDemo VALUES ('Widget', 10.0, 5), ('Gadget', 20.0, 5)")
cursor.execute("UPDATE #UpdDemo SET Price = Price * 1.1 WHERE CategoryID = 5")
# description is None for non-SELECT statements
if cursor.description is None:
print(f"Updated {cursor.rowcount} rows")
else:
results = cursor.fetchall()
行计数
跟踪受影响的行数
在 INSERT、 UPDATE或 DELETE之后 cursor.rowcount ，返回受该语句影响的行数：
cursor.execute("CREATE TABLE #RowDemo (Name NVARCHAR(50), Stock INT)")
cursor.execute("INSERT INTO #RowDemo VALUES ('A', 0), ('B', 5), ('C', 0)")
cursor.execute("UPDATE #RowDemo SET Stock = -1 WHERE Stock = 0")
print(f"Rows affected: {cursor.rowcount}")
cursor.execute("DELETE FROM #RowDemo WHERE Stock = -1")
print(f"Deleted {cursor.rowcount} rows")
处理行数未知的情况
# Some operations might not return row count
cursor.execute("EXEC dbo.uspGetEmployeeManagers @BusinessEntityID = 5")
if cursor.rowcount == -1:
print("Row count not available")
else:
print(f"Affected {cursor.rowcount} rows")
跳过行
使用跳过作为分页替代方案
cursor.skip() 在不提取行的情况下推进光标位置。 对于大型数据集，优先采用SQL级 OFFSET-FETCH 分页以获得更好的性能：
def get_page_using_skip(cursor, page: int, page_size: int):
"""Get a page of results using skip."""
cursor.execute("SELECT * FROM Production.Product ORDER BY ProductID")
# Skip rows from previous pages
cursor.skip((page - 1) * page_size)
# Fetch this page
return cursor.fetchmany(page_size)
# Get page 3
page_3 = get_page_using_skip(cursor, page=3, page_size=20)
注释
对于大型数据集，应使用 SQL 级分页（OFFSET-FETCH），而不是在客户端执行 skip 操作，因为这种方式效率更高。
诊断信息
访问cursor.messages
该 messages 属性存储在 SQL 语句执行过程中生成的信息消息，详见 PEP 249。 这些消息包括 PRINT 语句的输出，以及严重级别低于 11 的 RAISERROR 消息。
该属性是一个元组列表，每个元组包含消息类型代码和消息文本：
conn = mssql_python.connect(connection_string, autocommit=True)
cursor = conn.cursor()
cursor.execute("PRINT 'Hello world!'")
print(cursor.messages)
Output:
[('[01000] (0)', '[Microsoft][ODBC Driver 18 for SQL Server][SQL Server]Hello world!')]
消息文本包含驱动程序前缀信息，因为驱动程序通过 SQLGetDiagRec 以诊断记录的形式检索消息。
从存储过程捕获消息
执行后读取 cursor.messages 以获取前述语句中的任何 PRINT 输出或信息服务器消息：
cursor.execute("EXECUTE dbo.uspGetEmployeeManagers @BusinessEntityID = 5")
results = cursor.fetchall()
# Check for any informational messages
if cursor.messages:
for msg_type, msg_text in cursor.messages:
print(f"Server message: {msg_text}")
内存管理
高效处理大型结果集
使用 fetchmany() 分批获取数据，以处理那些大到无法一次性加载到内存中的表：
def process_large_table(cursor, batch_size: int = 10000):
"""Process large result set without loading all into memory."""
cursor.execute("SELECT * FROM VeryLargeTable")
total_processed = 0
while True:
rows = cursor.fetchmany(batch_size)
if not rows:
break
for row in rows:
process_row(row)
total_processed += len(rows)
print(f"Progress: {total_processed} rows processed")
return total_processed
基于生成器的处理
将批量获取封装到生成器中，以便一次处理一行，同时使内存占用保持恒定，而不受结果集大小影响：
def row_generator(cursor, batch_size: int = 1000):
"""Generate rows from cursor without loading all."""
while True:
rows = cursor.fetchmany(batch_size)
if not rows:
break
for row in rows:
yield row
cursor.execute("SELECT * FROM LargeTable")
for row in row_generator(cursor, batch_size=5000):
# Process one row at a time
print(row) # Replace with your own row-handling logic
立即关闭光标
即使发生异常，也始终关闭块内 finally 的光标以释放服务器端资源：
def get_product(conn, product_id: int):
"""Get product and properly close cursor."""
cursor = conn.cursor()
try:
cursor.execute(
"SELECT * FROM Production.Product WHERE ProductID = %(id)s",
{"id": product_id}
)
return cursor.fetchone()
finally:
cursor.close()
光标状态管理
检查光标是否有数据
通过检查 fetchone() 是否返回 None，测试查询是否返回了任何行：
cursor.execute("SELECT ProductID, Name FROM Production.Product WHERE ProductID = 999")
row = cursor.fetchone()
if row is None:
print("Product not found")
else:
print(f"Found: {row.Name}")
重用光标
单个光标可以顺序执行多个查询。 每次 execute() 调用都会替换前一个结果集：
cursor = conn.cursor()
# Execute multiple queries with same cursor
cursor.execute("SELECT TOP 5 * FROM Sales.Customer")
customers = cursor.fetchall()
cursor.execute("SELECT TOP 5 * FROM Production.Product")
products = cursor.fetchall()
cursor.execute("SELECT TOP 5 * FROM Sales.SalesOrderHeader")
orders = cursor.fetchall()
cursor.close()
最佳做法
模式：游标辅助类
将光标生命周期管理封装在辅助类中，以减少应用中的样板代码：
class CursorManager:
"""Helper for managing cursor lifecycle."""
def __init__(self, connection):
self.conn = connection
def execute_and_fetch(self, query: str, params: dict = None) -> list:
"""Execute query and return all results."""
cursor = self.conn.cursor()
try:
cursor.execute(query, params or {})
return cursor.fetchall()
finally:
cursor.close()
def execute_scalar(self, query: str, params: dict = None):
"""Execute query and return single value."""
cursor = self.conn.cursor()
try:
cursor.execute(query, params or {})
return cursor.fetchval()
finally:
cursor.close()
def execute_non_query(self, query: str, params: dict = None) -> int:
"""Execute non-SELECT and return row count."""
cursor = self.conn.cursor()
try:
cursor.execute(query, params or {})
return cursor.rowcount
finally:
cursor.close()
# Usage
db = CursorManager(conn)
products = db.execute_and_fetch("SELECT TOP 5 Name FROM Production.Product")
count = db.execute_scalar("SELECT COUNT(*) FROM Production.Product")
db.execute_non_query("CREATE TABLE #Logs (LogID INT, Age INT)")
db.execute_non_query("INSERT INTO #Logs VALUES (1, 45), (2, 20), (3, 60)")
affected = db.execute_non_query("DELETE FROM #Logs WHERE Age > 30")
不要让光标开着
未明确关闭的光标会保留服务器端资源，直到连接关闭。 使用 try/finally 来确保清理：
# Bad: cursor left open
def get_data_bad(conn):
cursor = conn.cursor()
cursor.execute("SELECT * FROM Data")
return cursor.fetchall()
# Cursor never closed!
# Good: always close cursor
def get_data_good(conn):
cursor = conn.cursor()
try:
cursor.execute("SELECT * FROM Data")
return cursor.fetchall()
finally:
cursor.close()
将光标寿命与操作匹配
为单次操作创建短寿命光标。 仅将同一光标用于一系列相关操作：
# Short-lived cursor for simple query
def get_user_count(conn) -> int:
cursor = conn.cursor()
try:
cursor.execute("SELECT COUNT(*) FROM Person.Person")
return cursor.fetchval()
finally:
cursor.close()
# Reuse cursor for related operations
def update_inventory(conn, items: list):
cursor = conn.cursor()
try:
for item in items:
cursor.execute(
"UPDATE Inventory SET Quantity = %(qty)s WHERE ProductID = %(id)s",
item
)
conn.commit()
finally:
cursor.close()