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Troubleshoot query, data, and operation issues with mssql-python

Use this article to diagnose query execution, data type, performance, transaction, and bulk copy issues with the mssql-python driver.

Query execution issues

Table or object not found

Symptoms:

ProgrammingError: [42S02] (208) Invalid object name 'TableName'.

Possible causes and solutions:

  • Incorrect database context

    cursor.execute("SELECT DB_NAME()")
print(cursor.fetchone()[0])

  • Schema not specified

    cursor.execute("SELECT * FROM dbo.TableName")

  • Table doesn't exist

    cursor.execute("""
    SELECT TABLE_NAME
    FROM INFORMATION_SCHEMA.TABLES
    WHERE TABLE_NAME = 'TableName'
""")

Syntax error

Symptoms:

ProgrammingError: [42000] (102) Incorrect syntax near '...'.

Solutions:

  1. Test the SQL statement in SQL Server Management Studio (SSMS) to verify the syntax.

  2. Use a parameterized query instead of string interpolation:

    # Don't use string interpolation for query parameters.
cursor.execute(f"SELECT * FROM Production.Product WHERE Name = '{name}'")

# Use parameters.
cursor.execute(
    "SELECT * FROM Production.Product WHERE Name = %(name)s",
    {"name": name},
)

Parameter errors

Symptoms:

ProgrammingError: [07001] Wrong number of parameters

Solutions:

  1. Count the placeholders and parameters. The counts must match.

  2. Choose the correct parameter style:

    # Qmark style: positional parameters
cursor.execute(
    "SELECT * FROM Production.Product "
    "WHERE ProductID = ? AND Name LIKE ?",
    (1, "Adjustable%"),
)
print(cursor.fetchone())

# Pyformat style: named parameters
cursor.execute(
    "SELECT * FROM Production.Product "
    "WHERE ProductID = %(id)s AND Name LIKE %(name)s",
    {"id": 1, "name": "Adjustable%"},
)
print(cursor.fetchone())

Data type issues

Datetime conversion errors

Symptoms:

DataError: [22007] Invalid datetime format

Solution:

Use Python datetime objects instead of strings.

from datetime import datetime

cursor.execute("CREATE TABLE #Events (EventDate DATETIME)")

# This value raises an error because the date is invalid.
try:
    cursor.execute(
        "INSERT INTO #Events (EventDate) VALUES (%(event_date)s)",
        {"event_date": "2024-13-45"},
    )
except Exception as e:
    print(f"Expected error: {e}")

# Use a Python datetime object.
cursor.execute(
    "INSERT INTO #Events (EventDate) VALUES (%(event_date)s)",
    {"event_date": datetime(2024, 3, 15)},
)
cursor.execute("SELECT EventDate FROM #Events")
print(cursor.fetchone())

Decimal precision issues

Symptoms:

Numbers appear truncated or rounded incorrectly.

Solution:

Use decimal.Decimal for precise numeric values:

from decimal import Decimal

cursor.execute("CREATE TABLE #PriceDemo (ListPrice DECIMAL(10, 2))")
cursor.execute(
    "INSERT INTO #PriceDemo (ListPrice) VALUES (%(list_price)s)",
    {"list_price": Decimal("19.99")},
)

Unicode encoding issues

Symptoms:

Special characters appear garbled or cause errors.

Solutions:

  1. Use nvarchar columns for Unicode data in your database.

  2. Pass strings directly. The driver handles encoding:

    cursor.execute("CREATE TABLE #UnicodeDemo (Name NVARCHAR(50))")
cursor.execute(
    "INSERT INTO #UnicodeDemo (Name) VALUES (%(name)s)",
    {"name": "日本語"},
)
cursor.execute("SELECT Name FROM #UnicodeDemo")
print(cursor.fetchone())

Performance issues

Slow query execution

Possible causes and solutions:

  • Missing indexes: Check the query execution plan in SSMS.

  • Large result sets: Use fetchmany() instead of fetchall():

    cursor.arraysize = 1000
while True:
    rows = cursor.fetchmany()
    if not rows:
        break
    process_rows(rows)

  • Connection pooling disabled: Enable pooling:

    import mssql_python

mssql_python.pooling(max_size=20, idle_timeout=300)

Memory issues with large results

Symptoms:

The Python process runs out of memory.

Solutions:

  1. Stream results instead of loading all rows into memory.

    cursor.execute("SELECT * FROM LargeTable")
for row in cursor:
    process_row(row)

  2. Use server-side pagination.

    page_size = 1000
offset = 0

while True:
    cursor.execute(
        "SELECT * FROM LargeTable ORDER BY ID "
        "OFFSET ? ROWS FETCH NEXT ? ROWS ONLY",
        (offset, page_size),
    )
    rows = cursor.fetchall()
    if not rows:
        break
    process_rows(rows)
    offset += page_size

Transaction issues

Temp table scoping with autocommit

Session temp tables (#tablename) you create inside a transaction disappear when the transaction rolls back. This behavior commonly causes confusion when autocommit is off, which is the default:

conn = mssql_python.connect(connection_string)
cursor = conn.cursor()

cursor.execute("CREATE TABLE #TempData (ID INT, Name NVARCHAR(50))")
cursor.execute("INSERT INTO #TempData VALUES (1, 'test')")

# An explicit rollback or an error removes #TempData.
conn.rollback()

# This statement fails with "Invalid object name '#TempData'".
cursor.execute("SELECT * FROM #TempData")

Commit immediately after you create a temp table, or use autocommit mode:

cursor.execute("CREATE TABLE #TempData (ID INT, Name NVARCHAR(50))")
conn.commit()

cursor.execute("INSERT INTO #TempData VALUES (1, 'test')")
conn.commit()

DDL statements that require autocommit mode, such as CREATE DATABASE, fail inside an open transaction. Set autocommit before you run them:

conn.autocommit = True
cursor.execute("CREATE DATABASE TestDB")
conn.autocommit = False

Transaction not committed

Symptoms:

Data changes don't persist after you close the connection.

Solution:

With autocommit=False, which is the default, call commit():

cursor.execute("CREATE TABLE #Products (Name NVARCHAR(100))")
cursor.execute(
    "INSERT INTO #Products (Name) VALUES (%(name)s)",
    {"name": "Widget"},
)
conn.commit()

Alternatively, use autocommit mode:

conn = mssql_python.connect(connection_string, autocommit=True)

Deadlock errors

Symptoms:

OperationalError: [40001] (1205) Transaction ... was deadlocked on lock resources with another process

Solution:

Retry logic handles the immediate failure, but recurring deadlocks indicate a design problem. Capture the deadlock graph, and analyze the statements and lock types. Common fixes include these changes:

  • Reorder operations so competing transactions acquire locks in the same sequence.
  • Reduce the transaction scope.
  • Add appropriate indexes to reduce lock duration.

For a full walkthrough of deadlock analysis, see Deadlocks guide. If you use Azure SQL Database, see Analyze and prevent deadlocks.

Bulk load issues

Constraint violations during bulkcopy

Symptoms:

RuntimeError: CHECK constraint ... Conflict occurred in database ...
RuntimeError: Cannot insert duplicate key ... violation of PRIMARY KEY constraint

Cause:

Data in your batch violates table constraints such as primary key, unique, check, or foreign key constraints.

Solution:

Validate data before you load it. For large datasets, load the data into a staging table, and then merge it into the target:

cursor.execute("CREATE TABLE ##Staging (ID INT, Name NVARCHAR(100))")
cursor.bulkcopy("##Staging", rows)

# Check for duplicate rows before the merge.
cursor.execute("""
    SELECT s.ID
    FROM ##Staging AS s
    INNER JOIN dbo.Target AS t
        ON s.ID = t.ID
""")
dupes = cursor.fetchall()
if dupes:
    print(f"Skipping {len(dupes)} duplicate rows")

# Insert rows that don't exist in the target.
cursor.execute("""
    INSERT INTO dbo.Target (ID, Name)
    SELECT s.ID, s.Name
    FROM ##Staging AS s
    WHERE NOT EXISTS (
        SELECT 1
        FROM dbo.Target AS t
        WHERE t.ID = s.ID
    )
""")
conn.commit()

For upsert patterns with staging tables, see Data loading and movement patterns.

Column mapping errors

Symptoms:

RuntimeError: Bulk copy failure - column count mismatch

Cause:

The number of columns in your data doesn't match the target table column count, or the columns are in the wrong order.

Solution:

Ensure that your data matches the table schema in order and count:

from decimal import Decimal

cursor.execute("""
    SELECT COLUMN_NAME, DATA_TYPE
    FROM INFORMATION_SCHEMA.COLUMNS
    WHERE TABLE_NAME = 'MyTable'
    ORDER BY ORDINAL_POSITION
""")
for col in cursor.fetchall():
    print(col)

rows = [
    (1, "Widget", Decimal("19.99")),
    (2, "Gadget", Decimal("29.99")),
]
cursor.bulkcopy("dbo.MyTable", rows)

Type mismatches during bulkcopy

Symptoms:

Data loads, but values are truncated, rounded, or incorrect.

Cause:

Python values don't map cleanly to the target column types. Common examples include float values loaded into decimal columns, which can lose precision, and oversized strings loaded into fixed-length columns.

Solution:

Use Python types that match your schema:

from decimal import Decimal

rows = [
    # Use Decimal for decimal and numeric columns.
    (1, "Widget", Decimal("19.99")),
    # Avoid float values because they can lose precision.
    # (1, "Widget", 19.99),
]
cursor.bulkcopy("dbo.Products", rows)

NumPy type binding failures

Symptoms:

Parameters silently fail or raise data type errors when you use NumPy integer or float types.

Cause:

NumPy types such as numpy.int64 and numpy.int32 don't pass isinstance(x, int) in NumPy 2.x. The driver's type inference doesn't recognize them, which causes unexpected behavior.

Solution:

Convert NumPy values to native Python types before you bind them:

import numpy as np

cursor.execute(
    "SELECT * FROM Production.Product WHERE ProductID = %(product_id)s",
    {"product_id": int(np.int64(42))},
)

for _, row in df.iterrows():
    cursor.execute(
        "INSERT INTO #Orders (ProductID, Qty) "
        "VALUES (%(product_id)s, %(qty)s)",
        {
            "product_id": int(row["ProductID"]),
            "qty": int(row["Qty"]),
        },
    )

For larger datasets, use the Arrow or pandas integration paths. These paths handle type conversion internally.

Bulk copy with temp tables

Symptoms:

cursor.bulkcopy("#TempTable", data) raises RuntimeError: Invalid object name '#TempTable'.

Cause:

bulkcopy() can't resolve session temp tables (#tablename) because of metadata lookup limitations. Global temp tables (##tablename) and permanent tables work.

Solution:

Use a global temp table or a regular staging table:

# A global temp table is visible to all sessions and is dropped
# when the last session disconnects.
cursor.execute("CREATE TABLE ##Staging (ID INT, Name NVARCHAR(50))")
cursor.bulkcopy("##Staging", rows)

# Alternatively, use a permanent staging table.
cursor.execute("CREATE TABLE dbo.Staging (ID INT, Name NVARCHAR(50))")
cursor.bulkcopy("dbo.Staging", rows)

For small datasets where you prefer a session temp table, use executemany():

cursor.execute("CREATE TABLE #Staging (ID INT, Name NVARCHAR(50))")
cursor.executemany(
    "INSERT INTO #Staging (ID, Name) VALUES (?, ?)",
    rows,
)

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