PySpark .load()函数连接Azure SQL数据库报错求助

问题描述 #

使用PySpark通过Python连接Azure SQL数据库时，调用.load()函数触发报错，移除该函数后代码可正常执行；但连接本地MySQL数据库时，.load()能正常加载完整数据。需求是通过PyCharm运行代码，连接Azure SQL数据库并将指定表数据读取为PySpark DataFrame格式。

代码示例 #

def load_azure(spark, table_name, server, username, password, database):
    source_properties = {
        "driver": 'com.microsoft.sqlserver.jdbc.SQLServerDriver',
        "url": f'jdbc:sqlserver://{server}:1433;database={database};user={username};password={password};',
        "user": username,
        "password": password,
        "dbtable": table_name
    }
    azure_df = spark.read \
        .format("jdbc") \
        .option("url", source_properties["url"]) \
        .option("dbtable", source_properties["dbtable"]) \
        .option("inferSchema", "True")
    azure_df = azure_df.load()
    print('connected before')
    print(azure_df.printSchema())
    print(azure_df.show())
    print(azure_df)
    print('connected')
    return azure_df

报错信息 #

File "D:\Aryan\python\pythonProject\test.py", line 61, in <module>
    data = load_azure(spark, table_name1, server1, username1, password1, database1)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Aryan\python\pythonProject\test.py", line 48, in load_azure
    azure_df = azure_df.load()
               ^^^^^^^^^^^^^^^
  File "C:\Spark\spark-3.5.0-bin-hadoop3\python\lib\pyspark.zip\pyspark\sql\readwriter.py", line 314, in load
  File "C:\Spark\spark-3.5.0-bin-hadoop3\python\lib\py4j-0.10.9.7-src.zip\py4j\java_gateway.py", line 1322, in __call__
  File "C:\Spark\spark-3.5.0-bin-hadoop3\python\lib\pyspark.zip\pyspark\errors\exceptions\captured.py", line 179, in deco
  File "C:\Spark\spark-3.5.0-bin-hadoop3\python\lib\py4j-0.10.9.7-src.zip\py4j\protocol.py", line 326, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o29.load.
: java.sql.SQLException: No suitable driver
    at java.sql/java.sql.DriverManager.getDriver(DriverManager.java:300)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.$anonfun$driverClass$2(JDBCOptions.scala:109)
    at scala.Option.getOrElse(Option.scala:189)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:109)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:41)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:34)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:346)
    at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:229)
    at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:211)
    at scala.Option.getOrElse(Option.scala:189)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:172)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:75)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:52)
    at java.base/java.lang.reflect.Method.invoke(Method.java:580)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
    at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
    at java.base/java.lang.Thread.run(Thread.java:1583)

解决方法 #

1. 补充JDBC驱动配置 #

报错根源是Spark找不到Azure SQL对应的JDBC驱动，代码中已定义driver属性但未传入Spark读取配置，需添加该行：

azure_df = spark.read \
    .format("jdbc") \
    .option("url", source_properties["url"]) \
    .option("dbtable", source_properties["dbtable"]) \
    .option("inferSchema", "True") \
    .option("driver", source_properties["driver"])  # 新增驱动配置

2. 确保Spark环境包含SQL Server JDBC驱动包 #

• 下载对应JDK版本的Microsoft SQL Server JDBC驱动包
• 将驱动包放入Spark安装目录的jars文件夹（如C:\Spark\spark-3.5.0-bin-hadoop3\jars）
• 若在PyCharm中运行，可在Run Configuration的环境变量中添加：SPARK_OPTS=--jars 驱动包本地路径

3. 优化代码写法 #

可通过.options(**source_properties)批量传入参数，同时简化URL（无需重复写入user和password）：

def load_azure(spark, table_name, server, username, password, database):
    source_properties = {
        "driver": 'com.microsoft.sqlserver.jdbc.SQLServerDriver',
        "url": f'jdbc:sqlserver://{server}:1433;database={database};',
        "user": username,
        "password": password,
        "dbtable": table_name,
        "inferSchema": "True"
    }
    azure_df = spark.read \
        .format("jdbc") \
        .options(**source_properties) \
        .load()
    print('connected before')
    print(azure_df.printSchema())
    print(azure_df.show())
    print(azure_df)
    print('connected')
    return azure_df

内容的提问来源于stack exchange，提问作者user24701821