PySpark读取Kafka流转DataFrame遇scala类缺失错误求助

尝试将Kafka主题数据转换为Spark DataFrame，使用以下PySpark代码：

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *

# Create a SparkSession
spark = SparkSession.builder \
    .appName("KafkaStreamToDataFrame") \
    .getOrCreate()

# Define the schema for the data in the Kafka stream
schema = StructType([
    StructField("key", StringType()),
    StructField("value", StringType())
])

# Read the data from the Kafka stream
df = spark \
    .readStream \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "kafka_host:9092") \
    .option("subscribe", "ext_device-measurement_10121") \
    .load() \
    .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") \
    .select(from_json(col("value"), schema).alias("data")) \
    .select("data.*")

# Start the stream and display the data in the DataFrame
query = df \
    .writeStream \
    .format("console") \
    .start()

query.awaitTermination()

执行命令：

spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.3.1 dev_ev.py 

Spark版本：3.3.1

执行时出现错误：

File "/home/avs/avnish_spark/dev_ev.py", line 21, in <module>
    .option("subscribe", "ext_device-measurement_10121") \
  File "/opt/avnish/spark-3.3.1-bin-hadoop3/python/lib/pyspark.zip/pyspark/sql/streaming.py", line 469, in load
  File "/opt/avnish/spark-3.3.1-bin-hadoop3/python/lib/py4j-0.10.9.5-src.zip/py4j/java_gateway.py", line 1322, in __call__
  File "/opt/avnish/spark-3.3.1-bin-hadoop3/python/lib/pyspark.zip/pyspark/sql/utils.py", line 190, in deco
  File "/opt/avnish/spark-3.3.1-bin-hadoop3/python/lib/py4j-0.10.9.5-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o35.load.
: java.lang.NoClassDefFoundError: scala/$less$colon$less
    at org.apache.spark.sql.kafka010.KafkaSourceProvider.org$apache$spark$sql$kafka010$KafkaSourceProvider$$validateStreamOptions(KafkaSourceProvider.scala:338)
    at org.apache.spark.sql.kafka010.KafkaSourceProvider.sourceSchema(KafkaSourceProvider.scala:71)
    at org.apache.spark.sql.execution.datasources.DataSource.sourceSchema(DataSource.scala:236)
    at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo$lzycompute(DataSource.scala:118)
    at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo(DataSource.scala:118)
    at org.apache.spark.sql.execution.streaming.StreamingRelation$.apply(StreamingRelation.scala:34)
    at org.apache.spark.sql.streaming.DataStreamReader.loadInternal(DataStreamReader.scala:168)
    at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:144)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    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.lang.Thread.run(Thread.java:750)
Caused by: java.lang.ClassNotFoundException: scala.$less$colon$less
    at java.net.URLClassLoader.findClass(URLClassLoader.java:387)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
    at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
    ... 20 more

已确认Kafka主题可访问且正在推送JSON记录，尝试过手动下载jar包放入SPARK_HOME/jars目录，使用命令spark-submit --jars $SPARK_HOME/jars/org.apache.spark:spark-sql-kafka-0-10_2.12:3.3.1 dev_ev.py执行，问题仍未解决，期望正常显示DataFrame。

1. 检查Scala版本兼容性 #

Spark 3.3.1对应Scala版本为2.12.x，执行scala -version确认环境中Scala版本是否匹配。若版本不符，需安装对应版本的Scala并配置环境变量。

2. 修正--jars参数用法 #

--jars参数需要指定jar文件的绝对路径，而非Maven坐标。正确用法示例：

spark-submit --jars /opt/avnish/spark-3.3.1-bin-hadoop3/jars/spark-sql-kafka-0-10_2.12-3.3.1.jar dev_ev.py

多jar包时用逗号分隔：

spark-submit --jars /path/to/jar1.jar,/path/to/jar2.jar dev_ev.py

3. 重新拉取完整依赖 #

使用--packages时，Spark会自动下载所有关联依赖（包括Scala库）。若之前下载失败，先清理本地缓存再重新执行：

rm -rf ~/.ivy2/cache/org.apache.spark
spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.3.1 dev_ev.py

4. 确保依赖jar包完整 #

手动放入SPARK_HOME/jars的jar包需版本匹配且完整，Spark SQL Kafka核心依赖包括：

• spark-sql-kafka-0-10_2.12-3.3.1.jar
• kafka-clients-2.8.1.jar
• spark-token-provider-kafka-0-10_2.12-3.3.1.jar
• commons-pool2-2.11.1.jar

确认这些jar包均存在于SPARK_HOME/jars目录，无版本冲突。

5. 验证Kafka消息结构（可选优化） #

若后续解析后数据为空，可先打印原始value确认JSON结构是否与代码中定义的schema匹配：

df = spark \
    .readStream \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "kafka_host:9092") \
    .option("subscribe", "ext_device-measurement_10121") \
    .load() \
    .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")

query = df.writeStream.format("console").start()
query.awaitTermination()

根据实际结构调整schema后再重新解析。

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