用pyspark把kafka主题数据经过etl导入另一个主题中的有关报错

首先看一下我们的示例代码

复制代码

import os
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
"""
------------------------------------------
  Description : TODO：
  SourceFile : etl_stream_kafka
  Author  : zxx
  Date  : 2024/11/14
-------------------------------------------
"""
if __name__ == '__main__':
    os.environ['JAVA_HOME'] = 'D:/bigdata/03-java/java-8/jdk'
    # 配置Hadoop的路径，就是前面解压的那个路径
    os.environ['HADOOP_HOME'] = 'D:/bigdata/04-Hadoop/hadoop/hadoop-3.3.1/hadoop-3.3.1'
    # 配置base环境Python解析器的路径
    os.environ['PYSPARK_PYTHON'] = 'D:/bigdata/22-spark/Miniconda3/python.exe'  # 配置base环境Python解析器的路径
    os.environ['PYSPARK_DRIVER_PYTHON'] = 'D:/bigdata/22-spark/Miniconda3/python.exe'
    spark = SparkSession.builder.master("local[2]").appName("etl_stream_kafka").config(
        "spark.sql.shuffle.partitions", 2).getOrCreate()
    # 连接kafka
    readDF = spark.readStream.format("kafka") \
    .option("kafka.bootstrap.servers", "bigdata01:9092") \
    .option("subscribe", "topicA") \
    .load()

    # 使用DSL语句
    etlDF = readDF.selectExpr("cast(value as STRING)").filter(F.col("value").contains("success"))

    etlDF.writeStream \
    .format("kafka") \
        .option("kafka.bootstrap.servers", "bigdata01:9092") \
        .option("topic", "etlTopic") \
        .option("checkpointLocation", "../../datas/kafka_stream") \
        .start().awaitTermination()
    # 关闭
    spark.stop()

运行发现报错

复制代码

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Traceback (most recent call last):
  File "D:\bigdata\18-python\pyspark_project\pythonProject1\main\streamingkafka\etl_stream_kafka.py", line 22, in <module>
    readDF = spark.readStream.format("kafka") \
  File "D:\bigdata\22-spark\Miniconda3\lib\site-packages\pyspark\sql\streaming.py", line 482, in load
    return self._df(self._jreader.load())
  File "D:\bigdata\22-spark\Miniconda3\lib\site-packages\py4j\java_gateway.py", line 1304, in __call__
    return_value = get_return_value(
  File "D:\bigdata\22-spark\Miniconda3\lib\site-packages\pyspark\sql\utils.py", line 117, in deco
    raise converted from None
pyspark.sql.utils.AnalysisException: Failed to find data source: kafka. Please deploy the application as per the deployment section of "Structured Streaming + Kafka Integration Guide".

报错 : org.apache.spark.sql.AnalysisException: Failed to find data source: kafka. Please deploy the application as per the deployment section of "Structured Streaming + Kafka Integration Guide".;

解决：这个是因为缺少了Kafka和Spark的集成包，前往https://mvnrepository.com/artifact/org.apache.spark

下载对应的jar包即可，比如我是SparkSql写入的Kafka，那么我就需要下载Spark-Sql-Kafka.x.x.x.jar

进入网站（已打包放入文章末尾）

找到对应有关spark 和kafka的模块

找到对应的版本，这里我用的kafka是3.0版本，下载的是3.1.2版本

点进去，下载jar包

再次运行会发现仍然报错，这是因为jar包之间的依赖关系，从刚才下载的界面下面再下载有关的jar包

再次运行即可

jar包下载链接

【免费】用pyspark把数据从kafka的一个主题用流处理后再导入kafka的另一个主题的有关报错资源-CSDN文库