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spark.py

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+import pymysql
+from pyspark.sql import SparkSession
+import pyspark.sql.functions as F
+from pyspark.sql.window import Window
+from pyspark.sql.streaming import DataStreamWriter
+
+def write_to_mysql(batch_df, batch_id):
+    # 将 DataFrame 转换为 Pandas DataFrame
+    pandas_df = batch_df.toPandas()
+
+    # 连接 MySQL 数据库
+    connection = pymysql.connect(
+        host='43.140.205.103',
+        user='kaku',
+        password='p4J7fY8mc6hcZfjG',
+        database='kaku',
+        charset='utf8mb4'  # 设置为 utf8mb4 编码，以支持所有 Unicode 字符
+    )
+
+    cursor = connection.cursor()
+
+    # 插入数据到 MySQL
+    for _, row in pandas_df.iterrows():
+        order_name = row['order_name']
+        status = row['status']
+        count = row['count']
+
+        # 插入语句
+        sql = """
+        INSERT INTO order_name_YN (order_name, status, count)
+        VALUES (%s, %s, %s)
+        ON DUPLICATE KEY UPDATE count = count + VALUES(count)
+        """
+
+        cursor.execute("SET NAMES 'utf8mb4';") 
+        cursor.execute(sql, (order_name, status, count))
+        
+    
+    # 提交事务并关闭连接
+    connection.commit()
+    cursor.close()
+    connection.close()
+
+if __name__ == '__main__':
+    # 1- 创建 SparkSession
+    spark = SparkSession.builder \
+        .config("spark.sql.shuffle.partitions", 1) \
+        .config("spark.jars.packages", "org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1") \
+        .appName('ss_kafka_push_to_mysql') \
+        .master('local[*]') \
+        .getOrCreate()
+
+    # 2- 读取 Kafka 数据流
+    kafka_stream = spark.readStream \
+        .format("kafka") \
+        .option("kafka.bootstrap.servers", "niit-node2:9092") \
+        .option("subscribePattern", "orders") \
+        .load()
+
+    # 3- 解析数据并使用英文列名
+    parsed_stream = kafka_stream.selectExpr("cast(value as string) as value", "timestamp") \
+        .withColumn("order_id", F.split(F.col("value"), "\t")[0]) \
+        .withColumn("order_type", F.split(F.col("value"), "\t")[1]) \
+        .withColumn("order_name", F.split(F.col("value"), "\t")[2]) \
+        .withColumn("order_quantity", F.split(F.col("value"), "\t")[3]) \
+        .withColumn("status", F.split(F.col("value"), "\t")[4]) \
+        .drop("value")
+
+    # 4- 定义时间窗口，并使用窗口聚合
+    windowed_stream = parsed_stream \
+        .groupBy(
+            F.window(parsed_stream.timestamp, "2 seconds"),  # 2分钟的时间窗口
+            parsed_stream.order_name,
+            parsed_stream.status
+        ) \
+        .agg(F.count("*").alias("count"))
+
+    # 5- 使用 foreachBatch 将数据推送到 MySQL
+    windowed_stream.writeStream \
+        .foreachBatch(write_to_mysql) \
+        .outputMode("update") \
+        .trigger(processingTime="2 seconds") \
+        .start()
+
+    # 等待流任务结束
+    spark.streams.awaitAnyTermination()