双十二大数据实时交互选购

# 示例代码：使用Flink进行实时数据处理
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import TableEnvironment

env = StreamExecutionEnvironment.get_execution_environment()
t_env = TableEnvironment.create(env)

# 定义数据源和接收器
source_ddl = """
    CREATE TABLE user_behavior (
        user_id INT,
        item_id INT,
        action STRING,
        event_time TIMESTAMP(3)
    ) WITH (
        'connector' = 'kafka',
        'topic' = 'user_behavior_topic',
        'properties.bootstrap.servers' = 'localhost:9092',
        'format' = 'json'
    )
"""

sink_ddl = """
    CREATE TABLE recommendation (
        user_id INT,
        item_id INT,
        score DOUBLE
    ) WITH (
        'connector' = 'kafka',
        'topic' = 'recommendation_topic',
        'format' = 'json'
    )
"""

t_env.execute_sql(source_ddl)
t_env.execute_sql(sink_ddl)

# 实时推荐逻辑
query = """
    SELECT user_id, item_id, score
    FROM user_behavior
    JOIN recommendation ON user_behavior.user_id = recommendation.user_id
    WHERE event_time >= CURRENT_TIMESTAMP - INTERVAL '1' MINUTE
"""
result_table = t_env.sql_query(query)
result_table.execute_insert(sink_ddl).wait()

# 示例代码：使用Spark进行分布式数据处理
from pyspark.sql import SparkSession
from pyspark.sql.functions import window

spark = SparkSession.builder.appName("RealTimeRecommendation").getOrCreate()

# 读取Kafka数据流
df = spark \
    .readStream \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("subscribe", "user_behavior_topic") \
    .load()

# 数据处理逻辑
processed_df = df.selectExpr("CAST(value AS STRING)") \
    .select(from_json("value", schema).alias("data")) \
    .select("data.*") \
    .groupBy(window("event_time", "1 minute"), "user_id") \
    .agg(collect_list("item_id").alias("items"))

# 输出结果到Kafka
query = processed_df \
    .writeStream \
    .outputMode("update") \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("topic", "recommendation_topic") \
    .start()

query.awaitTermination()

// 示例代码：使用React进行前端性能优化
import React, { useState, useEffect } from 'react';

function RecommendationList() {
    const [recommendations, setRecommendations] = useState([]);

    useEffect(() => {
        const fetchData = async () => {
            const response = await fetch('/api/recommendations');
            const data = await response.json();
            setRecommendations(data);
        };

        fetchData();
    }, []);

    return (
        <div>
            {recommendations.map(item => (
                <div key={item.id}>{item.name}</div>
            ))}
        </div>
    );
}

export default RecommendationList;