构建智能电商推荐系统：大数据实战中的Kudu、Flink和Mahout应用【上进小菜猪大数据】

# 使用Kudu进行数据存储和查询示例
from kudu.client import Partitioning

# 连接到Kudu集群
client = kudu.connect(host='kudu.master', port=7051)

# 创建Kudu表
schema = kudu.schema([(name='id', type='int64'), (name='name', type='string')])
table = client.table('my_table')
client.create_table('my_table', schema, partitioning=Partitioning(hash_partitions=4, columns=['id']))

# 插入数据
session = client.new_session()
insert = table.new_insert({'id': 1, 'name': 'John'})
session.apply(insert)
session.flush()

# 查询数据
scanner = table.scanner()
scanner.add_predicate(kudu.Predicate.compare('id', '>', 0))
scanner.open()
for row in scanner.read_all():
    print(row)

# 使用Flink进行实时流处理示例
from flink.streaming.api.environment import StreamExecutionEnvironment
from flink.streaming.api.functions.source import SourceFunction
from flink.streaming.api.functions.sink import SinkFunction

# 创建执行环境
env = StreamExecutionEnvironment.get_execution_environment()

# 定义数据源
class MySource(SourceFunction):
    def run(self, ctx):
        while True:
            ctx.collect('Hello, World!')

    def cancel(self):
        pass

# 定义数据接收器
class MySink(SinkFunction):
    def invoke(self, value):
        print(value)

# 添加数据源和接收器
source = env.add_source(MySource())
sink = env.add_sink(MySink())

# 执行任务
env.execute()

# 使用Mahout进行数据挖掘和智能分析示例
from mahout.cf.taste.impl.model.file import FileDataModel
from mahout.cf.taste.impl.neighborhood import NearestNUserNeighborhood
from mahout.cf.taste.impl.recommender import GenericUserBasedRecommender
from mahout.cf.taste.similarity import PearsonCorrelationSimilarity

# 加载数据模型
model = FileDataModel(File("data.csv"))

# 计算用户相似度
similarity = PearsonCorrelationSimilarity(model)
neighborhood = NearestNUserNeighborhood(3, similarity, model)

# 构建推荐模型
recommender = GenericUserBasedRecommender(model, neighborhood, similarity)

# 获取用户推荐结果
recommendations = recommender.recommend(1, 5)
for recommendation in recommendations:
    print(recommendation)

# 创建Kudu表
schema = kudu.schema([
    (name='user_id', type='int64'),
    (name='product_id', type='int64'),
    (name='purchase_time', type='timestamp')
])
table = client.table('purchase_history')
client.create_table('purchase_history', schema, partitioning=Partitioning(hash_partitions=4, columns=['user_id']))

# 定义Flink任务
from flink.streaming.api.functions import ProcessFunction

class RealTimeRecommendation(ProcessFunction):
    def process_element(self, value, ctx):
        # 实时推荐算法逻辑
        user_id = value['user_id']
        recommendations = recommender.recommend(user_id, 5)
        for recommendation in recommendations:
            # 将推荐结果存储到Kudu表中
            insert = recommendations_table.new_insert({'user_id': user_id, 'product_id': recommendation.product_id})
            session.apply(insert)
        session.flush()

# 从Kudu表读取数据流
source = env.add_source(KuduSource(table))

# 应用实时推荐算法
stream = source.process(RealTimeRecommendation())

# 将推荐结果存储到Kudu表
sink = KuduSink(recommendations_table)
stream.add_sink(sink)

# 执行任务
env.execute()

# 加载数据模型
model = FileDataModel(File("purchase_history.csv"))

# 计算用户相似度
similarity = PearsonCorrelationSimilarity(model)
neighborhood = NearestNUserNeighborhood(3, similarity, model)

# 构建推荐模型
recommender = GenericUserBasedRecommender(model, neighborhood, similarity)

# 获取用户推荐结果并存储到Kudu表
for user_id in users:
    recommendations = recommender.recommend(user_id, 5)
    for recommendation in recommendations:
        insert = recommendations_table.new_insert({'user_id': user_id, 'product_id': recommendation.product_id})
        session.apply(insert)
    session.flush()