pyspark之Structured Streaming结果保存到Mysql数据库-socket例子统计(含批次)

from pyspark.sql import SparkSession,DataFrame
from pyspark.sql.functions import explode,split,lit
"""
实现将数据保存到mysql数据库，同时将流计算batch保存到数据库中
"""
if __name__ == '__main__':
    
    spark = SparkSession.builder.getOrCreate()
    spark.sparkContext.setLogLevel("WARN")
    
    #配置socket数据源  配置host和port
    lines = spark.readStream.format("socket").option("host","localhost").option("port",9999).load()
    
    words = lines.select(explode(split(lines.value," ")).alias("word"))
    
    wordsCount = words.groupBy("word").count()

    """
    输出到console
    wordsCount.writeStream.outputMode("complete").format("console").trigger(processingTime="10 seconds").start().awaitTermination()
    """
    
    #mysql表结构如下: word varchar(32)   sl int   batch  int
    PROP = {}
    PROP['driver'] = 'com.mysql.jdbc.Driver'
    PROP['user'] = 'root'
    PROP['password'] = 'root'
    def insert_into_mysql(df:DataFrame,batch):
        print("batch:{} is start".format(batch))
        data = df.withColumn("batch",lit(batch)).withColumnRenamed("count","sl")
        """
        两种写法--->个人感觉第二种配置了batchsize速度块些，可能是数据库配置原因，测试过程中流数据一条一条submit到数据库，速度较慢
        data.write.mysql(url="jdbc:mysql://localhost:3306/spark",table="socket_test",mode="append",properties=PROP)  
        """
        data.write.format("jdbc").option("driver","com.mysql.jdbc.Driver").option("url","jdbc:mysql://localhost:3306/spark").option("dbtable","socket").option("user","root").option("password","root").option("batchsize",100).mode("append").save()
        print("batch:{} is end".format(batch))

    wordsCount.writeStream.outputMode("complete").foreachBatch(insert_into_mysql).trigger(processingTime="20 seconds").start().awaitTermination()