SparkSql输出数据的方式
一、普通文件输出方式
方式一：给定输出数据源的类型和地址
df.write.format("json").save(path)
df.write.format("csv").save(path)
df.write.format("parquet").save(path)方式二：直接调用对应数据源类型的方法
df.write.json(path)
df.write.csv(path)
df.write.parquet(path)append: 追加模式，当数据存在时，继续追加
overwrite: 覆写模式，当数据存在时，覆写以前数据，存储当前最新数据；
error/errorifexists: 如果目标存在就报错，默认的模式
ignore: 忽略，数据存在时不做任何操作代码编写模板：
df.write.mode(saveMode="append").format("csv").save(path)代码演示普通的文件输出格式：
import osfrom pyspark.sql import SparkSessionif __name__ == '__main__':# 配置环境os.environ['JAVA_HOME'] = 'C:/Program Files/Java/jdk1.8.0_241'# 配置Hadoop的路径，就是前面解压的那个路径os.environ['HADOOP_HOME'] = 'D:/hadoop-3.3.1'# 配置base环境Python解析器的路径os.environ['PYSPARK_PYTHON'] = 'C:/ProgramData/Miniconda3/python.exe' # 配置base环境Python解析器的路径os.environ['PYSPARK_DRIVER_PYTHON'] = 'C:/ProgramData/Miniconda3/python.exe'spark = SparkSession.builder.master("local[2]").appName("").config("spark.sql.shuffle.partitions", 2).getOrCreate()df = spark.read.json("../../datas/person.json")# 获取年龄最大的人的名字df.createOrReplaceTempView("persons")rsDf = spark.sql("""select name,age from persons where age = (select max(age) from persons)""")# 将结果打印到控制台#rsDf.write.format("console").save()#rsDf.write.json("../../datas/result",mode="overwrite")#rsDf.write.mode(saveMode='overwrite').format("json").save("../../datas/result")#rsDf.write.mode(saveMode='overwrite').format("csv").save("../../datas/result1")#rsDf.write.mode(saveMode='overwrite').format("parquet").save("../../datas/result2")#rsDf.write.mode(saveMode='append').format("csv").save("../../datas/result1")# text 保存路径为hdfs 直接报错，不支持#rsDf.write.mode(saveMode='overwrite').text("hdfs://bigdata01:9820/result")#rsDf.write.orc("hdfs://bigdata01:9820/result",mode="overwrite")rsDf.write.parquet("hdfs://bigdata01:9820/result", mode="overwrite")spark.stop()二、保存到数据库中
代码演示：
import os
# 导入pyspark模块
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSessionif __name__ == '__main__':# 配置环境os.environ['JAVA_HOME'] = 'D:\Download\Java\JDK'# 配置Hadoop的路径，就是前面解压的那个路径os.environ['HADOOP_HOME'] = 'D:\\bigdata\hadoop-3.3.1\hadoop-3.3.1'# 配置base环境Python解析器的路径os.environ['PYSPARK_PYTHON'] = 'C:/ProgramData/Miniconda3/python.exe' # 配置base环境Python解析器的路径os.environ['PYSPARK_DRIVER_PYTHON'] = 'C:/ProgramData/Miniconda3/python.exe'spark = SparkSession.builder.master('local[*]').appName('').config("spark.sql.shuffle.partitions", 2).getOrCreate()df5 = spark.read.format("csv").option("sep", "\t").load("../../datas/zuoye/emp.tsv")\.toDF('eid','ename','salary','sal','dept_id')df5.createOrReplaceTempView('emp')rsDf = spark.sql("select * from emp")rsDf.write.format("jdbc") \.option("driver", "com.mysql.cj.jdbc.Driver") \.option("url", "jdbc:mysql://bigdata01:3306/mysql") \.option("user", "root") \.option("password", "123456") \.option("dbtable", "emp1") \.save(mode="overwrite")spark.stop()# 使用完后，记得关闭
三、保存到hive中
代码演示：
import os
# 导入pyspark模块
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSessionif __name__ == '__main__':# 配置环境os.environ['JAVA_HOME'] = 'D:\Download\Java\JDK'# 配置Hadoop的路径，就是前面解压的那个路径os.environ['HADOOP_HOME'] = 'D:\\bigdata\hadoop-3.3.1\hadoop-3.3.1'# 配置base环境Python解析器的路径os.environ['PYSPARK_PYTHON'] = 'C:/ProgramData/Miniconda3/python.exe' # 配置base环境Python解析器的路径os.environ['PYSPARK_DRIVER_PYTHON'] = 'C:/ProgramData/Miniconda3/python.exe'os.environ['HADOOP_USER_NAME'] = 'root'spark = SparkSession \.builder \.appName("HiveAPP") \.master("local[2]") \.config("spark.sql.warehouse.dir", 'hdfs://bigdata01:9820/user/hive/warehouse') \.config('hive.metastore.uris', 'thrift://bigdata01:9083') \.config("spark.sql.shuffle.partitions", 2) \.enableHiveSupport() \.getOrCreate()df5 = spark.read.format("csv").option("sep", "\t").load("../../datas/zuoye/emp.tsv") \.toDF('eid', 'ename', 'salary', 'sal', 'dept_id')df5.createOrReplaceTempView('emp')rsDf = spark.sql("select * from emp")rsDf.write.saveAsTable("spark.emp")spark.stop()# 使用完后，记得关闭