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
sql
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
sql
df.write.json(path)
df.write.csv(path)
df.write.parquet(path)
sql
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:
sql
df.write.mode(saveMode="append").format("csv").save(path)
代码演示普通的文件输出格式:
sql
import os
from pyspark.sql import SparkSession
if __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:
sql
import os
# 导入pyspark模块
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
if __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:
sql
import os
# 导入pyspark模块
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
if __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