SparkSQL编程基本概念和基本用法

本节将介绍SparkSQL编程基本概念和基本用法。

不同于RDD编程的命令式编程范式，SparkSQL编程是一种声明式编程范式，我们可以通过SQL语句或者调用DataFrame的相关API描述我们想要实现的操作。

然后Spark会将我们的描述进行语法解析，找到相应的执行计划并对其进行流程优化，然后调用相应基础命令进行执行。

我们使用pyspark进行RDD编程时，在Excutor上跑的很多时候就是Python代码，当然，少数时候也会跑java字节码。

但我们使用pyspark进行SparkSQL编程时，在Excutor上跑的全部是java字节码，pyspark在Driver端就将相应的Python代码转换成了java任务然后放到Excutor上执行。

因此，使用SparkSQL的编程范式进行编程，我们能够取得几乎和直接使用scala/java进行编程相当的效率(忽略语法解析时间差异)。此外SparkSQL提供了非常方便的数据读写API，我们可以用它和Hive表，HDFS，mysql表，Cassandra，Hbase等各种存储媒介进行数据交换。

美中不足的是，SparkSQL的灵活性会稍差一些，其默认支持的数据类型通常只有 Int,Long,Float,Double,String,Boolean 等这些标准SQL数据类型, 类型扩展相对繁琐。对于一些较为SQL中不直接支持的功能，通常可以借助于用户自定义函数(UDF)来实现，如果功能更加复杂，则可以转成RDD来进行实现。

本节我们将主要介绍以下主要内容：

• RDD和DataFrame的对比

• 创建DataFrame

• DataFrame保存成文件

• DataFrame的API交互

• DataFrame的SQL交互

importfindspark

#指定spark_home为刚才的解压路径,指定python路径
spark_home="/Users/liangyun/ProgramFiles/spark-3.0.1-bin-hadoop3.2"
python_path="/Users/liangyun/anaconda3/bin/python"
findspark.init(spark_home,python_path)

importpyspark
frompyspark.sqlimportSparkSession

#SparkSQL的许多功能封装在SparkSession的方法接口中

spark=SparkSession.builder
.appName("test")
.config("master","local[4]")
.enableHiveSupport()
.getOrCreate()

sc=spark.sparkContext

一，RDD，DataFrame和DataSet对比

DataFrame参照了Pandas的思想，在RDD基础上增加了schma，能够获取列名信息。

DataSet在DataFrame基础上进一步增加了数据类型信息，可以在编译时发现类型错误。

DataFrame可以看成DataSet[Row]，两者的API接口完全相同。

DataFrame和DataSet都支持SQL交互式查询，可以和 Hive无缝衔接。

DataSet只有Scala语言和Java语言接口中才支持，在Python和R语言接口只支持DataFrame。

DataFrame数据结构本质上是通过RDD来实现的，但是RDD是一种行存储的数据结构，而DataFrame是一种列存储的数据结构。

二，创建DataFrame

1，通过toDF方法转换成DataFrame

可以将RDD用toDF方法转换成DataFrame

#将RDD转换成DataFrame
rdd=sc.parallelize([("LiLei",15,88),("HanMeiMei",16,90),("DaChui",17,60)])
df=rdd.toDF(["name","age","score"])
df.show()
df.printSchema()

+---------+---+-----+
|name|age|score|
+---------+---+-----+
|LiLei|15|88|
|HanMeiMei|16|90|
|DaChui|17|60|
+---------+---+-----+

root
|--name:string(nullable=true)
|--age:long(nullable=true)
|--score:long(nullable=true)

2, 通过createDataFrame方法将Pandas.DataFrame转换成pyspark中的DataFrame

importpandasaspd

pdf=pd.DataFrame([("LiLei",18),("HanMeiMei",17)],columns=["name","age"])
df=spark.createDataFrame(pdf)
df.show()

+---------+---+
|name|age|
+---------+---+
|LiLei|18|
|HanMeiMei|17|
+---------+---+

#也可以对列表直接转换
values=[("LiLei",18),("HanMeiMei",17)]
df=spark.createDataFrame(values,["name","age"])
df.show()

+---------+---+
|name|age|
+---------+---+
|LiLei|18|
|HanMeiMei|17|
+---------+---+

4, 通过createDataFrame方法指定schema动态创建DataFrame

可以通过createDataFrame的方法指定rdd和schema创建DataFrame。

这种方法比较繁琐，但是可以在预先不知道schema和数据类型的情况下在代码中动态创建DataFrame.

frompyspark.sql.typesimport*
frompyspark.sqlimportRow
fromdatetimeimportdatetime

schema=StructType([StructField("name",StringType(),nullable=False),
StructField("score",IntegerType(),nullable=True),
StructField("birthday",DateType(),nullable=True)])

rdd=sc.parallelize([Row("LiLei",87,datetime(2010,1,5)),
Row("HanMeiMei",90,datetime(2009,3,1)),
Row("DaChui",None,datetime(2008,7,2))])

dfstudent=spark.createDataFrame(rdd,schema)

dfstudent.show()

+---------+-----+----------+
|name|score|birthday|
+---------+-----+----------+
|LiLei|87|2010-01-05|
|HanMeiMei|90|2009-03-01|
|DaChui|null|2008-07-02|
+---------+-----+----------+

4，通过读取文件创建

可以读取json文件，csv文件，hive数据表或者mysql数据表得到DataFrame。

#读取json文件生成DataFrame
df=spark.read.json("data/people.json")
df.show()

+----+-------+
|age|name|
+----+-------+
|null|Michael|
|30|Andy|
|19|Justin|
+----+-------+

#读取csv文件
df=spark.read.option("header","true")
.option("inferSchema","true")
.option("delimiter",",")
.csv("data/iris.csv")
df.show(5)
df.printSchema()

+-----------+----------+-----------+----------+-----+
|sepallength|sepalwidth|petallength|petalwidth|label|
+-----------+----------+-----------+----------+-----+
|5.1|3.5|1.4|0.2|0|
|4.9|3.0|1.4|0.2|0|
|4.7|3.2|1.3|0.2|0|
|4.6|3.1|1.5|0.2|0|
|5.0|3.6|1.4|0.2|0|
+-----------+----------+-----------+----------+-----+
onlyshowingtop5rows

root
|--sepallength:double(nullable=true)
|--sepalwidth:double(nullable=true)
|--petallength:double(nullable=true)
|--petalwidth:double(nullable=true)
|--label:integer(nullable=true)

#读取csv文件
df=spark.read.format("com.databricks.spark.csv")
.option("header","true")
.option("inferSchema","true")
.option("delimiter",",")
.load("data/iris.csv")
df.show(5)
df.printSchema()

+-----------+----------+-----------+----------+-----+
|sepallength|sepalwidth|petallength|petalwidth|label|
+-----------+----------+-----------+----------+-----+
|5.1|3.5|1.4|0.2|0|
|4.9|3.0|1.4|0.2|0|
|4.7|3.2|1.3|0.2|0|
|4.6|3.1|1.5|0.2|0|
|5.0|3.6|1.4|0.2|0|
+-----------+----------+-----------+----------+-----+
onlyshowingtop5rows

root
|--sepallength:double(nullable=true)
|--sepalwidth:double(nullable=true)
|--petallength:double(nullable=true)
|--petalwidth:double(nullable=true)
|--label:integer(nullable=true)

#读取parquet文件
df=spark.read.parquet("data/users.parquet")
df.show()

+------+--------------+----------------+
|name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa|null|[3,9,15,20]|
|Ben|red|[]|
+------+--------------+----------------+

#读取hive数据表生成DataFrame

spark.sql("CREATETABLEIFNOTEXISTSsrc(keyINT,valueSTRING)USINGhive")
spark.sql("LOADDATALOCALINPATH'data/kv1.txt'INTOTABLEsrc")
df=spark.sql("SELECTkey,valueFROMsrcWHEREkey< 10 ORDER BY key")
df.show(5)

+---+-----+
|key|value|
+---+-----+
|0|val_0|
|0|val_0|
|0|val_0|
|0|val_0|
|0|val_0|
+---+-----+
onlyshowingtop5rows

#读取mysql数据表生成DataFrame
"""
url="jdbc//localhost:3306/test"
df=spark.read.format("jdbc")
.option("url",url)
.option("dbtable","runoob_tbl")
.option("user","root")
.option("password","0845")
.load()
df.show()
"""

三，DataFrame保存成文件

可以保存成csv文件，json文件，parquet文件或者保存成hive数据表

#保存成csv文件
df=spark.read.format("json").load("data/people.json")
df.write.format("csv").option("header","true").save("data/people_write.csv")

#先转换成rdd再保存成txt文件
df.rdd.saveAsTextFile("data/people_rdd.txt")

#保存成json文件
df.write.json("data/people_write.json")

#保存成parquet文件,压缩格式,占用存储小,且是spark内存中存储格式，加载最快
df.write.partitionBy("age").format("parquet").save("data/namesAndAges.parquet")
df.write.parquet("data/people_write.parquet")

#保存成hive数据表
df.write.bucketBy(42,"name").sortBy("age").saveAsTable("people_bucketed")

四，DataFrame的API交互

frompyspark.sqlimportRow
frompyspark.sql.functionsimport*

df=spark.createDataFrame(
[("LiLei",15,"male"),
("HanMeiMei",16,"female"),
("DaChui",17,"male")]).toDF("name","age","gender")

df.show()
df.printSchema()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
+---------+---+------+

root
|--name:string(nullable=true)
|--age:long(nullable=true)
|--gender:string(nullable=true)

1，Action操作

DataFrame的Action操作包括show,count,collect,,describe,take,head,first等操作。

#show
df.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
+---------+---+------+

#show(numRows:Int,truncate:Boolean)
#第二个参数设置是否当输出字段长度超过20时进行截取
df.show(2,False)

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
+---------+---+------+
onlyshowingtop2rows

#count
df.count()

3

#collect
df.collect()

[Row(name='LiLei',age=15,gender='male'),
Row(name='HanMeiMei',age=16,gender='female'),
Row(name='DaChui',age=17,gender='male')]

#first
df.first()

Row(name='LiLei',age=15,gender='male')

#take
df.take(2)

[Row(name='LiLei',age=15,gender='male'),
Row(name='HanMeiMei',age=16,gender='female')]

#head
df.head(2)

[Row(name='LiLei',age=15,gender='male'),
Row(name='HanMeiMei',age=16,gender='female')]

2，类RDD操作

DataFrame支持RDD中一些诸如distinct,cache,sample,foreach,intersect,except等操作。

可以把DataFrame当做数据类型为Row的RDD来进行操作，必要时可以将其转换成RDD来操作。

df=spark.createDataFrame([("HelloWorld",),("HelloChina",),("HelloSpark",)]).toDF("value")
df.show()

+-----------+
|value|
+-----------+
|HelloWorld|
|HelloChina|
|HelloSpark|
+-----------+

#map操作，需要先转换成rdd
rdd=df.rdd.map(lambdax:Row(x[0].upper()))
dfmap=rdd.toDF(["value"]).show()

+-----------+
|value|
+-----------+
|HELLOWORLD|
|HELLOCHINA|
|HELLOSPARK|
+-----------+

#flatMap，需要先转换成rdd
df_flat=df.rdd.flatMap(lambdax:x[0].split("")).map(lambdax:Row(x)).toDF(["value"])
df_flat.show()

+-----+
|value|
+-----+
|Hello|
|World|
|Hello|
|China|
|Hello|
|Spark|
+-----+

#filter过滤
df_filter=df.rdd.filter(lambdas:s[0].endswith("Spark")).toDF(["value"])

df_filter.show()

+-----------+
|value|
+-----------+
|HelloSpark|
+-----------+

#filter和broadcast混合使用
broads=sc.broadcast(["Hello","World"])

df_filter_broad=df_flat.filter(~col("value").isin(broads.value))

df_filter_broad.show()

+-----+
|value|
+-----+
|China|
|Spark|
+-----+

#distinct
df_distinct=df_flat.distinct()
df_distinct.show()

+-----+
|value|
+-----+
|World|
|China|
|Hello|
|Spark|
+-----+

#cache缓存
df.cache()
df.unpersist()

#sample抽样
dfsample=df.sample(False,0.6,0)

dfsample.show()

+-----------+
|value|
+-----------+
|HelloChina|
|HelloSpark|
+-----------+

df2=spark.createDataFrame([["HelloWorld"],["HelloScala"],["HelloSpark"]]).toDF("value")
df2.show()

+-----------+
|value|
+-----------+
|HelloWorld|
|HelloScala|
|HelloSpark|
+-----------+

#intersect交集
dfintersect=df.intersect(df2)

dfintersect.show()

+-----------+
|value|
+-----------+
|HelloSpark|
|HelloWorld|
+-----------+

#exceptAll补集

dfexcept=df.exceptAll(df2)
dfexcept.show()

+-----------+
|value|
+-----------+
|HelloChina|
+-----------+

3，类Excel操作

可以对DataFrame进行增加列，删除列，重命名列，排序等操作，去除重复行，去除空行，就跟操作Excel表格一样。

df=spark.createDataFrame([
("LiLei",15,"male"),
("HanMeiMei",16,"female"),
("DaChui",17,"male"),
("RuHua",16,None)
]).toDF("name","age","gender")

df.show()
df.printSchema()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|RuHua|16|null|
+---------+---+------+

root
|--name:string(nullable=true)
|--age:long(nullable=true)
|--gender:string(nullable=true)

#增加列
dfnew=df.withColumn("birthyear",-df["age"]+2020)

dfnew.show()

+---------+---+------+---------+
|name|age|gender|birthyear|
+---------+---+------+---------+
|LiLei|15|male|2005|
|HanMeiMei|16|female|2004|
|DaChui|17|male|2003|
|RuHua|16|null|2004|
+---------+---+------+---------+

#置换列的顺序
dfupdate=dfnew.select("name","age","birthyear","gender")
dfupdate.show()

#删除列
dfdrop=df.drop("gender")
dfdrop.show()

+---------+---+
|name|age|
+---------+---+
|LiLei|15|
|HanMeiMei|16|
|DaChui|17|
|RuHua|16|
+---------+---+

#重命名列
dfrename=df.withColumnRenamed("gender","sex")
dfrename.show()

+---------+---+------+
|name|age|sex|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|RuHua|16|null|
+---------+---+------+

#排序sort，可以指定升序降序
dfsorted=df.sort(df["age"].desc())
dfsorted.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|DaChui|17|male|
|RuHua|16|null|
|HanMeiMei|16|female|
|LiLei|15|male|
+---------+---+------+

#排序orderby,默认为升序,可以根据多个字段
dfordered=df.orderBy(df["age"].desc(),df["gender"].desc())

dfordered.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|DaChui|17|male|
|HanMeiMei|16|female|
|RuHua|16|null|
|LiLei|15|male|
+---------+---+------+

#去除nan值行
dfnotnan=df.na.drop()

dfnotnan.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
+---------+---+------+

#填充nan值
df_fill=df.na.fill("female")
df_fill.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|RuHua|16|female|
+---------+---+------+

#替换某些值
df_replace=df.na.replace({"":"female","RuHua":"SiYu"})
df_replace.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|SiYu|16|null|
+---------+---+------+

#去重，默认根据全部字段
df2=df.unionAll(df)
df2.show()
dfunique=df2.dropDuplicates()
dfunique.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|RuHua|16|null|
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|RuHua|16|null|
+---------+---+------+

+---------+---+------+
|name|age|gender|
+---------+---+------+
|RuHua|16|null|
|DaChui|17|male|
|HanMeiMei|16|female|
|LiLei|15|male|
+---------+---+------+

#去重,根据部分字段
dfunique_part=df.dropDuplicates(["age"])
dfunique_part.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|DaChui|17|male|
|LiLei|15|male|
|HanMeiMei|16|female|
+---------+---+------+

#简单聚合操作
dfagg=df.agg({"name":"count","age":"max"})

dfagg.show()

+-----------+--------+
|count(name)|max(age)|
+-----------+--------+
|4|17|
+-----------+--------+

#汇总信息
df_desc=df.describe()
df_desc.show()

+-------+------+-----------------+------+
|summary|name|age|gender|
+-------+------+-----------------+------+
|count|4|4|3|
|mean|null|16.0|null|
|stddev|null|0.816496580927726|null|
|min|DaChui|15|female|
|max|RuHua|17|male|
+-------+------+-----------------+------+

#频率超过0.5的年龄和性别
df_freq=df.stat.freqItems(("age","gender"),0.5)

df_freq.show()

+-------------+----------------+
|age_freqItems|gender_freqItems|
+-------------+----------------+
|[16]|[male]|
+-------------+----------------+

4，类SQL表操作

类SQL表操作主要包括表查询(select,selectExpr,where),表连接(join,union,unionAll),表分组(groupby,agg,pivot)等操作。

df=spark.createDataFrame([
("LiLei",15,"male"),
("HanMeiMei",16,"female"),
("DaChui",17,"male"),
("RuHua",16,None)]).toDF("name","age","gender")

df.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|RuHua|16|null|
+---------+---+------+

#表查询select
dftest=df.select("name").limit(2)
dftest.show()

+---------+
|name|
+---------+
|LiLei|
|HanMeiMei|
+---------+

dftest=df.select("name",df["age"]+1)
dftest.show()

+---------+---------+
|name|(age+1)|
+---------+---------+
|LiLei|16|
|HanMeiMei|17|
|DaChui|18|
|RuHua|17|
+---------+---------+

#表查询select
dftest=df.select("name",-df["age"]+2020).toDF("name","birth_year")
dftest.show()

+---------+----------+
|name|birth_year|
+---------+----------+
|LiLei|2005|
|HanMeiMei|2004|
|DaChui|2003|
|RuHua|2004|
+---------+----------+

#表查询selectExpr,可以使用UDF函数，指定别名等
importdatetime
spark.udf.register("getBirthYear",lambdaage:datetime.datetime.now().year-age)
dftest=df.selectExpr("name","getBirthYear(age)asbirth_year","UPPER(gender)asgender")
dftest.show()

+---------+----------+------+
|name|birth_year|gender|
+---------+----------+------+
|LiLei|2005|MALE|
|HanMeiMei|2004|FEMALE|
|DaChui|2003|MALE|
|RuHua|2004|null|
+---------+----------+------+

#表查询where,指定SQL中的where字句表达式
dftest=df.where("gender='male'andage>15")
dftest.show()

+------+---+------+
|name|age|gender|
+------+---+------+
|DaChui|17|male|
+------+---+------+

#表查询filter
dftest=df.filter(df["age"]>16)
dftest.show()

+------+---+------+
|name|age|gender|
+------+---+------+
|DaChui|17|male|
+------+---+------+

#表查询filter
dftest=df.filter("gender='male'")
dftest.show()

+------+---+------+
|name|age|gender|
+------+---+------+
|LiLei|15|male|
|DaChui|17|male|
+------+---+------+

#表连接join
dfscore=spark.createDataFrame([("LiLei","male",88),("HanMeiMei","female",90),("DaChui","male",50)])
.toDF("name","gender","score")

dfscore.show()

+---------+------+-----+
|name|gender|score|
+---------+------+-----+
|LiLei|male|88|
|HanMeiMei|female|90|
|DaChui|male|50|
+---------+------+-----+

#表连接join,根据单个字段
dfjoin=df.join(dfscore.select("name","score"),"name")
dfjoin.show()

+---------+---+------+-----+
|name|age|gender|score|
+---------+---+------+-----+
|LiLei|15|male|88|
|HanMeiMei|16|female|90|
|DaChui|17|male|50|
+---------+---+------+-----+

#表连接join,根据多个字段
dfjoin=df.join(dfscore,["name","gender"])
dfjoin.show()

+---------+------+---+-----+
|name|gender|age|score|
+---------+------+---+-----+
|HanMeiMei|female|16|90|
|DaChui|male|17|50|
|LiLei|male|15|88|
+---------+------+---+-----+

#表连接join,根据多个字段
#可以指定连接方式为"inner","left","right","outer","semi","full","leftanti","anti"等多种方式
dfjoin=df.join(dfscore,["name","gender"],"right")
dfjoin.show()

+---------+------+---+-----+
|name|gender|age|score|
+---------+------+---+-----+
|HanMeiMei|female|16|90|
|DaChui|male|17|50|
|LiLei|male|15|88|
+---------+------+---+-----+

dfjoin=df.join(dfscore,["name","gender"],"outer")
dfjoin.show()

+---------+------+---+-----+
|name|gender|age|score|
+---------+------+---+-----+
|HanMeiMei|female|16|90|
|DaChui|male|17|50|
|LiLei|male|15|88|
|RuHua|null|16|null|
+---------+------+---+-----+

#表连接，灵活指定连接关系
dfmark=dfscore.withColumnRenamed("gender","sex")
dfmark.show()

+---------+------+-----+
|name|sex|score|
+---------+------+-----+
|LiLei|male|88|
|HanMeiMei|female|90|
|DaChui|male|50|
+---------+------+-----+

dfjoin=df.join(dfmark,(df["name"]==dfmark["name"])&(df["gender"]==dfmark["sex"]),
"inner")
dfjoin.show()

+---------+---+------+---------+------+-----+
|name|age|gender|name|sex|score|
+---------+---+------+---------+------+-----+
|HanMeiMei|16|female|HanMeiMei|female|90|
|DaChui|17|male|DaChui|male|50|
|LiLei|15|male|LiLei|male|88|
+---------+---+------+---------+------+-----+

#表合并union
dfstudent=spark.createDataFrame([("Jim",18,"male"),("Lily",16,"female")]).toDF("name","age","gender")
dfstudent.show()

+----+---+------+
|name|age|gender|
+----+---+------+
|Jim|18|male|
|Lily|16|female|
+----+---+------+

dfunion=df.union(dfstudent)
dfunion.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|15|male|
|HanMeiMei|16|female|
|DaChui|17|male|
|RuHua|16|null|
|Jim|18|male|
|Lily|16|female|
+---------+---+------+

#表分组groupBy
frompyspark.sqlimportfunctionsasF
dfgroup=df.groupBy("gender").max("age")
dfgroup.show()

+------+--------+
|gender|max(age)|
+------+--------+
|null|16|
|female|16|
|male|17|
+------+--------+

#表分组后聚合，groupBy,agg
dfagg=df.groupBy("gender").agg(F.mean("age").alias("mean_age"),
F.collect_list("name").alias("names"))
dfagg.show()

+------+--------+---------------+
|gender|mean_age|names|
+------+--------+---------------+
|null|16.0|[RuHua]|
|female|16.0|[HanMeiMei]|
|male|16.0|[LiLei,DaChui]|
+------+--------+---------------+

#表分组聚合，groupBy,agg
dfagg=df.groupBy("gender").agg(F.expr("avg(age)"),F.expr("collect_list(name)"))
dfagg.show()

+------+--------+------------------+
|gender|avg(age)|collect_list(name)|
+------+--------+------------------+
|null|16.0|[RuHua]|
|female|16.0|[HanMeiMei]|
|male|16.0|[LiLei,DaChui]|
+------+--------+------------------+

#表分组聚合，groupBy,agg
df.groupBy("gender","age").agg(F.collect_list(col("name"))).show()

+------+---+------------------+
|gender|age|collect_list(name)|
+------+---+------------------+
|male|15|[LiLei]|
|male|17|[DaChui]|
|female|16|[HanMeiMei]|
|null|16|[RuHua]|
+------+---+------------------+

#表分组后透视，groupBy,pivot
dfstudent=spark.createDataFrame([("LiLei",18,"male",1),("HanMeiMei",16,"female",1),
("Jim",17,"male",2),("DaChui",20,"male",2)]).toDF("name","age","gender","class")
dfstudent.show()
dfstudent.groupBy("class").pivot("gender").max("age").show()

+---------+---+------+-----+
|name|age|gender|class|
+---------+---+------+-----+
|LiLei|18|male|1|
|HanMeiMei|16|female|1|
|Jim|17|male|2|
|DaChui|20|male|2|
+---------+---+------+-----+

+-----+------+----+
|class|female|male|
+-----+------+----+
|1|16|18|
|2|null|20|
+-----+------+----+

#窗口函数

df=spark.createDataFrame([("LiLei",78,"class1"),("HanMeiMei",87,"class1"),
("DaChui",65,"class2"),("RuHua",55,"class2")])
.toDF("name","score","class")

df.show()
dforder=df.selectExpr("name","score","class",
"row_number()over(partitionbyclassorderbyscoredesc)asorder")

dforder.show()

+---------+-----+------+
|name|score|class|
+---------+-----+------+
|LiLei|78|class1|
|HanMeiMei|87|class1|
|DaChui|65|class2|
|RuHua|55|class2|
+---------+-----+------+

+---------+-----+------+-----+
|name|score|class|order|
+---------+-----+------+-----+
|DaChui|65|class2|1|
|RuHua|55|class2|2|
|HanMeiMei|87|class1|1|
|LiLei|78|class1|2|
+---------+-----+------+-----+

六，DataFrame的SQL交互

将DataFrame注册为临时表视图或者全局表视图后，可以使用sql语句对DataFrame进行交互。

不仅如此，还可以通过SparkSQL对Hive表直接进行增删改查等操作。

1，注册视图后进行SQL交互

#注册为临时表视图,其生命周期和SparkSession相关联
df=spark.createDataFrame([("LiLei",18,"male"),("HanMeiMei",17,"female"),("Jim",16,"male")],
("name","age","gender"))

df.show()
df.createOrReplaceTempView("student")
dfmale=spark.sql("select*fromstudentwheregender='male'")
dfmale.show()

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|18|male|
|HanMeiMei|17|female|
|Jim|16|male|
+---------+---+------+

+-----+---+------+
|name|age|gender|
+-----+---+------+
|LiLei|18|male|
|Jim|16|male|
+-----+---+------+

#注册为全局临时表视图,其生命周期和整个Spark应用程序关联

df.createOrReplaceGlobalTempView("student")
query="""
selectt.gender
,collect_list(t.name)asnames
fromglobal_temp.studentt
groupbyt.gender
""".strip("
")

spark.sql(query).show()
#可以在新的Session中访问
spark.newSession().sql("select*fromglobal_temp.student").show()

+------+------------+
|gender|names|
+------+------------+
|female|[HanMeiMei]|
|male|[LiLei,Jim]|
+------+------------+

+---------+---+------+
|name|age|gender|
+---------+---+------+
|LiLei|18|male|
|HanMeiMei|17|female|
|Jim|16|male|
+---------+---+------+

2，对Hive表进行增删改查操作

#删除hive表

query="DROPTABLEIFEXISTSstudents"
spark.sql(query)

#建立hive分区表
#(注：不可以使用中文字段作为分区字段)

query="""CREATETABLEIFNOTEXISTS`students`
(`name`STRINGCOMMENT'姓名',
`age`INTCOMMENT'年龄'
)
PARTITIONEDBY(`class`STRINGCOMMENT'班级',`gender`STRINGCOMMENT'性别')
""".replace("
","")
spark.sql(query)

##动态写入数据到hive分区表
spark.conf.set("hive.exec.dynamic.partition.mode","nonstrict")#注意此处有一个设置操作
dfstudents=spark.createDataFrame([("LiLei",18,"class1","male"),
("HanMeimei",17,"class2","female"),
("DaChui",19,"class2","male"),
("Lily",17,"class1","female")]).toDF("name","age","class","gender")
dfstudents.show()

#动态写入分区
dfstudents.write.mode("overwrite").format("hive")
.partitionBy("class","gender").saveAsTable("students")

#写入到静态分区
dfstudents=spark.createDataFrame([("Jim",18,"class3","male"),
("Tom",19,"class3","male")]).toDF("name","age","class","gender")
dfstudents.createOrReplaceTempView("dfclass3")

#INSERTINTO尾部追加,INSERTOVERWRITETABLE覆盖分区
query="""
INSERTOVERWRITETABLE`students`
PARTITION(class='class3',gender='male')
SELECTname,agefromdfclass3
""".replace("
","")
spark.sql(query)

#写入到混合分区
dfstudents=spark.createDataFrame([("David",18,"class4","male"),
("Amy",17,"class4","female"),
("Jerry",19,"class4","male"),
("Ann",17,"class4","female")]).toDF("name","age","class","gender")
dfstudents.createOrReplaceTempView("dfclass4")

query="""
INSERTOVERWRITETABLE`students`
PARTITION(class='class4',gender)
SELECTname,age,genderfromdfclass4
""".replace("
","")
spark.sql(query)

#读取全部数据

dfdata=spark.sql("select*fromstudents")
dfdata.show()

+---------+---+------+------+
|name|age|class|gender|
+---------+---+------+------+
|Ann|17|class4|female|
|Amy|17|class4|female|
|HanMeimei|17|class2|female|
|DaChui|19|class2|male|
|LiLei|18|class1|male|
|Lily|17|class1|female|
|Jerry|19|class4|male|
|David|18|class4|male|
|Jim|18|class3|male|
|Tom|19|class3|male|
+---------+---+------+------+

责任编辑：haq