Hive几个常见窗口函数分组控制
简介
常规的窗口函数当然没有什么好说的，非常简单，这里介绍一下分组的，重点是分组、排序之后的rows between用法。
关键是理解rows between中关键字含义：
关键字 含义
preceding
往前
following
往后
current row
当前行
unbounded
开始行
unbounded preceding
表示从前面的起点
unbounded following
表示到后面的终点
直接看有些抽象，下面看例子。
max
select country,time,charge,max(charge) over (partition by country order by time) as normal,
max(charge) over (partition by country order by time rows between unbounded preceding and current row) as unb_pre_cur,
max(charge) over (partition by country order by time rows between 2 preceding and 1 following) as pre2_fol1,
max(charge) over (partition by country order by time rows between current row and unbounded following) as cur_unb_fol
from temp
默认是在分组类的当前行之前的行中计算。
rows between unbounded preceding and current row和默认的一样
rows between 2 preceding and 1 following表示在当前行的前2行和后1行中计算
rows between current row and unbounded following表示在当前行和到最后行中计算
rows between对于avg、min、max、sum这几个窗口函数的含义基本是一致的。
完整测试代码
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.junit.Before;
import org.junit.Test;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.FileWriter;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serializable;
import java.time.LocalDate;
import java.time.format.DateTimeFormatter;
import java.util.LinkedList;
import java.util.List;
import java.util.Random;
public class SparkHiveFunctionTest implements Serializable{
private static final String DATA_PATH = "F:\tmp\charge.csv";
private static final String DATA_OBJECT_PATH = "F:\tmp\charge";
private static String[] counties = {"中国","俄罗斯","美国","日本","韩国"};
private SparkSession sparkSession;
private Dataset<Charge> dataset;
@Before
public void setUp(){
sparkSession = SparkSession
.builder()
.appName("test")
.master("local")
.getOrCreate();
sparkSession.sparkContext().setLogLevel("WARN");
}
@Test
public void start() throws IOException, ClassNotFoundException {
// List<Charge> infos = getData(true);
List<Charge> infos = getData(false);
dataset = sparkSession.createDataset(infos, Encoders.bean(Charge.class));
// sum();
// avg();
// min();
max();
}
private void sum(){
dataset.createOrReplaceTempView("temp");
String sql = "select country,time,charge," +
"sum(charge) over (partition by country order by time) as normal," +
"sum(charge) over (partition by country order by time rows between unbounded preceding and current row) as unb_pre_cur," +
"sum(charge) over (partition by country order by time rows between 2 preceding and 1 following) as pre2_fol1," +
"sum(charge) over (partition by country order by time rows between current row and unbounded following) as cur_unb_fol" +
" from temp";
Dataset<Row> ds = sparkSession.sql(sql);
ds.show(100);
}
private void avg(){
dataset.createOrReplaceTempView("temp");
String sql = "select country,time,charge," +
"avg(charge) over (partition by country order by time) as normal," +
"avg(charge) over (partition by country order by time rows between unbounded preceding and current row) as unb_pre_cur," +
"avg(charge) over (partition by country order by time rows between 2 preceding and 1 following) as pre2_fol1," +
"avg(charge) over (partition by country order by time rows between current row and unbounded following) as cur_unb_fol" +
" from temp";
Dataset<Row> ds = sparkSession.sql(sql);
ds.show(100);
}
private void min(){
dataset.createOrReplaceTempView("temp");
String sql = "select country,time,charge," +
"min(charge) over (partition by country order by time) as normal," +
"min(charge) over (partition by country order by time rows between unbounded preceding and current row) as unb_pre_cur," +
"min(charge) over (partition by country order by time rows between 2 preceding and 1 following) as pre2_fol1," +
"min(charge) over (partition by country order by time rows between current row and unbounded following) as cur_unb_fol" +
" from temp";
Dataset<Row> ds = sparkSession.sql(sql);
ds.show(100);
}
private void max(){
dataset.createOrReplaceTempView("temp");
String sql = "select country,time,charge," +
"max(charge) over (partition by country order by time) as normal," +
"max(charge) over (partition by country order by time rows between unbounded preceding and current row) as unb_pre_cur," +
"max(charge) over (partition by country order by time rows between 2 preceding and 1 following) as pre2_fol1," +
"max(charge) over (partition by country order by time rows between current row and unbounded following) as cur_unb_fol" +
" from temp";
Dataset<Row> ds = sparkSession.sql(sql);
ds.show(100);
}
private static List<Charge> getData(Boolean newGen) throws IOException, ClassNotFoundException {
if(newGen != null && newGen == true){
return generateData();
}else {
return readList();
}
}
private static List<Charge> generateData() throws IOException {
FileWriter fileWriter = new FileWriter(DATA_PATH);
LinkedList<Charge> infos = new LinkedList<>();
Random random = new Random();
LocalDate localDate = LocalDate.of(2020, 1, 4);
DateTimeFormatter dateTimeFormatter = DateTimeFormatter.ofPattern("yyyy-MM-dd");
for(int i=0;i<50;i++){
Charge info = new Charge();
String county = counties[random.nextInt(counties.length)];
info.setCountry(county);
int day = random.nextInt(10);
LocalDate date = localDate.plusDays(day);
String time = date.format(dateTimeFormatter);
int charge = 10000 + random.nextInt(10000);
info.setCharge(charge);
info.setTime(time);
infos.add(info);
fileWriter.write(String.format("%s,%s,%d
",county,time,charge));
}
fileWriter.flush();
writeList(infos);
return infos;
}
private static void writeList(LinkedList<Charge> infos) throws IOException {
FileOutputStream fos = new FileOutputStream(DATA_OBJECT_PATH);
ObjectOutputStream oos = new ObjectOutputStream(fos);
oos.writeObject(infos);
}
private static LinkedList<Charge> readList() throws IOException, ClassNotFoundException {
FileInputStream fis = new FileInputStream(DATA_OBJECT_PATH);
ObjectInputStream ois = new ObjectInputStream(fis);
LinkedList<Charge> list = (LinkedList) ois.readObject();
return list;
}
/**
* 必须public，必须实现Serializable
*/
public static class Charge implements Serializable {
/**
* 国家
*/
private String country;
/**
* 充值时间
*/
private String time;
/**
* 充值金额
*/
private Integer charge;
public String getCountry() {
return country;
}
public void setCountry(String country) {
this.country = country;
}
public String getTime() {
return time;
}
public void setTime(String time) {
this.time = time;
}
public Integer getCharge() {
return charge;
}
public void setCharge(Integer charge) {
this.charge = charge;
}
}
}
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