Spark SQL区分大小写的筛选器打开列的模式

如何使用spark sql筛选器作为模式的列基础上的区分大小写的筛选器

例如，我有一个模式：

“Aaaa”

我的专栏有如下数据：

adaz
LssA ss 
Leds ST 
Pear QA 
Lear QA

我想检索具有“Aaaa”模式的行，关于字母大小写。这意味着所需的行将是“LED ST”、“Pear QA”、“Lear QA”

"Aaaa AA" => 'Leds ST' , 'Pear QA', 'Lear QA'
"AaaA aa" => 'LssA ss'
"aaaa" => 'adaz'

如何使用spark sql获得此结果？ 或者我们可以为这个结果编写任何正则表达式sql查询吗

我们可以使用Spark SQL函数

translate（）

为字符串创建分组列

使用Pypark： 用于测试的示例数据帧

from pyspark.sql.types import StringType

df = spark.createDataFrame(["adaz", "LssA ss", "Leds ST", "Pear QA","Lear QA"], StringType())

实际变换

from pyspark.sql.functions import translate, collect_list, col
import string

lowercases = string.ascii_lowercase
uppercases = string.ascii_uppercase
length_alphabet = len(uppercases)

ones = "1" * length_alphabet
zeroes = "0" * length_alphabet

old = uppercases + lowercases
new = ones + zeroes

df.withColumn("group", translate(df.value, old, new)) \
  .groupBy(col("group")).agg(collect_list(df.value).alias("strings")) \
  .show(truncate = False)

结果: 使用Scala Spark： 使用“regexp_extract”：

输出：

+-------+
|value  |
+-------+
|Leds ST|
|Pear QA|
|Lear QA|
+-------+

---

我在@pasha701上扩展

scala> val df=List(
     | "adaz",
     | "LssA ss",
     |   "Leds ST",
     |   "Pear QA",
     |   "Lear QA"
     | ).toDF("value")
df: org.apache.spark.sql.DataFrame = [value: string]

scala> val df2= df.withColumn("reg1", regexp_extract($"value","^[A-Z][a-z]{3} [A-Z]{2}$",0)=!=lit("")).withColumn("reg2",regexp_extract($"value","^[a-z]{4}$",0)=!=lit("")).withColumn("reg3", regexp_extract($"value","^[A-Z][a-z]{2}[A-Z] [a-z]{2}$",0)=!=lit(""))
df2: org.apache.spark.sql.DataFrame = [value: string, reg1: boolean ... 2 more fields]

scala> val df3=df2.withColumn("reg_patt", when('reg1,"1000 11").when('reg2,"0000").when('reg3,"1001 00").otherwise("9"))
df3: org.apache.spark.sql.DataFrame = [value: string, reg1: boolean ... 3 more fields]

scala> df3.groupBy("reg_patt").agg(collect_list('value) as "newval").show(false)
+--------+---------------------------+
|reg_patt|newval                     |
+--------+---------------------------+
|1000 11 |[Leds ST, Pear QA, Lear QA]|
|0000    |[adaz]                     |
|1001 00 |[LssA ss]                  |
+--------+---------------------------+


scala>

---

动态模式匹配，有趣。长度也由模式暗示？此解决方案可能给出问题的部分解决方案。

val df=List(
"adaz",
"LssA ss",
  "Leds ST",
  "Pear QA",
  "Lear QA"
).toDF("value")
df.filter(regexp_extract($"value","^[A-Z][a-z]{3} [A-Z]{2}$",0)=!=lit("")).show(false)

+-------+
|value  |
+-------+
|Leds ST|
|Pear QA|
|Lear QA|
+-------+

scala> val df=List(
     | "adaz",
     | "LssA ss",
     |   "Leds ST",
     |   "Pear QA",
     |   "Lear QA"
     | ).toDF("value")
df: org.apache.spark.sql.DataFrame = [value: string]

scala> val df2= df.withColumn("reg1", regexp_extract($"value","^[A-Z][a-z]{3} [A-Z]{2}$",0)=!=lit("")).withColumn("reg2",regexp_extract($"value","^[a-z]{4}$",0)=!=lit("")).withColumn("reg3", regexp_extract($"value","^[A-Z][a-z]{2}[A-Z] [a-z]{2}$",0)=!=lit(""))
df2: org.apache.spark.sql.DataFrame = [value: string, reg1: boolean ... 2 more fields]

scala> val df3=df2.withColumn("reg_patt", when('reg1,"1000 11").when('reg2,"0000").when('reg3,"1001 00").otherwise("9"))
df3: org.apache.spark.sql.DataFrame = [value: string, reg1: boolean ... 3 more fields]

scala> df3.groupBy("reg_patt").agg(collect_list('value) as "newval").show(false)
+--------+---------------------------+
|reg_patt|newval                     |
+--------+---------------------------+
|1000 11 |[Leds ST, Pear QA, Lear QA]|
|0000    |[adaz]                     |
|1001 00 |[LssA ss]                  |
+--------+---------------------------+


scala>