如何通过SQL查询检查列的数值是否包含字母答案

【问题标题】：How to check if numerical value of a column contains alphabets via SQL query如何通过SQL查询检查列的数值是否包含字母
【发布时间】：2017-10-16 17:55:38
【问题描述】：

我在 AWS S3 中有一个 CSV 文件，该文件正在加载到 AWS Glue，即用于对来自 S3 的源数据文件进行转换。它提供 PySpark 脚本环境。数据看起来有点像这样：

"ID","CNTRY_CD","SUB_ID","PRIME_KEY","DATE"    
"123","IND","25635525","11243749772","2017-10-17"    
"123","IND","25632349","112322abcd","2017-10-17"    
"123","IND","25635234","11243kjsd434","2017-10-17"    
"123","IND","25639822","1124374343","2017-10-17"

预期的结果应该是这样的：

"123","IND","25632349","112322abcd","2017-10-17"    
"123","IND","25635234","11243kjsd434","2017-10-17"

我正在处理名称为“PRIME_KEY”的整数类型字段，该字段可能包含导致数据格式错误的字母。

现在的要求是，我需要使用 SQL 查询找出 Integer 类型的主键列是否包含任何字母数字字符，而不仅仅是数字值。到目前为止，我已经尝试了几种正则表达式的变体来做到这一点，如下所示，但没有运气：

SELECT * 
FROM table_name
WHERE column_name IS NOT NULL AND 
CAST(column_name AS VARCHAR(100)) LIKE \'%[0-9a-z0-9]%\'

源脚本：

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
glueContext = GlueContext(SparkContext.getOrCreate())
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
# s3 output directory
output_dir = "s3://aws-glue-scripts../.."

# Data Catalog: database and table name
db_name = "sampledb"
glue_tbl_name = "sampleTable"

datasource = glueContext.create_dynamic_frame.from_catalog(database = db_name, table_name = glue_tbl_name)
datasource_df = datasource.toDF()
datasource_df.registerTempTable("sample_tbl")
invalid_primarykey_values_df = spark.sql("SELECT * FROM sample_tbl WHERE CAST(PRIME_KEY AS STRING) RLIKE '([a-z]+[0-9]+)|([0-9]+[a-z]+)'")
invalid_primarykey_values_df.show()

这个脚本的输出如下：

+---+--------+--------+------------+---------+ -----------+---------------+

+---+--------+--------+------------+---------+ -----------+---------------+

|123|IND|25635525|[11243749772,null]|2017-10-17|

|123|IND|25632349|[null,112322ab..|2017-10-17|

|123|IND|25635234|[null,11243kjsd..|2017-10-17|

|123|IND|25639822|[1124374343,null]|2017-10-17|

+--------+--------+--------+------- ---+-----------+---------------+

我已经突出显示了我正在处理的领域的值。它看起来与源数据有些不同。

对此的任何帮助将不胜感激。谢谢

【问题讨论】：

标签： mysql sql pyspark apache-spark-sql pyspark-sql

【解决方案1】：

您可以使用RLIKE

SELECT * 
FROM table_name
WHERE CAST(PRIME_KEY AS STRING) RLIKE '([0-9]+[a-z]+)'

更通用的字母数字过滤器匹配。

WHERE CAST(PRIME_KEY AS STRING) RLIKE '([a-z]+[0-9]+)|([0-9]+[a-z]+)'

编辑：根据评论

必要的导入和 udfs

val spark = SparkSession.builder
  .config(conf)
  .getOrCreate

import org.apache.spark.sql.functions._
val extract_pkey = udf((x: String) => x.replaceAll("null|\\]|\\[|,", "").trim)

import spark.implicits._

使用 UDF 设置用于测试和清洁的样本数据

val df = Seq(
  ("123", "IND", "25635525", "[11243749772,null]", "2017-10-17"),
  ("123", "IND", "25632349", "[null,112322abcd]", "2017-10-17"),
  ("123", "IND", "25635234", "[null,11243kjsd434]", "2017-10-17"),
  ("123", "IND", "25639822", "[1124374343,null]", "2017-10-17")
).toDF("ID", "CNTRY_CD", "SUB_ID", "PRIME_KEY", "DATE")
  .withColumn("PRIME_KEY", extract_pkey($"PRIME_KEY"))


df.registerTempTable("tbl")

spark.sql("SELECT *  FROM tbl WHERE PRIME_KEY RLIKE '([a-z]+[0-9]+)|([0-9]+[a-z]+)'")
  .show(false)

+---+--------+--------+------------+----------+
|ID |CNTRY_CD|SUB_ID  |PRIME_KEY   |DATE      |
+---+--------+--------+------------+----------+
|123|IND     |25632349|112322abcd  |2017-10-17|
|123|IND     |25635234|11243kjsd434|2017-10-17|
+---+--------+--------+------------+----------+

【讨论】：