AWS Glue, crawlers and issue with money datatype

Question

I'm using a AWS Glue crawler for a RDS Aurora PostgreSQL, the table contains some columns with the money datatype, the crawler create the table with those columns as double.

`AWSGlueDataCatalog_node1652381535100 = glueContext.create_dynamic_frame.from_catalog(
database="tmp_db",
table_name="postgres_public_list",
transformation_ctx="AWSGlueDataCatalog_node1652381535100",)
`

But then when I start to working on the AWS Glue, any action, even a simple AWSGlueDataCatalog_node1652381535100.count() gives me this kind of error:

> ... Caused by: org.postgresql.util.PSQLException: Bad value for type double : 2,400.00 ...

Doesn't matter if I try to change the columns datatype to string, keeps saying the same error.

Any ideas how to solve this issue?

Answer

Hello,

I would like to inform you that MONEY[] is not supported due to the JDBC driver for PostgreSQL can’t handle those types properly. To process this kind of data, please cast the column to decimal type and make sure to verify it using printschema. B

For dynamic frame you can use Applymapping:

```
## To verify the datatype of the column
datasource0.printtSchema()

## Cast it to decimal using ApplyMapping
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("v1", "string", "v1", "string"),("XX_threshold", "double", "XX_threshold", "decimal(25,10)")], transformation_ctx = "applymapping1")

##Verify the schma if it is counted to Decimal or not.
applymapping1.printSchema()

applymapping1.toDF().show()
```

For dataframe you can use cast function:

```
from pyspark.sql.types import *

df=datasource0.toDF()
df2 = df.withColumn("age",col("age").cast("decimal(25,10)"))
df2.printSchema()
df2.show()
```

AWS Glue, crawlers and issue with money datatype

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