Error when running a glue job to write data to data lake

I am writing a glue script to take data from s3(PSQL WAL LOGS) to write that data into a hudi data lake.

Whenever I am trying to do that I am getting unable to upsert data with commit time error with a trace of

 org.apache.hudi.exception.HoodieException: ts(Part -ts) field not found in record, Acceptable fields were : [my db columns]

I am not sure from where i am getting this ts into the scope.

Pasting the below glue script for reference. Note that i am trying to write the CDC logs to data lake, it will be great if some has a glue script to do that

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
import boto3
from pyspark.sql.types import DataType, StringType



glue_to_spark_types = {
    "boolean": "boolean",
    "tinyint": "tinyint",
    "smallint": "smallint",
    "int": "int",
    "bigint": "bigint",
    "float": "float",
    "double": "double",
    "decimal": "decimal",
    "string": "string",
    "date": "date",
    "timestamp": "timestamp",
    "array": "array",
    "struct": "struct",
    "map": "map"
}

args = getResolvedOptions(sys.argv, ["JOB_NAME"])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session

glue_client = boto3.client('glue')

job = Job(glueContext)
job.init(args["JOB_NAME"], args)

tables = glue_client.get_tables(DatabaseName="glue catalog db")

# for table in tables['TableList']:
for table in [{ 'Name': 'tableName' }]:

    table_name = table['Name']
    print("Table", table)

# Script generated for node S3 bucket
    S3bucket_node1 = glueContext.create_dynamic_frame.from_catalog(
        database="GLUE CATALOG DB",
        table_name=table_name,
        transformation_ctx="S3bucket_node1",
    )
    table_schema = S3bucket_node1.schema()
    
    mappings = []

    # Iterate over the columns in the schema and generate the mappings
    for field in table_schema.fields:
        print("field", field)
        column_name = field.name
        column_glue_type = field.dataType.typeName()
        print("col_glue_type", column_glue_type, column_name)

        # Get the corresponding Spark SQL data type from the dictionary
        column_spark_type = glue_to_spark_types.get(column_glue_type, "string")
        mappings.append((column_name, column_glue_type, column_name, column_spark_type))


    
    # Script generated for node ApplyMapping
    ApplyMapping_node2 = ApplyMapping.apply(
        frame=S3bucket_node1,
        mappings=mappings,
        transformation_ctx="ApplyMapping_node2",
    )
    
    # Script generated for node S3 bucket
    additional_options = {
        "hoodie.table.name": "asd" + table_name,
        "hoodie.datasource.write.table.type": "COPY_ON_WRITE",
        "hoodie.datasource.write.operation": "upsert",
        "hoodie.datasource.write.recordkey.field": "id",
        # "hoodie.datasource.write.precombine.field": "modified_at",
        "hoodie.datasource.write.hive_style_partitioning": "true",
        "hoodie.parquet.compression.codec": "gzip",
        "hoodie.datasource.hive_sync.enable": "true",
        "hoodie.datasource.hive_sync.database": "asdasdasdasd",
        "hoodie.datasource.hive_sync.table": "cl__" + table_name,
        "hoodie.datasource.hive_sync.partition_extractor_class": "org.apache.hudi.hive.MultiPartKeysValueExtractor",
        "hoodie.datasource.hive_sync.use_jdbc": "false",
        "hoodie.datasource.hive_sync.mode": "hms",
    }
    
    print("Apply mapping", additional_options)
    
    S3bucket_node3_df = ApplyMapping_node2.toDF()
    
    S3bucket_node3_df.write.format("hudi").options(**additional_options).mode(
        "append"
    ).save("s3://asdasdasdasdds/" + table_name + "/")
    
    print("table", table_name)

job.commit()