AWS Glue S3 to Redshift ETL job - ignored `preactions` and `postactions`

Question

I am building a data pipeline to Load data into Redshift from an S3 data lake.

Data are stored in Parquet format on S3 and I would like to load them into the respective Redshift tables using an AWS Glue ETL job. The whole job should just DROP the existing table and replace it with the new data.
My idea is to use the `preactions` field of the `glueContext.write_dynamic_frame.from_options()` method to execute the needed SQL commands.

Here is an example code that uses mimics the actual job I would like to perform:

```
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

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

raw_dyf = glueContext.create_dynamic_frame.from_catalog(
    database="medium_test",
    table_name="orders_csv",
    transformation_ctx="AWSGlueDataCatalog_node",
)

transformed_dyf = ApplyMapping.apply(
    frame=raw_dyf,
    mappings=[
        ("order_id", "string", "order_id", "string"),
        ("restaurant_id", "string", "restaurant_id", "string"),
        ("no_of_items", "long", "no_of_items", "long"),
        ("net_total", "long", "net_total", "long"),
        ("discount", "long", "discount", "long"),
        ("total", "long", "total", "long"),
        ("customer_id", "long", "customer_id", "long"),
        ("order_status", "string", "order_status", "string"),
        ("order_type", "string", "order_type", "string"),
        ("source", "string", "source", "string"),
        ("sales_date", "string", "sales_date", "string"),
    ],
    transformation_ctx="ChangeSchema_node",
)

datasink = glueContext.write_dynamic_frame.from_options(
    frame=transformed_dyf,
    connection_type="redshift",
    connection_options={
        "redshiftTmpDir": "s3://aws-glue-assets/temporary/",
        "useConnectionProperties": "true",
        "dbtable": "public.test_orders",
        "connectionName": "data_infra_glue_redshift_connection",
        "preactions": """DROP TABLE public.test_orders;
        CREATE TABLE IF NOT EXISTS public.test_orders (order_id VARCHAR(100), restaurant_id VARCHAR, no_of_items BIGINT, net_total BIGINT, discount BIGINT, total BIGINT, customer_id BIGINT, order_status VARCHAR, order_type VARCHAR, source VARCHAR, sales_date VARCHAR);
        TRUNCATE TABLE public.test_orders;
        """
    },
    transformation_ctx="AmazonRedshift_node",
)

job.commit()
```

Unfortunately, it turns out that when the Job is executed the `preactions` are ignored and data are appended to the existing table. The result is that, for example, running multiple times on the same data results in multiple copies of the data, which is not the intended behaviour as the table should be dropped and recreated at every run.

Is it evident to someone what I am missing? Is there any specific permission that the role running the script should have to perform SQL commands?

Thanks for your help!

Best regards,
Alberto

Answer

I believe the problem is your preactions contain a newline character.

> preactions: A semicolon-delimited list of SQL commands that are run before the COPY command. If the commands fail, then Amazon Redshift throws an exception.
>
>**Note: Be sure that the preaction parameter doesn't contain newline characters.**

https://repost.aws/knowledge-center/sql-commands-redshift-glue-job

Also, you should avoid the pattern of dropping and then recreating the table each time. Instead, just truncate the table. When you drop and recreate objects like this, it will contribute to catalog bloat.

AWS Glue S3 to Redshift ETL job - ignored `preactions` and `postactions`

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