I've tried the DROP/ TRUNCATE
scenario, but have not been able to do it with connections already created in Glue, but with a pure Python PostgreSQL driver, pg8000.
- Download the tar of pg8000 from pypi
- Create an empty
__init__.py
in the root folder
- Zip up the contents & upload to S3
- Reference the zip file in the
Python lib path
of the job
- Set the DB connection details as job params (make sure to prepend all key names with
--
). Tick the "Server-side encryption" box.
Then you can simply create a connection and execute SQL.
import sys
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame
from awsglue.job import Job
import pg8000
args = getResolvedOptions(sys.argv, [
'JOB_NAME',
'PW',
'HOST',
'USER',
'DB'
])
# ...
# Create Spark & Glue context
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
# ...
config_port = 5432
conn = pg8000.connect(
database=args['DB'],
user=args['USER'],
password=args['PW'],
host=args['HOST'],
port=config_port
)
query = "TRUNCATE TABLE {0};".format(".".join([schema, table]))
cur = conn.cursor()
cur.execute(query)
conn.commit()
cur.close()
conn.close()
与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…