awswrangler.athena.start_query_execution¶
-
awswrangler.athena.
start_query_execution
(sql: str, database: Optional[str] = None, s3_output: Optional[str] = None, workgroup: Optional[str] = None, encryption: Optional[str] = None, kms_key: Optional[str] = None, boto3_session: Optional[boto3.session.Session] = None, data_source: Optional[str] = None) → Any¶ Start a SQL Query against AWS Athena.
Note
Create the default Athena bucket if it doesn’t exist and s3_output is None. (E.g. s3://aws-athena-query-results-ACCOUNT-REGION/)
Note
This functions has arguments that can has default values configured globally through wr.config or environment variables:
database
workgroup
Check out the Global Configurations Tutorial for details.
- Parameters
sql (str) – SQL query.
database (str, optional) – AWS Glue/Athena database name.
s3_output (str, optional) – AWS S3 path.
workgroup (str, optional) – Athena workgroup.
encryption (str, optional) – None, ‘SSE_S3’, ‘SSE_KMS’, ‘CSE_KMS’.
kms_key (str, optional) – For SSE-KMS and CSE-KMS , this is the KMS key ARN or ID.
boto3_session (boto3.Session(), optional) – Boto3 Session. The default boto3 session will be used if boto3_session receive None.
data_source (str, optional) – Data Source / Catalog name. If None, ‘AwsDataCatalog’ will be used by default.
- Returns
Query execution ID
- Return type
str
Examples
Querying into the default data source (Amazon s3 - ‘AwsDataCatalog’)
>>> import awswrangler as wr >>> query_exec_id = wr.athena.start_query_execution(sql='...', database='...')
Querying into another data source (PostgreSQL, Redshift, etc)
>>> import awswrangler as wr >>> query_exec_id = wr.athena.start_query_execution(sql='...', database='...', data_source='...')