def execute_transformation_query(bq_client):
"""Executes transformation query to a new destination table.
Args:
bq_client: Object representing a reference to a BigQuery Client
"""
dataset_ref = bq_client.get_dataset(bigquery.DatasetReference(
project=config.billing_project_id,
dataset_id=config.output_dataset_id))
table_ref = dataset_ref.table(config.output_table_name)
job_config = bigquery.QueryJobConfig()
job_config.destination = table_ref
job_config.write_disposition = bigquery.WriteDisposition().WRITE_TRUNCATE
job_config.time_partitioning = bigquery.TimePartitioning(
field='usage_start_time',
expiration_ms=None)
sql = Template(file_to_string(config.sql_file_path))
sql = sql.safe_substitute(billing_table=config.billing_project_id +
'.' + config.billing_dataset_id +
'.' + config.billing_table_name,
allocation_method=config.allocation_method
)
logging.info('Attempting query on all dates...')
# Execute Query
query_job = bq_client.query(
sql,
job_config=job_config)
query_job.result() # Waits for the query to finish
logging.info('Transformation query complete. All partitions are updated.')
def execute_query(bq_client: bigquery.Client, env_vars: {}, query_path: object,
output_table_name: str, time_partition: bool) -> None:
"""Executes transformation query to a new destination table.
Args:
bq_client: bigquery.Client object
env_vars: Dictionary of key: value, where value is environment variable
query_path: Object representing location of SQL query to execute
output_table_name: String representing name of table that holds output
time_partition: Boolean indicating whether to time-partition output
"""
dataset_ref = bq_client.get_dataset(
bigquery.DatasetReference(project=bq_client.project,
dataset_id=env_vars['corrected_dataset_id']))
table_ref = dataset_ref.table(output_table_name)
job_config = bigquery.QueryJobConfig()
job_config.destination = table_ref
job_config.write_disposition = bigquery.WriteDisposition().WRITE_TRUNCATE
# Time Partitioning table is only needed for final output query
if time_partition:
job_config.time_partitioning = bigquery.TimePartitioning(
field='usage_start_time', expiration_ms=None)
logging.info('Attempting query...')
# Execute Query
query_job = bq_client.query(query=render_template(query_path, env_vars),
job_config=job_config)
query_job.result() # Waits for the query to finish
def csv_in_gcs_to_table(event, context):
from google.cloud import bigquery
client = bigquery.Client()
bucket_name = "egen-poc-bucket"
object_name = event['name']
table_id = 'cparkar-project-310718.egen_poc_dataset.egen_poc_table'
schema = [
bigquery.SchemaField('Sno', 'INTEGER'),
bigquery.SchemaField('State', 'STRING'),
bigquery.SchemaField('ConfirmedIndianNational', 'INTEGER'),
bigquery.SchemaField('ConfirmedForeignNational', 'INTEGER'),
bigquery.SchemaField('Cured', 'INTEGER'),
bigquery.SchemaField('Deaths', 'INTEGER'),
bigquery.SchemaField('Confirmed', 'INTEGER')
]
job_config = bigquery.LoadJobConfig()
job_config.schema = schema
job_config.source_format = bigquery.SourceFormat.CSV
job_config.write_disposition = bigquery.WriteDisposition().WRITE_APPEND
job_config.skip_leading_rows = 1
uri = "gs://{}/{}".format(bucket_name, object_name)
load_job = client.load_table_from_uri(uri, table_id, job_config=job_config)
load_job.result()
def execute_query(bq_client):
"""Executes transformation query to a new destination table.
Args:
bq_client: Object representing a reference to a BigQuery Client
"""
dataset_ref = bq_client.get_dataset(
bigquery.DatasetReference(
project=config.config_vars['project_id'],
dataset_id=config.config_vars['output_dataset_id']))
table_ref = dataset_ref.table(config.config_vars['output_table_name'])
job_config = bigquery.QueryJobConfig()
job_config.destination = table_ref
job_config.write_disposition = bigquery.WriteDisposition().WRITE_TRUNCATE
sql = file_to_string(config.config_vars['sql_file_path'])
logging.info('Attempting query on all dates...')
# Execute Query
query_job = bq_client.query(sql, job_config=job_config)
query_job.result() # Waits for the query to finish
logging.info('Query complete. The table is updated.')
def csv_in_gcs_to_table(bucket_name: str, object_name: str, dataset_id: str,
table_id: str,
schema: List[bigquery.SchemaField]) -> None:
"""Upload CSV to BigQuery table.
If the table already exists, it overwrites the table data.
Args:
bucket_name: Bucket name for holding the object
object_name: Name of object to be uploaded
dataset_id: Dataset id where the table is located.
table_id: String holding id of hte table.
schema: Schema of the table_id
"""
client = bigquery.Client()
dataset_ref = client.dataset(dataset_id)
job_config = bigquery.LoadJobConfig()
job_config.schema = schema
job_config.source_format = bigquery.SourceFormat.CSV
job_config.write_disposition = bigquery.WriteDisposition().WRITE_TRUNCATE
uri = "gs://{}/{}".format(bucket_name, object_name)
load_job = client.load_table_from_uri(uri,
dataset_ref.table(table_id),
job_config=job_config)
load_job.result()
