Apache Airflow: print query success on logging info, query failure on logging.error
My question is regarding the logging of query successes or failure, done by the BigQueryOperator of Apache Airflow 1.10.0 I am wondering if it is possible to print query success on logging.info
, and if it is a failure to print on logging.error
?
from airflow.contrib.operators import bigquery_operator
# Query recent StackOverflow questions.
bq_recent_questions_query = bigquery_operator.BigQueryOperator(
task_id='bq_recent_questions_query',
bql="""
SELECT owner_display_name, title, view_count
FROM `bigquery-public-data.stackoverflow.posts_questions`
WHERE creation_date < CAST('{max_date}' AS TIMESTAMP)
AND creation_date >= CAST('{min_date}' AS TIMESTAMP)
ORDER BY view_count DESC
LIMIT 100
""".format(max_date=max_query_date, min_date=min_query_date),
use_legacy_sql=False,
destination_dataset_table=bq_recent_questions_table_id)
https://cloud.google.com/composer/docs/how-to/using/writing-dags
python google-bigquery airflow
add a comment |
My question is regarding the logging of query successes or failure, done by the BigQueryOperator of Apache Airflow 1.10.0 I am wondering if it is possible to print query success on logging.info
, and if it is a failure to print on logging.error
?
from airflow.contrib.operators import bigquery_operator
# Query recent StackOverflow questions.
bq_recent_questions_query = bigquery_operator.BigQueryOperator(
task_id='bq_recent_questions_query',
bql="""
SELECT owner_display_name, title, view_count
FROM `bigquery-public-data.stackoverflow.posts_questions`
WHERE creation_date < CAST('{max_date}' AS TIMESTAMP)
AND creation_date >= CAST('{min_date}' AS TIMESTAMP)
ORDER BY view_count DESC
LIMIT 100
""".format(max_date=max_query_date, min_date=min_query_date),
use_legacy_sql=False,
destination_dataset_table=bq_recent_questions_table_id)
https://cloud.google.com/composer/docs/how-to/using/writing-dags
python google-bigquery airflow
add a comment |
My question is regarding the logging of query successes or failure, done by the BigQueryOperator of Apache Airflow 1.10.0 I am wondering if it is possible to print query success on logging.info
, and if it is a failure to print on logging.error
?
from airflow.contrib.operators import bigquery_operator
# Query recent StackOverflow questions.
bq_recent_questions_query = bigquery_operator.BigQueryOperator(
task_id='bq_recent_questions_query',
bql="""
SELECT owner_display_name, title, view_count
FROM `bigquery-public-data.stackoverflow.posts_questions`
WHERE creation_date < CAST('{max_date}' AS TIMESTAMP)
AND creation_date >= CAST('{min_date}' AS TIMESTAMP)
ORDER BY view_count DESC
LIMIT 100
""".format(max_date=max_query_date, min_date=min_query_date),
use_legacy_sql=False,
destination_dataset_table=bq_recent_questions_table_id)
https://cloud.google.com/composer/docs/how-to/using/writing-dags
python google-bigquery airflow
My question is regarding the logging of query successes or failure, done by the BigQueryOperator of Apache Airflow 1.10.0 I am wondering if it is possible to print query success on logging.info
, and if it is a failure to print on logging.error
?
from airflow.contrib.operators import bigquery_operator
# Query recent StackOverflow questions.
bq_recent_questions_query = bigquery_operator.BigQueryOperator(
task_id='bq_recent_questions_query',
bql="""
SELECT owner_display_name, title, view_count
FROM `bigquery-public-data.stackoverflow.posts_questions`
WHERE creation_date < CAST('{max_date}' AS TIMESTAMP)
AND creation_date >= CAST('{min_date}' AS TIMESTAMP)
ORDER BY view_count DESC
LIMIT 100
""".format(max_date=max_query_date, min_date=min_query_date),
use_legacy_sql=False,
destination_dataset_table=bq_recent_questions_table_id)
https://cloud.google.com/composer/docs/how-to/using/writing-dags
python google-bigquery airflow
python google-bigquery airflow
edited Nov 21 '18 at 8:32
Meghdeep Ray
2,56831838
2,56831838
asked Nov 20 '18 at 10:20
Paul VelthuisPaul Velthuis
141112
141112
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
You could make your own Operator by just copying the BigQueryOperator and making the following changes to the execute
and on_kill
functions inside it, or you could override the existing BigQueryOperator
as well.
def execute(self, context):
if self.bq_cursor is None:
self.log.info( "Beginnging Execution." )
hook = BigQueryHook(
bigquery_conn_id=self.bigquery_conn_id,
use_legacy_sql=self.use_legacy_sql,
delegate_to=self.delegate_to)
conn = hook.get_conn()
self.bq_cursor = conn.cursor()
self.bq_cursor.run_query(
self.sql,
destination_dataset_table=self.destination_dataset_table,
write_disposition=self.write_disposition,
allow_large_results=self.allow_large_results,
flatten_results=self.flatten_results,
udf_config=self.udf_config,
maximum_billing_tier=self.maximum_billing_tier,
maximum_bytes_billed=self.maximum_bytes_billed,
create_disposition=self.create_disposition,
query_params=self.query_params,
labels=self.labels,
schema_update_options=self.schema_update_options,
priority=self.priority,
time_partitioning=self.time_partitioning
)
self.log.info( "Executed: %s" % self.sql )
def on_kill(self):
super(BigQueryOperator, self).on_kill()
self.log.error( "Failed to Execute: %s" % self.sql )
if self.bq_cursor is not None:
self.log.info('Canceling running query due to execution timeout')
self.bq_cursor.cancel_query()
You have to put in custom operators into the plugins directory.
add a comment |
Looks like the code logs the query prior to execution, so the outcome is not known at the time the log is written.
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
You could make your own Operator by just copying the BigQueryOperator and making the following changes to the execute
and on_kill
functions inside it, or you could override the existing BigQueryOperator
as well.
def execute(self, context):
if self.bq_cursor is None:
self.log.info( "Beginnging Execution." )
hook = BigQueryHook(
bigquery_conn_id=self.bigquery_conn_id,
use_legacy_sql=self.use_legacy_sql,
delegate_to=self.delegate_to)
conn = hook.get_conn()
self.bq_cursor = conn.cursor()
self.bq_cursor.run_query(
self.sql,
destination_dataset_table=self.destination_dataset_table,
write_disposition=self.write_disposition,
allow_large_results=self.allow_large_results,
flatten_results=self.flatten_results,
udf_config=self.udf_config,
maximum_billing_tier=self.maximum_billing_tier,
maximum_bytes_billed=self.maximum_bytes_billed,
create_disposition=self.create_disposition,
query_params=self.query_params,
labels=self.labels,
schema_update_options=self.schema_update_options,
priority=self.priority,
time_partitioning=self.time_partitioning
)
self.log.info( "Executed: %s" % self.sql )
def on_kill(self):
super(BigQueryOperator, self).on_kill()
self.log.error( "Failed to Execute: %s" % self.sql )
if self.bq_cursor is not None:
self.log.info('Canceling running query due to execution timeout')
self.bq_cursor.cancel_query()
You have to put in custom operators into the plugins directory.
add a comment |
You could make your own Operator by just copying the BigQueryOperator and making the following changes to the execute
and on_kill
functions inside it, or you could override the existing BigQueryOperator
as well.
def execute(self, context):
if self.bq_cursor is None:
self.log.info( "Beginnging Execution." )
hook = BigQueryHook(
bigquery_conn_id=self.bigquery_conn_id,
use_legacy_sql=self.use_legacy_sql,
delegate_to=self.delegate_to)
conn = hook.get_conn()
self.bq_cursor = conn.cursor()
self.bq_cursor.run_query(
self.sql,
destination_dataset_table=self.destination_dataset_table,
write_disposition=self.write_disposition,
allow_large_results=self.allow_large_results,
flatten_results=self.flatten_results,
udf_config=self.udf_config,
maximum_billing_tier=self.maximum_billing_tier,
maximum_bytes_billed=self.maximum_bytes_billed,
create_disposition=self.create_disposition,
query_params=self.query_params,
labels=self.labels,
schema_update_options=self.schema_update_options,
priority=self.priority,
time_partitioning=self.time_partitioning
)
self.log.info( "Executed: %s" % self.sql )
def on_kill(self):
super(BigQueryOperator, self).on_kill()
self.log.error( "Failed to Execute: %s" % self.sql )
if self.bq_cursor is not None:
self.log.info('Canceling running query due to execution timeout')
self.bq_cursor.cancel_query()
You have to put in custom operators into the plugins directory.
add a comment |
You could make your own Operator by just copying the BigQueryOperator and making the following changes to the execute
and on_kill
functions inside it, or you could override the existing BigQueryOperator
as well.
def execute(self, context):
if self.bq_cursor is None:
self.log.info( "Beginnging Execution." )
hook = BigQueryHook(
bigquery_conn_id=self.bigquery_conn_id,
use_legacy_sql=self.use_legacy_sql,
delegate_to=self.delegate_to)
conn = hook.get_conn()
self.bq_cursor = conn.cursor()
self.bq_cursor.run_query(
self.sql,
destination_dataset_table=self.destination_dataset_table,
write_disposition=self.write_disposition,
allow_large_results=self.allow_large_results,
flatten_results=self.flatten_results,
udf_config=self.udf_config,
maximum_billing_tier=self.maximum_billing_tier,
maximum_bytes_billed=self.maximum_bytes_billed,
create_disposition=self.create_disposition,
query_params=self.query_params,
labels=self.labels,
schema_update_options=self.schema_update_options,
priority=self.priority,
time_partitioning=self.time_partitioning
)
self.log.info( "Executed: %s" % self.sql )
def on_kill(self):
super(BigQueryOperator, self).on_kill()
self.log.error( "Failed to Execute: %s" % self.sql )
if self.bq_cursor is not None:
self.log.info('Canceling running query due to execution timeout')
self.bq_cursor.cancel_query()
You have to put in custom operators into the plugins directory.
You could make your own Operator by just copying the BigQueryOperator and making the following changes to the execute
and on_kill
functions inside it, or you could override the existing BigQueryOperator
as well.
def execute(self, context):
if self.bq_cursor is None:
self.log.info( "Beginnging Execution." )
hook = BigQueryHook(
bigquery_conn_id=self.bigquery_conn_id,
use_legacy_sql=self.use_legacy_sql,
delegate_to=self.delegate_to)
conn = hook.get_conn()
self.bq_cursor = conn.cursor()
self.bq_cursor.run_query(
self.sql,
destination_dataset_table=self.destination_dataset_table,
write_disposition=self.write_disposition,
allow_large_results=self.allow_large_results,
flatten_results=self.flatten_results,
udf_config=self.udf_config,
maximum_billing_tier=self.maximum_billing_tier,
maximum_bytes_billed=self.maximum_bytes_billed,
create_disposition=self.create_disposition,
query_params=self.query_params,
labels=self.labels,
schema_update_options=self.schema_update_options,
priority=self.priority,
time_partitioning=self.time_partitioning
)
self.log.info( "Executed: %s" % self.sql )
def on_kill(self):
super(BigQueryOperator, self).on_kill()
self.log.error( "Failed to Execute: %s" % self.sql )
if self.bq_cursor is not None:
self.log.info('Canceling running query due to execution timeout')
self.bq_cursor.cancel_query()
You have to put in custom operators into the plugins directory.
answered Nov 21 '18 at 8:27
Meghdeep RayMeghdeep Ray
2,56831838
2,56831838
add a comment |
add a comment |
Looks like the code logs the query prior to execution, so the outcome is not known at the time the log is written.
add a comment |
Looks like the code logs the query prior to execution, so the outcome is not known at the time the log is written.
add a comment |
Looks like the code logs the query prior to execution, so the outcome is not known at the time the log is written.
Looks like the code logs the query prior to execution, so the outcome is not known at the time the log is written.
answered Nov 20 '18 at 14:32
joebjoeb
2,20611519
2,20611519
add a comment |
add a comment |
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