def test_table_lookup(self):
# type: () -> None
dataset_ref1 = DatasetReference('my_project', 'dataset1')
table_ref1 = TableReference(dataset_ref1, 'table1')
table1 = Table(table_ref1, _TEST_SCHEMA)
# Trying to get the same dataset/table in another project doesn't work.
with self.assertRaisesRegexp(NotFound, 'other_project'):
self.bq_client.get_table(
TableReference(DatasetReference('other_project', 'dataset1'),
'table1'))
# Trying to get the table before the dataset exists doesn't work
with self.assertRaisesRegexp(NotFound, 'dataset1'):
self.bq_client.get_table(table_ref1)
self.bq_client.create_dataset(Dataset(dataset_ref1))
# Trying to get the table before the table exists doesn't work
with self.assertRaises(NotFound):
self.bq_client.get_table(table_ref1)
self.bq_client.create_table(table1)
# Assert the created table has the expected properties.
table_found = self.bq_client.get_table(table_ref1)
self.assertEqual(table1.project, "my_project")
self.assertEqual(table1.dataset_id, "dataset1")
self.assertEqual(table1.table_id, "table1")
six.assertCountEqual(self, table_found.schema, _TEST_SCHEMA)
def test_listing_tables(self):
# type: () -> None
dataset_ref1 = DatasetReference('my_project', 'dataset1')
self.bq_client.create_dataset(Dataset(dataset_ref1))
self.bq_client.create_table(Table(TableReference(dataset_ref1, 'table1'), _TEST_SCHEMA))
self.bq_client.create_table(Table(TableReference(dataset_ref1, 'table2'), []))
six.assertCountEqual(
self,
[table_ref.table_id for table_ref in self.bq_client.list_tables(dataset_ref1)],
['table1', 'table2'])
def setUp(self):
super(ClientWriteFromQueryTest, self).setUp()
dataset_ref = DatasetReference(self.bq_client.project, 'my_dataset')
schema = [SchemaField(name="a", field_type='INT64'),
SchemaField(name="b", field_type='FLOAT64'),
]
self.source_table = Table(TableReference(dataset_ref, 'source_table'), schema)
self.destination_table = Table(TableReference(dataset_ref, 'destination_table'), schema)
self.bq_client.create_dataset(Dataset(dataset_ref))
self.bq_client.create_table(self.source_table)
# We don't create the destination table here; some tests do not want it created.
# Stick two rows into source_table
self.assertFalse(self.bq_client.insert_rows(self.source_table,
[{'a': 1, 'b': 2.5}, {'a': 3, 'b': 4.25}]))
def __create_job_config(
self, ems_query_job_config: EmsQueryJobConfig) -> QueryJobConfig:
job_config = QueryJobConfig()
job_config.priority = ems_query_job_config.priority.value
job_config.use_legacy_sql = False
job_config.use_query_cache = ems_query_job_config.use_query_cache
job_config.labels = ems_query_job_config.labels
if ems_query_job_config.destination_table is not None:
job_config.time_partitioning = TimePartitioning("DAY")
table_reference = TableReference(
DatasetReference(
ems_query_job_config.destination_project_id
or self.__project_id,
ems_query_job_config.destination_dataset),
ems_query_job_config.destination_table)
job_config.destination = table_reference
job_config.write_disposition = ems_query_job_config.write_disposition.value
job_config.create_disposition = ems_query_job_config.create_disposition.value
partitioning = ems_query_job_config.time_partitioning
if partitioning is not None:
job_config.time_partitioning = TimePartitioning(
partitioning.type.value, partitioning.field,
partitioning.expiration_ms,
partitioning.require_partition_filter)
if ems_query_job_config.table_definitions is not None:
job_config.table_definitions = ems_query_job_config.table_definitions
return job_config
def table_exists(self, table: str) -> bool:
try:
self.__bigquery_client.get_table(
table=TableReference.from_string(table))
return True
except NotFound:
return False
def log_bigquery_job(job: Union[bigquery.LoadJob, bigquery.QueryJob],
table: bigquery.TableReference,
message: Optional[str] = None,
severity: Optional[str] = 'NOTICE'):
"""
Creates a structured log which includes a BigQuery job and table reference
:param job:
:param table:
:param message:
:param severity:
"""
if job.error_result:
severity = "ERROR"
message = message or "BigQuery Job had errors."
elif severity == "ERROR":
message = message or ("BigQuery Job completed"
" but is considered an error.")
else:
severity = "NOTICE"
message = message or "BigQuery Job completed without errors."
print(
json.dumps(
dict(
message=message,
severity=severity,
job=job.to_api_repr(),
table=table.to_api_repr(),
errors=job.error_result,
)))
def make_table(project,
dataset_id,
table_id,
friendly_name=None,
description=None,
expires=None,
partitioning_type=None,
view_use_legacy_sql=None,
view_query=None,
schema=None,
labels=None):
dataset_ref = DatasetReference(project, dataset_id)
table_ref = TableReference(dataset_ref, table_id)
table = Table(table_ref)
table.friendly_name = friendly_name
table.description = description
table.expires = expires
table.partitioning_type = partitioning_type
if view_use_legacy_sql is not None:
table.view_use_legacy_sql = view_use_legacy_sql
if view_query is not None:
table.view_query = view_query
table.schema = schema
if labels is not None:
table.labels = labels
return table
def _table_ref(self, table_id):
"""Return a BigQuery client library table reference"""
from google.cloud.bigquery import DatasetReference
from google.cloud.bigquery import TableReference
return TableReference(
DatasetReference(self.project_id, self.dataset_id), table_id)
def test_generate_queries(self, mock_table_cols, mock_table_dates,
mock_sandbox):
table_cols_dict = {
"condition_occurrence": [
"condition_start_date", "condition_start_datetime",
"condition_end_date", "condition_end_datetime"
],
"measurement": ["measurement_date", "measurement_datetime"],
'drug_exposure': [
'drug_exposure_start_date', 'drug_exposure_start_datetime',
'drug_exposure_end_date', 'drug_exposure_end_datetime',
'verbatim_end_date'
]
}
table_cols_df = pd.DataFrame.from_dict({
"table_name": [
"condition_occurrence", "_mapping_condition_occurrence",
"measurement", "measurement_ext", "drug_exposure_ext",
"drug_exposure"
]
})
mock_table_cols.return_value = table_cols_df
mock_table_dates.return_value = table_cols_dict
mock_sandbox.return_value = self.dataset_id + '_sandbox'
pid_rid_table_ref = TableReference.from_string(
f"{self.project_id}.{self.pids_dataset_id}.{self.pids_table}")
deactivated_pids_table_ref = TableReference.from_string(
f"{self.project_id}.{self.deactivated_pids_dataset_id}.{self.deactivated_pids_table}"
)
queries = rdp.generate_queries(
client=self.mock_bq_client,
project_id=self.project_id,
dataset_id=self.dataset_id,
sandbox_dataset_id=self.dataset_id,
deact_pids_table_ref=deactivated_pids_table_ref,
pid_rid_table_ref=pid_rid_table_ref,
)
mock_table_cols.called_once_with(self.mock_bq_client, self.project_id,
self.dataset_id)
mock_table_dates.called_once_with(table_cols_df)
# count sandbox and clean queries
self.assertEqual(len(table_cols_dict) * 2, len(queries))
def test_listing_tables_with_max(self):
# type: () -> None
dataset_ref1 = DatasetReference('my_project', 'dataset1')
self.bq_client.create_dataset(Dataset(dataset_ref1))
for i in range(10):
self.bq_client.create_table(Table(TableReference(dataset_ref1, 'table{}'.format(i)),
_TEST_SCHEMA))
self.assertEqual(5, len(self.bq_client.list_tables(dataset_ref1, max_results=5)))
self.assertEqual(10, len(self.bq_client.list_tables(dataset_ref1, max_results=20)))
self.assertEqual(10, len(self.bq_client.list_tables(dataset_ref1)))
def __create_test_table(self, table_name, dataset_id):
table_schema = [
SchemaField("int_data", "INT64"),
SchemaField("str_data", "STRING")
]
table_reference = TableReference(dataset_id, table_name)
test_table = Table(table_reference, table_schema)
test_table.time_partitioning = TimePartitioning("DAY")
self.__delete_if_exists(test_table)
self.GCP_BIGQUERY_CLIENT.create_table(test_table)
return test_table
def run_async_load_job(self, job_id_prefix: str,
config: EmsLoadJobConfig) -> str:
return self.__bigquery_client.load_table_from_uri(
source_uris=config.source_uri_template,
destination=TableReference(
DatasetReference(config.destination_project_id,
config.destination_dataset),
config.destination_table),
job_id_prefix=job_id_prefix,
location=self.__location,
job_config=self.__create_load_job_config(config)).job_id
def run_async_extract_job(self, job_id_prefix: str, table: str,
destination_uris: List[str],
job_config: EmsExtractJobConfig) -> str:
extract_job_config = self.__create_extract_job_config(job_config)
return self.__bigquery_client.extract_table(
source=TableReference.from_string(table_id=table),
destination_uris=destination_uris,
job_id_prefix=job_id_prefix,
location=self.__location,
job_config=extract_job_config).job_id
def list_item_from_table_id(table_id: str) -> TableListItem:
"""
Get a table list item as returned by :meth:`bigquery.Client.list_tables`
from a table ID
:param table_id: A table ID including project ID, dataset ID, and table ID,
each separated by ``.``.
:return: a table list item
"""
resource = {
"tableReference": TableReference.from_string(table_id).to_api_repr()
}
return TableListItem(resource)
def get_raw_details(self, dataset_id, view_id):
if self.did_full_update:
return self.raw_details[dataset_id][view_id]
if dataset_id not in self.raw_details:
self.raw_details[dataset_id] = dict()
if view_id not in self.raw_details[dataset_id]:
try:
self.raw_details[dataset_id][view_id] = self.bq_client._client.get_table( \
TableReference(DatasetReference(self.bq_conductor_conf.GOOGLE_CLOUD_PROJECT,
dataset_id.split('.')[-1]), view_id)).to_api_repr()
except:
self.raw_details[dataset_id][view_id] = None
return self.raw_details[dataset_id][view_id]
def log_api_error(table: bigquery.TableReference, message: str,
error: Union[GoogleAPIError, ClientError]):
"""
Creates a structured api error log which includes a BigQuery table reference
:param table:
:param message:
:param error:
:return:
"""
print(
json.dumps(
dict(message=message or error.message,
severity='ERROR',
table=table.to_api_repr(),
errors=error.errors or error.message or message)))
def handle_bq_lock(gcs_client: storage.Client,
lock_blob: storage.Blob,
next_job_id: Optional[str],
table: bigquery.TableReference,
retry_attempt_cnt: Optional[int] = None):
"""Reclaim the lock blob for the new job id (in-place) or delete the lock
blob if next_job_id is None."""
try:
if next_job_id:
lock_blob_contents = json.dumps(
dict(job_id=next_job_id,
table=table.to_api_repr(),
retry_attempt_cnt=retry_attempt_cnt))
logging.log_with_table(
table,
f"Writing the following content to lock_blob {lock_blob.name}:"
f" {dict(job_id=next_job_id, table=table.to_api_repr(), retry_attempt_cnt=retry_attempt_cnt)}"
)
if lock_blob.exists(client=gcs_client):
lock_blob.upload_from_string(
lock_blob_contents,
if_generation_match=lock_blob.generation,
client=gcs_client)
else: # This happens when submitting the first job in the backlog
lock_blob.upload_from_string(
lock_blob_contents,
if_generation_match=0, # noqa: E126
client=gcs_client)
else:
logging.log_with_table(
table, "releasing lock at: "
f"gs://{lock_blob.bucket.name}/{lock_blob.name}")
lock_blob.delete(
if_generation_match=lock_blob.generation,
client=gcs_client,
)
except (google.api_core.exceptions.PreconditionFailed,
google.api_core.exceptions.NotFound) as err:
if isinstance(err, google.api_core.exceptions.PreconditionFailed):
raise exceptions.BacklogException(
f"The lock at gs://{lock_blob.bucket.name}/{lock_blob.name} "
f"was changed by another process.") from err
logging.log_with_table(
table,
"Tried deleting a lock blob that was either already deleted "
"or never existed.")
def send_to_bq(dataset, table, row):
bigquery_client = bigquery.Client(project='theo-home')
table_ref = TableReference(
dataset_ref=DatasetReference(dataset_id=dataset, project='theo-home'),
table_id=table,
)
schema = [SchemaField(name=field, field_type=bq_types[type(data)]) for field, data in row.items()]
table = bigquery_client.create_table(
Table(table_ref, schema=schema),
exists_ok=True
)
errors = bigquery_client.insert_rows(table, [row])
if errors:
print(errors, file=sys.stderr)
def to_table(dataset_ref, model):
schema = model.schema
if schema:
schema = tuple(
BigQuerySchemaField.to_schema_field(s) for s in schema)
else:
schema = None
table_ref = TableReference(dataset_ref, model.table_id)
table = Table(table_ref, schema)
table.friendly_name = model.friendly_name
table.description = model.description
table.expires = model.expires
table.partitioning_type = model.partitioning_type
if model.view_use_legacy_sql is not None:
table.view_use_legacy_sql = model.view_use_legacy_sql
if model.view_query is not None:
table.view_query = model.view_query
table.labels = model.labels if model.labels is not None else dict()
return table
def test_load_table_from_file(self, column_to_null):
# type: (int) -> None
dataset_ref = DatasetReference('my_project', 'my_dataset')
dataset = Dataset(dataset_ref)
table = Table(TableReference(dataset_ref, 'table1'), _TEST_SCHEMA)
self.bq_client.create_dataset(dataset)
self.bq_client.create_table(table)
output = cStringIO()
csv_out = csv.writer(output)
input_row = list(self.INPUT_ROW)
expected_row = list(self.EXPECTED_ROW)
if column_to_null is not None:
input_row[column_to_null] = 'NULL'
expected_row[column_to_null] = None
csv_out.writerow(input_row)
self.bq_client.load_table_from_file(output, table.reference, job_config=None, rewind=True)
self.assertRowsExpected(
self.bq_client.query('SELECT * FROM `my_project.my_dataset.table1`',
QueryJobConfig()),
[expected_row])
def log_with_table(
table: bigquery.TableReference,
message: str,
severity: Optional[str] = 'NOTICE',
):
"""
Creates a structured log which includes a BigQuery table reference
:param table:
:param message:
:param severity:
:return:
"""
table_json = None
if table is not None:
table_json = table.to_api_repr()
print(
json.dumps(dict(
message=message,
severity=severity,
table=table_json,
)))
def test_delete_table(self):
# type: () -> None
dataset_ref1 = DatasetReference('my_project', 'dataset1')
table_ref1 = TableReference(dataset_ref1, 'table1')
# Can't delete table, dataset not created yet.
with self.assertRaisesRegexp(NotFound, 'dataset1'):
self.bq_client.delete_table(table_ref1)
self.bq_client.create_dataset(Dataset(dataset_ref1))
# Can't delete table, table not created yet.
with self.assertRaisesRegexp(NotFound, 'table1'):
self.bq_client.delete_table(table_ref1)
table1 = Table(table_ref1, _TEST_SCHEMA)
self.bq_client.create_table(table1)
self.bq_client.delete_table(table_ref1)
with self.assertRaisesRegexp(NotFound, 'table1'):
self.bq_client.get_table(table_ref1)
def test_insert_rows(self):
# type: () -> None
dataset_ref = DatasetReference('my_project', 'my_dataset')
dataset = Dataset(dataset_ref)
table1_ref = TableReference(dataset_ref, 'table1')
schema = [
SchemaField(name="a", field_type='INT64'),
SchemaField(name="b", field_type='FLOAT64'),
]
table = Table(table1_ref, schema)
self.bq_client.create_dataset(dataset)
self.bq_client.create_table(table)
# Insert two rows, check that they landed
self.assertFalse(
self.bq_client.insert_rows(table, [{
'a': 1,
'b': 2.5
}, {
'a': 3,
'b': 4.25
}]))
self.assertRowsExpected(
self.bq_client.query(
'SELECT * FROM `my_project.my_dataset.table1`',
QueryJobConfig()), [[1, 2.5], [3, 4.25]])
# Insert two more rows, check that all four rows are now present.
self.assertFalse(
self.bq_client.insert_rows(table, [{
'a': 5,
'b': 6.5
}, {
'a': 7,
'b': 8.25
}]))
self.assertRowsExpected(
self.bq_client.query(
'SELECT * FROM `my_project.my_dataset.table1`',
QueryJobConfig()), [[1, 2.5], [3, 4.25], [5, 6.5], [7, 8.25]])
def write_to_table_from_json_file(path: Path) -> LoadJob:
table = TableReference(DatasetReference(PROJECT_NAME, BQ_DATASET),
BQ_TABLE)
bq = BQ()
job_config = job.LoadJobConfig()
# job_config.autodetect = True
job_config.source_format = job.SourceFormat.NEWLINE_DELIMITED_JSON
job_config.write_disposition = job.WriteDisposition.WRITE_APPEND
job_config.create_disposition = job.CreateDisposition.CREATE_NEVER
try:
with path.open("rb") as f:
load_job: LoadJob = bq.load_table_from_file(f,
destination=table,
job_config=job_config)
load_job.result()
os.remove(str(path))
logging.info(f"{load_job.job_id} completed at {load_job.ended}")
except Exception as ex:
logging.info(f"{load_job.job_id} crashed with {load_job.errors}")
return load_job
def create(self, table_id, schema):
"""Create a table in Google BigQuery given a table and schema
Parameters
----------
table : str
Name of table to be written
schema : str
Use the generate_bq_schema to generate your table schema from a
dataframe.
"""
from google.cloud.bigquery import DatasetReference
from google.cloud.bigquery import Table
from google.cloud.bigquery import TableReference
if self.exists(table_id):
raise TableCreationError(
"Table {0} already exists".format(table_id)
)
if not _Dataset(self.project_id, credentials=self.credentials).exists(
self.dataset_id
):
_Dataset(
self.project_id,
credentials=self.credentials,
location=self.location,
).create(self.dataset_id)
table_ref = TableReference(
DatasetReference(self.project_id, self.dataset_id), table_id
)
table = Table(table_ref)
table.schema = pandas_gbq.schema.to_google_cloud_bigquery(schema)
try:
self.client.create_table(table)
except self.http_error as ex:
self.process_http_error(ex)
def test_dataset_delete(self):
# type: () -> None
dataset1 = Dataset(DatasetReference('my_project', 'dataset1'))
# Can't delete dataset, doesn't exist yet.
with self.assertRaisesRegexp(NotFound, 'dataset1'):
self.bq_client.delete_dataset(dataset1.reference)
self.bq_client.create_dataset(dataset1)
self.bq_client.create_table(Table(TableReference(dataset1.reference, 'table1'),
_TEST_SCHEMA))
# Can't delete dataset, not empty
with self.assertRaises(BadRequest):
self.bq_client.delete_dataset(dataset1.reference)
# Okay to delete, specifically requesting to delete contents.
self.bq_client.delete_dataset(dataset1.reference, delete_contents=True)
# And now dataset is gone again.
with self.assertRaisesRegexp(NotFound, 'dataset1'):
self.bq_client.get_dataset(DatasetReference('my_project', 'dataset1'))
def __inner(s, i, client):
# check if weather station (data) exists in BQ
# not all stations are active now/data may not be available
# Supply NaN's for missing weather columns
log.info(f"Download data for station: {i}, site: {s}")
this_station_ref = TableReference(dataset_ref_, str(s))
if client.table_exists(this_station_ref):
df = client.bq_to_df(this_station_ref)
# remove any null year/month/day, sometimes we get dirty data
df = df[(~df['year'].isnull()) & (~df['month'].isnull()) &
(~df['day'].isnull())]
df.index = df.apply(lambda x: pd.datetime(int(x[
'year']), int(x['month']), int(x['day'])),
axis=1)
for dt in weather_data_types:
if dt not in df.columns:
df[dt] = np.nan
df = df_index.join(df)
return df[weather_data_types]
return pd.DataFrame() # else return empty dataframe
def load_from_gcs(self, table_id, input_uri):
job_results = {}
client = self.bq_client
project_id, ds_id, table_name = table_id.split(".")
dataset = DatasetReference(project_id, ds_id)
output_table = TableReference(dataset, table_name)
ending = input_uri.split(".")[-1]
file_type = self.__get_file_type(ending)
config = LoadJobConfig(autodetect=True,
write_disposition=WriteDisposition.WRITE_APPEND,
source_format=file_type)
loadJob = LoadJob(self.__create_job_id(project_id, "loadJob"),
input_uri,
output_table,
client,
job_config=config)
job_results["loadJob"] = self.__get_results(loadJob)
return job_results
def external_query( # pylint: disable=too-many-arguments
gcs_client: storage.Client, bq_client: bigquery.Client, gsurl: str,
query: str, job_id: str, table: bigquery.TableReference):
"""Load from query over external table from GCS.
This hinges on a SQL query defined in GCS at _config/*.sql and
an external table definition
_config/{constants.BQ_EXTERNAL_TABLE_CONFIG_FILENAME} (otherwise will assume
PARQUET external table)
"""
external_table_config = read_gcs_file_if_exists(
gcs_client,
f"{gsurl}_config/{constants.BQ_EXTERNAL_TABLE_CONFIG_FILENAME}")
if not external_table_config:
external_table_config = look_for_config_in_parents(
gcs_client, gsurl, constants.BQ_EXTERNAL_TABLE_CONFIG_FILENAME)
if external_table_config:
external_table_def = json.loads(external_table_config)
else:
print(f" {gsurl}_config/{constants.BQ_EXTERNAL_TABLE_CONFIG_FILENAME} "
f"not found in parents of {gsurl}. "
"Falling back to default PARQUET external table: "
f"{json.dumps(constants.DEFAULT_EXTERNAL_TABLE_DEFINITION)}")
external_table_def = constants.DEFAULT_EXTERNAL_TABLE_DEFINITION
print(
json.dumps(
dict(message="Found external table definition.",
table=table.to_api_repr(),
external_table_def=external_table_def)))
# Reduce the amount of sourceUris by using wildcards with common
# prefixes. This is done to keep the cloud logging audit metadata
# below 100k in size, otherwise the metadata is omitted in the event.
source_uris_with_wildcards = compact_source_uris_with_wildcards(
flatten2dlist(get_batches_for_gsurl(gcs_client, gsurl)))
# This may cause an issue if >10,000 files.
external_table_def["sourceUris"] = source_uris_with_wildcards
# Check if hivePartitioningOptions
# https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#hivepartitioningoptions
# is set in external.json file
if external_table_def.get("hivePartitioningOptions"):
external_table_def["hivePartitioningOptions"] = {
"mode":
external_table_def["hivePartitioningOptions"].get("mode")
or "AUTO",
"sourceUriPrefix":
get_hive_partitioning_source_uri_prefix(
external_table_def["sourceUris"][0])
}
external_config = bigquery.ExternalConfig.from_api_repr(external_table_def)
job_config = bigquery.QueryJobConfig(
table_definitions={"temp_ext": external_config}, use_legacy_sql=False)
# drop partition decorator if present.
table_id = table.table_id.split("$")[0]
# similar syntax to str.format but doesn't require escaping braces
# elsewhere in query (e.g. in a regex)
rendered_query = query.replace(
"{dest_dataset}", f"`{table.project}`.{table.dataset_id}").replace(
"{dest_table}", table_id)
job: bigquery.QueryJob = bq_client.query(rendered_query,
job_config=job_config,
job_id=job_id)
logging.log_bigquery_job(
job, table, f"Submitted asynchronous query job: {job.job_id}")
start_poll_for_errors = time.monotonic()
# Check if job failed quickly
while time.monotonic(
) - start_poll_for_errors < constants.WAIT_FOR_JOB_SECONDS:
job.reload(client=bq_client)
if job.state == "DONE":
check_for_bq_job_and_children_errors(bq_client, job, table)
return
time.sleep(constants.JOB_POLL_INTERVAL_SECONDS)
ch.setFormatter(formatter)
# add ch to logger
log.addHandler(ch)
return log
if __name__ == '__main__':
log = _setup_logging()
bqclient = BQClient()
dataset_id = 'weather'
dataset_ref_ = bqclient.client.dataset(dataset_id)
stations_ref = TableReference(dataset_ref_, 'stations_site_list')
# https://www.matthewproctor.com/australian_postcodes
postcodes_ref = TableReference(dataset_ref_, 'australian_postcodes')
stations = bqclient.bq_to_df(stations_ref)
postcodes = bqclient.bq_to_df(postcodes_ref)
kdtree = cKDTree(stations[['lat', 'lon']])
weather_file_on_disc = 'historical_weather.pk'
import pickle
if Path(weather_file_on_disc).exists():
historical_weather = pickle.load(open(weather_file_on_disc, 'rb'))
else:
