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# Copyright 2016 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testable usage examples for Google BigQuery API wrapper
Each example function takes a ``client`` argument (which must be an instance
of :class:`google.cloud.bigquery.client.Client`) and uses it to perform a task
with the API.
To facilitate running the examples as system tests, each example is also passed
a ``to_delete`` list; the function adds to the list any objects created which
need to be deleted during teardown.
"""
import os
import time
import pytest
import six
try:
import fastparquet
except (ImportError, AttributeError):
fastparquet = None
try:
import pandas
except (ImportError, AttributeError):
pandas = None
try:
import pyarrow
except (ImportError, AttributeError):
pyarrow = None
from google.api_core import datetime_helpers
from google.api_core.exceptions import InternalServerError
from google.api_core.exceptions import ServiceUnavailable
from google.api_core.exceptions import TooManyRequests
from google.cloud import bigquery
from google.cloud import storage
from test_utils.retry import RetryErrors
ORIGINAL_FRIENDLY_NAME = "Original friendly name"
ORIGINAL_DESCRIPTION = "Original description"
LOCALLY_CHANGED_FRIENDLY_NAME = "Locally-changed friendly name"
LOCALLY_CHANGED_DESCRIPTION = "Locally-changed description"
UPDATED_FRIENDLY_NAME = "Updated friendly name"
UPDATED_DESCRIPTION = "Updated description"
SCHEMA = [
bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
]
ROWS = [
("Phred Phlyntstone", 32),
("Bharney Rhubble", 33),
("Wylma Phlyntstone", 29),
("Bhettye Rhubble", 27),
]
QUERY = (
"SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` "
'WHERE state = "TX"'
)
retry_429 = RetryErrors(TooManyRequests)
retry_storage_errors = RetryErrors(
(TooManyRequests, InternalServerError, ServiceUnavailable)
)
@pytest.fixture(scope="module")
def client():
return bigquery.Client()
@pytest.fixture
def to_delete(client):
doomed = []
yield doomed
for item in doomed:
if isinstance(item, (bigquery.Dataset, bigquery.DatasetReference)):
retry_429(client.delete_dataset)(item, delete_contents=True)
elif isinstance(item, storage.Bucket):
retry_storage_errors(item.delete)()
else:
retry_429(item.delete)()
def _millis():
return int(time.time() * 1000)
class _CloseOnDelete(object):
def __init__(self, wrapped):
self._wrapped = wrapped
def delete(self):
self._wrapped.close()
def test_create_client_default_credentials():
"""Create a BigQuery client with Application Default Credentials"""
# [START bigquery_client_default_credentials]
from google.cloud import bigquery
# If you don't specify credentials when constructing the client, the
# client library will look for credentials in the environment.
client = bigquery.Client()
# [END bigquery_client_default_credentials]
assert client is not None
def test_list_datasets_by_label(client, to_delete):
dataset_id = "list_datasets_by_label_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
dataset.labels = {"color": "green"}
dataset = client.create_dataset(dataset) # API request
to_delete.append(dataset)
# [START bigquery_list_datasets_by_label]
# from google.cloud import bigquery
# client = bigquery.Client()
# The following label filter example will find datasets with an
# arbitrary 'color' label set to 'green'
label_filter = "labels.color:green"
datasets = list(client.list_datasets(filter=label_filter))
if datasets:
print("Datasets filtered by {}:".format(label_filter))
for dataset in datasets: # API request(s)
print("\t{}".format(dataset.dataset_id))
else:
print("No datasets found with this filter.")
# [END bigquery_list_datasets_by_label]
found = set([dataset.dataset_id for dataset in datasets])
assert dataset_id in found
# [START bigquery_dataset_exists]
def dataset_exists(client, dataset_reference):
"""Return if a dataset exists.
Args:
client (google.cloud.bigquery.client.Client):
A client to connect to the BigQuery API.
dataset_reference (google.cloud.bigquery.dataset.DatasetReference):
A reference to the dataset to look for.
Returns:
bool: ``True`` if the dataset exists, ``False`` otherwise.
"""
from google.cloud.exceptions import NotFound
try:
client.get_dataset(dataset_reference)
return True
except NotFound:
return False
# [END bigquery_dataset_exists]
def test_dataset_exists(client, to_delete):
"""Determine if a dataset exists."""
DATASET_ID = "get_table_dataset_{}".format(_millis())
dataset_ref = client.dataset(DATASET_ID)
dataset = bigquery.Dataset(dataset_ref)
dataset = client.create_dataset(dataset)
to_delete.append(dataset)
assert dataset_exists(client, dataset_ref)
assert not dataset_exists(client, client.dataset("i_dont_exist"))
@pytest.mark.skip(
reason=(
"update_dataset() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5588"
)
)
def test_manage_dataset_labels(client, to_delete):
dataset_id = "label_dataset_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
dataset = client.create_dataset(dataset)
to_delete.append(dataset)
# [START bigquery_label_dataset]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')
# dataset = client.get_dataset(dataset_ref) # API request
assert dataset.labels == {}
labels = {"color": "green"}
dataset.labels = labels
dataset = client.update_dataset(dataset, ["labels"]) # API request
assert dataset.labels == labels
# [END bigquery_label_dataset]
# [START bigquery_get_dataset_labels]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
dataset_ref = client.dataset(dataset_id)
dataset = client.get_dataset(dataset_ref) # API request
# View dataset labels
print("Dataset ID: {}".format(dataset_id))
print("Labels:")
if dataset.labels:
for label, value in dataset.labels.items():
print("\t{}: {}".format(label, value))
else:
print("\tDataset has no labels defined.")
# [END bigquery_get_dataset_labels]
assert dataset.labels == labels
# [START bigquery_delete_label_dataset]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')
# dataset = client.get_dataset(dataset_ref) # API request
# This example dataset starts with one label
assert dataset.labels == {"color": "green"}
# To delete a label from a dataset, set its value to None
dataset.labels["color"] = None
dataset = client.update_dataset(dataset, ["labels"]) # API request
assert dataset.labels == {}
# [END bigquery_delete_label_dataset]
def test_create_table_nested_repeated_schema(client, to_delete):
dataset_id = "create_table_nested_repeated_{}".format(_millis())
dataset_ref = client.dataset(dataset_id)
dataset = bigquery.Dataset(dataset_ref)
client.create_dataset(dataset)
to_delete.append(dataset)
# [START bigquery_nested_repeated_schema]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')
schema = [
bigquery.SchemaField("id", "STRING", mode="NULLABLE"),
bigquery.SchemaField("first_name", "STRING", mode="NULLABLE"),
bigquery.SchemaField("last_name", "STRING", mode="NULLABLE"),
bigquery.SchemaField("dob", "DATE", mode="NULLABLE"),
bigquery.SchemaField(
"addresses",
"RECORD",
mode="REPEATED",
fields=[
bigquery.SchemaField("status", "STRING", mode="NULLABLE"),
bigquery.SchemaField("address", "STRING", mode="NULLABLE"),
bigquery.SchemaField("city", "STRING", mode="NULLABLE"),
bigquery.SchemaField("state", "STRING", mode="NULLABLE"),
bigquery.SchemaField("zip", "STRING", mode="NULLABLE"),
bigquery.SchemaField("numberOfYears", "STRING", mode="NULLABLE"),
],
),
]
table_ref = dataset_ref.table("my_table")
table = bigquery.Table(table_ref, schema=schema)
table = client.create_table(table) # API request
print("Created table {}".format(table.full_table_id))
# [END bigquery_nested_repeated_schema]
def test_create_table_cmek(client, to_delete):
dataset_id = "create_table_cmek_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
client.create_dataset(dataset)
to_delete.append(dataset)
# [START bigquery_create_table_cmek]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
table_ref = client.dataset(dataset_id).table("my_table")
table = bigquery.Table(table_ref)
# Set the encryption key to use for the table.
# TODO: Replace this key with a key you have created in Cloud KMS.
kms_key_name = "projects/{}/locations/{}/keyRings/{}/cryptoKeys/{}".format(
"cloud-samples-tests", "us-central1", "test", "test"
)
table.encryption_configuration = bigquery.EncryptionConfiguration(
kms_key_name=kms_key_name
)
table = client.create_table(table) # API request
assert table.encryption_configuration.kms_key_name == kms_key_name
# [END bigquery_create_table_cmek]
def test_create_partitioned_table(client, to_delete):
dataset_id = "create_table_partitioned_{}".format(_millis())
dataset_ref = bigquery.Dataset(client.dataset(dataset_id))
dataset = client.create_dataset(dataset_ref)
to_delete.append(dataset)
# [START bigquery_create_table_partitioned]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_ref = client.dataset('my_dataset')
table_ref = dataset_ref.table("my_partitioned_table")
schema = [
bigquery.SchemaField("name", "STRING"),
bigquery.SchemaField("post_abbr", "STRING"),
bigquery.SchemaField("date", "DATE"),
]
table = bigquery.Table(table_ref, schema=schema)
table.time_partitioning = bigquery.TimePartitioning(
type_=bigquery.TimePartitioningType.DAY,
field="date", # name of column to use for partitioning
expiration_ms=7776000000,
) # 90 days
table = client.create_table(table)
print(
"Created table {}, partitioned on column {}".format(
table.table_id, table.time_partitioning.field
)
)
# [END bigquery_create_table_partitioned]
assert table.time_partitioning.type_ == "DAY"
assert table.time_partitioning.field == "date"
assert table.time_partitioning.expiration_ms == 7776000000
def test_load_and_query_partitioned_table(client, to_delete):
dataset_id = "load_partitioned_table_dataset_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
client.create_dataset(dataset)
to_delete.append(dataset)
# [START bigquery_load_table_partitioned]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
table_id = "us_states_by_date"
dataset_ref = client.dataset(dataset_id)
job_config = bigquery.LoadJobConfig()
job_config.schema = [
bigquery.SchemaField("name", "STRING"),
bigquery.SchemaField("post_abbr", "STRING"),
bigquery.SchemaField("date", "DATE"),
]
job_config.skip_leading_rows = 1
job_config.time_partitioning = bigquery.TimePartitioning(
type_=bigquery.TimePartitioningType.DAY,
field="date", # name of column to use for partitioning
expiration_ms=7776000000,
) # 90 days
uri = "gs://cloud-samples-data/bigquery/us-states/us-states-by-date.csv"
load_job = client.load_table_from_uri(
uri, dataset_ref.table(table_id), job_config=job_config
) # API request
assert load_job.job_type == "load"
load_job.result() # Waits for table load to complete.
table = client.get_table(dataset_ref.table(table_id))
print("Loaded {} rows to table {}".format(table.num_rows, table_id))
# [END bigquery_load_table_partitioned]
assert table.num_rows == 50
project_id = client.project
# [START bigquery_query_partitioned_table]
import datetime
# from google.cloud import bigquery
# client = bigquery.Client()
# project_id = 'my-project'
# dataset_id = 'my_dataset'
table_id = "us_states_by_date"
sql_template = """
SELECT *
FROM `{}.{}.{}`
WHERE date BETWEEN @start_date AND @end_date
"""
sql = sql_template.format(project_id, dataset_id, table_id)
job_config = bigquery.QueryJobConfig()
job_config.query_parameters = [
bigquery.ScalarQueryParameter("start_date", "DATE", datetime.date(1800, 1, 1)),
bigquery.ScalarQueryParameter("end_date", "DATE", datetime.date(1899, 12, 31)),
]
# API request
query_job = client.query(sql, job_config=job_config)
rows = list(query_job)
print("{} states were admitted to the US in the 1800s".format(len(rows)))
# [END bigquery_query_partitioned_table]
assert len(rows) == 29
# [START bigquery_table_exists]
def table_exists(client, table_reference):
"""Return if a table exists.
Args:
client (google.cloud.bigquery.client.Client):
A client to connect to the BigQuery API.
table_reference (google.cloud.bigquery.table.TableReference):
A reference to the table to look for.
Returns:
bool: ``True`` if the table exists, ``False`` otherwise.
"""
from google.cloud.exceptions import NotFound
try:
client.get_table(table_reference)
return True
except NotFound:
return False
# [END bigquery_table_exists]
def test_table_exists(client, to_delete):
"""Determine if a table exists."""
DATASET_ID = "get_table_dataset_{}".format(_millis())
TABLE_ID = "get_table_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(DATASET_ID))
dataset = client.create_dataset(dataset)
to_delete.append(dataset)
table_ref = dataset.table(TABLE_ID)
table = bigquery.Table(table_ref, schema=SCHEMA)
table = client.create_table(table)
assert table_exists(client, table_ref)
assert not table_exists(client, dataset.table("i_dont_exist"))
@pytest.mark.skip(
reason=(
"update_table() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5589"
)
)
def test_manage_table_labels(client, to_delete):
dataset_id = "label_table_dataset_{}".format(_millis())
table_id = "label_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
client.create_dataset(dataset)
to_delete.append(dataset)
table = bigquery.Table(dataset.table(table_id), schema=SCHEMA)
table = client.create_table(table)
# [START bigquery_label_table]
# from google.cloud import bigquery
# client = bigquery.Client()
# table_ref = client.dataset('my_dataset').table('my_table')
# table = client.get_table(table_ref) # API request
assert table.labels == {}
labels = {"color": "green"}
table.labels = labels
table = client.update_table(table, ["labels"]) # API request
assert table.labels == labels
# [END bigquery_label_table]
# [START bigquery_get_table_labels]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
# table_id = 'my_table'
dataset_ref = client.dataset(dataset_id)
table_ref = dataset_ref.table(table_id)
table = client.get_table(table_ref) # API Request
# View table labels
print("Table ID: {}".format(table_id))
print("Labels:")
if table.labels:
for label, value in table.labels.items():
print("\t{}: {}".format(label, value))
else:
print("\tTable has no labels defined.")
# [END bigquery_get_table_labels]
assert table.labels == labels
# [START bigquery_delete_label_table]
# from google.cloud import bigquery
# client = bigquery.Client()
# table_ref = client.dataset('my_dataset').table('my_table')
# table = client.get_table(table_ref) # API request
# This example table starts with one label
assert table.labels == {"color": "green"}
# To delete a label from a table, set its value to None
table.labels["color"] = None
table = client.update_table(table, ["labels"]) # API request
assert table.labels == {}
# [END bigquery_delete_label_table]
@pytest.mark.skip(
reason=(
"update_table() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5589"
)
)
def test_update_table_description(client, to_delete):
"""Update a table's description."""
dataset_id = "update_table_description_dataset_{}".format(_millis())
table_id = "update_table_description_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
client.create_dataset(dataset)
to_delete.append(dataset)
table = bigquery.Table(dataset.table(table_id), schema=SCHEMA)
table.description = "Original description."
table = client.create_table(table)
# [START bigquery_update_table_description]
# from google.cloud import bigquery
# client = bigquery.Client()
# table_ref = client.dataset('my_dataset').table('my_table')
# table = client.get_table(table_ref) # API request
assert table.description == "Original description."
table.description = "Updated description."
table = client.update_table(table, ["description"]) # API request
assert table.description == "Updated description."
# [END bigquery_update_table_description]
@pytest.mark.skip(
reason=(
"update_table() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5589"
)
)
def test_update_table_expiration(client, to_delete):
"""Update a table's expiration time."""
dataset_id = "update_table_expiration_dataset_{}".format(_millis())
table_id = "update_table_expiration_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
client.create_dataset(dataset)
to_delete.append(dataset)
table = bigquery.Table(dataset.table(table_id), schema=SCHEMA)
table = client.create_table(table)
# [START bigquery_update_table_expiration]
import datetime
import pytz
# from google.cloud import bigquery
# client = bigquery.Client()
# table_ref = client.dataset('my_dataset').table('my_table')
# table = client.get_table(table_ref) # API request
assert table.expires is None
# set table to expire 5 days from now
expiration = datetime.datetime.now(pytz.utc) + datetime.timedelta(days=5)
table.expires = expiration
table = client.update_table(table, ["expires"]) # API request
# expiration is stored in milliseconds
margin = datetime.timedelta(microseconds=1000)
assert expiration - margin <= table.expires <= expiration + margin
# [END bigquery_update_table_expiration]
@pytest.mark.skip(
reason=(
"update_table() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5589"
)
)
def test_add_empty_column(client, to_delete):
"""Adds an empty column to an existing table."""
dataset_id = "add_empty_column_dataset_{}".format(_millis())
table_id = "add_empty_column_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
dataset = client.create_dataset(dataset)
to_delete.append(dataset)
table = bigquery.Table(dataset.table(table_id), schema=SCHEMA)
table = client.create_table(table)
# [START bigquery_add_empty_column]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
# table_id = 'my_table'
table_ref = client.dataset(dataset_id).table(table_id)
table = client.get_table(table_ref) # API request
original_schema = table.schema
new_schema = original_schema[:] # creates a copy of the schema
new_schema.append(bigquery.SchemaField("phone", "STRING"))
table.schema = new_schema
table = client.update_table(table, ["schema"]) # API request
assert len(table.schema) == len(original_schema) + 1 == len(new_schema)
# [END bigquery_add_empty_column]
@pytest.mark.skip(
reason=(
"update_table() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5589"
)
)
def test_relax_column(client, to_delete):
"""Updates a schema field from required to nullable."""
dataset_id = "relax_column_dataset_{}".format(_millis())
table_id = "relax_column_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
dataset = client.create_dataset(dataset)
to_delete.append(dataset)
# [START bigquery_relax_column]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
# table_id = 'my_table'
original_schema = [
bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
]
table_ref = client.dataset(dataset_id).table(table_id)
table = bigquery.Table(table_ref, schema=original_schema)
table = client.create_table(table)
assert all(field.mode == "REQUIRED" for field in table.schema)
# SchemaField properties cannot be edited after initialization.
# To make changes, construct new SchemaField objects.
relaxed_schema = [
bigquery.SchemaField("full_name", "STRING", mode="NULLABLE"),
bigquery.SchemaField("age", "INTEGER", mode="NULLABLE"),
]
table.schema = relaxed_schema
table = client.update_table(table, ["schema"])
assert all(field.mode == "NULLABLE" for field in table.schema)
# [END bigquery_relax_column]
@pytest.mark.skip(
reason=(
"update_table() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5589"
)
)
def test_update_table_cmek(client, to_delete):
"""Patch a table's metadata."""
dataset_id = "update_table_cmek_{}".format(_millis())
table_id = "update_table_cmek_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
client.create_dataset(dataset)
to_delete.append(dataset)
table = bigquery.Table(dataset.table(table_id))
original_kms_key_name = "projects/{}/locations/{}/keyRings/{}/cryptoKeys/{}".format(
"cloud-samples-tests", "us-central1", "test", "test"
)
table.encryption_configuration = bigquery.EncryptionConfiguration(
kms_key_name=original_kms_key_name
)
table = client.create_table(table)
# [START bigquery_update_table_cmek]
# from google.cloud import bigquery
# client = bigquery.Client()
assert table.encryption_configuration.kms_key_name == original_kms_key_name
# Set a new encryption key to use for the destination.
# TODO: Replace this key with a key you have created in KMS.
updated_kms_key_name = (
"projects/cloud-samples-tests/locations/us-central1/"
"keyRings/test/cryptoKeys/otherkey"
)
table.encryption_configuration = bigquery.EncryptionConfiguration(
kms_key_name=updated_kms_key_name
)
table = client.update_table(table, ["encryption_configuration"]) # API request
assert table.encryption_configuration.kms_key_name == updated_kms_key_name
assert original_kms_key_name != updated_kms_key_name
# [END bigquery_update_table_cmek]
def test_browse_table_data(client, to_delete, capsys):
"""Retreive selected row data from a table."""
# [START bigquery_browse_table]
# from google.cloud import bigquery
# client = bigquery.Client()
dataset_ref = client.dataset("samples", project="bigquery-public-data")
table_ref = dataset_ref.table("shakespeare")
table = client.get_table(table_ref) # API call
# Load all rows from a table
rows = client.list_rows(table)
assert len(list(rows)) == table.num_rows
# Load the first 10 rows
rows = client.list_rows(table, max_results=10)
assert len(list(rows)) == 10
# Specify selected fields to limit the results to certain columns
fields = table.schema[:2] # first two columns
rows = client.list_rows(table, selected_fields=fields, max_results=10)
assert len(rows.schema) == 2
assert len(list(rows)) == 10
# Use the start index to load an arbitrary portion of the table
rows = client.list_rows(table, start_index=10, max_results=10)
# Print row data in tabular format
format_string = "{!s:<16} " * len(rows.schema)
field_names = [field.name for field in rows.schema]
print(format_string.format(*field_names)) # prints column headers
for row in rows:
print(format_string.format(*row)) # prints row data
# [END bigquery_browse_table]
out, err = capsys.readouterr()
out = list(filter(bool, out.split("\n"))) # list of non-blank lines
assert len(out) == 11
@pytest.mark.skip(
reason=(
"update_table() is flaky "
"https://github.com/GoogleCloudPlatform/google-cloud-python/issues/5589"
)
)
def test_manage_views(client, to_delete):
project = client.project
source_dataset_id = "source_dataset_{}".format(_millis())
source_dataset_ref = client.dataset(source_dataset_id)
source_dataset = bigquery.Dataset(source_dataset_ref)
source_dataset = client.create_dataset(source_dataset)
to_delete.append(source_dataset)
job_config = bigquery.LoadJobConfig()
job_config.schema = [
bigquery.SchemaField("name", "STRING"),
bigquery.SchemaField("post_abbr", "STRING"),
]
job_config.skip_leading_rows = 1
uri = "gs://cloud-samples-data/bigquery/us-states/us-states.csv"
source_table_id = "us_states"
load_job = client.load_table_from_uri(
uri, source_dataset.table(source_table_id), job_config=job_config
)
load_job.result()
shared_dataset_id = "shared_dataset_{}".format(_millis())
shared_dataset_ref = client.dataset(shared_dataset_id)
shared_dataset = bigquery.Dataset(shared_dataset_ref)
shared_dataset = client.create_dataset(shared_dataset)
to_delete.append(shared_dataset)
# [START bigquery_create_view]
# from google.cloud import bigquery
# client = bigquery.Client()
# project = 'my-project'
# source_dataset_id = 'my_source_dataset'
# source_table_id = 'us_states'
# shared_dataset_ref = client.dataset('my_shared_dataset')
# This example shows how to create a shared view of a source table of
# US States. The source table contains all 50 states, while the view will
# contain only states with names starting with 'W'.
view_ref = shared_dataset_ref.table("my_shared_view")
view = bigquery.Table(view_ref)
sql_template = 'SELECT name, post_abbr FROM `{}.{}.{}` WHERE name LIKE "W%"'
view.view_query = sql_template.format(project, source_dataset_id, source_table_id)
view = client.create_table(view) # API request
print("Successfully created view at {}".format(view.full_table_id))
# [END bigquery_create_view]
# [START bigquery_update_view_query]
# from google.cloud import bigquery
# client = bigquery.Client()
# project = 'my-project'
# source_dataset_id = 'my_source_dataset'
# source_table_id = 'us_states'
# shared_dataset_ref = client.dataset('my_shared_dataset')
# This example shows how to update a shared view of a source table of
# US States. The view's query will be updated to contain only states with
# names starting with 'M'.
view_ref = shared_dataset_ref.table("my_shared_view")
view = bigquery.Table(view_ref)
sql_template = 'SELECT name, post_abbr FROM `{}.{}.{}` WHERE name LIKE "M%"'
view.view_query = sql_template.format(project, source_dataset_id, source_table_id)
view = client.update_table(view, ["view_query"]) # API request
# [END bigquery_update_view_query]
# [START bigquery_get_view]
# from google.cloud import bigquery
# client = bigquery.Client()
# shared_dataset_id = 'my_shared_dataset'
view_ref = client.dataset(shared_dataset_id).table("my_shared_view")
view = client.get_table(view_ref) # API Request
# Display view properties
print("View at {}".format(view.full_table_id))
print("View Query:\n{}".format(view.view_query))
# [END bigquery_get_view]
assert view.view_query is not None
analyst_group_email = "example-analyst-group@google.com"
# [START bigquery_grant_view_access]
# from google.cloud import bigquery
# client = bigquery.Client()
# Assign access controls to the dataset containing the view
# shared_dataset_id = 'my_shared_dataset'
# analyst_group_email = 'data_analysts@example.com'
shared_dataset = client.get_dataset(
client.dataset(shared_dataset_id)
) # API request
access_entries = shared_dataset.access_entries
access_entries.append(
bigquery.AccessEntry("READER", "groupByEmail", analyst_group_email)
)
shared_dataset.access_entries = access_entries
shared_dataset = client.update_dataset(
shared_dataset, ["access_entries"]
) # API request
# Authorize the view to access the source dataset
# project = 'my-project'
# source_dataset_id = 'my_source_dataset'
source_dataset = client.get_dataset(
client.dataset(source_dataset_id)
) # API request
view_reference = {
"projectId": project,
"datasetId": shared_dataset_id,
"tableId": "my_shared_view",
}
access_entries = source_dataset.access_entries
access_entries.append(bigquery.AccessEntry(None, "view", view_reference))
source_dataset.access_entries = access_entries
source_dataset = client.update_dataset(
source_dataset, ["access_entries"]
) # API request
# [END bigquery_grant_view_access]
def test_table_insert_rows(client, to_delete):
"""Insert / fetch table data."""
dataset_id = "table_insert_rows_dataset_{}".format(_millis())
table_id = "table_insert_rows_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
dataset = client.create_dataset(dataset)
dataset.location = "US"
to_delete.append(dataset)
table = bigquery.Table(dataset.table(table_id), schema=SCHEMA)
table = client.create_table(table)
# [START bigquery_table_insert_rows]
# TODO(developer): Uncomment the lines below and replace with your values.
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset' # replace with your dataset ID
# For this sample, the table must already exist and have a defined schema
# table_id = 'my_table' # replace with your table ID
# table_ref = client.dataset(dataset_id).table(table_id)
# table = client.get_table(table_ref) # API request
rows_to_insert = [(u"Phred Phlyntstone", 32), (u"Wylma Phlyntstone", 29)]
errors = client.insert_rows(table, rows_to_insert) # API request
assert errors == []
# [END bigquery_table_insert_rows]
def test_load_table_from_file(client, to_delete):
"""Upload table data from a CSV file."""
dataset_id = "load_table_from_file_dataset_{}".format(_millis())
table_id = "load_table_from_file_table_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
dataset.location = "US"
client.create_dataset(dataset)
to_delete.append(dataset)
snippets_dir = os.path.abspath(os.path.dirname(__file__))
filename = os.path.join(
snippets_dir, "..", "..", "bigquery", "tests", "data", "people.csv"
)
# [START bigquery_load_from_file]
# from google.cloud import bigquery
# client = bigquery.Client()
# filename = '/path/to/file.csv'
# dataset_id = 'my_dataset'
# table_id = 'my_table'
dataset_ref = client.dataset(dataset_id)
table_ref = dataset_ref.table(table_id)
job_config = bigquery.LoadJobConfig()
job_config.source_format = bigquery.SourceFormat.CSV
job_config.skip_leading_rows = 1
job_config.autodetect = True
with open(filename, "rb") as source_file:
job = client.load_table_from_file(source_file, table_ref, job_config=job_config)
job.result() # Waits for table load to complete.
print("Loaded {} rows into {}:{}.".format(job.output_rows, dataset_id, table_id))
# [END bigquery_load_from_file]
table = client.get_table(table_ref)
rows = list(client.list_rows(table)) # API request
assert len(rows) == 2
# Order is not preserved, so compare individually
row1 = bigquery.Row(("Wylma Phlyntstone", 29), {"full_name": 0, "age": 1})
assert row1 in rows
row2 = bigquery.Row(("Phred Phlyntstone", 32), {"full_name": 0, "age": 1})
assert row2 in rows
def test_load_table_from_uri_avro(client, to_delete, capsys):
dataset_id = "load_table_from_uri_avro_{}".format(_millis())
dataset = bigquery.Dataset(client.dataset(dataset_id))
client.create_dataset(dataset)
to_delete.append(dataset)
# [START bigquery_load_table_gcs_avro]
# from google.cloud import bigquery
# client = bigquery.Client()
# dataset_id = 'my_dataset'
dataset_ref = client.dataset(dataset_id)
job_config = bigquery.LoadJobConfig()
job_config.source_format = bigquery.SourceFormat.AVRO
uri = "gs://cloud-samples-data/bigquery/us-states/us-states.avro"
load_job = client.load_table_from_uri(
uri, dataset_ref.table("us_states"), job_config=job_config
) # API request