testbed/googleapis__python-bigquery/docs/snippets.py

# 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

try:
    import pandas
except (ImportError, AttributeError):
    pandas = None

try:
    import pyarrow
except (ImportError, AttributeError):
    pyarrow = None

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


@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())
    project = client.project
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = bigquery.Dataset(dataset_ref)
    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()
    # project = client.project
    # dataset_ref = bigquery.DatasetReference(project, dataset_id)
    # table_ref = dataset_ref.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_cmek(client, to_delete):
    """Patch a table's metadata."""
    dataset_id = "update_table_cmek_{}".format(_millis())
    table_id = "update_table_cmek_{}".format(_millis())
    project = client.project
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = bigquery.Dataset(dataset_ref)
    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", "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/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_load_table_add_column(client, to_delete):
    dataset_id = "load_table_add_column_{}".format(_millis())
    project = client.project
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = bigquery.Dataset(dataset_ref)
    dataset.location = "US"
    dataset = client.create_dataset(dataset)
    to_delete.append(dataset)

    snippets_dir = os.path.abspath(os.path.dirname(__file__))
    filepath = os.path.join(snippets_dir, "..", "tests", "data", "people.csv")
    table_ref = dataset_ref.table("my_table")
    old_schema = [bigquery.SchemaField("full_name", "STRING", mode="REQUIRED")]
    table = client.create_table(bigquery.Table(table_ref, schema=old_schema))

    # [START bigquery_add_column_load_append]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # project = client.project
    # dataset_ref = bigquery.DatasetReference(project, 'my_dataset')
    # filepath = 'path/to/your_file.csv'

    # Retrieves the destination table and checks the length of the schema
    table_id = "my_table"
    table_ref = dataset_ref.table(table_id)
    table = client.get_table(table_ref)
    print("Table {} contains {} columns.".format(table_id, len(table.schema)))

    # Configures the load job to append the data to the destination table,
    # allowing field addition
    job_config = bigquery.LoadJobConfig()
    job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND
    job_config.schema_update_options = [
        bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION
    ]
    # In this example, the existing table contains only the 'full_name' column.
    # 'REQUIRED' fields cannot be added to an existing schema, so the
    # additional column must be 'NULLABLE'.
    job_config.schema = [
        bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
        bigquery.SchemaField("age", "INTEGER", mode="NULLABLE"),
    ]
    job_config.source_format = bigquery.SourceFormat.CSV
    job_config.skip_leading_rows = 1

    with open(filepath, "rb") as source_file:
        job = client.load_table_from_file(
            source_file,
            table_ref,
            location="US",  # Must match the destination dataset location.
            job_config=job_config,
        )  # API request

    job.result()  # Waits for table load to complete.
    print(
        "Loaded {} rows into {}:{}.".format(
            job.output_rows, dataset_id, table_ref.table_id
        )
    )

    # Checks the updated length of the schema
    table = client.get_table(table)
    print("Table {} now contains {} columns.".format(table_id, len(table.schema)))
    # [END bigquery_add_column_load_append]
    assert len(table.schema) == 2
    assert table.num_rows > 0


def test_load_table_relax_column(client, to_delete):
    dataset_id = "load_table_relax_column_{}".format(_millis())
    project = client.project
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = bigquery.Dataset(dataset_ref)
    dataset.location = "US"
    dataset = client.create_dataset(dataset)
    to_delete.append(dataset)

    snippets_dir = os.path.abspath(os.path.dirname(__file__))
    filepath = os.path.join(snippets_dir, "..", "tests", "data", "people.csv")
    table_ref = dataset_ref.table("my_table")
    old_schema = [
        bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
        bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
        bigquery.SchemaField("favorite_color", "STRING", mode="REQUIRED"),
    ]
    table = client.create_table(bigquery.Table(table_ref, schema=old_schema))

    # [START bigquery_relax_column_load_append]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # project = client.project
    # dataset_ref = bigquery.DatasetReference(project, 'my_dataset')
    # filepath = 'path/to/your_file.csv'

    # Retrieves the destination table and checks the number of required fields
    table_id = "my_table"
    table_ref = dataset_ref.table(table_id)
    table = client.get_table(table_ref)
    original_required_fields = sum(field.mode == "REQUIRED" for field in table.schema)
    # In this example, the existing table has 3 required fields.
    print("{} fields in the schema are required.".format(original_required_fields))

    # Configures the load job to append the data to a destination table,
    # allowing field relaxation
    job_config = bigquery.LoadJobConfig()
    job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND
    job_config.schema_update_options = [
        bigquery.SchemaUpdateOption.ALLOW_FIELD_RELAXATION
    ]
    # In this example, the existing table contains three required fields
    # ('full_name', 'age', and 'favorite_color'), while the data to load
    # contains only the first two fields.
    job_config.schema = [
        bigquery.SchemaField("full_name", "STRING", mode="REQUIRED"),
        bigquery.SchemaField("age", "INTEGER", mode="REQUIRED"),
    ]
    job_config.source_format = bigquery.SourceFormat.CSV
    job_config.skip_leading_rows = 1

    with open(filepath, "rb") as source_file:
        job = client.load_table_from_file(
            source_file,
            table_ref,
            location="US",  # Must match the destination dataset location.
            job_config=job_config,
        )  # API request

    job.result()  # Waits for table load to complete.
    print(
        "Loaded {} rows into {}:{}.".format(
            job.output_rows, dataset_id, table_ref.table_id
        )
    )

    # Checks the updated number of required fields
    table = client.get_table(table)
    current_required_fields = sum(field.mode == "REQUIRED" for field in table.schema)
    print("{} fields in the schema are now required.".format(current_required_fields))
    # [END bigquery_relax_column_load_append]
    assert original_required_fields - current_required_fields == 1
    assert len(table.schema) == 3
    assert table.schema[2].mode == "NULLABLE"
    assert table.num_rows > 0


def test_extract_table(client, to_delete):
    bucket_name = "extract_shakespeare_{}".format(_millis())
    storage_client = storage.Client()
    bucket = retry_storage_errors(storage_client.create_bucket)(bucket_name)
    to_delete.append(bucket)

    # [START bigquery_extract_table]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # bucket_name = 'my-bucket'
    project = "bigquery-public-data"
    dataset_id = "samples"
    table_id = "shakespeare"

    destination_uri = "gs://{}/{}".format(bucket_name, "shakespeare.csv")
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    table_ref = dataset_ref.table(table_id)

    extract_job = client.extract_table(
        table_ref,
        destination_uri,
        # Location must match that of the source table.
        location="US",
    )  # API request
    extract_job.result()  # Waits for job to complete.

    print(
        "Exported {}:{}.{} to {}".format(project, dataset_id, table_id, destination_uri)
    )
    # [END bigquery_extract_table]

    blob = retry_storage_errors(bucket.get_blob)("shakespeare.csv")
    assert blob.exists
    assert blob.size > 0
    to_delete.insert(0, blob)


def test_extract_table_json(client, to_delete):
    bucket_name = "extract_shakespeare_json_{}".format(_millis())
    storage_client = storage.Client()
    bucket = retry_storage_errors(storage_client.create_bucket)(bucket_name)
    to_delete.append(bucket)
    project = "bigquery-public-data"
    dataset_id = "samples"

    # [START bigquery_extract_table_json]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # bucket_name = 'my-bucket'

    destination_uri = "gs://{}/{}".format(bucket_name, "shakespeare.json")
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    table_ref = dataset_ref.table("shakespeare")
    job_config = bigquery.job.ExtractJobConfig()
    job_config.destination_format = bigquery.DestinationFormat.NEWLINE_DELIMITED_JSON

    extract_job = client.extract_table(
        table_ref,
        destination_uri,
        job_config=job_config,
        # Location must match that of the source table.
        location="US",
    )  # API request
    extract_job.result()  # Waits for job to complete.
    # [END bigquery_extract_table_json]

    blob = retry_storage_errors(bucket.get_blob)("shakespeare.json")
    assert blob.exists
    assert blob.size > 0
    to_delete.insert(0, blob)


def test_extract_table_compressed(client, to_delete):
    bucket_name = "extract_shakespeare_compress_{}".format(_millis())
    storage_client = storage.Client()
    bucket = retry_storage_errors(storage_client.create_bucket)(bucket_name)
    to_delete.append(bucket)
    project = "bigquery-public-data"
    dataset_id = "samples"

    # [START bigquery_extract_table_compressed]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    # bucket_name = 'my-bucket'

    destination_uri = "gs://{}/{}".format(bucket_name, "shakespeare.csv.gz")
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    table_ref = dataset_ref.table("shakespeare")
    job_config = bigquery.job.ExtractJobConfig()
    job_config.compression = bigquery.Compression.GZIP

    extract_job = client.extract_table(
        table_ref,
        destination_uri,
        # Location must match that of the source table.
        location="US",
        job_config=job_config,
    )  # API request
    extract_job.result()  # Waits for job to complete.
    # [END bigquery_extract_table_compressed]

    blob = retry_storage_errors(bucket.get_blob)("shakespeare.csv.gz")
    assert blob.exists
    assert blob.size > 0
    to_delete.insert(0, blob)


def test_client_query_total_rows(client, capsys):
    """Run a query and just check for how many rows."""
    # [START bigquery_query_total_rows]
    # from google.cloud import bigquery
    # client = bigquery.Client()

    query = (
        "SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` "
        'WHERE state = "TX" '
        "LIMIT 100"
    )
    results = client.query_and_wait(
        query,
        # Location must match that of the dataset(s) referenced in the query.
        location="US",
    )  # API request - starts the query and waits for results.

    print("Got {} rows.".format(results.total_rows))
    # [END bigquery_query_total_rows]

    out, _ = capsys.readouterr()
    assert "Got 100 rows." in out


def test_ddl_create_view(client, to_delete, capsys):
    """Create a view via a DDL query."""
    project = client.project
    dataset_id = "ddl_view_{}".format(_millis())
    table_id = "new_view"
    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    dataset = bigquery.Dataset(dataset_ref)
    client.create_dataset(dataset)
    to_delete.append(dataset)

    # [START bigquery_ddl_create_view]
    # from google.cloud import bigquery
    # project = 'my-project'
    # dataset_id = 'my_dataset'
    # table_id = 'new_view'
    # client = bigquery.Client(project=project)

    sql = """
    CREATE VIEW `{}.{}.{}`
    OPTIONS(
        expiration_timestamp=TIMESTAMP_ADD(
            CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
        friendly_name="new_view",
        description="a view that expires in 2 days",
        labels=[("org_unit", "development")]
    )
    AS SELECT name, state, year, number
        FROM `bigquery-public-data.usa_names.usa_1910_current`
        WHERE state LIKE 'W%'
    """.format(
        project, dataset_id, table_id
    )

    job = client.query(sql)  # API request.
    job.result()  # Waits for the query to finish.

    print(
        'Created new view "{}.{}.{}".'.format(
            job.destination.project,
            job.destination.dataset_id,
            job.destination.table_id,
        )
    )
    # [END bigquery_ddl_create_view]

    out, _ = capsys.readouterr()
    assert 'Created new view "{}.{}.{}".'.format(project, dataset_id, table_id) in out

    # Test that listing query result rows succeeds so that generic query
    # processing tools work with DDL statements.
    rows = list(job)
    assert len(rows) == 0

    if pandas is not None:
        df = job.to_dataframe()
        assert len(df) == 0


@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_query_results_as_dataframe(client):
    # [START bigquery_query_results_dataframe]
    # from google.cloud import bigquery
    # client = bigquery.Client()

    sql = """
        SELECT name, SUM(number) as count
        FROM `bigquery-public-data.usa_names.usa_1910_current`
        GROUP BY name
        ORDER BY count DESC
        LIMIT 10
    """

    df = client.query_and_wait(sql).to_dataframe()
    # [END bigquery_query_results_dataframe]
    assert isinstance(df, pandas.DataFrame)
    assert len(list(df)) == 2  # verify the number of columns
    assert len(df) == 10  # verify the number of rows


@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_list_rows_as_dataframe(client):
    # [START bigquery_list_rows_dataframe]
    # from google.cloud import bigquery
    # client = bigquery.Client()
    project = "bigquery-public-data"
    dataset_id = "samples"

    dataset_ref = bigquery.DatasetReference(project, dataset_id)
    table_ref = dataset_ref.table("shakespeare")
    table = client.get_table(table_ref)

    df = client.list_rows(table).to_dataframe()
    # [END bigquery_list_rows_dataframe]
    assert isinstance(df, pandas.DataFrame)
    assert len(list(df)) == len(table.schema)  # verify the number of columns
    assert len(df) == table.num_rows  # verify the number of rows


if __name__ == "__main__":
    pytest.main()