As of January 1, 2020 this library no longer supports Python 2 on the latest released version.
Library versions released prior to that date will continue to be available. For more information please
visit Python 2 support on Google Cloud.
Source code for google.cloud.bigquery.client
# Copyright 2015 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.
"""Client for interacting with the Google BigQuery API."""
from __future__ import absolute_import
from __future__ import division
from collections import abc as collections_abc
import copy
import functools
import gzip
import io
import itertools
import json
import math
import os
import tempfile
import uuid
import warnings
try:
import pyarrow
except ImportError: # pragma: NO COVER
pyarrow = None
from google import resumable_media
from google.resumable_media.requests import MultipartUpload
from google.resumable_media.requests import ResumableUpload
import google.api_core.client_options
import google.api_core.exceptions
from google.api_core.iam import Policy
from google.api_core import page_iterator
import google.cloud._helpers
from google.cloud import exceptions
from google.cloud.client import ClientWithProject
from google.cloud.bigquery._helpers import _del_sub_prop
from google.cloud.bigquery._helpers import _get_sub_prop
from google.cloud.bigquery._helpers import _record_field_to_json
from google.cloud.bigquery._helpers import _str_or_none
from google.cloud.bigquery._helpers import _verify_job_config_type
from google.cloud.bigquery._http import Connection
from google.cloud.bigquery import _pandas_helpers
from google.cloud.bigquery.dataset import Dataset
from google.cloud.bigquery.dataset import DatasetListItem
from google.cloud.bigquery.dataset import DatasetReference
from google.cloud.bigquery.opentelemetry_tracing import create_span
from google.cloud.bigquery import job
from google.cloud.bigquery.model import Model
from google.cloud.bigquery.model import ModelReference
from google.cloud.bigquery.model import _model_arg_to_model_ref
from google.cloud.bigquery.query import _QueryResults
from google.cloud.bigquery.retry import DEFAULT_RETRY
from google.cloud.bigquery.routine import Routine
from google.cloud.bigquery.routine import RoutineReference
from google.cloud.bigquery.schema import SchemaField
from google.cloud.bigquery.table import _table_arg_to_table
from google.cloud.bigquery.table import _table_arg_to_table_ref
from google.cloud.bigquery.table import Table
from google.cloud.bigquery.table import TableListItem
from google.cloud.bigquery.table import TableReference
from google.cloud.bigquery.table import RowIterator
_DEFAULT_CHUNKSIZE = 1048576 # 1024 * 1024 B = 1 MB
_MAX_MULTIPART_SIZE = 5 * 1024 * 1024
_DEFAULT_NUM_RETRIES = 6
_BASE_UPLOAD_TEMPLATE = "{host}/upload/bigquery/v2/projects/{project}/jobs?uploadType="
_MULTIPART_URL_TEMPLATE = _BASE_UPLOAD_TEMPLATE + "multipart"
_RESUMABLE_URL_TEMPLATE = _BASE_UPLOAD_TEMPLATE + "resumable"
_GENERIC_CONTENT_TYPE = "*/*"
_READ_LESS_THAN_SIZE = (
"Size {:d} was specified but the file-like object only had " "{:d} bytes remaining."
)
_NEED_TABLE_ARGUMENT = (
"The table argument should be a table ID string, Table, or TableReference"
)
_LIST_ROWS_FROM_QUERY_RESULTS_FIELDS = "jobReference,totalRows,pageToken,rows"
# In microbenchmarks, it's been shown that even in ideal conditions (query
# finished, local data), requests to getQueryResults can take 10+ seconds.
# In less-than-ideal situations, the response can take even longer, as it must
# be able to download a full 100+ MB row in that time. Don't let the
# connection timeout before data can be downloaded.
# https://github.com/googleapis/python-bigquery/issues/438
_MIN_GET_QUERY_RESULTS_TIMEOUT = 120
class Project(object):
"""Wrapper for resource describing a BigQuery project.
Args:
project_id (str): Opaque ID of the project
numeric_id (int): Numeric ID of the project
friendly_name (str): Display name of the project
"""
def __init__(self, project_id, numeric_id, friendly_name):
self.project_id = project_id
self.numeric_id = numeric_id
self.friendly_name = friendly_name
@classmethod
def from_api_repr(cls, resource):
"""Factory: construct an instance from a resource dict."""
return cls(resource["id"], resource["numericId"], resource["friendlyName"])
[docs]class Client(ClientWithProject):
"""Client to bundle configuration needed for API requests.
Args:
project (Optional[str]):
Project ID for the project which the client acts on behalf of.
Will be passed when creating a dataset / job. If not passed,
falls back to the default inferred from the environment.
credentials (Optional[google.auth.credentials.Credentials]):
The OAuth2 Credentials to use for this client. If not passed
(and if no ``_http`` object is passed), falls back to the
default inferred from the environment.
_http (Optional[requests.Session]):
HTTP object to make requests. Can be any object that
defines ``request()`` with the same interface as
:meth:`requests.Session.request`. If not passed, an ``_http``
object is created that is bound to the ``credentials`` for the
current object.
This parameter should be considered private, and could change in
the future.
location (Optional[str]):
Default location for jobs / datasets / tables.
default_query_job_config (Optional[google.cloud.bigquery.job.QueryJobConfig]):
Default ``QueryJobConfig``.
Will be merged into job configs passed into the ``query`` method.
client_info (Optional[google.api_core.client_info.ClientInfo]):
The client info used to send a user-agent string along with API
requests. If ``None``, then default info will be used. Generally,
you only need to set this if you're developing your own library
or partner tool.
client_options (Optional[Union[google.api_core.client_options.ClientOptions, Dict]]):
Client options used to set user options on the client. API Endpoint
should be set through client_options.
Raises:
google.auth.exceptions.DefaultCredentialsError:
Raised if ``credentials`` is not specified and the library fails
to acquire default credentials.
"""
SCOPE = (
"https://www.googleapis.com/auth/bigquery",
"https://www.googleapis.com/auth/cloud-platform",
)
"""The scopes required for authenticating as a BigQuery consumer."""
[docs] def __init__(
self,
project=None,
credentials=None,
_http=None,
location=None,
default_query_job_config=None,
client_info=None,
client_options=None,
):
super(Client, self).__init__(
project=project,
credentials=credentials,
client_options=client_options,
_http=_http,
)
kw_args = {"client_info": client_info}
if client_options:
if type(client_options) == dict:
client_options = google.api_core.client_options.from_dict(
client_options
)
if client_options.api_endpoint:
api_endpoint = client_options.api_endpoint
kw_args["api_endpoint"] = api_endpoint
self._connection = Connection(self, **kw_args)
self._location = location
self._default_query_job_config = copy.deepcopy(default_query_job_config)
@property
def location(self):
"""Default location for jobs / datasets / tables."""
return self._location
[docs] def close(self):
"""Close the underlying transport objects, releasing system resources.
.. note::
The client instance can be used for making additional requests even
after closing, in which case the underlying connections are
automatically re-created.
"""
self._http._auth_request.session.close()
self._http.close()
[docs] def get_service_account_email(
self, project=None, retry=DEFAULT_RETRY, timeout=None
):
"""Get the email address of the project's BigQuery service account
Note:
This is the service account that BigQuery uses to manage tables
encrypted by a key in KMS.
Args:
project (Optional[str]):
Project ID to use for retreiving service account email.
Defaults to the client's project.
retry (Optional[google.api_core.retry.Retry]): How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
str: service account email address
Example:
>>> from google.cloud import bigquery
>>> client = bigquery.Client()
>>> client.get_service_account_email()
my_service_account@my-project.iam.gserviceaccount.com
"""
if project is None:
project = self.project
path = "/projects/%s/serviceAccount" % (project,)
span_attributes = {"path": path}
api_response = self._call_api(
retry,
span_name="BigQuery.getServiceAccountEmail",
span_attributes=span_attributes,
method="GET",
path=path,
timeout=timeout,
)
return api_response["email"]
[docs] def list_projects(
self, max_results=None, page_token=None, retry=DEFAULT_RETRY, timeout=None
):
"""List projects for the project associated with this client.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/projects/list
Args:
max_results (Optional[int]):
Maximum number of projects to return, If not passed,
defaults to a value set by the API.
page_token (Optional[str]):
Token representing a cursor into the projects. If not passed,
the API will return the first page of projects. The token marks
the beginning of the iterator to be returned and the value of
the ``page_token`` can be accessed at ``next_page_token`` of the
:class:`~google.api_core.page_iterator.HTTPIterator`.
retry (Optional[google.api_core.retry.Retry]): How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.api_core.page_iterator.Iterator:
Iterator of :class:`~google.cloud.bigquery.client.Project`
accessible to the current client.
"""
span_attributes = {"path": "/projects"}
def api_request(*args, **kwargs):
return self._call_api(
retry,
span_name="BigQuery.listProjects",
span_attributes=span_attributes,
*args,
timeout=timeout,
**kwargs,
)
return page_iterator.HTTPIterator(
client=self,
api_request=api_request,
path="/projects",
item_to_value=_item_to_project,
items_key="projects",
page_token=page_token,
max_results=max_results,
)
[docs] def list_datasets(
self,
project=None,
include_all=False,
filter=None,
max_results=None,
page_token=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""List datasets for the project associated with this client.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets/list
Args:
project (Optional[str]):
Project ID to use for retreiving datasets. Defaults to the
client's project.
include_all (Optional[bool]):
True if results include hidden datasets. Defaults to False.
filter (Optional[str]):
An expression for filtering the results by label.
For syntax, see
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets/list#body.QUERY_PARAMETERS.filter
max_results (Optional[int]):
Maximum number of datasets to return.
page_token (Optional[str]):
Token representing a cursor into the datasets. If not passed,
the API will return the first page of datasets. The token marks
the beginning of the iterator to be returned and the value of
the ``page_token`` can be accessed at ``next_page_token`` of the
:class:`~google.api_core.page_iterator.HTTPIterator`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.api_core.page_iterator.Iterator:
Iterator of :class:`~google.cloud.bigquery.dataset.DatasetListItem`.
associated with the project.
"""
extra_params = {}
if project is None:
project = self.project
if include_all:
extra_params["all"] = True
if filter:
# TODO: consider supporting a dict of label -> value for filter,
# and converting it into a string here.
extra_params["filter"] = filter
path = "/projects/%s/datasets" % (project,)
span_attributes = {"path": path}
def api_request(*args, **kwargs):
return self._call_api(
retry,
span_name="BigQuery.listDatasets",
span_attributes=span_attributes,
*args,
timeout=timeout,
**kwargs,
)
return page_iterator.HTTPIterator(
client=self,
api_request=api_request,
path=path,
item_to_value=_item_to_dataset,
items_key="datasets",
page_token=page_token,
max_results=max_results,
extra_params=extra_params,
)
[docs] def dataset(self, dataset_id, project=None):
"""Deprecated: Construct a reference to a dataset.
.. deprecated:: 1.24.0
Construct a
:class:`~google.cloud.bigquery.dataset.DatasetReference` using its
constructor or use a string where previously a reference object
was used.
As of ``google-cloud-bigquery`` version 1.7.0, all client methods
that take a
:class:`~google.cloud.bigquery.dataset.DatasetReference` or
:class:`~google.cloud.bigquery.table.TableReference` also take a
string in standard SQL format, e.g. ``project.dataset_id`` or
``project.dataset_id.table_id``.
Args:
dataset_id (str): ID of the dataset.
project (Optional[str]):
Project ID for the dataset (defaults to the project of the client).
Returns:
google.cloud.bigquery.dataset.DatasetReference:
a new ``DatasetReference`` instance.
"""
if project is None:
project = self.project
warnings.warn(
"Client.dataset is deprecated and will be removed in a future version. "
"Use a string like 'my_project.my_dataset' or a "
"cloud.google.bigquery.DatasetReference object, instead.",
PendingDeprecationWarning,
stacklevel=2,
)
return DatasetReference(project, dataset_id)
def _create_bqstorage_client(self):
"""Create a BigQuery Storage API client using this client's credentials.
If a client cannot be created due to missing dependencies, raise a
warning and return ``None``.
Returns:
Optional[google.cloud.bigquery_storage.BigQueryReadClient]:
A BigQuery Storage API client.
"""
try:
from google.cloud import bigquery_storage
except ImportError:
warnings.warn(
"Cannot create BigQuery Storage client, the dependency "
"google-cloud-bigquery-storage is not installed."
)
return None
return bigquery_storage.BigQueryReadClient(credentials=self._credentials)
[docs] def create_dataset(
self, dataset, exists_ok=False, retry=DEFAULT_RETRY, timeout=None
):
"""API call: create the dataset via a POST request.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets/insert
Args:
dataset (Union[ \
google.cloud.bigquery.dataset.Dataset, \
google.cloud.bigquery.dataset.DatasetReference, \
str, \
]):
A :class:`~google.cloud.bigquery.dataset.Dataset` to create.
If ``dataset`` is a reference, an empty dataset is created
with the specified ID and client's default location.
exists_ok (Optional[bool]):
Defaults to ``False``. If ``True``, ignore "already exists"
errors when creating the dataset.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.dataset.Dataset:
A new ``Dataset`` returned from the API.
Raises:
google.cloud.exceptions.Conflict:
If the dataset already exists.
Example:
>>> from google.cloud import bigquery
>>> client = bigquery.Client()
>>> dataset = bigquery.Dataset('my_project.my_dataset')
>>> dataset = client.create_dataset(dataset)
"""
if isinstance(dataset, str):
dataset = DatasetReference.from_string(
dataset, default_project=self.project
)
if isinstance(dataset, DatasetReference):
dataset = Dataset(dataset)
path = "/projects/%s/datasets" % (dataset.project,)
data = dataset.to_api_repr()
if data.get("location") is None and self.location is not None:
data["location"] = self.location
try:
span_attributes = {"path": path}
api_response = self._call_api(
retry,
span_name="BigQuery.createDataset",
span_attributes=span_attributes,
method="POST",
path=path,
data=data,
timeout=timeout,
)
return Dataset.from_api_repr(api_response)
except google.api_core.exceptions.Conflict:
if not exists_ok:
raise
return self.get_dataset(dataset.reference, retry=retry)
[docs] def create_routine(
self, routine, exists_ok=False, retry=DEFAULT_RETRY, timeout=None
):
"""[Beta] Create a routine via a POST request.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/routines/insert
Args:
routine (google.cloud.bigquery.routine.Routine):
A :class:`~google.cloud.bigquery.routine.Routine` to create.
The dataset that the routine belongs to must already exist.
exists_ok (Optional[bool]):
Defaults to ``False``. If ``True``, ignore "already exists"
errors when creating the routine.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.routine.Routine:
A new ``Routine`` returned from the service.
Raises:
google.cloud.exceptions.Conflict:
If the routine already exists.
"""
reference = routine.reference
path = "/projects/{}/datasets/{}/routines".format(
reference.project, reference.dataset_id
)
resource = routine.to_api_repr()
try:
span_attributes = {"path": path}
api_response = self._call_api(
retry,
span_name="BigQuery.createRoutine",
span_attributes=span_attributes,
method="POST",
path=path,
data=resource,
timeout=timeout,
)
return Routine.from_api_repr(api_response)
except google.api_core.exceptions.Conflict:
if not exists_ok:
raise
return self.get_routine(routine.reference, retry=retry)
[docs] def create_table(self, table, exists_ok=False, retry=DEFAULT_RETRY, timeout=None):
"""API call: create a table via a PUT request
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables/insert
Args:
table (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
A :class:`~google.cloud.bigquery.table.Table` to create.
If ``table`` is a reference, an empty table is created
with the specified ID. The dataset that the table belongs to
must already exist.
exists_ok (Optional[bool]):
Defaults to ``False``. If ``True``, ignore "already exists"
errors when creating the table.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.table.Table:
A new ``Table`` returned from the service.
Raises:
google.cloud.exceptions.Conflict:
If the table already exists.
"""
table = _table_arg_to_table(table, default_project=self.project)
dataset_id = table.dataset_id
path = "/projects/%s/datasets/%s/tables" % (table.project, dataset_id)
data = table.to_api_repr()
try:
span_attributes = {"path": path, "dataset_id": dataset_id}
api_response = self._call_api(
retry,
span_name="BigQuery.createTable",
span_attributes=span_attributes,
method="POST",
path=path,
data=data,
timeout=timeout,
)
return Table.from_api_repr(api_response)
except google.api_core.exceptions.Conflict:
if not exists_ok:
raise
return self.get_table(table.reference, retry=retry)
def _call_api(
self, retry, span_name=None, span_attributes=None, job_ref=None, **kwargs
):
call = functools.partial(self._connection.api_request, **kwargs)
if retry:
call = retry(call)
if span_name is not None:
with create_span(
name=span_name, attributes=span_attributes, client=self, job_ref=job_ref
):
return call()
return call()
[docs] def get_dataset(self, dataset_ref, retry=DEFAULT_RETRY, timeout=None):
"""Fetch the dataset referenced by ``dataset_ref``
Args:
dataset_ref (Union[ \
google.cloud.bigquery.dataset.DatasetReference, \
str, \
]):
A reference to the dataset to fetch from the BigQuery API.
If a string is passed in, this method attempts to create a
dataset reference from a string using
:func:`~google.cloud.bigquery.dataset.DatasetReference.from_string`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.dataset.Dataset:
A ``Dataset`` instance.
"""
if isinstance(dataset_ref, str):
dataset_ref = DatasetReference.from_string(
dataset_ref, default_project=self.project
)
path = dataset_ref.path
span_attributes = {"path": path}
api_response = self._call_api(
retry,
span_name="BigQuery.getDataset",
span_attributes=span_attributes,
method="GET",
path=path,
timeout=timeout,
)
return Dataset.from_api_repr(api_response)
def get_iam_policy(
self, table, requested_policy_version=1, retry=DEFAULT_RETRY, timeout=None,
):
if not isinstance(table, (Table, TableReference)):
raise TypeError("table must be a Table or TableReference")
if requested_policy_version != 1:
raise ValueError("only IAM policy version 1 is supported")
body = {"options": {"requestedPolicyVersion": 1}}
path = "{}:getIamPolicy".format(table.path)
span_attributes = {"path": path}
response = self._call_api(
retry,
span_name="BigQuery.getIamPolicy",
span_attributes=span_attributes,
method="POST",
path=path,
data=body,
timeout=timeout,
)
return Policy.from_api_repr(response)
def set_iam_policy(
self, table, policy, updateMask=None, retry=DEFAULT_RETRY, timeout=None,
):
if not isinstance(table, (Table, TableReference)):
raise TypeError("table must be a Table or TableReference")
if not isinstance(policy, (Policy)):
raise TypeError("policy must be a Policy")
body = {"policy": policy.to_api_repr()}
if updateMask is not None:
body["updateMask"] = updateMask
path = "{}:setIamPolicy".format(table.path)
span_attributes = {"path": path}
response = self._call_api(
retry,
span_name="BigQuery.setIamPolicy",
span_attributes=span_attributes,
method="POST",
path=path,
data=body,
timeout=timeout,
)
return Policy.from_api_repr(response)
def test_iam_permissions(
self, table, permissions, retry=DEFAULT_RETRY, timeout=None,
):
if not isinstance(table, (Table, TableReference)):
raise TypeError("table must be a Table or TableReference")
body = {"permissions": permissions}
path = "{}:testIamPermissions".format(table.path)
span_attributes = {"path": path}
response = self._call_api(
retry,
span_name="BigQuery.testIamPermissions",
span_attributes=span_attributes,
method="POST",
path=path,
data=body,
timeout=timeout,
)
return response
[docs] def get_model(self, model_ref, retry=DEFAULT_RETRY, timeout=None):
"""[Beta] Fetch the model referenced by ``model_ref``.
Args:
model_ref (Union[ \
google.cloud.bigquery.model.ModelReference, \
str, \
]):
A reference to the model to fetch from the BigQuery API.
If a string is passed in, this method attempts to create a
model reference from a string using
:func:`google.cloud.bigquery.model.ModelReference.from_string`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.model.Model: A ``Model`` instance.
"""
if isinstance(model_ref, str):
model_ref = ModelReference.from_string(
model_ref, default_project=self.project
)
path = model_ref.path
span_attributes = {"path": path}
api_response = self._call_api(
retry,
span_name="BigQuery.getModel",
span_attributes=span_attributes,
method="GET",
path=path,
timeout=timeout,
)
return Model.from_api_repr(api_response)
[docs] def get_routine(self, routine_ref, retry=DEFAULT_RETRY, timeout=None):
"""[Beta] Get the routine referenced by ``routine_ref``.
Args:
routine_ref (Union[ \
google.cloud.bigquery.routine.Routine, \
google.cloud.bigquery.routine.RoutineReference, \
str, \
]):
A reference to the routine to fetch from the BigQuery API. If
a string is passed in, this method attempts to create a
reference from a string using
:func:`google.cloud.bigquery.routine.RoutineReference.from_string`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the API call.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.routine.Routine:
A ``Routine`` instance.
"""
if isinstance(routine_ref, str):
routine_ref = RoutineReference.from_string(
routine_ref, default_project=self.project
)
path = routine_ref.path
span_attributes = {"path": path}
api_response = self._call_api(
retry,
span_name="BigQuery.getRoutine",
span_attributes=span_attributes,
method="GET",
path=path,
timeout=timeout,
)
return Routine.from_api_repr(api_response)
[docs] def get_table(self, table, retry=DEFAULT_RETRY, timeout=None):
"""Fetch the table referenced by ``table``.
Args:
table (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
A reference to the table to fetch from the BigQuery API.
If a string is passed in, this method attempts to create a
table reference from a string using
:func:`google.cloud.bigquery.table.TableReference.from_string`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.table.Table:
A ``Table`` instance.
"""
table_ref = _table_arg_to_table_ref(table, default_project=self.project)
path = table_ref.path
span_attributes = {"path": path}
api_response = self._call_api(
retry,
span_name="BigQuery.getTable",
span_attributes=span_attributes,
method="GET",
path=path,
timeout=timeout,
)
return Table.from_api_repr(api_response)
[docs] def update_dataset(self, dataset, fields, retry=DEFAULT_RETRY, timeout=None):
"""Change some fields of a dataset.
Use ``fields`` to specify which fields to update. At least one field
must be provided. If a field is listed in ``fields`` and is ``None`` in
``dataset``, it will be deleted.
If ``dataset.etag`` is not ``None``, the update will only
succeed if the dataset on the server has the same ETag. Thus
reading a dataset with ``get_dataset``, changing its fields,
and then passing it to ``update_dataset`` will ensure that the changes
will only be saved if no modifications to the dataset occurred
since the read.
Args:
dataset (google.cloud.bigquery.dataset.Dataset):
The dataset to update.
fields (Sequence[str]):
The properties of ``dataset`` to change. These are strings
corresponding to the properties of
:class:`~google.cloud.bigquery.dataset.Dataset`.
For example, to update the default expiration times, specify
both properties in the ``fields`` argument:
.. code-block:: python
bigquery_client.update_dataset(
dataset,
[
"default_partition_expiration_ms",
"default_table_expiration_ms",
]
)
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.dataset.Dataset:
The modified ``Dataset`` instance.
"""
partial = dataset._build_resource(fields)
if dataset.etag is not None:
headers = {"If-Match": dataset.etag}
else:
headers = None
path = dataset.path
span_attributes = {"path": path, "fields": fields}
api_response = self._call_api(
retry,
span_name="BigQuery.updateDataset",
span_attributes=span_attributes,
method="PATCH",
path=path,
data=partial,
headers=headers,
timeout=timeout,
)
return Dataset.from_api_repr(api_response)
[docs] def update_model(self, model, fields, retry=DEFAULT_RETRY, timeout=None):
"""[Beta] Change some fields of a model.
Use ``fields`` to specify which fields to update. At least one field
must be provided. If a field is listed in ``fields`` and is ``None``
in ``model``, the field value will be deleted.
If ``model.etag`` is not ``None``, the update will only succeed if
the model on the server has the same ETag. Thus reading a model with
``get_model``, changing its fields, and then passing it to
``update_model`` will ensure that the changes will only be saved if
no modifications to the model occurred since the read.
Args:
model (google.cloud.bigquery.model.Model): The model to update.
fields (Sequence[str]):
The properties of ``model`` to change. These are strings
corresponding to the properties of
:class:`~google.cloud.bigquery.model.Model`.
For example, to update the descriptive properties of the model,
specify them in the ``fields`` argument:
.. code-block:: python
bigquery_client.update_model(
model, ["description", "friendly_name"]
)
retry (Optional[google.api_core.retry.Retry]):
A description of how to retry the API call.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.model.Model:
The model resource returned from the API call.
"""
partial = model._build_resource(fields)
if model.etag:
headers = {"If-Match": model.etag}
else:
headers = None
path = model.path
span_attributes = {"path": path, "fields": fields}
api_response = self._call_api(
retry,
span_name="BigQuery.updateModel",
span_attributes=span_attributes,
method="PATCH",
path=path,
data=partial,
headers=headers,
timeout=timeout,
)
return Model.from_api_repr(api_response)
[docs] def update_routine(self, routine, fields, retry=DEFAULT_RETRY, timeout=None):
"""[Beta] Change some fields of a routine.
Use ``fields`` to specify which fields to update. At least one field
must be provided. If a field is listed in ``fields`` and is ``None``
in ``routine``, the field value will be deleted.
.. warning::
During beta, partial updates are not supported. You must provide
all fields in the resource.
If :attr:`~google.cloud.bigquery.routine.Routine.etag` is not
``None``, the update will only succeed if the resource on the server
has the same ETag. Thus reading a routine with
:func:`~google.cloud.bigquery.client.Client.get_routine`, changing
its fields, and then passing it to this method will ensure that the
changes will only be saved if no modifications to the resource
occurred since the read.
Args:
routine (google.cloud.bigquery.routine.Routine):
The routine to update.
fields (Sequence[str]):
The fields of ``routine`` to change, spelled as the
:class:`~google.cloud.bigquery.routine.Routine` properties.
For example, to update the description property of the routine,
specify it in the ``fields`` argument:
.. code-block:: python
bigquery_client.update_routine(
routine, ["description"]
)
retry (Optional[google.api_core.retry.Retry]):
A description of how to retry the API call.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.routine.Routine:
The routine resource returned from the API call.
"""
partial = routine._build_resource(fields)
if routine.etag:
headers = {"If-Match": routine.etag}
else:
headers = None
# TODO: remove when routines update supports partial requests.
partial["routineReference"] = routine.reference.to_api_repr()
path = routine.path
span_attributes = {"path": path, "fields": fields}
api_response = self._call_api(
retry,
span_name="BigQuery.updateRoutine",
span_attributes=span_attributes,
method="PUT",
path=path,
data=partial,
headers=headers,
timeout=timeout,
)
return Routine.from_api_repr(api_response)
[docs] def update_table(self, table, fields, retry=DEFAULT_RETRY, timeout=None):
"""Change some fields of a table.
Use ``fields`` to specify which fields to update. At least one field
must be provided. If a field is listed in ``fields`` and is ``None``
in ``table``, the field value will be deleted.
If ``table.etag`` is not ``None``, the update will only succeed if
the table on the server has the same ETag. Thus reading a table with
``get_table``, changing its fields, and then passing it to
``update_table`` will ensure that the changes will only be saved if
no modifications to the table occurred since the read.
Args:
table (google.cloud.bigquery.table.Table): The table to update.
fields (Sequence[str]):
The fields of ``table`` to change, spelled as the
:class:`~google.cloud.bigquery.table.Table` properties.
For example, to update the descriptive properties of the table,
specify them in the ``fields`` argument:
.. code-block:: python
bigquery_client.update_table(
table,
["description", "friendly_name"]
)
retry (Optional[google.api_core.retry.Retry]):
A description of how to retry the API call.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.table.Table:
The table resource returned from the API call.
"""
partial = table._build_resource(fields)
if table.etag is not None:
headers = {"If-Match": table.etag}
else:
headers = None
path = table.path
span_attributes = {"path": path, "fields": fields}
api_response = self._call_api(
retry,
span_name="BigQuery.updateTable",
span_attributes=span_attributes,
method="PATCH",
path=path,
data=partial,
headers=headers,
timeout=timeout,
)
return Table.from_api_repr(api_response)
[docs] def list_models(
self,
dataset,
max_results=None,
page_token=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""[Beta] List models in the dataset.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/models/list
Args:
dataset (Union[ \
google.cloud.bigquery.dataset.Dataset, \
google.cloud.bigquery.dataset.DatasetReference, \
str, \
]):
A reference to the dataset whose models to list from the
BigQuery API. If a string is passed in, this method attempts
to create a dataset reference from a string using
:func:`google.cloud.bigquery.dataset.DatasetReference.from_string`.
max_results (Optional[int]):
Maximum number of models to return. If not passed, defaults to a
value set by the API.
page_token (Optional[str]):
Token representing a cursor into the models. If not passed,
the API will return the first page of models. The token marks
the beginning of the iterator to be returned and the value of
the ``page_token`` can be accessed at ``next_page_token`` of the
:class:`~google.api_core.page_iterator.HTTPIterator`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.api_core.page_iterator.Iterator:
Iterator of
:class:`~google.cloud.bigquery.model.Model` contained
within the requested dataset.
"""
if isinstance(dataset, str):
dataset = DatasetReference.from_string(
dataset, default_project=self.project
)
if not isinstance(dataset, (Dataset, DatasetReference)):
raise TypeError("dataset must be a Dataset, DatasetReference, or string")
path = "%s/models" % dataset.path
span_attributes = {"path": path}
def api_request(*args, **kwargs):
return self._call_api(
retry,
span_name="BigQuery.listModels",
span_attributes=span_attributes,
*args,
timeout=timeout,
**kwargs,
)
result = page_iterator.HTTPIterator(
client=self,
api_request=api_request,
path=path,
item_to_value=_item_to_model,
items_key="models",
page_token=page_token,
max_results=max_results,
)
result.dataset = dataset
return result
[docs] def list_routines(
self,
dataset,
max_results=None,
page_token=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""[Beta] List routines in the dataset.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/routines/list
Args:
dataset (Union[ \
google.cloud.bigquery.dataset.Dataset, \
google.cloud.bigquery.dataset.DatasetReference, \
str, \
]):
A reference to the dataset whose routines to list from the
BigQuery API. If a string is passed in, this method attempts
to create a dataset reference from a string using
:func:`google.cloud.bigquery.dataset.DatasetReference.from_string`.
max_results (Optional[int]):
Maximum number of routines to return. If not passed, defaults
to a value set by the API.
page_token (Optional[str]):
Token representing a cursor into the routines. If not passed,
the API will return the first page of routines. The token marks
the beginning of the iterator to be returned and the value of the
``page_token`` can be accessed at ``next_page_token`` of the
:class:`~google.api_core.page_iterator.HTTPIterator`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.api_core.page_iterator.Iterator:
Iterator of all
:class:`~google.cloud.bigquery.routine.Routine`s contained
within the requested dataset, limited by ``max_results``.
"""
if isinstance(dataset, str):
dataset = DatasetReference.from_string(
dataset, default_project=self.project
)
if not isinstance(dataset, (Dataset, DatasetReference)):
raise TypeError("dataset must be a Dataset, DatasetReference, or string")
path = "{}/routines".format(dataset.path)
span_attributes = {"path": path}
def api_request(*args, **kwargs):
return self._call_api(
retry,
span_name="BigQuery.listRoutines",
span_attributes=span_attributes,
*args,
timeout=timeout,
**kwargs,
)
result = page_iterator.HTTPIterator(
client=self,
api_request=api_request,
path=path,
item_to_value=_item_to_routine,
items_key="routines",
page_token=page_token,
max_results=max_results,
)
result.dataset = dataset
return result
[docs] def list_tables(
self,
dataset,
max_results=None,
page_token=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""List tables in the dataset.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables/list
Args:
dataset (Union[ \
google.cloud.bigquery.dataset.Dataset, \
google.cloud.bigquery.dataset.DatasetReference, \
str, \
]):
A reference to the dataset whose tables to list from the
BigQuery API. If a string is passed in, this method attempts
to create a dataset reference from a string using
:func:`google.cloud.bigquery.dataset.DatasetReference.from_string`.
max_results (Optional[int]):
Maximum number of tables to return. If not passed, defaults
to a value set by the API.
page_token (Optional[str]):
Token representing a cursor into the tables. If not passed,
the API will return the first page of tables. The token marks
the beginning of the iterator to be returned and the value of
the ``page_token`` can be accessed at ``next_page_token`` of the
:class:`~google.api_core.page_iterator.HTTPIterator`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.api_core.page_iterator.Iterator:
Iterator of
:class:`~google.cloud.bigquery.table.TableListItem` contained
within the requested dataset.
"""
if isinstance(dataset, str):
dataset = DatasetReference.from_string(
dataset, default_project=self.project
)
if not isinstance(dataset, (Dataset, DatasetReference)):
raise TypeError("dataset must be a Dataset, DatasetReference, or string")
path = "%s/tables" % dataset.path
span_attributes = {"path": path}
def api_request(*args, **kwargs):
return self._call_api(
retry,
span_name="BigQuery.listTables",
span_attributes=span_attributes,
*args,
timeout=timeout,
**kwargs,
)
result = page_iterator.HTTPIterator(
client=self,
api_request=api_request,
path=path,
item_to_value=_item_to_table,
items_key="tables",
page_token=page_token,
max_results=max_results,
)
result.dataset = dataset
return result
[docs] def delete_dataset(
self,
dataset,
delete_contents=False,
retry=DEFAULT_RETRY,
timeout=None,
not_found_ok=False,
):
"""Delete a dataset.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets/delete
Args:
dataset (Union[ \
google.cloud.bigquery.dataset.Dataset, \
google.cloud.bigquery.dataset.DatasetReference, \
str, \
]):
A reference to the dataset to delete. If a string is passed
in, this method attempts to create a dataset reference from a
string using
:func:`google.cloud.bigquery.dataset.DatasetReference.from_string`.
delete_contents (Optional[bool]):
If True, delete all the tables in the dataset. If False and
the dataset contains tables, the request will fail.
Default is False.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
not_found_ok (Optional[bool]):
Defaults to ``False``. If ``True``, ignore "not found" errors
when deleting the dataset.
"""
if isinstance(dataset, str):
dataset = DatasetReference.from_string(
dataset, default_project=self.project
)
if not isinstance(dataset, (Dataset, DatasetReference)):
raise TypeError("dataset must be a Dataset or a DatasetReference")
params = {}
path = dataset.path
if delete_contents:
params["deleteContents"] = "true"
span_attributes = {"path": path, "deleteContents": delete_contents}
else:
span_attributes = {"path": path}
try:
self._call_api(
retry,
span_name="BigQuery.deleteDataset",
span_attributes=span_attributes,
method="DELETE",
path=path,
query_params=params,
timeout=timeout,
)
except google.api_core.exceptions.NotFound:
if not not_found_ok:
raise
[docs] def delete_model(
self, model, retry=DEFAULT_RETRY, timeout=None, not_found_ok=False
):
"""[Beta] Delete a model
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/models/delete
Args:
model (Union[ \
google.cloud.bigquery.model.Model, \
google.cloud.bigquery.model.ModelReference, \
str, \
]):
A reference to the model to delete. If a string is passed in,
this method attempts to create a model reference from a
string using
:func:`google.cloud.bigquery.model.ModelReference.from_string`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
not_found_ok (Optional[bool]):
Defaults to ``False``. If ``True``, ignore "not found" errors
when deleting the model.
"""
if isinstance(model, str):
model = ModelReference.from_string(model, default_project=self.project)
if not isinstance(model, (Model, ModelReference)):
raise TypeError("model must be a Model or a ModelReference")
path = model.path
try:
span_attributes = {"path": path}
self._call_api(
retry,
span_name="BigQuery.deleteModel",
span_attributes=span_attributes,
method="DELETE",
path=path,
timeout=timeout,
)
except google.api_core.exceptions.NotFound:
if not not_found_ok:
raise
[docs] def delete_routine(
self, routine, retry=DEFAULT_RETRY, timeout=None, not_found_ok=False
):
"""[Beta] Delete a routine.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/routines/delete
Args:
routine (Union[ \
google.cloud.bigquery.routine.Routine, \
google.cloud.bigquery.routine.RoutineReference, \
str, \
]):
A reference to the routine to delete. If a string is passed
in, this method attempts to create a routine reference from a
string using
:func:`google.cloud.bigquery.routine.RoutineReference.from_string`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
not_found_ok (Optional[bool]):
Defaults to ``False``. If ``True``, ignore "not found" errors
when deleting the routine.
"""
if isinstance(routine, str):
routine = RoutineReference.from_string(
routine, default_project=self.project
)
path = routine.path
if not isinstance(routine, (Routine, RoutineReference)):
raise TypeError("routine must be a Routine or a RoutineReference")
try:
span_attributes = {"path": path}
self._call_api(
retry,
span_name="BigQuery.deleteRoutine",
span_attributes=span_attributes,
method="DELETE",
path=path,
timeout=timeout,
)
except google.api_core.exceptions.NotFound:
if not not_found_ok:
raise
[docs] def delete_table(
self, table, retry=DEFAULT_RETRY, timeout=None, not_found_ok=False
):
"""Delete a table
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables/delete
Args:
table (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
A reference to the table to delete. If a string is passed in,
this method attempts to create a table reference from a
string using
:func:`google.cloud.bigquery.table.TableReference.from_string`.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
not_found_ok (Optional[bool]):
Defaults to ``False``. If ``True``, ignore "not found" errors
when deleting the table.
"""
table = _table_arg_to_table_ref(table, default_project=self.project)
if not isinstance(table, TableReference):
raise TypeError("Unable to get TableReference for table '{}'".format(table))
try:
path = table.path
span_attributes = {"path": path}
self._call_api(
retry,
span_name="BigQuery.deleteTable",
span_attributes=span_attributes,
method="DELETE",
path=path,
timeout=timeout,
)
except google.api_core.exceptions.NotFound:
if not not_found_ok:
raise
def _get_query_results(
self, job_id, retry, project=None, timeout_ms=None, location=None, timeout=None,
):
"""Get the query results object for a query job.
Args:
job_id (str): Name of the query job.
retry (google.api_core.retry.Retry):
How to retry the RPC.
project (Optional[str]):
Project ID for the query job (defaults to the project of the client).
timeout_ms (Optional[int]):
Number of milliseconds the the API call should wait for the query
to complete before the request times out.
location (Optional[str]): Location of the query job.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``. If set, this connection timeout may be
increased to a minimum value. This prevents retries on what
would otherwise be a successful response.
Returns:
google.cloud.bigquery.query._QueryResults:
A new ``_QueryResults`` instance.
"""
extra_params = {"maxResults": 0}
if timeout is not None:
timeout = max(timeout, _MIN_GET_QUERY_RESULTS_TIMEOUT)
if project is None:
project = self.project
if timeout_ms is not None:
extra_params["timeoutMs"] = timeout_ms
if location is None:
location = self.location
if location is not None:
extra_params["location"] = location
path = "/projects/{}/queries/{}".format(project, job_id)
# This call is typically made in a polling loop that checks whether the
# job is complete (from QueryJob.done(), called ultimately from
# QueryJob.result()). So we don't need to poll here.
span_attributes = {"path": path}
resource = self._call_api(
retry,
span_name="BigQuery.getQueryResults",
span_attributes=span_attributes,
method="GET",
path=path,
query_params=extra_params,
timeout=timeout,
)
return _QueryResults.from_api_repr(resource)
[docs] def job_from_resource(self, resource):
"""Detect correct job type from resource and instantiate.
Args:
resource (Dict): one job resource from API response
Returns:
Union[ \
google.cloud.bigquery.job.LoadJob, \
google.cloud.bigquery.job.CopyJob, \
google.cloud.bigquery.job.ExtractJob, \
google.cloud.bigquery.job.QueryJob \
]:
The job instance, constructed via the resource.
"""
config = resource.get("configuration", {})
if "load" in config:
return job.LoadJob.from_api_repr(resource, self)
elif "copy" in config:
return job.CopyJob.from_api_repr(resource, self)
elif "extract" in config:
return job.ExtractJob.from_api_repr(resource, self)
elif "query" in config:
return job.QueryJob.from_api_repr(resource, self)
return job.UnknownJob.from_api_repr(resource, self)
[docs] def create_job(self, job_config, retry=DEFAULT_RETRY, timeout=None):
"""Create a new job.
Args:
job_config (dict): configuration job representation returned from the API.
Keyword Arguments:
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
Union[ \
google.cloud.bigquery.job.LoadJob, \
google.cloud.bigquery.job.CopyJob, \
google.cloud.bigquery.job.ExtractJob, \
google.cloud.bigquery.job.QueryJob \
]:
A new job instance.
"""
if "load" in job_config:
load_job_config = google.cloud.bigquery.job.LoadJobConfig.from_api_repr(
job_config
)
destination = _get_sub_prop(job_config, ["load", "destinationTable"])
source_uris = _get_sub_prop(job_config, ["load", "sourceUris"])
destination = TableReference.from_api_repr(destination)
return self.load_table_from_uri(
source_uris,
destination,
job_config=load_job_config,
retry=retry,
timeout=timeout,
)
elif "copy" in job_config:
copy_job_config = google.cloud.bigquery.job.CopyJobConfig.from_api_repr(
job_config
)
destination = _get_sub_prop(job_config, ["copy", "destinationTable"])
destination = TableReference.from_api_repr(destination)
sources = []
source_configs = _get_sub_prop(job_config, ["copy", "sourceTables"])
if source_configs is None:
source_configs = [_get_sub_prop(job_config, ["copy", "sourceTable"])]
for source_config in source_configs:
table_ref = TableReference.from_api_repr(source_config)
sources.append(table_ref)
return self.copy_table(
sources,
destination,
job_config=copy_job_config,
retry=retry,
timeout=timeout,
)
elif "extract" in job_config:
extract_job_config = google.cloud.bigquery.job.ExtractJobConfig.from_api_repr(
job_config
)
source = _get_sub_prop(job_config, ["extract", "sourceTable"])
if source:
source_type = "Table"
source = TableReference.from_api_repr(source)
else:
source = _get_sub_prop(job_config, ["extract", "sourceModel"])
source_type = "Model"
source = ModelReference.from_api_repr(source)
destination_uris = _get_sub_prop(job_config, ["extract", "destinationUris"])
return self.extract_table(
source,
destination_uris,
job_config=extract_job_config,
retry=retry,
timeout=timeout,
source_type=source_type,
)
elif "query" in job_config:
copy_config = copy.deepcopy(job_config)
_del_sub_prop(copy_config, ["query", "destinationTable"])
query_job_config = google.cloud.bigquery.job.QueryJobConfig.from_api_repr(
copy_config
)
query = _get_sub_prop(copy_config, ["query", "query"])
return self.query(
query, job_config=query_job_config, retry=retry, timeout=timeout
)
else:
raise TypeError("Invalid job configuration received.")
[docs] def get_job(
self, job_id, project=None, location=None, retry=DEFAULT_RETRY, timeout=None
):
"""Fetch a job for the project associated with this client.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/get
Args:
job_id (str): Unique job identifier.
Keyword Arguments:
project (Optional[str]):
ID of the project which owns the job (defaults to the client's project).
location (Optional[str]): Location where the job was run.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
Union[ \
google.cloud.bigquery.job.LoadJob, \
google.cloud.bigquery.job.CopyJob, \
google.cloud.bigquery.job.ExtractJob, \
google.cloud.bigquery.job.QueryJob \
]:
Job instance, based on the resource returned by the API.
"""
extra_params = {"projection": "full"}
if project is None:
project = self.project
if location is None:
location = self.location
if location is not None:
extra_params["location"] = location
path = "/projects/{}/jobs/{}".format(project, job_id)
span_attributes = {"path": path, "job_id": job_id, "location": location}
resource = self._call_api(
retry,
span_name="BigQuery.getJob",
span_attributes=span_attributes,
method="GET",
path=path,
query_params=extra_params,
timeout=timeout,
)
return self.job_from_resource(resource)
[docs] def cancel_job(
self, job_id, project=None, location=None, retry=DEFAULT_RETRY, timeout=None
):
"""Attempt to cancel a job from a job ID.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/cancel
Args:
job_id (str): Unique job identifier.
Keyword Arguments:
project (Optional[str]):
ID of the project which owns the job (defaults to the client's project).
location (Optional[str]): Location where the job was run.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
Union[ \
google.cloud.bigquery.job.LoadJob, \
google.cloud.bigquery.job.CopyJob, \
google.cloud.bigquery.job.ExtractJob, \
google.cloud.bigquery.job.QueryJob, \
]:
Job instance, based on the resource returned by the API.
"""
extra_params = {"projection": "full"}
if project is None:
project = self.project
if location is None:
location = self.location
if location is not None:
extra_params["location"] = location
path = "/projects/{}/jobs/{}/cancel".format(project, job_id)
span_attributes = {"path": path, "job_id": job_id, "location": location}
resource = self._call_api(
retry,
span_name="BigQuery.cancelJob",
span_attributes=span_attributes,
method="POST",
path=path,
query_params=extra_params,
timeout=timeout,
)
return self.job_from_resource(resource["job"])
[docs] def list_jobs(
self,
project=None,
parent_job=None,
max_results=None,
page_token=None,
all_users=None,
state_filter=None,
retry=DEFAULT_RETRY,
timeout=None,
min_creation_time=None,
max_creation_time=None,
):
"""List jobs for the project associated with this client.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/list
Args:
project (Optional[str]):
Project ID to use for retreiving datasets. Defaults
to the client's project.
parent_job (Optional[Union[ \
google.cloud.bigquery.job._AsyncJob, \
str, \
]]):
If set, retrieve only child jobs of the specified parent.
max_results (Optional[int]):
Maximum number of jobs to return.
page_token (Optional[str]):
Opaque marker for the next "page" of jobs. If not
passed, the API will return the first page of jobs. The token
marks the beginning of the iterator to be returned and the
value of the ``page_token`` can be accessed at
``next_page_token`` of
:class:`~google.api_core.page_iterator.HTTPIterator`.
all_users (Optional[bool]):
If true, include jobs owned by all users in the project.
Defaults to :data:`False`.
state_filter (Optional[str]):
If set, include only jobs matching the given state. One of:
* ``"done"``
* ``"pending"``
* ``"running"``
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
min_creation_time (Optional[datetime.datetime]):
Min value for job creation time. If set, only jobs created
after or at this timestamp are returned. If the datetime has
no time zone assumes UTC time.
max_creation_time (Optional[datetime.datetime]):
Max value for job creation time. If set, only jobs created
before or at this timestamp are returned. If the datetime has
no time zone assumes UTC time.
Returns:
google.api_core.page_iterator.Iterator:
Iterable of job instances.
"""
if isinstance(parent_job, job._AsyncJob):
parent_job = parent_job.job_id
extra_params = {
"allUsers": all_users,
"stateFilter": state_filter,
"minCreationTime": _str_or_none(
google.cloud._helpers._millis_from_datetime(min_creation_time)
),
"maxCreationTime": _str_or_none(
google.cloud._helpers._millis_from_datetime(max_creation_time)
),
"projection": "full",
"parentJobId": parent_job,
}
extra_params = {
param: value for param, value in extra_params.items() if value is not None
}
if project is None:
project = self.project
path = "/projects/%s/jobs" % (project,)
span_attributes = {"path": path}
def api_request(*args, **kwargs):
return self._call_api(
retry,
span_name="BigQuery.listJobs",
span_attributes=span_attributes,
*args,
timeout=timeout,
**kwargs,
)
return page_iterator.HTTPIterator(
client=self,
api_request=api_request,
path=path,
item_to_value=_item_to_job,
items_key="jobs",
page_token=page_token,
max_results=max_results,
extra_params=extra_params,
)
[docs] def load_table_from_uri(
self,
source_uris,
destination,
job_id=None,
job_id_prefix=None,
location=None,
project=None,
job_config=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""Starts a job for loading data into a table from CloudStorage.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfigurationload
Args:
source_uris (Union[str, Sequence[str]]):
URIs of data files to be loaded; in format
``gs://<bucket_name>/<object_name_or_glob>``.
destination (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
Table into which data is to be loaded. If a string is passed
in, this method attempts to create a table reference from a
string using
:func:`google.cloud.bigquery.table.TableReference.from_string`.
Keyword Arguments:
job_id (Optional[str]): Name of the job.
job_id_prefix (Optional[str]):
The user-provided prefix for a randomly generated job ID.
This parameter will be ignored if a ``job_id`` is also given.
location (Optional[str]):
Location where to run the job. Must match the location of the
destination table.
project (Optional[str]):
Project ID of the project of where to run the job. Defaults
to the client's project.
job_config (Optional[google.cloud.bigquery.job.LoadJobConfig]):
Extra configuration options for the job.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.job.LoadJob: A new load job.
Raises:
TypeError:
If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.LoadJobConfig`
class.
"""
job_id = _make_job_id(job_id, job_id_prefix)
if project is None:
project = self.project
if location is None:
location = self.location
job_ref = job._JobReference(job_id, project=project, location=location)
if isinstance(source_uris, str):
source_uris = [source_uris]
destination = _table_arg_to_table_ref(destination, default_project=self.project)
if job_config:
job_config = copy.deepcopy(job_config)
_verify_job_config_type(job_config, google.cloud.bigquery.job.LoadJobConfig)
load_job = job.LoadJob(job_ref, source_uris, destination, self, job_config)
load_job._begin(retry=retry, timeout=timeout)
return load_job
[docs] def load_table_from_file(
self,
file_obj,
destination,
rewind=False,
size=None,
num_retries=_DEFAULT_NUM_RETRIES,
job_id=None,
job_id_prefix=None,
location=None,
project=None,
job_config=None,
timeout=None,
):
"""Upload the contents of this table from a file-like object.
Similar to :meth:`load_table_from_uri`, this method creates, starts and
returns a :class:`~google.cloud.bigquery.job.LoadJob`.
Args:
file_obj (file): A file handle opened in binary mode for reading.
destination (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
Table into which data is to be loaded. If a string is passed
in, this method attempts to create a table reference from a
string using
:func:`google.cloud.bigquery.table.TableReference.from_string`.
Keyword Arguments:
rewind (Optional[bool]):
If True, seek to the beginning of the file handle before
reading the file.
size (Optional[int]):
The number of bytes to read from the file handle. If size is
``None`` or large, resumable upload will be used. Otherwise,
multipart upload will be used.
num_retries (Optional[int]): Number of upload retries. Defaults to 6.
job_id (Optional[str]): Name of the job.
job_id_prefix (Optional[str]):
The user-provided prefix for a randomly generated job ID.
This parameter will be ignored if a ``job_id`` is also given.
location (Optional[str]):
Location where to run the job. Must match the location of the
destination table.
project (Optional[str]):
Project ID of the project of where to run the job. Defaults
to the client's project.
job_config (Optional[google.cloud.bigquery.job.LoadJobConfig]):
Extra configuration options for the job.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.job.LoadJob: A new load job.
Raises:
ValueError:
If ``size`` is not passed in and can not be determined, or if
the ``file_obj`` can be detected to be a file opened in text
mode.
TypeError:
If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.LoadJobConfig`
class.
"""
job_id = _make_job_id(job_id, job_id_prefix)
if project is None:
project = self.project
if location is None:
location = self.location
destination = _table_arg_to_table_ref(destination, default_project=self.project)
job_ref = job._JobReference(job_id, project=project, location=location)
if job_config:
job_config = copy.deepcopy(job_config)
_verify_job_config_type(job_config, google.cloud.bigquery.job.LoadJobConfig)
load_job = job.LoadJob(job_ref, None, destination, self, job_config)
job_resource = load_job.to_api_repr()
if rewind:
file_obj.seek(0, os.SEEK_SET)
_check_mode(file_obj)
try:
if size is None or size >= _MAX_MULTIPART_SIZE:
response = self._do_resumable_upload(
file_obj, job_resource, num_retries, timeout, project=project
)
else:
response = self._do_multipart_upload(
file_obj, job_resource, size, num_retries, timeout, project=project
)
except resumable_media.InvalidResponse as exc:
raise exceptions.from_http_response(exc.response)
return self.job_from_resource(response.json())
[docs] def load_table_from_dataframe(
self,
dataframe,
destination,
num_retries=_DEFAULT_NUM_RETRIES,
job_id=None,
job_id_prefix=None,
location=None,
project=None,
job_config=None,
parquet_compression="snappy",
timeout=None,
):
"""Upload the contents of a table from a pandas DataFrame.
Similar to :meth:`load_table_from_uri`, this method creates, starts and
returns a :class:`~google.cloud.bigquery.job.LoadJob`.
.. note::
REPEATED fields are NOT supported when using the CSV source format.
They are supported when using the PARQUET source format, but
due to the way they are encoded in the ``parquet`` file,
a mismatch with the existing table schema can occur, so
100% compatibility cannot be guaranteed for REPEATED fields when
using the parquet format.
https://github.com/googleapis/python-bigquery/issues/17
Args:
dataframe (pandas.DataFrame):
A :class:`~pandas.DataFrame` containing the data to load.
destination (google.cloud.bigquery.table.TableReference):
The destination table to use for loading the data. If it is an
existing table, the schema of the :class:`~pandas.DataFrame`
must match the schema of the destination table. If the table
does not yet exist, the schema is inferred from the
:class:`~pandas.DataFrame`.
If a string is passed in, this method attempts to create a
table reference from a string using
:func:`google.cloud.bigquery.table.TableReference.from_string`.
Keyword Arguments:
num_retries (Optional[int]): Number of upload retries.
job_id (Optional[str]): Name of the job.
job_id_prefix (Optional[str]):
The user-provided prefix for a randomly generated
job ID. This parameter will be ignored if a ``job_id`` is
also given.
location (Optional[str]):
Location where to run the job. Must match the location of the
destination table.
project (Optional[str]):
Project ID of the project of where to run the job. Defaults
to the client's project.
job_config (Optional[google.cloud.bigquery.job.LoadJobConfig]):
Extra configuration options for the job.
To override the default pandas data type conversions, supply
a value for
:attr:`~google.cloud.bigquery.job.LoadJobConfig.schema` with
column names matching those of the dataframe. The BigQuery
schema is used to determine the correct data type conversion.
Indexes are not loaded. Requires the :mod:`pyarrow` library.
By default, this method uses the parquet source format. To
override this, supply a value for
:attr:`~google.cloud.bigquery.job.LoadJobConfig.source_format`
with the format name. Currently only
:attr:`~google.cloud.bigquery.job.SourceFormat.CSV` and
:attr:`~google.cloud.bigquery.job.SourceFormat.PARQUET` are
supported.
parquet_compression (Optional[str]):
[Beta] The compression method to use if intermittently
serializing ``dataframe`` to a parquet file.
The argument is directly passed as the ``compression``
argument to the underlying ``pyarrow.parquet.write_table()``
method (the default value "snappy" gets converted to uppercase).
https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html#pyarrow-parquet-write-table
If the job config schema is missing, the argument is directly
passed as the ``compression`` argument to the underlying
``DataFrame.to_parquet()`` method.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_parquet.html#pandas.DataFrame.to_parquet
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.job.LoadJob: A new load job.
Raises:
ValueError:
If a usable parquet engine cannot be found. This method
requires :mod:`pyarrow` to be installed.
TypeError:
If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.LoadJobConfig`
class.
"""
job_id = _make_job_id(job_id, job_id_prefix)
if job_config:
_verify_job_config_type(job_config, google.cloud.bigquery.job.LoadJobConfig)
# Make a copy so that the job config isn't modified in-place.
job_config_properties = copy.deepcopy(job_config._properties)
job_config = job.LoadJobConfig()
job_config._properties = job_config_properties
else:
job_config = job.LoadJobConfig()
supported_formats = {job.SourceFormat.CSV, job.SourceFormat.PARQUET}
if job_config.source_format is None:
# default value
job_config.source_format = job.SourceFormat.PARQUET
if job_config.source_format not in supported_formats:
raise ValueError(
"Got unexpected source_format: '{}'. Currently, only PARQUET and CSV are supported".format(
job_config.source_format
)
)
if pyarrow is None and job_config.source_format == job.SourceFormat.PARQUET:
# pyarrow is now the only supported parquet engine.
raise ValueError("This method requires pyarrow to be installed")
if location is None:
location = self.location
# If table schema is not provided, we try to fetch the existing table
# schema, and check if dataframe schema is compatible with it - except
# for WRITE_TRUNCATE jobs, the existing schema does not matter then.
if (
not job_config.schema
and job_config.write_disposition != job.WriteDisposition.WRITE_TRUNCATE
):
try:
table = self.get_table(destination)
except google.api_core.exceptions.NotFound:
table = None
else:
columns_and_indexes = frozenset(
name
for name, _ in _pandas_helpers.list_columns_and_indexes(dataframe)
)
# schema fields not present in the dataframe are not needed
job_config.schema = [
field for field in table.schema if field.name in columns_and_indexes
]
job_config.schema = _pandas_helpers.dataframe_to_bq_schema(
dataframe, job_config.schema
)
if not job_config.schema:
# the schema could not be fully detected
warnings.warn(
"Schema could not be detected for all columns. Loading from a "
"dataframe without a schema will be deprecated in the future, "
"please provide a schema.",
PendingDeprecationWarning,
stacklevel=2,
)
tmpfd, tmppath = tempfile.mkstemp(
suffix="_job_{}.{}".format(job_id[:8], job_config.source_format.lower())
)
os.close(tmpfd)
try:
if job_config.source_format == job.SourceFormat.PARQUET:
if job_config.schema:
if parquet_compression == "snappy": # adjust the default value
parquet_compression = parquet_compression.upper()
_pandas_helpers.dataframe_to_parquet(
dataframe,
job_config.schema,
tmppath,
parquet_compression=parquet_compression,
)
else:
dataframe.to_parquet(tmppath, compression=parquet_compression)
else:
dataframe.to_csv(
tmppath,
index=False,
header=False,
encoding="utf-8",
float_format="%.17g",
date_format="%Y-%m-%d %H:%M:%S.%f",
)
with open(tmppath, "rb") as tmpfile:
file_size = os.path.getsize(tmppath)
return self.load_table_from_file(
tmpfile,
destination,
num_retries=num_retries,
rewind=True,
size=file_size,
job_id=job_id,
job_id_prefix=job_id_prefix,
location=location,
project=project,
job_config=job_config,
timeout=timeout,
)
finally:
os.remove(tmppath)
[docs] def load_table_from_json(
self,
json_rows,
destination,
num_retries=_DEFAULT_NUM_RETRIES,
job_id=None,
job_id_prefix=None,
location=None,
project=None,
job_config=None,
timeout=None,
):
"""Upload the contents of a table from a JSON string or dict.
Args:
json_rows (Iterable[Dict[str, Any]]):
Row data to be inserted. Keys must match the table schema fields
and values must be JSON-compatible representations.
.. note::
If your data is already a newline-delimited JSON string,
it is best to wrap it into a file-like object and pass it
to :meth:`~google.cloud.bigquery.client.Client.load_table_from_file`::
import io
from google.cloud import bigquery
data = u'{"foo": "bar"}'
data_as_file = io.StringIO(data)
client = bigquery.Client()
client.load_table_from_file(data_as_file, ...)
destination (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
Table into which data is to be loaded. If a string is passed
in, this method attempts to create a table reference from a
string using
:func:`google.cloud.bigquery.table.TableReference.from_string`.
Keyword Arguments:
num_retries (Optional[int]): Number of upload retries.
job_id (Optional[str]): Name of the job.
job_id_prefix (Optional[str]):
The user-provided prefix for a randomly generated job ID.
This parameter will be ignored if a ``job_id`` is also given.
location (Optional[str]):
Location where to run the job. Must match the location of the
destination table.
project (Optional[str]):
Project ID of the project of where to run the job. Defaults
to the client's project.
job_config (Optional[google.cloud.bigquery.job.LoadJobConfig]):
Extra configuration options for the job. The ``source_format``
setting is always set to
:attr:`~google.cloud.bigquery.job.SourceFormat.NEWLINE_DELIMITED_JSON`.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.job.LoadJob: A new load job.
Raises:
TypeError:
If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.LoadJobConfig`
class.
"""
job_id = _make_job_id(job_id, job_id_prefix)
if job_config:
_verify_job_config_type(job_config, google.cloud.bigquery.job.LoadJobConfig)
# Make a copy so that the job config isn't modified in-place.
job_config = copy.deepcopy(job_config)
else:
job_config = job.LoadJobConfig()
job_config.source_format = job.SourceFormat.NEWLINE_DELIMITED_JSON
if job_config.schema is None:
job_config.autodetect = True
if project is None:
project = self.project
if location is None:
location = self.location
destination = _table_arg_to_table_ref(destination, default_project=self.project)
data_str = "\n".join(json.dumps(item) for item in json_rows)
encoded_str = data_str.encode()
data_file = io.BytesIO(encoded_str)
return self.load_table_from_file(
data_file,
destination,
size=len(encoded_str),
num_retries=num_retries,
job_id=job_id,
job_id_prefix=job_id_prefix,
location=location,
project=project,
job_config=job_config,
timeout=timeout,
)
def _do_resumable_upload(
self, stream, metadata, num_retries, timeout, project=None
):
"""Perform a resumable upload.
Args:
stream (IO[bytes]): A bytes IO object open for reading.
metadata (Dict): The metadata associated with the upload.
num_retries (int):
Number of upload retries. (Deprecated: This
argument will be removed in a future release.)
timeout (float):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
project (Optional[str]):
Project ID of the project of where to run the upload. Defaults
to the client's project.
Returns:
requests.Response:
The "200 OK" response object returned after the final chunk
is uploaded.
"""
upload, transport = self._initiate_resumable_upload(
stream, metadata, num_retries, timeout, project=project
)
while not upload.finished:
response = upload.transmit_next_chunk(transport)
return response
def _initiate_resumable_upload(
self, stream, metadata, num_retries, timeout, project=None
):
"""Initiate a resumable upload.
Args:
stream (IO[bytes]): A bytes IO object open for reading.
metadata (Dict): The metadata associated with the upload.
num_retries (int):
Number of upload retries. (Deprecated: This
argument will be removed in a future release.)
timeout (float):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
project (Optional[str]):
Project ID of the project of where to run the upload. Defaults
to the client's project.
Returns:
Tuple:
Pair of
* The :class:`~google.resumable_media.requests.ResumableUpload`
that was created
* The ``transport`` used to initiate the upload.
"""
chunk_size = _DEFAULT_CHUNKSIZE
transport = self._http
headers = _get_upload_headers(self._connection.user_agent)
if project is None:
project = self.project
# TODO: Increase the minimum version of google-cloud-core to 1.6.0
# and remove this logic. See:
# https://github.com/googleapis/python-bigquery/issues/509
hostname = (
self._connection.API_BASE_URL
if not hasattr(self._connection, "get_api_base_url_for_mtls")
else self._connection.get_api_base_url_for_mtls()
)
upload_url = _RESUMABLE_URL_TEMPLATE.format(host=hostname, project=project)
# TODO: modify ResumableUpload to take a retry.Retry object
# that it can use for the initial RPC.
upload = ResumableUpload(upload_url, chunk_size, headers=headers)
if num_retries is not None:
upload._retry_strategy = resumable_media.RetryStrategy(
max_retries=num_retries
)
upload.initiate(
transport,
stream,
metadata,
_GENERIC_CONTENT_TYPE,
stream_final=False,
timeout=timeout,
)
return upload, transport
def _do_multipart_upload(
self, stream, metadata, size, num_retries, timeout, project=None
):
"""Perform a multipart upload.
Args:
stream (IO[bytes]): A bytes IO object open for reading.
metadata (Dict): The metadata associated with the upload.
size (int):
The number of bytes to be uploaded (which will be read
from ``stream``). If not provided, the upload will be
concluded once ``stream`` is exhausted (or :data:`None`).
num_retries (int):
Number of upload retries. (Deprecated: This
argument will be removed in a future release.)
timeout (float):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
project (Optional[str]):
Project ID of the project of where to run the upload. Defaults
to the client's project.
Returns:
requests.Response:
The "200 OK" response object returned after the multipart
upload request.
Raises:
ValueError:
if the ``stream`` has fewer than ``size``
bytes remaining.
"""
data = stream.read(size)
if len(data) < size:
msg = _READ_LESS_THAN_SIZE.format(size, len(data))
raise ValueError(msg)
headers = _get_upload_headers(self._connection.user_agent)
if project is None:
project = self.project
# TODO: Increase the minimum version of google-cloud-core to 1.6.0
# and remove this logic. See:
# https://github.com/googleapis/python-bigquery/issues/509
hostname = (
self._connection.API_BASE_URL
if not hasattr(self._connection, "get_api_base_url_for_mtls")
else self._connection.get_api_base_url_for_mtls()
)
upload_url = _MULTIPART_URL_TEMPLATE.format(host=hostname, project=project)
upload = MultipartUpload(upload_url, headers=headers)
if num_retries is not None:
upload._retry_strategy = resumable_media.RetryStrategy(
max_retries=num_retries
)
response = upload.transmit(
self._http, data, metadata, _GENERIC_CONTENT_TYPE, timeout=timeout
)
return response
[docs] def copy_table(
self,
sources,
destination,
job_id=None,
job_id_prefix=None,
location=None,
project=None,
job_config=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""Copy one or more tables to another table.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfigurationtablecopy
Args:
sources (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
Sequence[ \
Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
] \
], \
]):
Table or tables to be copied.
destination (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
Table into which data is to be copied.
Keyword Arguments:
job_id (Optional[str]): The ID of the job.
job_id_prefix (Optional[str]):
The user-provided prefix for a randomly generated job ID.
This parameter will be ignored if a ``job_id`` is also given.
location (Optional[str]):
Location where to run the job. Must match the location of any
source table as well as the destination table.
project (Optional[str]):
Project ID of the project of where to run the job. Defaults
to the client's project.
job_config (Optional[google.cloud.bigquery.job.CopyJobConfig]):
Extra configuration options for the job.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.job.CopyJob: A new copy job instance.
Raises:
TypeError:
If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.CopyJobConfig`
class.
"""
job_id = _make_job_id(job_id, job_id_prefix)
if project is None:
project = self.project
if location is None:
location = self.location
job_ref = job._JobReference(job_id, project=project, location=location)
# sources can be one of many different input types. (string, Table,
# TableReference, or a sequence of any of those.) Convert them all to a
# list of TableReferences.
#
# _table_arg_to_table_ref leaves lists unmodified.
sources = _table_arg_to_table_ref(sources, default_project=self.project)
if not isinstance(sources, collections_abc.Sequence):
sources = [sources]
sources = [
_table_arg_to_table_ref(source, default_project=self.project)
for source in sources
]
destination = _table_arg_to_table_ref(destination, default_project=self.project)
if job_config:
_verify_job_config_type(job_config, google.cloud.bigquery.job.CopyJobConfig)
job_config = copy.deepcopy(job_config)
copy_job = job.CopyJob(
job_ref, sources, destination, client=self, job_config=job_config
)
copy_job._begin(retry=retry, timeout=timeout)
return copy_job
[docs] def extract_table(
self,
source,
destination_uris,
job_id=None,
job_id_prefix=None,
location=None,
project=None,
job_config=None,
retry=DEFAULT_RETRY,
timeout=None,
source_type="Table",
):
"""Start a job to extract a table into Cloud Storage files.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfigurationextract
Args:
source (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
google.cloud.bigquery.model.Model, \
google.cloud.bigquery.model.ModelReference, \
src, \
]):
Table or Model to be extracted.
destination_uris (Union[str, Sequence[str]]):
URIs of Cloud Storage file(s) into which table data is to be
extracted; in format
``gs://<bucket_name>/<object_name_or_glob>``.
Keyword Arguments:
job_id (Optional[str]): The ID of the job.
job_id_prefix (Optional[str]):
The user-provided prefix for a randomly generated job ID.
This parameter will be ignored if a ``job_id`` is also given.
location (Optional[str]):
Location where to run the job. Must match the location of the
source table.
project (Optional[str]):
Project ID of the project of where to run the job. Defaults
to the client's project.
job_config (Optional[google.cloud.bigquery.job.ExtractJobConfig]):
Extra configuration options for the job.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
source_type (Optional[str]):
Type of source to be extracted.``Table`` or ``Model``. Defaults to ``Table``.
Returns:
google.cloud.bigquery.job.ExtractJob: A new extract job instance.
Raises:
TypeError:
If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.ExtractJobConfig`
class.
ValueError:
If ``source_type`` is not among ``Table``,``Model``.
"""
job_id = _make_job_id(job_id, job_id_prefix)
if project is None:
project = self.project
if location is None:
location = self.location
job_ref = job._JobReference(job_id, project=project, location=location)
src = source_type.lower()
if src == "table":
source = _table_arg_to_table_ref(source, default_project=self.project)
elif src == "model":
source = _model_arg_to_model_ref(source, default_project=self.project)
else:
raise ValueError(
"Cannot pass `{}` as a ``source_type``, pass Table or Model".format(
source_type
)
)
if isinstance(destination_uris, str):
destination_uris = [destination_uris]
if job_config:
_verify_job_config_type(
job_config, google.cloud.bigquery.job.ExtractJobConfig
)
job_config = copy.deepcopy(job_config)
extract_job = job.ExtractJob(
job_ref, source, destination_uris, client=self, job_config=job_config
)
extract_job._begin(retry=retry, timeout=timeout)
return extract_job
[docs] def query(
self,
query,
job_config=None,
job_id=None,
job_id_prefix=None,
location=None,
project=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""Run a SQL query.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfigurationquery
Args:
query (str):
SQL query to be executed. Defaults to the standard SQL
dialect. Use the ``job_config`` parameter to change dialects.
Keyword Arguments:
job_config (Optional[google.cloud.bigquery.job.QueryJobConfig]):
Extra configuration options for the job.
To override any options that were previously set in
the ``default_query_job_config`` given to the
``Client`` constructor, manually set those options to ``None``,
or whatever value is preferred.
job_id (Optional[str]): ID to use for the query job.
job_id_prefix (Optional[str]):
The prefix to use for a randomly generated job ID. This parameter
will be ignored if a ``job_id`` is also given.
location (Optional[str]):
Location where to run the job. Must match the location of the
any table used in the query as well as the destination table.
project (Optional[str]):
Project ID of the project of where to run the job. Defaults
to the client's project.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
google.cloud.bigquery.job.QueryJob: A new query job instance.
Raises:
TypeError:
If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.QueryJobConfig`
class.
"""
job_id = _make_job_id(job_id, job_id_prefix)
if project is None:
project = self.project
if location is None:
location = self.location
job_config = copy.deepcopy(job_config)
if self._default_query_job_config:
if job_config:
_verify_job_config_type(
job_config, google.cloud.bigquery.job.QueryJobConfig
)
# anything that's not defined on the incoming
# that is in the default,
# should be filled in with the default
# the incoming therefore has precedence
job_config = job_config._fill_from_default(
self._default_query_job_config
)
else:
_verify_job_config_type(
self._default_query_job_config,
google.cloud.bigquery.job.QueryJobConfig,
)
job_config = copy.deepcopy(self._default_query_job_config)
job_ref = job._JobReference(job_id, project=project, location=location)
query_job = job.QueryJob(job_ref, query, client=self, job_config=job_config)
query_job._begin(retry=retry, timeout=timeout)
return query_job
[docs] def insert_rows(self, table, rows, selected_fields=None, **kwargs):
"""Insert rows into a table via the streaming API.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/insertAll
Args:
table (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
The destination table for the row data, or a reference to it.
rows (Union[Sequence[Tuple], Sequence[Dict]]):
Row data to be inserted. If a list of tuples is given, each
tuple should contain data for each schema field on the
current table and in the same order as the schema fields. If
a list of dictionaries is given, the keys must include all
required fields in the schema. Keys which do not correspond
to a field in the schema are ignored.
selected_fields (Sequence[google.cloud.bigquery.schema.SchemaField]):
The fields to return. Required if ``table`` is a
:class:`~google.cloud.bigquery.table.TableReference`.
kwargs (Dict):
Keyword arguments to
:meth:`~google.cloud.bigquery.client.Client.insert_rows_json`.
Returns:
Sequence[Mappings]:
One mapping per row with insert errors: the "index" key
identifies the row, and the "errors" key contains a list of
the mappings describing one or more problems with the row.
Raises:
ValueError: if table's schema is not set or `rows` is not a `Sequence`.
"""
if not isinstance(rows, (collections_abc.Sequence, collections_abc.Iterator)):
raise TypeError("rows argument should be a sequence of dicts or tuples")
table = _table_arg_to_table(table, default_project=self.project)
if not isinstance(table, Table):
raise TypeError(_NEED_TABLE_ARGUMENT)
schema = table.schema
# selected_fields can override the table schema.
if selected_fields is not None:
schema = selected_fields
if len(schema) == 0:
raise ValueError(
(
"Could not determine schema for table '{}'. Call client.get_table() "
"or pass in a list of schema fields to the selected_fields argument."
).format(table)
)
json_rows = [_record_field_to_json(schema, row) for row in rows]
return self.insert_rows_json(table, json_rows, **kwargs)
[docs] def insert_rows_from_dataframe(
self, table, dataframe, selected_fields=None, chunk_size=500, **kwargs
):
"""Insert rows into a table from a dataframe via the streaming API.
Args:
table (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
The destination table for the row data, or a reference to it.
dataframe (pandas.DataFrame):
A :class:`~pandas.DataFrame` containing the data to load. Any
``NaN`` values present in the dataframe are omitted from the
streaming API request(s).
selected_fields (Sequence[google.cloud.bigquery.schema.SchemaField]):
The fields to return. Required if ``table`` is a
:class:`~google.cloud.bigquery.table.TableReference`.
chunk_size (int):
The number of rows to stream in a single chunk. Must be positive.
kwargs (Dict):
Keyword arguments to
:meth:`~google.cloud.bigquery.client.Client.insert_rows_json`.
Returns:
Sequence[Sequence[Mappings]]:
A list with insert errors for each insert chunk. Each element
is a list containing one mapping per row with insert errors:
the "index" key identifies the row, and the "errors" key
contains a list of the mappings describing one or more problems
with the row.
Raises:
ValueError: if table's schema is not set
"""
insert_results = []
chunk_count = int(math.ceil(len(dataframe) / chunk_size))
rows_iter = _pandas_helpers.dataframe_to_json_generator(dataframe)
for _ in range(chunk_count):
rows_chunk = itertools.islice(rows_iter, chunk_size)
result = self.insert_rows(table, rows_chunk, selected_fields, **kwargs)
insert_results.append(result)
return insert_results
[docs] def insert_rows_json(
self,
table,
json_rows,
row_ids=None,
skip_invalid_rows=None,
ignore_unknown_values=None,
template_suffix=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""Insert rows into a table without applying local type conversions.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/insertAll
Args:
table (Union[ \
google.cloud.bigquery.table.Table \
google.cloud.bigquery.table.TableReference, \
str \
]):
The destination table for the row data, or a reference to it.
json_rows (Sequence[Dict]):
Row data to be inserted. Keys must match the table schema fields
and values must be JSON-compatible representations.
row_ids (Optional[Sequence[Optional[str]]]):
Unique IDs, one per row being inserted. An ID can also be
``None``, indicating that an explicit insert ID should **not**
be used for that row. If the argument is omitted altogether,
unique IDs are created automatically.
skip_invalid_rows (Optional[bool]):
Insert all valid rows of a request, even if invalid rows exist.
The default value is ``False``, which causes the entire request
to fail if any invalid rows exist.
ignore_unknown_values (Optional[bool]):
Accept rows that contain values that do not match the schema.
The unknown values are ignored. Default is ``False``, which
treats unknown values as errors.
template_suffix (Optional[str]):
Treat ``name`` as a template table and provide a suffix.
BigQuery will create the table ``<name> + <template_suffix>``
based on the schema of the template table. See
https://cloud.google.com/bigquery/streaming-data-into-bigquery#template-tables
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
Returns:
Sequence[Mappings]:
One mapping per row with insert errors: the "index" key
identifies the row, and the "errors" key contains a list of
the mappings describing one or more problems with the row.
Raises:
TypeError: if `json_rows` is not a `Sequence`.
"""
if not isinstance(
json_rows, (collections_abc.Sequence, collections_abc.Iterator)
):
raise TypeError("json_rows argument should be a sequence of dicts")
# Convert table to just a reference because unlike insert_rows,
# insert_rows_json doesn't need the table schema. It's not doing any
# type conversions.
table = _table_arg_to_table_ref(table, default_project=self.project)
rows_info = []
data = {"rows": rows_info}
for index, row in enumerate(json_rows):
info = {"json": row}
if row_ids is not None:
info["insertId"] = row_ids[index]
else:
info["insertId"] = str(uuid.uuid4())
rows_info.append(info)
if skip_invalid_rows is not None:
data["skipInvalidRows"] = skip_invalid_rows
if ignore_unknown_values is not None:
data["ignoreUnknownValues"] = ignore_unknown_values
if template_suffix is not None:
data["templateSuffix"] = template_suffix
path = "%s/insertAll" % table.path
# We can always retry, because every row has an insert ID.
span_attributes = {"path": path}
response = self._call_api(
retry,
span_name="BigQuery.insertRowsJson",
span_attributes=span_attributes,
method="POST",
path=path,
data=data,
timeout=timeout,
)
errors = []
for error in response.get("insertErrors", ()):
errors.append({"index": int(error["index"]), "errors": error["errors"]})
return errors
[docs] def list_partitions(self, table, retry=DEFAULT_RETRY, timeout=None):
"""List the partitions in a table.
Args:
table (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
The table or reference from which to get partition info
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
If multiple requests are made under the hood, ``timeout``
applies to each individual request.
Returns:
List[str]:
A list of the partition ids present in the partitioned table
"""
table = _table_arg_to_table_ref(table, default_project=self.project)
meta_table = self.get_table(
TableReference(
DatasetReference(table.project, table.dataset_id),
"%s$__PARTITIONS_SUMMARY__" % table.table_id,
),
retry=retry,
timeout=timeout,
)
subset = [col for col in meta_table.schema if col.name == "partition_id"]
return [
row[0]
for row in self.list_rows(
meta_table, selected_fields=subset, retry=retry, timeout=timeout
)
]
[docs] def list_rows(
self,
table,
selected_fields=None,
max_results=None,
page_token=None,
start_index=None,
page_size=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""List the rows of the table.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/list
.. note::
This method assumes that the provided schema is up-to-date with the
schema as defined on the back-end: if the two schemas are not
identical, the values returned may be incomplete. To ensure that the
local copy of the schema is up-to-date, call ``client.get_table``.
Args:
table (Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableListItem, \
google.cloud.bigquery.table.TableReference, \
str, \
]):
The table to list, or a reference to it. When the table
object does not contain a schema and ``selected_fields`` is
not supplied, this method calls ``get_table`` to fetch the
table schema.
selected_fields (Sequence[google.cloud.bigquery.schema.SchemaField]):
The fields to return. If not supplied, data for all columns
are downloaded.
max_results (Optional[int]):
Maximum number of rows to return.
page_token (Optional[str]):
Token representing a cursor into the table's rows.
If not passed, the API will return the first page of the
rows. The token marks the beginning of the iterator to be
returned and the value of the ``page_token`` can be accessed
at ``next_page_token`` of the
:class:`~google.cloud.bigquery.table.RowIterator`.
start_index (Optional[int]):
The zero-based index of the starting row to read.
page_size (Optional[int]):
The maximum number of rows in each page of results from this request.
Non-positive values are ignored. Defaults to a sensible value set by the API.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``.
If multiple requests are made under the hood, ``timeout``
applies to each individual request.
Returns:
google.cloud.bigquery.table.RowIterator:
Iterator of row data
:class:`~google.cloud.bigquery.table.Row`-s. During each
page, the iterator will have the ``total_rows`` attribute
set, which counts the total number of rows **in the table**
(this is distinct from the total number of rows in the
current page: ``iterator.page.num_items``).
"""
table = _table_arg_to_table(table, default_project=self.project)
if not isinstance(table, Table):
raise TypeError(_NEED_TABLE_ARGUMENT)
schema = table.schema
# selected_fields can override the table schema.
if selected_fields is not None:
schema = selected_fields
# No schema, but no selected_fields. Assume the developer wants all
# columns, so get the table resource for them rather than failing.
elif len(schema) == 0:
table = self.get_table(table.reference, retry=retry, timeout=timeout)
schema = table.schema
params = {}
if selected_fields is not None:
params["selectedFields"] = ",".join(field.name for field in selected_fields)
if start_index is not None:
params["startIndex"] = start_index
params["formatOptions.useInt64Timestamp"] = True
row_iterator = RowIterator(
client=self,
api_request=functools.partial(self._call_api, retry, timeout=timeout),
path="%s/data" % (table.path,),
schema=schema,
page_token=page_token,
max_results=max_results,
page_size=page_size,
extra_params=params,
table=table,
# Pass in selected_fields separately from schema so that full
# tables can be fetched without a column filter.
selected_fields=selected_fields,
total_rows=getattr(table, "num_rows", None),
)
return row_iterator
def _list_rows_from_query_results(
self,
job_id,
location,
project,
schema,
total_rows=None,
destination=None,
max_results=None,
start_index=None,
page_size=None,
retry=DEFAULT_RETRY,
timeout=None,
):
"""List the rows of a completed query.
See
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/getQueryResults
Args:
job_id (str):
ID of a query job.
location (str): Location of the query job.
project (str):
ID of the project where the query job was run.
schema (Sequence[google.cloud.bigquery.schema.SchemaField]):
The fields expected in these query results. Used to convert
from JSON to expected Python types.
total_rows (Optional[int]):
Total number of rows in the query results.
destination (Optional[Union[ \
google.cloud.bigquery.table.Table, \
google.cloud.bigquery.table.TableListItem, \
google.cloud.bigquery.table.TableReference, \
str, \
]]):
Destination table reference. Used to fetch the query results
with the BigQuery Storage API.
max_results (Optional[int]):
Maximum number of rows to return across the whole iterator.
start_index (Optional[int]):
The zero-based index of the starting row to read.
page_size (Optional[int]):
The maximum number of rows in each page of results from this request.
Non-positive values are ignored. Defaults to a sensible value set by the API.
retry (Optional[google.api_core.retry.Retry]):
How to retry the RPC.
timeout (Optional[float]):
The number of seconds to wait for the underlying HTTP transport
before using ``retry``. If set, this connection timeout may be
increased to a minimum value. This prevents retries on what
would otherwise be a successful response.
If multiple requests are made under the hood, ``timeout``
applies to each individual request.
Returns:
google.cloud.bigquery.table.RowIterator:
Iterator of row data
:class:`~google.cloud.bigquery.table.Row`-s.
"""
params = {
"fields": _LIST_ROWS_FROM_QUERY_RESULTS_FIELDS,
"location": location,
}
if timeout is not None:
timeout = max(timeout, _MIN_GET_QUERY_RESULTS_TIMEOUT)
if start_index is not None:
params["startIndex"] = start_index
params["formatOptions.useInt64Timestamp"] = True
row_iterator = RowIterator(
client=self,
api_request=functools.partial(self._call_api, retry, timeout=timeout),
path=f"/projects/{project}/queries/{job_id}",
schema=schema,
max_results=max_results,
page_size=page_size,
table=destination,
extra_params=params,
total_rows=total_rows,
)
return row_iterator
def _schema_from_json_file_object(self, file_obj):
"""Helper function for schema_from_json that takes a
file object that describes a table schema.
Returns:
List of schema field objects.
"""
json_data = json.load(file_obj)
return [SchemaField.from_api_repr(field) for field in json_data]
def _schema_to_json_file_object(self, schema_list, file_obj):
"""Helper function for schema_to_json that takes a schema list and file
object and writes the schema list to the file object with json.dump
"""
json.dump(schema_list, file_obj, indent=2, sort_keys=True)
[docs] def schema_from_json(self, file_or_path):
"""Takes a file object or file path that contains json that describes
a table schema.
Returns:
List of schema field objects.
"""
if isinstance(file_or_path, io.IOBase):
return self._schema_from_json_file_object(file_or_path)
with open(file_or_path) as file_obj:
return self._schema_from_json_file_object(file_obj)
[docs] def schema_to_json(self, schema_list, destination):
"""Takes a list of schema field objects.
Serializes the list of schema field objects as json to a file.
Destination is a file path or a file object.
"""
json_schema_list = [f.to_api_repr() for f in schema_list]
if isinstance(destination, io.IOBase):
return self._schema_to_json_file_object(json_schema_list, destination)
with open(destination, mode="w") as file_obj:
return self._schema_to_json_file_object(json_schema_list, file_obj)
# pylint: disable=unused-argument
def _item_to_project(iterator, resource):
"""Convert a JSON project to the native object.
Args:
iterator (google.api_core.page_iterator.Iterator): The iterator that is currently in use.
resource (Dict): An item to be converted to a project.
Returns:
google.cloud.bigquery.client.Project: The next project in the page.
"""
return Project.from_api_repr(resource)
# pylint: enable=unused-argument
def _item_to_dataset(iterator, resource):
"""Convert a JSON dataset to the native object.
Args:
iterator (google.api_core.page_iterator.Iterator): The iterator that is currently in use.
resource (Dict): An item to be converted to a dataset.
Returns:
google.cloud.bigquery.dataset.DatasetListItem: The next dataset in the page.
"""
return DatasetListItem(resource)
def _item_to_job(iterator, resource):
"""Convert a JSON job to the native object.
Args:
iterator (google.api_core.page_iterator.Iterator): The iterator that is currently in use.
resource (Dict): An item to be converted to a job.
Returns:
job instance: The next job in the page.
"""
return iterator.client.job_from_resource(resource)
def _item_to_model(iterator, resource):
"""Convert a JSON model to the native object.
Args:
iterator (google.api_core.page_iterator.Iterator):
The iterator that is currently in use.
resource (Dict): An item to be converted to a model.
Returns:
google.cloud.bigquery.model.Model: The next model in the page.
"""
return Model.from_api_repr(resource)
def _item_to_routine(iterator, resource):
"""Convert a JSON model to the native object.
Args:
iterator (google.api_core.page_iterator.Iterator):
The iterator that is currently in use.
resource (Dict): An item to be converted to a routine.
Returns:
google.cloud.bigquery.routine.Routine: The next routine in the page.
"""
return Routine.from_api_repr(resource)
def _item_to_table(iterator, resource):
"""Convert a JSON table to the native object.
Args:
iterator (google.api_core.page_iterator.Iterator): The iterator that is currently in use.
resource (Dict): An item to be converted to a table.
Returns:
google.cloud.bigquery.table.Table: The next table in the page.
"""
return TableListItem(resource)
def _make_job_id(job_id, prefix=None):
"""Construct an ID for a new job.
Args:
job_id (Optional[str]): the user-provided job ID.
prefix (Optional[str]): the user-provided prefix for a job ID.
Returns:
str: A job ID
"""
if job_id is not None:
return job_id
elif prefix is not None:
return str(prefix) + str(uuid.uuid4())
else:
return str(uuid.uuid4())
def _check_mode(stream):
"""Check that a stream was opened in read-binary mode.
Args:
stream (IO[bytes]): A bytes IO object open for reading.
Raises:
ValueError:
if the ``stream.mode`` is a valid attribute
and is not among ``rb``, ``r+b`` or ``rb+``.
"""
mode = getattr(stream, "mode", None)
if isinstance(stream, gzip.GzipFile):
if mode != gzip.READ:
raise ValueError(
"Cannot upload gzip files opened in write mode: use "
"gzip.GzipFile(filename, mode='rb')"
)
else:
if mode is not None and mode not in ("rb", "r+b", "rb+"):
raise ValueError(
"Cannot upload files opened in text mode: use "
"open(filename, mode='rb') or open(filename, mode='r+b')"
)
def _get_upload_headers(user_agent):
"""Get the headers for an upload request.
Args:
user_agent (str): The user-agent for requests.
Returns:
Dict: The headers to be used for the request.
"""
return {
"Accept": "application/json",
"Accept-Encoding": "gzip, deflate",
"User-Agent": user_agent,
"content-type": "application/json",
}
