support team,
I am using google bigquery API python lib to test some operations. One purpose is to get the job information. Then we can better control all our queries from the API. I found there is a get() method mentioned in the REST reference here, which can get the job information. But in the API python lib here, I can not find any doc about this get() method or something can finish the same operation.
Can you help to provide me any guide doc about any method in the python lib can get the job information?
Thanks
Zhihong
You are looking at the documentation for the translate API rather than BigQuery. See job_from_resource under the BigQuery client documentation.
Based on Elliott's suggestion, I got the job info I need after run a query, but not figure out how to fetch the job info for an exist job, which I think not needed anymore if got query info after each operation. The python code is as below:
from google.cloud import bigquery
client = bigquery.Client()
query = client.run_sunc_query(sql)
query.use_legacy_sql = False
query.use_query_cache = True
query.run()
trows = query.total_rows
billed_byte = query.total_bytes_processed
More query info parameters can be found here and more example code can be found here.
Related
I am trying to learn ML models for predicting stock prices and initially, I tried using DataReader
import pandas_datareader as web
df = web.DataReader('AAPL', data_source='yahoo', start='2016-01-01', end='2021-08-01')
But I get a RemoteDataError and kept hitting a dead end trying to figure it out so I tried using tiingo
https://tiingo-python.readthedocs.io/en/latest/readme.html
I read through the documentation and tried passing a dictionary with 'api_key' as a key
into my tiingo client, ie.
from tiingo import TiingoClient
client = TiingoClient()
config = {}
config['session'] = True
config['api_key'] = 'my_api_key'
client = TiingoClient(config)
The documentation says I can now use TiingoClient to make API calls, however,
RuntimeError: Tiingo API Key not provided. Please provide via environment variable or config argument.
It is quite challenging learning the ML models and its syntax but what compounds the difficulty for me is what some data-scientests consider to be trivial as they don't typically deal with gathering or scraping data. Maybe my question is trivial but I've spent about an hour trying to figure out how to import data properly for stock prices and the only method that worked for me so far is
df = web.get_data_yahoo('stock symbol')
but I would like to grasp the other ways of importing stock prices via Tiingo and DataReader so if anyone can provide explanations/tips/suggestions I'd greatly appreciate it.
EDIT: for my tiingo account I did not buy any subscription plan for using their data as I was under the impression I can access data for free with my api-key
This is what I use, but its identical to what you are using it seems.
config = {}
config['session'] = True
config['api_key'] = "key here"
client = TiingoClient(config)
Remove this line: TiingoClient()
On Google Cloud Platform, I am trying to submit a pyspark job that writes a dataframe to BigQuery.
The code that executes the writing is as the following:
finalDF.write.format("bigquery")\
.mode('overwrite')\
.option("table","[PROJECT_ID].dataset.table")\
.save()
And I get the mentioned error in the title. How can I set the GCS temporary path?
As the github repository of spark-bigquery-connector states
One can specify it when writing:
df.write
.format("bigquery")
.option("temporaryGcsBucket","some-bucket")
.save("dataset.table")
Or in a global manner:
spark.conf.set("temporaryGcsBucket","some-bucket")
Property "temporaryGcsBucket" needs to be set either at the time of writing dataframe or while creating sparkSession.
.option("temporaryGcsBucket","some-bucket")
or like .option("temporaryGcsBucket","some-bucket/optional_path")
1. finalDF.write.format("bigquery") .mode('overwrite').option("temporaryGcsBucket","some-bucket").option("table","[PROJECT_ID].dataset.table") .save()
I really liked BigQuery's Data Transfer Service. I have flat files in the exact schema sitting to be loaded into BQ. It would have been awesome to just setup DTS schedule that picked up GCS files that match a pattern and load the into BQ. I like the built in option to delete source files after copy and email in case of trouble. But the biggest bummer is that the minimum interval is 60 minutes. That is crazy. I could have lived with a 10 min delay perhaps.
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
StartManualTransferRuns is part of the RPC library but does not have a REST API equivalent as of now. How to use that will depend on your environment. For instance, you can use the Python Client Library (docs).
As an example, I used the following code (you'll need to run pip install google-cloud-bigquery-datatransfer for the depencencies):
import time
from google.cloud import bigquery_datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = bigquery_datatransfer_v1.DataTransferServiceClient()
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = '5e6...7bc' # alphanumeric ID you'll find in the UI
parent = client.project_transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = bigquery_datatransfer_v1.types.Timestamp(seconds=int(time.time() + 10))
response = client.start_manual_transfer_runs(parent, requested_run_time=start_time)
print(response)
Note that you'll need to use the right Transfer Config ID and the requested_run_time has to be of type bigquery_datatransfer_v1.types.Timestamp (for which there was no example in the docs). I set a start time 10 seconds ahead of the current execution time.
You should get a response such as:
runs {
name: "projects/PROJECT_NUMBER/locations/us/transferConfigs/5e6...7bc/runs/5e5...c04"
destination_dataset_id: "DATASET_NAME"
schedule_time {
seconds: 1579358571
nanos: 922599371
}
...
data_source_id: "google_cloud_storage"
state: PENDING
params {
...
}
run_time {
seconds: 1579358581
}
user_id: 28...65
}
and the transfer is triggered as expected (nevermind the error):
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
With this you can set a cron job to execute your function every ten minutes. As discussed in the comments, the minimum interval is 60 minutes so it won't pick up files less than one hour old (docs).
Apart from that, this is not a very robust solution and here come into play your follow-up questions. I think these might be too broad to address in a single StackOverflow question but I would say that, for on-demand refresh, Cloud Scheduler + Cloud Functions/Cloud Run can work very well.
Dataflow would be best if you needed ETL but it has a GCS connector that can watch a file pattern (example). With this you would skip the transfer, set the watch interval and the load job triggering frequency to write the files into BigQuery. VM(s) would be running constantly in a streaming pipeline as opposed to the previous approach but a 10-minute watch period is possible.
If you have complex workflows/dependencies, Airflow has recently introduced operators to start manual runs.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
You can use wildcards to match a file pattern when you create the transfer:
Also, this can be done on a file-by-file basis using Pub/Sub notifications for Cloud Storage to trigger a Cloud Function.
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
There is already a Feature Request here. Feel free to star it to show your interest and receive updates
Now your can easy manual run transfer Bigquery data use RESTApi:
HTTP request
POST https://bigquerydatatransfer.googleapis.com/v1/{parent=projects/*/locations/*/transferConfigs/*}:startManualRuns
About this part > {parent=projects//locations//transferConfigs/*}, check on CONFIGURATION of your Transfer then notice part like image bellow.
Here
More here:
https://cloud.google.com/bigquery-transfer/docs/reference/datatransfer/rest/v1/projects.locations.transferConfigs/startManualRuns
following the Guillem's answer and the API updates, this is my new code:
import time
from google.cloud.bigquery import datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = datatransfer_v1.DataTransferServiceClient()
config = '34y....654'
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = config
parent = client.transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = Timestamp(seconds=int(time.time()))
request = datatransfer_v1.types.StartManualTransferRunsRequest(
{ "parent": parent, "requested_run_time": start_time }
)
response = client.start_manual_transfer_runs(request, timeout=360)
print(response)
For this to work, you need to know the correct TRANSFER_CONFIG_ID.
In my case, I wanted to list all the BigQuery Scheduled queries, to get a specific ID. You can do it like that :
# Put your projetID here
PROJECT_ID = 'PROJECT_ID'
from google.cloud import bigquery_datatransfer_v1
bq_transfer_client = bigquery_datatransfer_v1.DataTransferServiceClient()
parent = bq_transfer_client.project_path(PROJECT_ID)
# Iterate over all results
for element in bq_transfer_client.list_transfer_configs(parent):
# Print Display Name for each Scheduled Query
print(f'[Schedule Query Name]:\t{element.display_name}')
# Print name of all elements (it contains the ID)
print(f'[Name]:\t\t{element.name}')
# Extract the IDs:
TRANSFER_CONFIG_ID= element.name.split('/')[-1]
print(f'[TRANSFER_CONFIG_ID]:\t\t{TRANSFER_CONFIG_ID}')
# You can print the entire element for debug purposes
print(element)
We are using the Beta Scheduled query feature of BigQuery.
Details: https://cloud.google.com/bigquery/docs/scheduling-queries
We have few ETL scheduled queries running overnight to optimize the aggregation and reduce query cost. It works well and there hasn't been much issues.
The problem arises when the person who scheduled the query using their own credentials leaves the organization. I know we can do "update credential" in such cases.
I read through the document and also gave it some try but couldn't really find if we can use a service account instead of individual accounts to schedule queries.
Service accounts are cleaner and ties up to the rest of the IAM framework and is not dependent on a single user.
So if you have any additional information regarding scheduled queries and service account please share.
Thanks for taking time to read the question and respond to it.
Regards
BigQuery Scheduled Query now does support creating a scheduled query with a service account and updating a scheduled query with a service account. Will these work for you?
While it's not supported in BigQuery UI, it's possible to create a transfer (including a scheduled query) using python GCP SDK for DTS, or from BQ CLI.
The following is an example using Python SDK:
r"""Example of creating TransferConfig using service account.
Usage Example:
1. Install GCP BQ python client library.
2. If it has not been done, please grant p4 service account with
iam.serviceAccout.GetAccessTokens permission on your project.
$ gcloud projects add-iam-policy-binding {user_project_id} \
--member='serviceAccount:service-{user_project_number}#'\
'gcp-sa-bigquerydatatransfer.iam.gserviceaccount.com' \
--role='roles/iam.serviceAccountTokenCreator'
where {user_project_id} and {user_project_number} are the user project's
project id and project number, respectively. E.g.,
$ gcloud projects add-iam-policy-binding my-test-proj \
--member='serviceAccount:service-123456789#'\
'gcp-sa-bigquerydatatransfer.iam.gserviceaccount.com'\
--role='roles/iam.serviceAccountTokenCreator'
3. Set environment var PROJECT to your user project, and
GOOGLE_APPLICATION_CREDENTIALS to the service account key path. E.g.,
$ export PROJECT_ID='my_project_id'
$ export GOOGLE_APPLICATION_CREDENTIALS=./serviceacct-creds.json'
4. $ python3 ./create_transfer_config.py
"""
import os
from google.cloud import bigquery_datatransfer
from google.oauth2 import service_account
from google.protobuf.struct_pb2 import Struct
PROJECT = os.environ["PROJECT_ID"]
SA_KEY_PATH = os.environ["GOOGLE_APPLICATION_CREDENTIALS"]
credentials = (
service_account.Credentials.from_service_account_file(SA_KEY_PATH))
client = bigquery_datatransfer.DataTransferServiceClient(
credentials=credentials)
# Get full path to project
parent_base = client.project_path(PROJECT)
params = Struct()
params["query"] = "SELECT CURRENT_DATE() as date, RAND() as val"
transfer_config = {
"destination_dataset_id": "my_data_set",
"display_name": "scheduled_query_test",
"data_source_id": "scheduled_query",
"params": params,
}
parent = parent_base + "/locations/us"
response = client.create_transfer_config(parent, transfer_config)
print response
As far as I know, unfortunately you can't use a service account to directly schedule queries yet. Maybe a Googler will correct me, but the BigQuery docs implicitly state this:
https://cloud.google.com/bigquery/docs/scheduling-queries#quotas
A scheduled query is executed with the creator's credentials and
project, as if you were executing the query yourself
If you need to use a service account (which is great practice BTW), then there are a few workarounds listed here. I've raised a FR here for posterity.
This question is very old and came on this thread while I was searching for same.
Yes, It is possible to use service account to schedule big query jobs.
While creating schedule query job, click on "Advance options", you will get option to select service account.
By default is uses credential of requesting user.
Image from bigquery "create schedule query"1
I am trying to do something which is very simple in other data services. I am trying to make a relatively simple SQL query and return it as a dataframe in python. I am on Windows 10 and using Phython 2.7 (specifically Canopy 1.7.4)
Typically this would be done with pandas.read_sql_query but due to some specifics with BigQuery they require a different method pandas.io.gbq.read_gbq
This method works fine unless you want to make a Big Query. If you make a Big Query on BigQuery you get the error
GenericGBQException: Reason: responseTooLarge, Message: Response too large to return. Consider setting allowLargeResults to true in your job configuration. For more information, see https://cloud.google.com/bigquery/troubleshooting-errors
This was asked and answered before in this ticket but neither of the solutions are relevant for my case
Python BigQuery allowLargeResults with pandas.io.gbq
One solution is for python 3 so it is a nonstarter. The other is giving an error due to me being unable to set my credentials as an environment variable in windows.
ApplicationDefaultCredentialsError: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https://developers.google.com/accounts/docs/application-default-credentials for more information.
I was able to download the JSON credentials file and I have set it as an environment variable in the few ways I know how but I still get the above error. Do I need to load this in some way in python? It seems to be looking for it but unable to find is correctly. Is there a special way to set it as an environment variable in this case?
You can do it in Python 2.7 by changing the default dialect from legacy to standard in pd.read_gbq function.
pd.read_gbq(query, 'my-super-project', dialect='standard')
Indeed, you can read in Big Query documentation for the parameter AllowLargeResults:
AllowLargeResults: For standard SQL queries, this flag is
ignored and large results are always allowed.
I have found two ways of directly importing the JSON credentials file. Both based on the original answer in Python BigQuery allowLargeResults with pandas.io.gbq
1) Credit to Tim Swast
First
pip install google-api-python-client
pip install google-auth
pip install google-cloud-core
then
replace
credentials = GoogleCredentials.get_application_default()
in create_service() with
from google.oauth2 import service_account
credentials = service_account.Credentials.from_service_account_file('path/file.json')
2)
Set the environment variable manually in the code like
import os,os.path
os.environ['GOOGLE_APPLICATION_CREDENTIALS']=os.path.expanduser('path/file.json')
I prefer method 2 since it does not require new modules to be installed and is also closer to the intended use of the JSON credentials.
Note:
You must create a destinationTable and add the information to run_query()
Here is a code that fully works within python 2.7 on Windows:
import pandas as pd
my_qry="<insert your big query here>"
### Here Put the data from your credentials file of the service account - all fields are available from there###
my_file="""{
"type": "service_account",
"project_id": "cb4recs",
"private_key_id": "<id>",
"private_key": "<your private key>\n",
"client_email": "<email>",
"client_id": "<id>",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://accounts.google.com/o/oauth2/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "<x509 url>"
}"""
df=pd.read_gbq(qry,project_id='<your project id>',private_key=my_file)
That's it :)