I have to send the results from a bigquery to a google storage bucket. I'm used to send it to tables like this:
{
"schedule": null,
"owner":"agf#jdfgdfgs.es",
"email":["l1o3t0y2h5o3v6o3#jggvgfvf.com"],
"task_config":{
"orders":{
"destination_dataset_table":"international_reporting.orders"
}
Which I write in a JSON inside a Github repository, said repository is then called by Airflow.Those github repos are almost all the time a config JSON and the sql queries to execute. I don't know how to point to said bucket in Google Cloud storage. I would prefer to do it this way in order to keep the same style as the other, i.e cant use python -.-
Can you help me?
Please and thank you
Related
We're trying to use AWS Glue for ETL operations in our nodejs project. The workflow will be like below
user uploads csv file
data transformation from XYZ format to ABC format(mapping and changing field names)
download transformed csv file to local system
Note that, this flow should happen programmatically(creating crawlers, job triggers should be done programmatically not using the console). I don't know why documentation and other articles always show how to create crawlers, create jobs from glue console?
I believe that we have to create lambda functions and triggers. but not quite sure how to achieve this end to end flow. can anyone please help me. Thanks
Is there a way to bulk tag bigquery tables with python google.cloud.datacatalog?
If you want to take a look at sample code which uses the python google.cloud.datacatalog client library, I've put together a utilities open source script, that creates bulk Tags using a CSV as source. If you want to use a different source, you may use this script as reference, hope it helps.
create bulk tags from csv
For this purpose you may consider using DataCatalogClient() method which is included in google.cloud.datacatalog_v1 class as a part of PyPI Python google-cloud-datacatalog package leveraging Google Cloud Data Catalog API service.
By the first, you have to enable Data Catalog and BigQuery APIs
in your project;
Install Python Cloud Client Libraries for the Data Catalog API:
pip install --upgrade google-cloud-datacatalog
Set up authentication, exporting
GOOGLE_APPLICATION_CREDENTIALS environment variable holding JSON
file that contains your service account key:
export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/[FILE_NAME].json"
Refer to this example from official documentation that
intelligibly reflects a way creating Data catalog tag template,
attaching appropriate tag fields to the target Bigquery table using
create_tag_template() function.
Having any doubts feel free to extend you initial question or add a comment below this answer, thus we can address particular use case according to your needs.
I have successfully scheduled my query in BigQuery, and the result is saved as a table in my dataset. I see a lot of information about scheduling data transfer in to BigQuery or Cloud Storage, but I haven't found anything regarding scheduling an export from a BigQuery table to Cloud Storage yet.
Is it possible to schedule an export of a BigQuery table to Cloud Storage so that I can further schedule having it SFTP-ed to me via Google BigQuery Data Transfer Services?
There isn't a managed service for scheduling BigQuery table exports, but one viable approach is to use Cloud Functions in conjunction with Cloud Scheduler.
The Cloud Function would contain the necessary code to export to Cloud Storage from the BigQuery table. There are multiple programming languages to choose from for that, such as Python, Node.JS, and Go.
Cloud Scheduler would send an HTTP call periodically in a cron format to the Cloud Function which would in turn, get triggered and run the export programmatically.
As an example and more specifically, you can follow these steps:
Create a Cloud Function using Python with an HTTP trigger. To interact with BigQuery from within the code you need to use the BigQuery client library. Import it with from google.cloud import bigquery. Then, you can use the following code in main.py to create an export job from BigQuery to Cloud Storage:
# Imports the BigQuery client library
from google.cloud import bigquery
def hello_world(request):
# Replace these values according to your project
project_name = "YOUR_PROJECT_ID"
bucket_name = "YOUR_BUCKET"
dataset_name = "YOUR_DATASET"
table_name = "YOUR_TABLE"
destination_uri = "gs://{}/{}".format(bucket_name, "bq_export.csv.gz")
bq_client = bigquery.Client(project=project_name)
dataset = bq_client.dataset(dataset_name, project=project_name)
table_to_export = dataset.table(table_name)
job_config = bigquery.job.ExtractJobConfig()
job_config.compression = bigquery.Compression.GZIP
extract_job = bq_client.extract_table(
table_to_export,
destination_uri,
# Location must match that of the source table.
location="US",
job_config=job_config,
)
return "Job with ID {} started exporting data from {}.{} to {}".format(extract_job.job_id, dataset_name, table_name, destination_uri)
Specify the client library dependency in the requirements.txt file
by adding this line:
google-cloud-bigquery
Create a Cloud Scheduler job. Set the Frequency you wish for
the job to be executed with. For instance, setting it to 0 1 * * 0
would run the job once a week at 1 AM every Sunday morning. The
crontab tool is pretty useful when it comes to experimenting
with cron scheduling.
Choose HTTP as the Target, set the URL as the Cloud
Function's URL (it can be found by selecting the Cloud Function and
navigating to the Trigger tab), and as HTTP method choose GET.
Once created, and by pressing the RUN NOW button, you can test how the export
behaves. However, before doing so, make sure the default App Engine service account has at least the Cloud IAM roles/storage.objectCreator role, or otherwise the operation might fail with a permission error. The default App Engine service account has a form of YOUR_PROJECT_ID#appspot.gserviceaccount.com.
If you wish to execute exports on different tables,
datasets and buckets for each execution, but essentially employing the same Cloud Function, you can use the HTTP POST method
instead, and configure a Body containing said parameters as data, which
would be passed on to the Cloud Function - although, that would imply doing
some small changes in its code.
Lastly, when the job is created, you can use the Cloud Function's returned job ID and the bq CLI to view the status of the export job with bq show -j <job_id>.
Not sure if this was in GA when this question was asked, but at least now there is an option to run an export to Cloud Storage via a regular SQL query. See the SQL tab in Exporting table data.
Example:
EXPORT DATA
OPTIONS (
uri = 'gs://bucket/folder/*.csv',
format = 'CSV',
overwrite = true,
header = true,
field_delimiter = ';')
AS (
SELECT field1, field2
FROM mydataset.table1
ORDER BY field1
);
This could as well be trivially setup via a Scheduled Query if you need a periodic export. And, of course, you need to make sure the user or service account running this has permissions to read the source datasets and tables and to write to the destination bucket.
Hopefully this is useful for other peeps visiting this question if not for OP :)
You have an alternative to the second part of the Maxim answer. The code for extracting the table and store it into Cloud Storage should work.
But, when you schedule a query, you can also define a PubSub topic where the BigQuery scheduler will post a message when the job is over. Thereby, the scheduler set up, as described by Maxim is optional and you can simply plug the function to the PubSub notification.
Before performing the extraction, don't forget to check the error status of the pubsub notification. You have also a lot of information about the scheduled query; useful is you want to perform more checks or if you want to generalize the function.
So, another point about the SFTP transfert. I open sourced a projet for querying BigQuery, build a CSV file and transfert this file to FTP server (sFTP and FTPs aren't supported, because my previous company only used FTP protocol!). If your file is smaller than 1.5Gb, I can update my project for adding the SFTP support is you want to use this. Let me know
I have a few GCP projects with log sinks to different storage buckets. I'd like to combine them into a single bucket. But the stackdriver export doesn't add any distinguishing information to the object names it creates; they all look like cloudaudit.googleapis.com/activity/2017/11/14/00:00:00_00:59:59_S0.json
What will happen if I start pushing them all to a single bucket? Will the different project sinks overwrite each other's objects? Is there any way to distinguish which project created the logs just from the object?
If not, I guess I should switch to pubsub sinks, and then write some code that produces objects with more desirable names. Are there any established patterns or examples for doing this?
Update: I filed https://issuetracker.google.com/issues/69371200 for this issue.
To enable this, just select custom destination on the sink and point to the bucket with this format: storage.googleapis.com/[BUCKET_ID].
I've just enabled this in a couple of my projects, as I'm curious to see the results when exporting to a bucket. However, I have been using a single BQ sink for all my projects, and the tables created have all the logs mixed, so no logs lost when using a single BQ sink.
I'm assuming for a GCS sink will work in the same way, but I'll tell you in a couple of days.
If a single bucket sink does not work, you can always use a single BQ sink (that will help in analyzing the logs), and when you no longer want to have them in BQ, export them and store the files wherever you want.
Also, since you'll be writing to your sink constantly, you can't use nearline or coldline, so the storage pricing is better in BQ than a regional bucket (0.02 USD/GB in BQ vs somewhere between 0.02 and 0.35 USD/GB for regional storage, depending on the region; BQ has 10GB free monthly, GCS 5GB).
I would generally recommend using a BQ sink, but I'll tell you what happens with my bucket logs.
Update:
A few hours later, and I've verified that shared bucket sinks work pretty much as you would expect. It concatenates logs chronologically regardless of the project origin, and only creates a single file for each time window. Hope this helps! (I still prefer BQ as a log sink...)
Update 2:
For the behavior you seek in the feature request, I would use BQ, but you could just as easily grep the project ID and separate the logs:
grep '"logName":"projects/<your-project-id>/' mixed-log.json > single-project-log.json
Or just get a cloud function triggered by bucket updates (so, every time you receive a log file in the sink) to run this for you.
Or namespace you buckets and have a cloud function moving them to wherever you need as soon as they are written.
The possibilities are endless!
If you have an organization or folder which includes all the projects that you want to collect logs from, then you can create a sink that collects from all projects in that org/folder.
Unfortunatlely, you cannot do this from the Cloud Console. Instead you must use gcloud with the --organization or --folder option or the API.
Looks like Parse.com stores the PFFile objects on AWS S3 and only stores a reference to the actual files on S3 in Parse for the PFFile object types.
So my problem here is I only get a link to AWS S3 link for my PFFile if I export the data using the out of the box Parse.com export functionality. After I import the same data to my Parse application, for some reason the security setting on those PFFiles on S3 is changed in a way that all PFFiles won't be accessible to me after an import due to security error.
My question is, does anyone know how the security is being set on the PFFiles? Here's a link to PFFile https://parse.com/docs/osx/api/Classes/PFFile.html but I guess this is rather an advanced topic and wasn't revealed on this page.
Also looking a solution for this, all I found is this from their forum:
In this case, the PFFiles are stored in a different app. You might
need to download these files and upload them again to the new app and
update the pointers. I know this is not a great answer but we're
working on making this process more straightforward.
https://www.parse.com/questions/import-pffile-object-not-working-in-iphone-application