I have many TBs in about 1 million tables in a single BigQuery project hosted in multiple datasets that are located in the US. I need to move all of this data to datasets hosted in the EU. What is my best option for doing so?
I'd export the tables to Google Cloud Storage and reimport using load jobs, but there's a 10K limit on load jobs per project per day
I'd do it as queries w/"allow large results" and save to a destination table, but that doesn't work cross-region
The only option I see right now is to reinsert all of the data using the BQ streaming API, which would be cost prohibitive.
What's the best way to move a large volume of data in many tables cross-region in BigQuery?
You have a couple of options:
Use load jobs, and contact Google Cloud Support to ask for a quota exception. They're likely to grant 100k or so on a temporary basis (if not, contact me, tigani#google, and I can do so).
Use federated query jobs. That is, move the data into a GCS bucket in the EU, then re-import the data via BigQuery queries with GCS data sources. More info here.
I'll also look into whether we can increase this quota limit across the board.
You can copy dataset using BigQuery Copy Dataset (in/cross-region). The copy dataset UI is similar to copy table. Just click "copy dataset" button from the source dataset, and specify the destination dataset in the pop-up form. See screenshot below. Check out the public documentation for more use cases.
A few other options that are now available since Jordan answered a few years ago. These options might be useful for some folks:
Use Cloud Composer to orchestrate the export and load via GCS buckets. See here.
Use Cloud Dataflow to orchestrate the export and load via GCS buckets. See here.
Disclaimer: I wrote the article for the 2nd option (using Cloud Dataflow).
Related
I am new to GCP and recently created a bucket on Google Cloud Storage. RAW files are dumping every hour on GCS bucket in every hour in CSV format.
I would like to load all the CSV files from Cloud storage to BigQuery and there will be a scheduling option to load the recent files from Cloud Storage and append the data to the same table on BigQuery.
Please help me to setup this.
There is many options. But I will present only 2:
You can do nothing and use external table in BigQuery, that means you let the data in Cloud Storage and ask BigQuery to request the data directly from Cloud Storage. You don't duplicate the data (and pay less for storage), but the query are slower (need to load the data from a less performant storage and to parse, on the fly, the CSV) and you process all the file for all queries. You can't use BigQuery advanced feature such as partitioning, clustering and others...
Perform a BigQuery load operation to load all the existing file in a BigQuery table (I recommend to partition the table if you can). For the new file, forget the old school scheduled ingestion process. With cloud, you can be event driven. Catch the event that notify a new file on Cloud Storage and load it directly in BigQuery. You have to write a small Cloud Functions for that, but it's the most efficient and the most recommended pattern. You can find code sample here
Just a warning on the latest solution, you can perform "only" 1500 load job per day and per table (about 1 per minute)
We are evaluating bigquery and snowflake for our new cloud warehouse. Does bigquery has a cloning feature built-in? This will enable our developers to create multiple development environments quickly and we can also restore to point-in-time .Snowflake has a zero-copy clone to minimize the storage footprint. For managing DEV/QA environments in bigquery do we need to manually copy the datasets from prod? Please share some insights.
You can use a pre-GA feature Big query data transfer service to create copies of datasets, you can also schedule and configure the jobs to run periodically so that the target dataset is in sync with source dataset. Restoring to a point in time is available via FOR SYSTEM_TIME AS OF in FROM clause
I don't think there is an exact snowflake clone equivalent on Big query. What would this mean?
You will be charged for additional storage and for data transfer if its cross-region (pricing equivalent to Compute Engine network egress between regions)
Cloning is not instantaneous, for large tables(> 1 TB) you might still have to wait for a while before you see a new copy is created
I want to send data from BigQuery (about 500K rows) to a custom endpoint via post method, how can I do this?
These are my options:
A PHP process to read and send the data (I have already tried this one, but it is too slow and the max execution time pops up).
I was looking for Google Cloud Dataflow, but I don't know Java.
Running it into Google Cloud Function, but I don't know how to send data via post.
Do you know another option?
As mentioned in the comments, 500k rows for a POST method is far too much data to be considered as an option.
Dataflow is a product oriented for pipelines development, intended to run several data transformations during its jobs. You can use BigQueryIO (with python sample codes) but, If you just need to migrate the data to a certain machine/endpoint, creating a Dataflow job will add complexity to your task.
The suggested approach is to export to a GCS bucket and then download the data from it.
For instance, if the size of Data that you are trying to retrieve is less than 1GB, you can export to a GCS bucket from the Command Line Interface like: bq extract --compression GZIP 'mydataset.mytable' gs://example-bucket/myfile.csv. Otherwise, you will need to export the data in more files using wildcard URI defining your bucket destination as indicated ('gs://my-bucket/file-name-*.json').
And finally, using gsutil command gsutil cp gs://[BUCKET_NAME]/[OBJECT_NAME] [SAVE_TO_LOCATION] you will download the data from your bucket.
Note: you have more available ways to do that in the Cloud documentation links provided, including the BigQuery web UI.
Also, bear in mind that there are no charges for exporting data from BigQuery, but you do incur charges for storing the exported data in Cloud Storage. BigQuery exports are subject to the limits on export jobs.
I have an app that sending me data from an API. The data is semi-structured (json data)
I would like to send this data to Google Big Query in order to stock all the information.
However, I'm not able to find how can I do it properly.
So far I have used Node JS on my own server to get the data using POST request.
Could you please help me ? Thnak.
You can use bigquery API to do streaming inserts.
You can also write the data to PubSub or Google Cloud Storage and use dataflow pipelines to load them into bigquery (you can either use streaming inserts (incur costs) or batch load jobs (free))
You can also log in stackdriver and from there you can select and send to bigquery (there already exists direct options for it in GCP, note that under the hood it performs streaming inserts)
If you feel that setting up dataflow is complicated, you can store your files and perform batch load jobs by directly calling bigquery API. Note that there are limits on number of batch loads you can make in a day over a particular table (1000 per day)
There is a page in the official documentation that lists all the possibilities of loading data to BigQuery.
For the simplicity, you can just send data from your local data soruce. You should use the Google Cloud client libraries for Big Query. Here you have a guide on how to do that as well as a relevant code example.
But my honest recommendation is to send data to Google Cloud Storage and from there, to load it to BigQuery. This way the whole process will be more stable.
You can check all the options from the first link that I've posted and choose what you think that will fit best with your workflow.
Keep in mind the limitations of this process.
I run a dataset in bigquery on a daily basis which i need to export to my google storage bucket. The dataset is greater than 10MB which means i'm unable to use app-scripts.
Essentially, I'd like to automate a data load using my bigquery script which exports the dataset as a CSV file to google storage.
Can anyone point me into the right direction in terms of which programme/method to use. Please also share your experiences.
Thanks
Here you can find some details on how to export data from BigQuery to Cloud Storage along with a sample written in Python.
https://cloud.google.com/bigquery/exporting-data-from-bigquery
You can implement a simple application running on App Engine that will contain cron job scheduled to run once a day and perform the steps described in the tutorial above.
https://cloud.google.com/appengine/docs/python/config/cron