I am currently exporting logs from Stackdriver to BigQuery using sinks. But i am only interessted in the jsonPayload. I would like to ignore pretty much everything else.
But since the table creation and data insertion happens automatically, i could not do this.
Is there a way to preprocess data coming from sink to store only what matters?
If the answer is no, is there a way to run a cron job each day to copy yesterday data into a seperate table and then remove it? (knowing that the tables are named using timestamps which makes it possible to query them by day)
As far as I know both options mentioned are currently not possible in the GCP platform. On my end I've also tried to create an internal reproduction of your request and noticed that there isn't a way to solely filter the jsonPayload.
I would therefore suggest creating a feature request in regards to your ask on the following public issue tracker link. Note that feature requests do not have an ETA as to when they'll processed or if they'll be implemented.
Related
I'm looking for some best (simplest;)) practices here.
I have Google Analytics data that is send to BigQuery on a daily basis. I have a query running on a daily basis that uses the data from the previous day's table.
However, I can't be sure this table and the data is there at the time the query runs and I'd like to check if it does. If it isn't there I want to retry later.
Ideally I have some monitoring/alerting around this as well.
Of course this can be done within the Google Cloud in many ways, I'm looking for some best practices how others do this?
I'm used to working with Airflow, but using Composer just for this seems a bit over the top. Cloud Run would be an option and I'm sure there are others. Also I've seen this question discussing how to handle a dependency in SQL, I'm just not sure if I could have it retry using just SQL as well?
EDIT:
I've got the check for the table working in SQL. I guess I just have to see if BigQuery has a way to build in delay like 'WAITFOR'
To track BQ usage we created a new dataset and configured it in Billing export. But after waiting for a day also the dataset seems to be empty as no new tables is created.
Is there any other setup needs to be done for this to work.
Refer this link,
https://cloud.google.com/billing/docs/how-to/export-data-bigquery
Thanks and regards,
Gour
You just need to follow the How to enable billing export to BigQuery steps to begin using this functionality. Keep in mind that you have to wait certain time to start seeing your data, as mentioned in the Export Billing Data to BigQuery documentation.
After you enable BigQuery export, it might take a few hours to start seeing your data. Billing data automatically exports your data to BigQuery in regular intervals, but the frequency of updates in BigQuery varies depending on the services you're using.
In case you continue having this issue, I recommend you to take a look the Issue Tracker tool that you can use to raise a BigQuery ticket in order to verify this scenario with the Google Technical Support Team. Since this is an automated process, you might need some of their help to review your Project's internal configuration.
I am looking for someway where my code on compute engine needs to be notified when my bigquery table gets updated ? Similar to object change notification on google cloud storage ? If not any work around ? My bigquery tables get updated using Data transfer service.
To the best of my knowledge, BigQuery does not have any type of notification functionality. It's probably a worthwhile feature request though.
Your best bet is to write a script, schedule it (e.g. a cron), and have it check the last modified timestamp for the table/dataset and fire off a notification when it detects a change/update.
I want to stream some time series data into BigQuery with insertAll but only retain the last 3 months (say) to avoid unbounded storage costs. The usual answer is to save each day of data into a separate table but AFAICT this would require each such table to be created in advance. I intend to stream data directly from unsecured clients authorized with a token that only has bigquery.insertdata scope, so they wouldn't be able to create the daily tables themselves. The only solution I can think of would be to run a secure daily cron job to create the tables -- not ideal, especially since if it misfires data will be dropped until the table is created.
Another approach would be to stream data into a single table and use table decorators to control query costs as the table grows. (I expect all queries to be for specific time ranges so the decorators should be pretty effective here.) However, there's no way to delete old data from the table, so storage costs will become unsustainable after a while. I can't figure out any way to "copy and truncate" the table atomically either, so that I can partition old data into daily tables without losing rows being streamed at that time.
Any ideas on how to solve this? Bonus points if your solution lets me re-aggregate old data into temporally coarser rows to retain more history for the same storage cost. Thanks.
Edit: just realized this is a partial duplicate of Bigquery event streaming and table creation.
If you look at the streaming API discovery document, there's a curious new experimental field called "templateSuffix", with a very relevant description.
I'd also point out that no official documentation has been released, so special care should probably go into using this field -- especially in a production setting. Experimental fields could possibly have bugs etc. Things I could think to be careful of off the top of my head are:
Modifying the schema of the base table in non-backwards-compatible ways.
Modifying the schema of a created table directly in a way that is incompatible with the base table.
Streaming to a created table directly and via this suffix -- row insert ids might not apply across boundaries.
Performing operations on the created table while it's actively being streamed to.
And I'm sure other things. Anyway, just thought I'd point that out. I'm sure official documentation will be much more thorough.
Most of us are doing the same thing as you described.
But we don't use a cron, as we create tables advance for 1 year or on some project for 5 years in advance. You may wonder why we do so, and when.
We do this when the schema is changed by us, by the developers. We do a deploy and we run a script that takes care of the schema changes for old/existing tables, and the script deletes all those empty tables from the future and simply recreates them. We didn't complicated our life with a cron, as we know the exact moment the schema changes, that's the deploy and there is no disadvantage to create tables in advance for such a long period. We do this based on tenants too on SaaS based system when the user is created or they close their accounts.
This way we don't need a cron, we just to know that the deploy needs to do this additional step when the schema changed.
As regarding don't lose streaming inserts while I do some maintenance on your tables, you need to address in your business logic at the application level. You probably have some sort of message queue, like Beanstalkd to queue all the rows into a tube and later a worker pushes to BigQuery. You may have this to cover the issue when BigQuery API responds with error and you need to retry. It's easy to do this with a simple message queue. So you would relly on this retry phase when you stop or rename some table for a while. The streaming insert will fail, most probably because the table is not ready for streaming insert eg: have been temporary renamed to do some ETL work.
If you don't have this retry phase you should consider adding it, as it not just helps retrying for BigQuery failed calls, but also allows you do have some maintenance window.
you've already solved it by partitioning. if table creation is an issue have an hourly cron in appengine that verifies today and tomorrow tables are always created.
very likely the appengine wont go over the free quotas and it has 99.95% SLO for uptime. the cron will never go down.
I want to stream some time series data into BigQuery with insertAll but only retain the last 3 months (say) to avoid unbounded storage costs. The usual answer is to save each day of data into a separate table but AFAICT this would require each such table to be created in advance. I intend to stream data directly from unsecured clients authorized with a token that only has bigquery.insertdata scope, so they wouldn't be able to create the daily tables themselves. The only solution I can think of would be to run a secure daily cron job to create the tables -- not ideal, especially since if it misfires data will be dropped until the table is created.
Another approach would be to stream data into a single table and use table decorators to control query costs as the table grows. (I expect all queries to be for specific time ranges so the decorators should be pretty effective here.) However, there's no way to delete old data from the table, so storage costs will become unsustainable after a while. I can't figure out any way to "copy and truncate" the table atomically either, so that I can partition old data into daily tables without losing rows being streamed at that time.
Any ideas on how to solve this? Bonus points if your solution lets me re-aggregate old data into temporally coarser rows to retain more history for the same storage cost. Thanks.
Edit: just realized this is a partial duplicate of Bigquery event streaming and table creation.
If you look at the streaming API discovery document, there's a curious new experimental field called "templateSuffix", with a very relevant description.
I'd also point out that no official documentation has been released, so special care should probably go into using this field -- especially in a production setting. Experimental fields could possibly have bugs etc. Things I could think to be careful of off the top of my head are:
Modifying the schema of the base table in non-backwards-compatible ways.
Modifying the schema of a created table directly in a way that is incompatible with the base table.
Streaming to a created table directly and via this suffix -- row insert ids might not apply across boundaries.
Performing operations on the created table while it's actively being streamed to.
And I'm sure other things. Anyway, just thought I'd point that out. I'm sure official documentation will be much more thorough.
Most of us are doing the same thing as you described.
But we don't use a cron, as we create tables advance for 1 year or on some project for 5 years in advance. You may wonder why we do so, and when.
We do this when the schema is changed by us, by the developers. We do a deploy and we run a script that takes care of the schema changes for old/existing tables, and the script deletes all those empty tables from the future and simply recreates them. We didn't complicated our life with a cron, as we know the exact moment the schema changes, that's the deploy and there is no disadvantage to create tables in advance for such a long period. We do this based on tenants too on SaaS based system when the user is created or they close their accounts.
This way we don't need a cron, we just to know that the deploy needs to do this additional step when the schema changed.
As regarding don't lose streaming inserts while I do some maintenance on your tables, you need to address in your business logic at the application level. You probably have some sort of message queue, like Beanstalkd to queue all the rows into a tube and later a worker pushes to BigQuery. You may have this to cover the issue when BigQuery API responds with error and you need to retry. It's easy to do this with a simple message queue. So you would relly on this retry phase when you stop or rename some table for a while. The streaming insert will fail, most probably because the table is not ready for streaming insert eg: have been temporary renamed to do some ETL work.
If you don't have this retry phase you should consider adding it, as it not just helps retrying for BigQuery failed calls, but also allows you do have some maintenance window.
you've already solved it by partitioning. if table creation is an issue have an hourly cron in appengine that verifies today and tomorrow tables are always created.
very likely the appengine wont go over the free quotas and it has 99.95% SLO for uptime. the cron will never go down.