Referring to item: Watching for new files matching a filepattern in Apache Beam
Can you use this for simple use cases? My use case is that I have user uploads data to Cloud Storage -> Pipeline (Process csv to json) -> Big Query. I know Cloud Storage is bounded collection so it represents Batch Dataflow.
What I would like is to do is keep pipeline running in streaming mode and as soon as a file is uploaded to Cloud Storage, it will be processed through pipeline. Is this possible with watchfornewfiles?
I wrote my code as follows:
p.apply(TextIO.read().from("<bucketname>")
.watchForNewFiles(
// Check for new files every 30 seconds
Duration.standardSeconds(30),
// Never stop checking for new files
Watch.Growth.<String>never()));
None of the contents is being forwarded to Big Query, but the pipeline shows that it is streaming.
You may use Google Cloud Storage Triggers here :
https://cloud.google.com/functions/docs/calling/storage#functions-calling-storage-python
These triggers uses Cloud Functions similar to Cloud Pub/Sub which gets triggered on objects if they were: created/ deleted/archived/ or metadata change.
These event are sent using Pub/Sub notifications from Cloud Storage, but pay attention not to set many functions over the same bucket as there is some notification limits.
Also, at the end of the document there is a link to a sample implementation.
Related
I have an ETL application which is suppose to migrate to AWS infra. The scheduler being used in my application is Tivoli Work Scheduler and we want to use the same on cloud as well which has file dependencies.
Now when we move to aws , the files to be watched will land in S3 Bucket. Can we put the OPEN dependency for files in S3? If yes, What would be the hostname ( HOST#Filepath ) ?
If Not, what services should be aligned to serve the purpose. I have both time as well as file dependency in my SCHEDULES.
Eg. The file might get uploaded on S3 at 1AM. AT 3 AM my schedule will get triggered, look for the file in S3 bucket. If present, starts execution and if not then it should wait as per other parameters on tws.
Any help or advice would be nice to have.
If I understand this correctly, job triggered at 3am will identify all files uploaded within last e.g. 24 hours.
You can list all s3 files to list everything uploaded within specific period of time.
Better solution would be to create S3 upload trigger which will send information to SQS and have your code inspect the depth (number of messages) there and start processing the files one by one. An additional benefit would be an assurance that all items are processed without having to worry about time overalpse.
I have written a cloud storage trigger based cloud function. I have 10-15 files landing at 5 secs interval in cloud bucket which loads data into a bigquery table(truncate and load).
While there are 10 files in the bucket I want cloud function to process them in sequential manner i.e 1 file at a time as all the files accesses the same table for operation.
Currently cloud function is getting triggered for multiple files at a time and it fails in BIgquery operation as multiple files trying to access the same table.
Is there any way to configure this in cloud function??
Thanks in Advance!
You can achieve this by using pubsub, and the max instance param on Cloud Function.
Firstly, use the notification capability of Google Cloud Storage and sink the event into a PubSub topic.
Now you will receive a message every time that a event occur on the bucket. If you want to filter on file creation only (object finalize) you can apply a filter on the subscription. I wrote an article on this
Then, create an HTTP functions (http function is required if you want to apply a filter) with the max instance set to 1. Like this, only 1 function can be executed in the same time. So, no concurrency!
Finally, create a PubSub subscription on the topic, with a filter or not, to call your function in HTTP.
EDIT
Thanks to your code, I understood what happens. In fact, BigQuery is a declarative system. When you perform a request or a load job, a job is created and it works in background.
In python, you can explicitly wait the end on the job, but, with pandas, I didn't find how!!
I just found a Google Cloud page to explain how to migrate from pandas to BigQuery client library. As you can see, there is a line at the end
# Wait for the load job to complete.
job.result()
than wait the end of the job.
You did it well in the _insert_into_bigquery_dwh function but it's not the case in the staging _insert_into_bigquery_staging one. This can lead to 2 issues:
The dwh function work on the old data because the staging isn't yet finish when you trigger this job
If the staging take, let's say, 10 seconds and run in "background" (you don't wait the end explicitly in your code) and the dwh take 1 seconds, the next file is processed at the end of the dwh function, even if the staging one continue to run in background. And that leads to your issue.
The architecture you describe isn't the same as the one from the documentation you linked. Note that in the flow diagram and the code samples the storage events triggers the cloud function which will stream the data directly to the destination table. Since BigQuery allow for multiple streaming insert jobs several functions could be executed at the same time without problems. In your use case the intermediate table used to load with write-truncate for data cleaning makes a big difference because each execution needs the previous one to finish thus requiring a sequential processing approach.
I would like to point out that PubSub doesn't allow to configure the rate at which messages are sent, if 10 messages arrive to the topic they all will be sent to the subscriber, even if processed one at a time. Limiting the function to one instance may lead to overhead for the above reason and could increase latency as well. That said, since the expected workload is 15-30 files a day the above maybe isn't a big concern.
If you'd like to have parallel executions you may try creating a new table for each message and set a short expiration deadline for it using table.expires(exp_datetime) setter method so that multiple executions don't conflict with each other. Here is the related library reference. Otherwise the great answer from Guillaume would completely get the job done.
I'm currently loading data from a csv file in Cloud Storage to BigQuery using a Cloud Function.
However, Cloud Functions have a timeout limit and I expect these files to get big enough where it will cause issues.
In addition, inserting into big query is limited to 10mb/insert_rows.
What else can I use instead of Cloud Functions to get this working?
Need it to be triggered upon file drop into Cloud Storage.
If you are inserting files from a .CSV use a batch load job. Asking BigQuery to load a file is a sub-second job, and BigQuery will proceed loading them in an async mode.
These loads are free, and your cloud function won't need to wait for it to finish.
You will be able to set up a notification that calls the cloud function each time a new file is dropped into your GCS bucket.
https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv
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.
Considering the fact that there is no data pipeline available in Singapore region, are there any alternatives available to efficiently push csv data to dynamodb?
If it was me, I would setup an s3 event notification on a bucket that fires a lambda function each time a CSV file was dropped into it.
The Notification would let Lambda know that a new file was available and a lambda function would be responsible for loading the data into dynamodb.
This would work better (because of the limits of lambda) if the CSV files were not huge, so they could be processed in a reasonable amount of time, and the bonus is the only worked that would need to be done once it was working would be to simply drop the new files into the right bucket - no server required.
Here is a github repository that has a CSV->Dynamodb loader written in java - it might help get you started.