I have some data sets in Google cloud storage. I could find how I can append more data to this dataset. But if I want to merge the data set(Insert else update), how do I do it?
I have one option of using Hive - Insert overwrite. Is there any other better option?
Is there any option with Google cloud storage API itself?
Maybe this could be helpful: https://cloud.google.com/storage/docs/json_api/v1/objects/compose
Objects: compose
Concatenates a list of existing objects into a new object in the same bucket.
GCS treats your objects (files) as blobs, there are no in-built GCS operations on the text in your objects. There is an easier way to do the same as you are doing though.
App-engine hosted MapReduce provides in-built adapters to work with GCS. You can find the example code in this repo.
Related
I have an API where users can query some time-series data. But now I want to make the entire data set available for users to download for their own uses. How would I go about doing something like this? I have RDS, an EC2 instance setup. What would my next steps be?
In this scenario and without any other data or restrictions given, I would use S3 bucket in the center of this process.
Create an S3 Bucket to save the database/dataset dump.
Dump the database/dataset to S3. ( examples: docker, lambda )
Manually transform dataset to CSV or use a Lambda triggered on every dataset dump. (not sure if pg_dump can give you CSV out of the box)
Host those datasets in a bucket accessible to your users and allow access to them as per case:
You can create a publicly available bucket and share its HTTP URL.
You can create a pre-signed URL to allow limited access to your dataset
S3 is proposed since its cheap and you can find a lot of readily available tooling to work with.
I have two sources:
A csv that will be uploaded to a cloud storage service, probably GCP Cloud Storage.
The output of a scrapping process done with Python.
When a user updates 1) (the cloud stored file) an event should be triggered to execute 2) (the scrapping process) and then some transformation should take place in order to merge these two sources into one in a JSON format. Finally, the content of this JSON file should be stored in a DB of easy access and low cost. The files the user will update are of max 5MB and the updates will take place once weekly.
From what I've read, I can use GCP Cloud Functions to accomplish this whole process or I can use Dataflow too. I've even considered using both. I've also thought of using MongoDB to store the JSON objects of the two sources final merge.
Why should I use Cloud Functions, Dataflow or both? What are your thoughts on the DB? I'm open to different approaches. Thanks.
Regarding de use of Cloud Functions and Dataflow. In your case I will go for Cloud Functions as you don't have a big volume of data. Dataflow is more complex, more expensive and you will have to use Apache Beam. If you are confortable with python and having into consideration your scenario I will choose Cloud Functions. Easy, convenient...
To trigger a Cloud Functions when Cloud Storage object is updated you will have to configure the triggers. Pretty easy.
https://cloud.google.com/functions/docs/calling/storage
Regarding the DB. MongoDB is a good option but if you wanth something quick an inexpensive consider DataStore
As a managed service it will make your life easy with a lot of native integrations. Also it has a very interesting free tier.
Recently I face an issue while writing the dataframe data into BigQuery using pyspark. Here it was:
pyspark.sql.utils.IllegalArgumentException: u'Temporary or persistent GCS bucket must be informed
After research the issue I found that Temporary GCS bucket to be mentioned spark.conf.
bucket = "temp_bucket"
spark.conf.set('temporaryGcsBucket', bucket)
I think there is no concept to have a file for a table in Biquery like Hive.
I would like to know more about it, why we need to have temp-gcs-bucket to write the data into bigquery?
I was searching for the reason behind this but I couldn't.
Please clarify.
Spark BigQuery connector has two write modes(writeMethod), 1. Direct 2.Indirect while writing data into BigQuery. This is a optional parameter, default is Indirect.
Indirect
You can specify indirect option like this option("writeMethod","indirect"). Its optional, and Indirect is default. This requires you to specify a temporary gcs bucket, if not you will get the error.
The need of temporary bucket is .
The connector writes the data to BigQuery by first buffering all the
data into a Cloud Storage temporary table. Then it copies all data
from into BigQuery in one operation.
Taken from the GCFS spark example docs here
Direct
In this method the data is written directly to BigQuery using the BigQuery Storage Write API
In scala you can specify like this option("writeMethod","direct"). which eliminates the need for a temporary bucket.
You can read more about the bigquery connector here
I've got a customer requirement to replicate a multiregion storage bucket (Mr Bucket A --> Mr Bucket B) so that every new object gets copied. Would Cloud Functions be the way to go here?
Cloud Storage already has multiregion replication. But if that's not what you're looking for, a Cloud Functions trigger that copies each new file might be the only efficient mechanism.
We are planning a solution that will be storing images in a Google Cloud Storage bucket. Nothing complex - just a bucket with a number of images. Each image will have custom metadata associated which will contain a number of key-value pairs including a userId of the person creating the image.
What we would like to do is run a query against the bucket objects passing in a userId as a search param and get back a list of image objects that have the custom metadata key 'userId' set that that user's id.
Is such a thing possible in Cloud Storage, and if so what is the mechanism?
Cloud Storage doesn't have a "query" engine behind it. You should instead duplicate the metadata (and the file path in Storage) to a database that's capable of the kinds of queries you want, then query the database directly instead. When you find objects of interest, then you can deal with them directly from Cloud Storage. This is the typical pattern.