I have a Nifi Flow, which fetches a data from RDS tables and load into S3 as flat files, now i need to generate another file which will be having the name of the file that I am loading into S3 bucket, this needs to be a separate flow;
example: if the RDS extracted flat file name is RDS.txt, then the new generated file should have rds.txt as content and I need to load this file to same S3 bucket.
Problem I face is I am using a generate flowfile processor and adding the flat file name as custom text in flowfile, but i could not set up any upstream for Generate flow file processor, so this is generating more files, if I use the merge content processor after the generate flow file processor, I could see duplicate values in the flowfile.
Can anyone help me out in this
I have a Nifi Flow, which fetches a data from RDS tables and load into S3 as flat files, now i need to generate another file which will be having the name of the file that I am loading into S3 bucket, this needs to be a separate flow;
Easiest path to do this is to chain something after PutS3Object that will update the flowfile contents with what you want. It would be really simple to write with ExecuteScript. Something like this:
def ff = session.get()
if (ff) {
def updated = session.write(ff, {
it.write(ff.getAttribute("filename").bytes)
} as OutputStreamCallback)
updated = session.putAttribute(updated, "is_updated", "true")
session.transfer(updated, REL_SUCCESS)
}
Then you can put a RouteOnAttribute after PutS3Object and have it route to either a null route if it detects the attribute is_updated or route back to PutS3Object if it's not been updated.
I got a simple solution for this I have added a funnel before the put s3 object, and upstream of the funnel will receive two file, one with the extract and the other with the file name, down stream of the funnel is connected to the puts3 object, so this will load both the files at the same time
Related
My original data is from Bigquery. I have created a dag job to extract the relevant fields data based on a "WHERE" condition into a csv file stored in Google Cloud Storage
As a next step, I am aiming to use "LOAD CSV WITH EHADERS FROM gs://link-to-bucket/file.csv to read the data from the CSV to Neo4j database
It seems however that I cannot just give the the gcs uri as the CSV link. Is there anyway to establish a secure connection to read the file, other then making the the bucket public?
My attempt
uri = "gs://link-to-bucket/file.csv"
def create_LP_query(uri):
query_string = f"""
LOAD CSV WITH HEADERS FROM '{uri}' AS row
MERGE (l:Limited_Partner:Company {{id: row.id}})
SET l.Name = row.Name """
It is not possible, you would have to create a Neo4j plugin that acts as a new ProtocolHandler.
I did one in the past for S3, you might take it as inspiration, it can be similar for GS.
https://github.com/ikwattro/neo4j-load-csv-s3-protocol
I am new to AWS. I am writing **AWS Glue job** for some transformation and I could do it. But now after the transformation I used **'from_options' in DynamicFrameWriter Class** to transfer the data frame as csv file. But the file copied to S3 without any extension. Also is there any way to rename the file copied, using DynamicFrameWriter or any other. Please help....
Step1: Triggered an AWS glue job for trnsforming files in S3 to RDS instance..
Step2: On successful job completion transfer the contents of file to another S3 using from_options' in DynamicFrameWriter class. But the file dosen't have any extension.
you have to set the format of the file you are writing.
eg: format=csv
This should set the csv file extension.. You however cannot choose the name of the file that you want to write it as. The only option you have is to have some sort of s3 operation where you change the key name of the file.
I'm testing out S3 Select and as far as I understand from the examples, you can treat a single object (CSV or JSON) as a data store.
I wanted to have a single JSON document per S3 object and search the entire bucket as a 'database'. I'm saving each 'file' as <ID>.json and each file has JSON documents with the same schema.
Is it possible to search multiple objects in a single call? i.e. Find all JSON documents where customerId = 123 ?
It appears that Amazon S3 Select operates on only one object.
You can use Amazon Athena to run queries across paths, which will include all files within that path. It also supports partitioning.
Simple, just iterate over the folder key in which you have all the files and grab the key and use the same to leverage S3 Select.
So what I am trying to do is to crawl data on S3 bucket with AWS Glue. Data stored as nested json and path looks like this:
s3://my-bucket/some_id/some_subfolder/datetime.json
When running default crawler (no custom classifiers) it does partition it based on path and deserializes json as expected, however, I would like to get a timestamp from the file name as well in a separate field. For now Crawler omits it.
For example if I run crawler on:
s3://my-bucket/10001/fromage/2017-10-10.json
I get table schema like this:
Partition 1: 10001
Partition 2: fromage
Array: JSON data
I did try to add custom classifier based on Grok pattern:
%{INT:id}/%{WORD:source}/%{TIMESTAMP_ISO8601:timestamp}
However, whenever I re-run crawler it skips custom classifier and uses default JSON one. As a solution obviously I could append file name to the JSON itself before running a crawler, but was wondering if I can avoid this step?
Classifiers only analyze the data within the file, not the filename itself. What you want to do is not possible today. If you can change the path where the files land, you could add the date as another partition:
s3://my-bucket/id=10001/source=fromage/timestamp=2017-10-10/data-file-2017-10-10.json
I have large numbers of files within s3 bucket and usually import it to Redshift. Since number of files is large I need a column in Redshift table which should contain source file name from s3 location.
Is there any means to carried out problem ?
Agree with Ketan that this is currently not possible in Redshift. If this is what you would want to achieve, it is possible through either
Reading the S3 files programmatically and write a new S3 files with file name as the column and load the new file
Alternatively, use Hive. Create external table on S3 file bucket location and use INPUT__FILE__NAME to get the file names, create a new table and then write back to S3. You can also do some pre-processing in Hive.
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+VirtualColumns
Hope this helps.
That isn't possible. During a Copy operation, Redshift only loads file contents into a table; it doesn't provide access to S3 file names.
To achieve what you want, you need to preprocess the data to add additional information inside the files.