I'm downloading data from an API and writing it to a csv file that I store in an S3 bucket. I'm then copying my file from this input bucket into an output bucket with a Lambda function. From the output bucket I'm ingesting it into a MySQL RDS instance with another Lambda function.
The copy-to-another-bucket and upload-to-RDS lambda functions both get triggered when I create a new object in a bucket. Since I'm appending to my csv file, the upload-to-RDS function gets triggered way more than it should and I end up with ~30 rows in my database instead of 6.
I thought by copying the files between S3 buckets I could avoid this, but it doesn't help. Is there any way to only upload the csv file to the database once it has been written and not while it's being updated? Can I delay the trigger maybe?
The only other solution I can think of is to skip the copy-to-another-bucket function altogether and to schedule the upload-to-RDS function.
You need to realize that S3 doesn't support updating an existing file. If you are appending a row to an existing CSV file in S3, then that operation requires uploading the entire contents of the CSV file to S3 again, which S3 sees as a new object.
If you need to store a temporary version of the CSV file in S3 while you are updating it, then you should store it in a separate path, like s3://your_bucket/tmp and then when you have completed your updates, move it to the final path like s3://your_bucket/complete and only configure the Lambda trigger on the /complete path.
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I need a solution for entering new data in csv that is stored in S3 bucket in AWS.
At this point we are downloading the file, editing and then uploading it again in s3 and we would like to automatize this process.
We need to add one row in a three column.
Thank you in advance!
I think you will be able to do that using Lambda Functions. You will need to programmatically make the modifications you need over the CSV but there are multiple programming languages that allow you to do that. One quick example is using python and the csv library
Then you can invoke that lambda or add more logic to the operations you want to do using an AWS API Gateway.
You can access the CSV file (object) inside the S3 Bucket from the lambda code using the AWS SDK and append the new rows with data you pass as parameters to the function
There is no way to directly modify the csv stored in S3 (if that is what you're asking). The process will always entail some version of download, modify, upload. There are many examples of how you can do this, for example here
I have an S3 bucket into which clients drop data files (CSV files) each month. I was wondering there was a way that I could automatically create a new "folder" (object) every time the files are dropped each month and put the newest files into that "folder". I need the CSV files separated by month so that AWS Glue can create new partitions when I run incremental crawlers on this bucket.
For example, let's say I have a S3 bucket called "client." On December 1st, a new CSV file ("DecClientData") will be dropped into that "client" bucket. I want to know if there is a way to automate the following two processes:
Create a "folder" (let's call it "dec") within "client".
Place the "DecClientData" file in the "dec" "folder".
Thanks in advance for any assistance you can provide!
S3 doesn't have the notion of folders commonly found in file systems but instead has a flat structure, more details can be found here.
Instead, the full path of an object is stored in its Key (filename). For example, an object can be stored in Amazon S3 with a Key of files/2020-12/data.txt regardless of the existence of files and 2020-12 directories (they are not really directories but zero-length objects).
In your case, to solve both points you are mentioning, you should leverage S3 event notifications and use them as a Lambda Trigger. When the Lambda function is triggered, it is passed the name of the object (Key) as an argument, at that point you can simply change its Key.
I.e. Object is uploaded in s3://my_bucket/uploads/file.txt, this creates an event notification that triggers a Lambda function. The functions gets the object and re-uploads it to s3://my_bucket/files/dec/file.txt (and deletes the original one).
Write an AWS Lambda function to create a folder in the client bucket and move the most recent .csv file (or files) in the new folder.
Then, configure the client S3 bucket to trigger the AWS Lambda function on new uploads through the event notification settings.
Initially a csv file is uploaded to S3 bucket and we often append that file by scripting when new row is added to that csv file. what we want is we want the script to run only when the csv file is modified, is there any watchers which can notify the script to run when the csv file is changed?
There is S3 event notification for that, you would be interested in the s3:ObjectCreated event
https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html
You should also take a look at the s3 documentation and note the difference between S3 and a File system. An "update" or "append" operation on s3 is actually replacing the whole object, just for your information
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.
We have a requirement to append to the existing S3 object, when we run the spark application every hour. I have tried this code:
df.coalesce(1).write.partitionBy("name").mode("append").option("compression", "gzip").parquet("s3n://path")
This application is creating new parquet files for every run. Hence, I am looking for a workaround to achieve this requirement.
Question is:
How we can configure the S3 bucket to get append to the existing object?
It is not possible to append to objects in Amazon S3. They can be overwritten, but not appended.
There is apparently a sneaky method where a file can be multi-part copied, with the 'source' set to the file and then set to some additional data. However, that cannot be accomplished in the method you show.
If you wish to add additional data to an External Table (eg used by EMR or Athena), then simply add an additional file in the correct folder for the desired partition.