I am currently dumping kafka messages to s3 folders partitioned by hour. That means that every x minutes I get a file in the hour path for a certain topic. I want to merge all the files in a certain hour to a single file. How can I achieve this?

To merge files from Kafka messages dumped into S3 folders partitioned by hour into a single file, you can create a Dagster asset that represents the merged file for each hourly partition. This asset would depend on the individual files as its inputs. Here's a high-level approach based on the documentation: 1. Define an Asset for Hourly Partitions: Create an asset that represents the hourly partitions of Kafka messages. This asset would be partitioned by hour, and each partition would correspond to the set of files in the S3 folder for that hour. 2. Create a Merging Asset: Define another asset that represents the merged file. This asset would have a dependency on the hourly partitioned asset. In the computation for this asset, you would read all the files for a given hour, merge them, and write the merged content to a new file in S3. 3. Use S3 Resources: Utilize the S3 resources provided by Dagster, such as

S3FileManagerResource

or

S3PickleIOManager

, to interact with S3. These resources can help you manage connections to S3 and handle file operations. 4. Implement the Merging Logic: Write the logic to read the files from S3, merge their contents, and write the merged content back to S3. This could be done using Python's built-in file handling capabilities or libraries like

pandas

if the files are in a format like CSV or Parquet. 5. Schedule or Trigger the Asset Computation: Set up a schedule or a sensor to trigger the computation of the merged asset. This could be done based on the availability of new files in the hourly S3 folders. Here is a conceptual example based on the documentation's guidance on assets and S3 interaction:

python
from dagster import asset, DailyPartitionsDefinition
from dagster_aws.s3 import S3Resource

# Define your S3 resource configuration
s3_resource = S3Resource(...)

@asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01"))
def hourly_partitioned_files(context):
    # Logic to represent the hourly files
    # This could be a list of file paths or S3 keys for the given hour
    pass

@asset
def merged_hourly_file(context, hourly_partitioned_files):
    # Logic to merge the files for a given hour
    # Use the S3 resource to interact with S3
    merged_content = merge_files(hourly_partitioned_files)
    # Write the merged content to a new file in S3
    s3_resource.put_object(Bucket='my-bucket', Key='merged/hourly_file.parquet', Body=merged_content)

Please note that this is a high-level approach and the actual implementation would require more details specific to your data and infrastructure. You would need to define the

merge_files

function to handle the merging of file contents and ensure that the S3 resource is properly configured with your AWS credentials and buck

@Scout what if i need to merge every time the file is written

If you need to merge files every time a new file is written to an S3 bucket, you would likely want to set up a sensor that triggers the merge operation whenever a new file is detected. This sensor would monitor the S3 bucket for changes and initiate the merge process for the relevant hourly partition when a new file arrives. Here's a conceptual approach to setting up such a system: 1. Create a Sensor: Define a sensor that checks the S3 bucket for new files within the hourly partition. The sensor can be configured to poll the S3 bucket at a certain frequency to detect new files. 2. Trigger the Merge Operation: When the sensor detects a new file, it triggers the merge operation. This operation would read all the files for the relevant hour, merge them, and write the merged content back to S3. 3. Implement the Merge Logic: Write the logic to perform the actual merging of the files. This could involve downloading the files, combining their contents, and then uploading the merged file back to S3. 4. Handle Concurrency and State: Since files can arrive at any time, you need to handle concurrency and state management carefully. This might involve using S3 object versioning or implementing a locking mechanism to ensure that only one merge operation happens at a time for each hourly partition. Here is a conceptual example of how you might set up a sensor in Dagster:

python
from dagster import sensor, RunRequest
from dagster_aws.s3 import s3_resource

@sensor(job=merge_hourly_file_job)
def s3_file_sensor(context):
    new_files = check_for_new_files()  # Implement this function to check S3 for new files
    if new_files:
        # If new files are detected, create a run request to trigger the merge operation
        return RunRequest(run_key="merge-hourly-files", run_config={})
    return None

def check_for_new_files():
    # Logic to list objects in the S3 bucket and detect new files
    # This could use the S3 client from the `boto3` library
    pass

In this example,

merge_hourly_file_job

would be a Dagster job that defines the merge operation. The

check_for_new_files

function would contain the logic to interact with S3 and determine if new files have been written to the hourly partition. Please note that this is a simplified example and does not include the actual implementation details for interacting with S3, handling file merging, or managing concurrency. You would need to fill in these details based on your specific use case and infrastructure. Additionally, you would need to ensure that your AWS credentials and permissions are correctly configured to allow Dagster to interact with your S3 bucket. Upserting Records | Dagster Glossary button Upserting Records | Dagster Glossary button Merge materialized Asset along with code... button Runtime / dynamic asset partitions button Thank you for your feedback!