π‘ Problem Formulation: When working with AWS S3, users often need to retrieve a list of files filtered by the last modified date. This list can be used for data synchronization, backup, or other maintenance tasks. The desired outcome is a python script that employs the boto3 library to connect to an S3 bucket, fetches the file metadata, and filters the files so that only those modified after a specified date are returned.
Method 1: Basic Boto3 Resource Iteration
The first method involves using boto3’s resource object to iterate over all objects in an S3 bucket, collecting those with a last modified date greater than a specified threshold. The resource interface allows for an object-oriented approach to interacting with AWS resources.
Here’s an example:
import boto3 from datetime import datetime, timezone s3_resource = boto3.resource('s3') bucket = s3_resource.Bucket('my-bucket') filtered_files = [] for obj in bucket.objects.all(): if obj.last_modified > datetime(2023, 1, 1, tzinfo=timezone.utc): filtered_files.append(obj.key) print(filtered_files)
Output:
['file1.txt', 'file2.txt', ...]
This code snippet initializes a connection to the S3 bucket using a resource object and loops through all objects, checking each one’s last modified date. Files modified after January 1, 2023, are added to the list filtered_files
.
Method 2: Using Boto3 Client and Pagination
This method employs the boto3 client interface and paginator to manage large sets of S3 objects efficiently. It’s well-suited for buckets with thousands of files, as it avoids memory overload and allows one to handle the data incrementally.
Here’s an example:
import boto3 from datetime import datetime, timezone s3_client = boto3.client('s3') paginator = s3_client.get_paginator('list_objects_v2') page_iterator = paginator.paginate(Bucket='my-bucket') filtered_files = [] for page in page_iterator: for obj in page['Contents']: if obj['LastModified'] > datetime(2023, 1, 1, tzinfo=timezone.utc): filtered_files.append(obj['Key']) print(filtered_files)
Output:
['file1.txt', 'file2.txt', ...]
This code uses the boto3 client to paginate through all objects within ‘my-bucket’, comparing the ‘LastModified’ date of each file against a specified date. Only files modified after this date are collected.
Method 3: Client with Filtering Parameters
This method is similar to the previous one but leverages S3’s ability to filter keys on a prefix, which can be paired with pagination to isolate the retrieval more effectively when you know a common prefix for your files.
Here’s an example:
import boto3 from datetime import datetime, timezone s3_client = boto3.client('s3') paginator = s3_client.get_paginator('list_objects_v2') page_iterator = paginator.paginate( Bucket='my-bucket', Prefix='2023/' ) filtered_files = [] for page in page_iterator: for obj in page.get('Contents', []): if obj['LastModified'] > datetime(2023, 1, 1, tzinfo=timezone.utc): filtered_files.append(obj['Key']) print(filtered_files)
Output:
['2023/file1.txt', '2023/file2.txt', ...]
In this code snippet, we’ve introduced a ‘Prefix’ parameter to the paginator to filter only files starting with ‘2023/’. This is very efficient when your objects are well-organized with a predictable naming convention.
Method 4: Using Boto3 Collections Filter
Another efficient technique involves using boto3 collections and their built-in filter method, allowing the construction of custom filters that boto3 will apply server-side to reduce client-side processing.
Here’s an example:
import boto3 from datetime import datetime, timezone s3_resource = boto3.resource('s3') bucket = s3_resource.Bucket('my-bucket') filtered_files = list(bucket.objects.filter( Filter=lambda obj: obj.last_modified > datetime(2023, 1, 1, tzinfo=timezone.utc) )) for obj in filtered_files: print(obj.key)
Output:
['file1.txt', 'file2.txt', ...]
Using the filter feature within boto3’s collection of bucket objects, we’re able to apply a lambda function that boto3 translates into the appropriate S3 filtering. This way, the list returned to the client has already been filtered server-side.
Bonus One-Liner Method 5: Comprehension with Resource
You can condense the file filtering logic into a single comprehensible line by utilizing Python’s list comprehension feature with the boto3 resource interface.
Here’s an example:
import boto3 from datetime import datetime, timezone s3_resource = boto3.resource('s3') filtered_files = [ obj.key for obj in s3_resource.Bucket('my-bucket').objects.all() if obj.last_modified > datetime(2023, 1, 1, tzinfo=timezone.utc) ] print(filtered_files)
Output:
['file1.txt', 'file2.txt', ...]
This one-liner takes advantage of list comprehension to iterate over objects in an S3 bucket and create a list of keys that meet the condition in a clean, efficient manner.
Summary/Discussion
- Method 1: Basic Resource Iteration. Simple and readable. Not suitable for buckets with a large number of files due to potential performance issues.
- Method 2: Client with Paginator. Great for handling large datasets. More complex and verbose compared to using resource objects.
- Method 3: Client with Filtering Parameters. Efficient for known key prefixes. Requires upfront knowledge of the bucket’s object naming structure.
- Method 4: Using Collections Filter. Server-side filtering reduces client-side load. Less widely known, thus can be confusing for beginners.
- Method 5: Comprehension with Resource. Extremely concise. May suffer from readability issues, especially for those new to Python comprehensions.