Note: log files I've pulled down already are in logs/ if you want to skip this step.

You'll need the Amazon Web Services credentials with access to the bucket entered as environmental variables (or credentials file):

• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_DEFAULT_REGION

Now either install the Amazon Web Services command line tool (awscli) or drop into a docker container with AWS installed.

docker run --rm -ti -v $(pwd):/data -w /data \
  -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
  -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
  -e AWS_DEFAULT_REGION=$AWS_DEFAULT_REGION \
  cboettig/aws

Copy the log files into the local logs directory:

aws s3 cp --recursive s3://drat/logs logs

Great, you now have the log files.

Note: I've parsed the first several logs into log.csv already if you want to start there.

Install the python dependencies (pandas, dateutil) or drop into the docker container as above. Then run the parses3logs.py script (from the repository root directory) to generate a parsed version of the logs, log.csv.

Now one needs to filter out all the activity that involves the aws-internal or S3-Console user agent, etc., to isolate which events are actual downloads. More tricks may be necessary to handle incomplete downloads etc.