I'm trying to get a zip file from my s3 bucket and then attached it in my email using boto3. I tried this but it doesn't work :
msg = MIMEMultipart()
def get_object(bucket,key):
client = boto3.client("s3")
return client.get_object(Bucket=bucket, Key=key)
file = get_object(BUCKET,key)
from email import encoders
from email.mime.base import MIMEBase
msg_1 = MIMEBase('application')
msg_1.set_payload(file['Body'].read())
encoders.encode_base64(msg_1)
msg_1.add_header('Content-Disposition', 'attachment',
filename='file.zip')
msg.attach(msg_1)
Here the code to initialize the GoogleRefreshTokenClient using the credentials from json key file.
oauth2_client = oauth2.GoogleServiceAccountClient(key_file, oauth2.GetAPIScope('ad_manager'))
Key_file .json is stored in S3 bucket.
Is there any way to pass .json file (with credentials) stored in s3 to GoogleServiceAccountClient?
Ps. Info to DalmTo stackoverflow member. Do not close or merge this question, please :)
You can read the file from S3 and write it as a json file to your /tmp folder
def readFileFromS3(file_name):
tmp_path = "/tmp/"+file_name
file_path = Path(tmp_path)
if file_path.is_file():
return tmp_path
s3 = boto3.resource(
's3',
aws_access_key_id = <AWS_ACCESS_KEY>,
aws_secret_access_key = <AWS_SECRET>,
region_name = <YOUR_REGION_NAME>
)
content_object = s3.Object(<BUCKET_NAME>, file_name)
file_content = content_object.get()['Body'].read().decode('utf-8')
json_content = json.loads(file_content)
with open(tmp_path, 'w') as res_file:
json.dump(json_content, res_file, indent=4)
return tmp_path
Then use the path returned from the above function in GoogleServiceAccountClient
key_file = readFileFromS3(<key_file_name_in_s3>)
oauth2_client = oauth2.GoogleServiceAccountClient(key_file, oauth2.GetAPIScope('ad_manager'))
boto3 documentation does not clearly specify how to update the user metadata of an already existing S3 Object.
It can be done using the copy_from() method -
import boto3
s3 = boto3.resource('s3')
s3_object = s3.Object('bucket-name', 'key')
s3_object.metadata.update({'id':'value'})
s3_object.copy_from(CopySource={'Bucket':'bucket-name', 'Key':'key'}, Metadata=s3_object.metadata, MetadataDirective='REPLACE')
You can do this using copy_from() on the resource (like this answer) mentions, but you can also use the client's copy_object() and specify the same source and destination. The methods are equivalent and invoke the same code underneath.
import boto3
s3 = boto3.client("s3")
src_key = "my-key"
src_bucket = "my-bucket"
s3.copy_object(Key=src_key, Bucket=src_bucket,
CopySource={"Bucket": src_bucket, "Key": src_key},
Metadata={"my_new_key": "my_new_val"},
MetadataDirective="REPLACE")
The 'REPLACE' value specifies that the metadata passed in the request should overwrite the source metadata entirely. If you mean to only add new key-values, or delete only some keys, you'd have to first read the original data, edit it and call the update.
To replacing only a subset of the metadata correctly:
Retrieve the original metadata with head_object(Key=src_key, Bucket=src_bucket). Also take note of the Etag in the response
Make desired changes to the metadata locally.
Call copy_object as above to upload the new metadata, but pass CopySourceIfMatch=original_etag in the request to ensure the remote object has the metadata you expect before overwriting it. original_etag is the one you got in step 1. In case the metadata (or the data itself) has changed since head_object was called (e.g. by another program running simultaneously), copy_object will fail with an HTTP 412 error.
Reference: boto3 issue 389
Similar to this answer but with the existing Metadata preserved while modifying only what is needed. From the system defined meta data, I've only preserved ContentType and ContentDisposition in this example. Other system defined meta data can also be preserved similarly.
import boto3
s3 = boto3.client('s3')
response = s3.head_object(Bucket=bucket_name, Key=object_name)
response['Metadata']['new_meta_key'] = "new_value"
response['Metadata']['existing_meta_key'] = "new_value"
result = s3.copy_object(Bucket=bucket_name, Key=object_name,
CopySource={'Bucket': bucket_name,
'Key': object_name},
Metadata=response['Metadata'],
MetadataDirective='REPLACE', TaggingDirective='COPY',
ContentDisposition=response['ContentDisposition'],
ContentType=response['ContentType'])
You can either update metadata by adding something or updating a current metadata value with a new one, here is the piece of code I am using :
import sys
import os
import boto3
import pprint
from boto3 import client
from botocore.utils import fix_s3_host
param_1= YOUR_ACCESS_KEY
param_2= YOUR_SECRETE_KEY
param_3= YOUR_END_POINT
param_4= YOUR_BUCKET
#Create the S3 client
s3ressource = client(
service_name='s3',
endpoint_url= param_3,
aws_access_key_id= param_1,
aws_secret_access_key=param_2,
use_ssl=True,
)
# Building a list of of object per bucket
def BuildObjectListPerBucket (variablebucket):
global listofObjectstobeanalyzed
listofObjectstobeanalyzed = []
extensions = ['.jpg','.png']
for key in s3ressource.list_objects(Bucket=variablebucket)["Contents"]:
#print (key ['Key'])
onemoreObject=key['Key']
if onemoreObject.endswith(tuple(extensions)):
listofObjectstobeanalyzed.append(onemoreObject)
#print listofObjectstobeanalyzed
else :
s3ressource.delete_object(Bucket=variablebucket,Key=onemoreObject)
return listofObjectstobeanalyzed
# for a given existing object, create metadata
def createmetdata(bucketname,objectname):
s3ressource.upload_file(objectname, bucketname, objectname, ExtraArgs={"Metadata": {"metadata1":"ImageName","metadata2":"ImagePROPERTIES" ,"metadata3":"ImageCREATIONDATE"}})
# for a given existing object, add new metadata
def ADDmetadata(bucketname,objectname):
s3_object = s3ressource.get_object(Bucket=bucketname, Key=objectname)
k = s3ressource.head_object(Bucket = bucketname, Key = objectname)
m = k["Metadata"]
m["new_metadata"] = "ImageNEWMETADATA"
s3ressource.copy_object(Bucket = bucketname, Key = objectname, CopySource = bucketname + '/' + objectname, Metadata = m, MetadataDirective='REPLACE')
# for a given existing object, update a metadata with new value
def CHANGEmetadata(bucketname,objectname):
s3_object = s3ressource.get_object(Bucket=bucketname, Key=objectname)
k = s3ressource.head_object(Bucket = bucketname, Key = objectname)
m = k["Metadata"]
m.update({'watson_visual_rec_dic':'ImageCREATIONDATEEEEEEEEEEEEEEEEEEEEEEEEEE'})
s3ressource.copy_object(Bucket = bucketname, Key = objectname, CopySource = bucketname + '/' + objectname, Metadata = m, MetadataDirective='REPLACE')
def readmetadata (bucketname,objectname):
ALLDATAOFOBJECT = s3ressource.get_object(Bucket=bucketname, Key=objectname)
ALLDATAOFOBJECTMETADATA=ALLDATAOFOBJECT['Metadata']
print ALLDATAOFOBJECTMETADATA
# create the list of object on a per bucket basis
BuildObjectListPerBucket (param_4)
# Call functions to see the results
for objectitem in listofObjectstobeanalyzed:
# CALL The function you want
readmetadata(param_4,objectitem)
ADDmetadata(param_4,objectitem)
readmetadata(param_4,objectitem)
CHANGEmetadata(param_4,objectitem)
readmetadata(param_4,objectitem)
I am using Sagemaker and have a bunch of model.tar.gz files that I need to unpack and load in sklearn. I've been testing using list_objects with delimiter to get to the tar.gz files:
response = s3.list_objects(
Bucket = bucket,
Prefix = 'aleks-weekly/models/',
Delimiter = '.csv'
)
for i in response['Contents']:
print(i['Key'])
And then I plan to extract with
import tarfile
tf = tarfile.open(model.read())
tf.extractall()
But how do I get to the actual tar.gz file from s3 instead of a some boto3 object?
You can download objects to files using s3.download_file(). This will make your code look like:
s3 = boto3.client('s3')
bucket = 'my-bukkit'
prefix = 'aleks-weekly/models/'
# List objects matching your criteria
response = s3.list_objects(
Bucket = bucket,
Prefix = prefix,
Delimiter = '.csv'
)
# Iterate over each file found and download it
for i in response['Contents']:
key = i['Key']
dest = os.path.join('/tmp',key)
print("Downloading file",key,"from bucket",bucket)
s3.download_file(
Bucket = bucket,
Key = key,
Filename = dest
)
I am trying to change ACL of 500k files within a S3 bucket folder from 'private' to 'public-read'
Is there any way to speed this up?
I am using the below snippet.
from boto3.session import Session
from multiprocessing.pool import ThreadPool
pool = ThreadPool(processes=100)
BUCKET_NAME = ""
aws_access_key_id = ""
aws_secret_access_key = ""
Prefix='pics/'
session = Session(aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key)
_s3 = session.resource("s3")
_bucket = _s3.Bucket(BUCKET_NAME)
def upload(eachObject):
eachObject.Acl().put(ACL='public-read')
counter = 0
filenames = []
for eachObject in _bucket.objects.filter(Prefix=Prefix):
counter += 1
filenames.append(eachObject)
if counter % 100 == 0:
pool.map(upload, filenames)
print(counter)
if filenames:
pool.map(upload, filenames)
As far as i can tell, without applying the ACL to the entire bucket, there is no way to simply apply the ACL to all items containing the same prefix without iterating through each item like below:
bucketName='YOUR_BUCKET_NAME'
prefix="YOUR_FOLDER_PREFIX"
s3 = boto3.resource('s3')
bucket = s3.Bucket(bucketName)
[obj.Acl().put(ACL='public-read') for obj in bucket.objects.filter(Prefix=prefix).all()]