ChatGPT解决这个技术问题 Extra ChatGPT

How to write a file or data to an S3 object using boto3

In boto 2, you can write to an S3 object using these methods:

Key.set_contents_from_string()

Key.set_contents_from_file()

Key.set_contents_from_filename()

Key.set_contents_from_stream()

Is there a boto 3 equivalent? What is the boto3 method for saving data to an object stored on S3?


C
Community

In boto 3, the 'Key.set_contents_from_' methods were replaced by

Object.put()

Client.put_object()

For example:

import boto3

some_binary_data = b'Here we have some data'
more_binary_data = b'Here we have some more data'

# Method 1: Object.put()
s3 = boto3.resource('s3')
object = s3.Object('my_bucket_name', 'my/key/including/filename.txt')
object.put(Body=some_binary_data)

# Method 2: Client.put_object()
client = boto3.client('s3')
client.put_object(Body=more_binary_data, Bucket='my_bucket_name', Key='my/key/including/anotherfilename.txt')

Alternatively, the binary data can come from reading a file, as described in the official docs comparing boto 2 and boto 3:

Storing Data Storing data from a file, stream, or string is easy: # Boto 2.x from boto.s3.key import Key key = Key('hello.txt') key.set_contents_from_file('/tmp/hello.txt') # Boto 3 s3.Object('mybucket', 'hello.txt').put(Body=open('/tmp/hello.txt', 'rb'))


botocore.exceptions.NoCredentialsError: Unable to locate credentials how to fix this ?
@deepakmurthy I'm not sure why you're getting that error... You'd need to ask a new Stack Overflow question and provide more details about the issue.
When I try s3.Object().put() I end up with an object with zero content-length. For me put() only accepts string data, but put(str(binarydata)) seems to have some sort of encoding issues. I end up with an object roughly 3times the size of the original data, which makes it useless for me.
@user1129682 I'm not sure why that is. Could you please ask a new question and provide more details?
@jkdev It'd be great if you could take a look.
m
mathetes

boto3 also has a method for uploading a file directly:

s3 = boto3.resource('s3')    
s3.Bucket('bucketname').upload_file('/local/file/here.txt','folder/sub/path/to/s3key')

http://boto3.readthedocs.io/en/latest/reference/services/s3.html#S3.Bucket.upload_file


This is good, but it doesn't allow for data currently in memory to be stored.
@Reid: for in-memory files you can use the s3.Bucket(...).upload_fileobj() method instead.
How does writing from in-memory perform vs. uploading to s3 from locally written file?
F
Franke

You no longer have to convert the contents to binary before writing to the file in S3. The following example creates a new text file (called newfile.txt) in an S3 bucket with string contents:

import boto3

s3 = boto3.resource(
    's3',
    region_name='us-east-1',
    aws_access_key_id=KEY_ID,
    aws_secret_access_key=ACCESS_KEY
)
content="String content to write to a new S3 file"
s3.Object('my-bucket-name', 'newfile.txt').put(Body=content)

Have no idea my 'put' action has no access. I created this bucket and put my canonical id under the access list.
How do you give a prefix in this case? Meaning, what if you want to store the file in my-bucket-name/subfolder/ ?
@kev you can specify that along with the filename 'subfolder/newfile.txt' instead of 'newfile.txt'
Re "You no longer have to convert the contents to binary before writing to the file in S3.", is this documented somewhere? I was looking at boto3.amazonaws.com/v1/documentation/api/latest/reference/…, and thought it only accepted bytes. I'm not sure what exactly constitutes a "seekable file-like object", but didn't think that included strings.
I may have comparing this with download_fileobj() which is for large multipart file uploads. The upload methods require seekable file objects, but put() lets you write strings directly to a file in the bucket, which is handy for lambda functions to dynamically create and write files to an S3 bucket.
U
Uri Goren

Here's a nice trick to read JSON from s3:

import json, boto3
s3 = boto3.resource("s3").Bucket("bucket")
json.load_s3 = lambda f: json.load(s3.Object(key=f).get()["Body"])
json.dump_s3 = lambda obj, f: s3.Object(key=f).put(Body=json.dumps(obj))

Now you can use json.load_s3 and json.dump_s3 with the same API as load and dump

data = {"test":0}
json.dump_s3(data, "key") # saves json to s3://bucket/key
data = json.load_s3("key") # read json from s3://bucket/key

Excellent. To get it to work, I added this extra bit: ...["Body"].read().decode('utf-8').
Great idea. Anyway, it provides some space for naming improvements.
Proposed rewrite of this nice idea: gist.github.com/vlcinsky/bbeda4321208aa98745afc29b58e90ac
k
kev

A cleaner and concise version which I use to upload files on the fly to a given S3 bucket and sub-folder-

import boto3

BUCKET_NAME = 'sample_bucket_name'
PREFIX = 'sub-folder/'

s3 = boto3.resource('s3')

# Creating an empty file called "_DONE" and putting it in the S3 bucket
s3.Object(BUCKET_NAME, PREFIX + '_DONE').put(Body="")

Note: You should ALWAYS put your AWS credentials (aws_access_key_id and aws_secret_access_key) in a separate file, for example- ~/.aws/credentials


What's the Windows equivalent location for the AWS credentials file, since Windows won't support ~
@HammanSamuel you may store it like C:\Users\username\.aws\credentials
Better to store it in environment variables of lambda.
o
ouflak

After some research, I found this. It can be achieved using a simple csv writer. It is to write a dictionary to CSV directly to S3 bucket.

eg: data_dict = [{"Key1": "value1", "Key2": "value2"}, {"Key1": "value4", "Key2": "value3"}] assuming that the keys in all the dictionary are uniform.

import csv
import boto3

# Sample input dictionary
data_dict = [{"Key1": "value1", "Key2": "value2"}, {"Key1": "value4", "Key2": "value3"}]
data_dict_keys = data_dict[0].keys()

# creating a file buffer
file_buff = StringIO()
# writing csv data to file buffer
writer = csv.DictWriter(file_buff, fieldnames=data_dict_keys)
writer.writeheader()
for data in data_dict:
    writer.writerow(data)
# creating s3 client connection
client = boto3.client('s3')
# placing file to S3, file_buff.getvalue() is the CSV body for the file
client.put_object(Body=file_buff.getvalue(), Bucket='my_bucket_name', Key='my/key/including/anotherfilename.txt')

U
Uri Goren

it is worth mentioning smart-open that uses boto3 as a back-end.

smart-open is a drop-in replacement for python's open that can open files from s3, as well as ftp, http and many other protocols.

for example

from smart_open import open
import json
with open("s3://your_bucket/your_key.json", 'r') as f:
    data = json.load(f)

The aws credentials are loaded via boto3 credentials, usually a file in the ~/.aws/ dir or an environment variable.


while this respone is informative, it doesn't adhere to answering the original question - which is, what are the boto3 equivalents of certain boto methods.
Smart open uses boto3
@UriGoren can you share an example to ftp to s3 using smart-open?
P
Prateek Bhuwania

You may use the below code to write, for example an image to S3 in 2019. To be able to connect to S3 you will have to install AWS CLI using command pip install awscli, then enter few credentials using command aws configure:

import urllib3
import uuid
from pathlib import Path
from io import BytesIO
from errors import custom_exceptions as cex

BUCKET_NAME = "xxx.yyy.zzz"
POSTERS_BASE_PATH = "assets/wallcontent"
CLOUDFRONT_BASE_URL = "https://xxx.cloudfront.net/"


class S3(object):
    def __init__(self):
        self.client = boto3.client('s3')
        self.bucket_name = BUCKET_NAME
        self.posters_base_path = POSTERS_BASE_PATH

    def __download_image(self, url):
        manager = urllib3.PoolManager()
        try:
            res = manager.request('GET', url)
        except Exception:
            print("Could not download the image from URL: ", url)
            raise cex.ImageDownloadFailed
        return BytesIO(res.data)  # any file-like object that implements read()

    def upload_image(self, url):
        try:
            image_file = self.__download_image(url)
        except cex.ImageDownloadFailed:
            raise cex.ImageUploadFailed

        extension = Path(url).suffix
        id = uuid.uuid1().hex + extension
        final_path = self.posters_base_path + "/" + id
        try:
            self.client.upload_fileobj(image_file,
                                       self.bucket_name,
                                       final_path
                                       )
        except Exception:
            print("Image Upload Error for URL: ", url)
            raise cex.ImageUploadFailed

        return CLOUDFRONT_BASE_URL + id