Skip to main content

for saving dictionaries using s3 with bz2 compression

Project description

S3Bz

save and load dictionary to s3 using bz compression

Install

pip install s3bz

How to use

Create a bucket and make sure that it has transfer acceleration enabled

create a buket

aws s3 mb s3://<bucketname>

put transfer acceleration

aws s3api put-bucket-accelerate-configuration --bucket <bucketname> --accelerate-configuration Status=Enabled

First, import the s3 module

import package

from importlib import reload
from s3bz.s3bz import S3

set up dummy data

BZ2 compression

save object using bz2 compression

result = S3.save(key = key, 
       objectToSave = sampleDict,
       bucket = bucket,
       user=USER,
       pw = PW,
       accelerate = True)
print(('failed', 'success')[result])
success

load object with bz2 compression

result = S3.load(key = key,
       bucket = bucket,
       user = USER,
       pw = PW,
       accelerate = True)
print(result[0])
{'ib_prcode': '75233', 'ib_brcode': '1004', 'ib_cf_qty': '155', 'new_ib_vs_stock_cv': '880'}

other compressions

Zl : zlib compression with json string encoding pklzl : zlib compression with pickle encoding

print(bucket)
%time S3.saveZl(key,sampleDict,bucket)
%time S3.loadZl(key,bucket)
%time S3.savePklZl(key,sampleDict,bucket)
%time result =S3.loadPklZl(key,bucket)
pybz-test
CPU times: user 22.1 ms, sys: 728 µs, total: 22.9 ms
Wall time: 134 ms
CPU times: user 42.6 ms, sys: 0 ns, total: 42.6 ms
Wall time: 542 ms
CPU times: user 19.3 ms, sys: 0 ns, total: 19.3 ms
Wall time: 150 ms
CPU times: user 41 ms, sys: 3.28 ms, total: 44.3 ms
Wall time: 503 ms

Bring your own compressor and encoder

import gzip, json
compressor=lambda x: gzip.compress(x)
encoder=lambda x: json.dumps(x).encode()
decompressor=lambda x: gzip.decompress(x)
decoder=lambda x: json.loads(x.decode())

%time S3.generalSave(key, sampleDict, bucket = bucket, compressor=compressor, encoder=encoder )
%time result = S3.generalLoad(key, bucket , decompressor=decompressor, decoder=decoder)
assert result == sampleDict, 'not the same as sample dict'
CPU times: user 30.4 ms, sys: 0 ns, total: 30.4 ms
Wall time: 128 ms
CPU times: user 44.8 ms, sys: 0 ns, total: 44.8 ms
Wall time: 416 ms

check if an object exist

result = S3.exist('', bucket, user=USER, pw=PW, accelerate = True)
print(('doesnt exist', 'exist')[result])
exist

presign download object

url = S3.presign(key=key,
              bucket=bucket,
              expiry = 1000,
              user=USER,
              pw=PW)
print(url)

download using signed link

from s3bz.s3bz import Requests
result = Requests.getContentFromUrl(url)

File operations

save without compression

inputPath = '/tmp/tmpFile.txt'
key = 'tmpFile'
downloadPath = '/tmp/downloadTmpFile.txt'
with open(inputPath , 'w')as f:
  f.write('hello world')
S3.saveFile(key =key ,path = inputPath,bucket = bucket)
##test
S3.exist(key,bucket)

load without compression

S3.loadFile(key= key , path = downloadPath, bucket = bucket)
##test
with open(downloadPath, 'r') as f:
  print(f.read())

delete

result = S3.deleteFile(key, bucket)
## test
S3.exist(key,bucket)

save and load pandas dataframe

### please install in pandas, 
### this is not include in the requirements to minimize the size impact
import pandas as pd
df = pd.DataFrame({'test':[1,2,3,4,5],'test2':[2,3,4,5,6]})
S3.saveDataFrame(bucket,key,df)
S3.loadDataFrame(bucket,key)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

s3bz-0.1.6.tar.gz (13.0 kB view hashes)

Uploaded Source

Built Distribution

s3bz-0.1.6-py3-none-any.whl (11.0 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page