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Merge pull request #889 from tahoe-lafs/3496.mutable-tests-python-3-part-3

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Port mutable tests to Python 3, part 3 of N

Fixes ticket:3496
This commit is contained in:
Itamar Turner-Trauring 2020-11-11 11:41:22 -05:00 committed by GitHub
commit 8a5702d846
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 106 additions and 48 deletions
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0
newsfragments/3496.minor Normal file
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6
src/allmydata/mutable/retrieve.py
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@ -1,3 +1,5 @@
from past.builtins import unicode
import time
from itertools import count
@ -906,9 +908,11 @@ class Retrieve(object):
def notify_server_corruption(self, server, shnum, reason):
if isinstance(reason, unicode):
reason = reason.encode("utf-8")
storage_server = server.get_storage_server()
storage_server.advise_corrupt_share(
"mutable",
b"mutable",
self._storage_index,
shnum,
reason,

6
src/allmydata/mutable/servermap.py
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@ -1,5 +1,7 @@
from __future__ import print_function
from past.builtins import unicode
import sys, time, copy
from zope.interface import implementer
from itertools import count
@ -800,9 +802,11 @@ class ServermapUpdater(object):
def notify_server_corruption(self, server, shnum, reason):
if isinstance(reason, unicode):
reason = reason.encode("utf-8")
ss = server.get_storage_server()
ss.advise_corrupt_share(
"mutable",
b"mutable",
self._storage_index,
shnum,
reason,

40
src/allmydata/test/mutable/test_interoperability.py
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@ -1,3 +1,15 @@
"""
Ported to Python 3.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from future.utils import PY2
if PY2:
from future.builtins import filter, map, zip, ascii, chr, hex, input, next, oct, open, pow, round, super, bytes, dict, list, object, range, str, max, min # noqa: F401
import os, base64
from twisted.trial import unittest
from allmydata import uri
@ -8,24 +20,24 @@ from ..no_network import GridTestMixin
class Interoperability(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
sdmf_old_shares = {}
sdmf_old_shares[0] = "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"
sdmf_old_shares[1] = "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"
sdmf_old_shares[2] = "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"
sdmf_old_shares[3] = "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"
sdmf_old_shares[4] = "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"
sdmf_old_shares[5] = "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"
sdmf_old_shares[6] = "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"
sdmf_old_shares[7] = "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"
sdmf_old_shares[8] = "VGFob2UgbXV0YWJsZSBjb250YWluZXIgdjEKdQlEA47ESLbTdKdpLJXCpBxd5OH239tl5hvAiz1dvGdE5rIOpf8cbfxbPcwNF+Y5dM92uBVbmV6KAAAAAAAAB/wAAAAAAAAJ0AAAAAFOWSw7jSx7WXzaMpdleJYXwYsRCV82jNA5oex9m2YhXSnb2POh+vvC1LE1NAfRc9GOb2zQG84Xdsx1Jub2brEeKkyt0sRIttN0p2kslcKkHF3k4fbf22XmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABamJprL6ecrsOoFKdrXUmWveLq8nzEGDOjFnyK9detI3noX3uyK2MwSnFdAfyN0tuAwoAAAAAAAAAFQAAAAAAAAAVAAABjwAAAo8AAAMXAAADNwAAAAAAAAM+AAAAAAAAB/wwggEgMA0GCSqGSIb3DQEBAQUAA4IBDQAwggEIAoIBAQC1IkainlJF12IBXBQdpRK1zXB7a26vuEYqRmQM09YjC6sQjCs0F2ICk8n9m/2Kw4l16eIEboB2Au9pODCE+u/dEAakEFh4qidTMn61rbGUbsLK8xzuWNW22ezzz9/nPia0HDrulXt51/FYtfnnAuD1RJGXJv/8tDllE9FL/18TzlH4WuB6Fp8FTgv7QdbZAfWJHDGFIpVCJr1XxOCsSZNFJIqGwZnD2lsChiWw5OJDbKd8otqN1hIbfHyMyfMOJ/BzRzvZXaUt4Dv5nf93EmQDWClxShRwpuX/NkZ5B2K9OFonFTbOCexm/MjMAdCBqebKKaiHFkiknUCn9eJQpZ5bAgERgV50VKj+AVTDfgTpqfO2vfo4wrufi6ZBb8QV7hllhUFBjYogQ9C96dnS7skv0s+cqFuUjwMILr5/rsbEmEMGvl0T0ytyAbtlXuowEFVj/YORNknM4yjY72YUtEPTlMpk0Cis7aIgTvu5qWMPER26PMApZuRqiwRsGIkaJIvOVOTHHjFYe3/YzdMkc7OZtqRMfQLtwVl2/zKQQV8b/a9vaT6q3mRLRd4P3esaAFe/+7sR/t+9tmB+a8kxtKM6kmaVQJMbXJZ4aoHGfeLX0m35Rcvu2Bmph7QfSDjk/eaE3q55zYSoGWShmlhlw4Kwg84sMuhmcVhLvo0LovR8bKmbdgABUSzNKiMx0E91q51/WH6ASL0fDEOLef9oxuyBX5F5cpoABojmWkDX3k3FKfgNHIeptE3lxB8HHzxDfSD250psyfNCAAwGsKbMxbmI2NpdTozZ3SICrySwgGkatA1gsDOJmOnTzgAYmqKY7A9vQChuYa17fYSyKerIb3682jxiIneQvCMWCK5WcuI4PMeIsUAj8yxdxHvV+a9vtSCEsDVvymrrooDKX1GK98t37yoDE0Wua7Lx6Bnad5n91qmHAnwSEJE5YIhQM634omd6cq9Wk4seJCUIn+ucoknrpxp0IR9QMxpKSMRHRUg2K8ZegnY3YqFunRZKCfsq9ufQEKgjZN12AFqi551KPBdn4/3V5HK6xTv0P4robSsE/BvuIfByvRf/W7ZrDx+CFC4EEcsBOACOZCrkhhqd5TkYKbe9RA+vs56+9N5qZGurkxcoKviiyEncxvTuShD65DK/6x6kMDMgQv/EdZDI3x9GtHTnRBYXwDGnPJ19w+q2zC3e2XarbxTGYQIPEC5mYx0gAA0sbjf018NGfwBhl6SB54iGsa8uLvR3jHv6OSRJgwxL6j7P0Ts4Hv2EtO12P0Lv21pwi3JC1O/WviSrKCvrQD5lMHL9Uym3hwFi2zu0mqwZvxOAbGy7kfOPXkLYKOHTZLthzKj3PsdjeceWBfYIvPGKYcd6wDr36d1aXSYS4IWeApTS2AQ2lu0DUcgSefAvsA8NkgOklvJY1cjTMSg6j6cxQo48Bvl8RAWGLbr4h2S/8KwDGxwLsSv0Gop/gnFc3GzCsmL0EkEyHHWkCA8YRXCghfW80KLDV495ff7yF5oiwK56GniqowZ3RG9Jxp5MXoJQgsLV1VMQFMAmsY69yz8eoxRH3wl9L0dMyndLulhWWzNwPMQ2I0yAWdzA/pksVmwTJTFenB3MHCiWc5rEwJ3yofe6NZZnZQrYyL9r1TNnVwfTwRUiykPiLSk4x9Mi6DX7RamDAxc8u3gDVfjPsTOTagBOEGUWlGAL54KE/E6sgCQ5DEAt12chk8AxbjBFLPgV+/idrzS0lZHOL+IVBI9D0i3Bq1yZcSIqcjZB0M3IbxbPm4gLAYOWEiTUN2ecsEHHg9nt6rhgffVoqSbCCFPbpC0xf7WOC3+BQORIZECOCC7cUAciXq3xn+GuxpFE40RWRJeKAK7bBQ21X89ABIXlQFkFddZ9kRvlZ2Pnl0oeF+2pjnZu0Yc2czNfZEQF2P7BKIdLrgMgxG89snxAY8qAYTCKyQw6xTG87wkjDcpy1wzsZLP3WsOuO7cAm7b27xU0jRKq8Cw4d1hDoyRG+RdS53F8RFJzVMaNNYgxU2tfRwUvXpTRXiOheeRVvh25+YGVnjakUXjx/dSDnOw4ETHGHD+7styDkeSfc3BdSZxswzc6OehgMI+xsCxeeRym15QUm9hxvg8X7Bfz/0WulgFwgzrm11TVynZYOmvyHpiZKoqQyQyKahIrfhwuchCr7lMsZ4a+umIkNkKxCLZnI+T7jd+eGFMgKItjz3kTTxRl3IhaJG3LbPmwRUJynMxQKdMi4Uf0qy0U7+i8hIJ9m50QXc+3tw2bwDSbx22XYJ9Wf14gxx5G5SPTb1JVCbhe4fxNt91xIxCow2zk62tzbYfRe6dfmDmgYHkv2PIEtMJZK8iKLDjFfu2ZUxsKT2A5g1q17og6o9MeXeuFS3mzJXJYFQZd+3UzlFR9qwkFkby9mg5y4XSeMvRLOHPt/H/r5SpEqBE6a9MadZYt61FBV152CUEzd43ihXtrAa0XH9HdsiySBcWI1SpM3mv9rRP0DiLjMUzHw/K1D8TE2f07zW4t/9kvE11tFj/NpICixQAAAAA="
sdmf_old_shares[9] = "VGFob2UgbXV0YWJsZSBjb250YWluZXIgdjEKdQlEA47ESLbTdKdpLJXCpBxd5OH239tl5hvAiz1dvGdE5rIOpf8cbfxbPcwNF+Y5dM92uBVbmV6KAAAAAAAAB/wAAAAAAAAJ0AAAAAFOWSw7jSx7WXzaMpdleJYXwYsRCV82jNA5oex9m2YhXSnb2POh+vvC1LE1NAfRc9GOb2zQG84Xdsx1Jub2brEeKkyt0sRIttN0p2kslcKkHF3k4fbf22XmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABamJprL6ecrsOoFKdrXUmWveLq8nzEGDOjFnyK9detI3noX3uyK2MwSnFdAfyN0tuAwoAAAAAAAAAFQAAAAAAAAAVAAABjwAAAo8AAAMXAAADNwAAAAAAAAM+AAAAAAAAB/wwggEgMA0GCSqGSIb3DQEBAQUAA4IBDQAwggEIAoIBAQC1IkainlJF12IBXBQdpRK1zXB7a26vuEYqRmQM09YjC6sQjCs0F2ICk8n9m/2Kw4l16eIEboB2Au9pODCE+u/dEAakEFh4qidTMn61rbGUbsLK8xzuWNW22ezzz9/nPia0HDrulXt51/FYtfnnAuD1RJGXJv/8tDllE9FL/18TzlH4WuB6Fp8FTgv7QdbZAfWJHDGFIpVCJr1XxOCsSZNFJIqGwZnD2lsChiWw5OJDbKd8otqN1hIbfHyMyfMOJ/BzRzvZXaUt4Dv5nf93EmQDWClxShRwpuX/NkZ5B2K9OFonFTbOCexm/MjMAdCBqebKKaiHFkiknUCn9eJQpZ5bAgERgV50VKj+AVTDfgTpqfO2vfo4wrufi6ZBb8QV7hllhUFBjYogQ9C96dnS7skv0s+cqFuUjwMILr5/rsbEmEMGvl0T0ytyAbtlXuowEFVj/YORNknM4yjY72YUtEPTlMpk0Cis7aIgTvu5qWMPER26PMApZuRqiwRsGIkaJIvOVOTHHjFYe3/YzdMkc7OZtqRMfQLtwVl2/zKQQV8b/a9vaT6q3mRLRd4P3esaAFe/+7sR/t+9tmB+a8kxtKM6kmaVQJMbXJZ4aoHGfeLX0m35Rcvu2Bmph7QfSDjk/eaE3q55zYSoGWShmlhlw4Kwg84sMuhmcVhLvo0LovR8bKmbdgABUSzNKiMx0E91q51/WH6ASL0fDEOLef9oxuyBX5F5cpoABojmWkDX3k3FKfgNHIeptE3lxB8HHzxDfSD250psyfNCAAwGsKbMxbmI2NpdTozZ3SICrySwgGkatA1gsDOJmOnTzgAXVnLiODzHiLFAI/MsXcR71fmvb7UghLA1b8pq66KAyl+aopjsD29AKG5hrXt9hLIp6shvfrzaPGIid5C8IxYIrjgBj1YohGgDE0Wua7Lx6Bnad5n91qmHAnwSEJE5YIhQM634omd6cq9Wk4seJCUIn+ucoknrpxp0IR9QMxpKSMRHRUg2K8ZegnY3YqFunRZKCfsq9ufQEKgjZN12AFqi551KPBdn4/3V5HK6xTv0P4robSsE/BvuIfByvRf/W7ZrDx+CFC4EEcsBOACOZCrkhhqd5TkYKbe9RA+vs56+9N5qZGurkxcoKviiyEncxvTuShD65DK/6x6kMDMgQv/EdZDI3x9GtHTnRBYXwDGnPJ19w+q2zC3e2XarbxTGYQIPEC5mYx0gAA0sbjf018NGfwBhl6SB54iGsa8uLvR3jHv6OSRJgwxL6j7P0Ts4Hv2EtO12P0Lv21pwi3JC1O/WviSrKCvrQD5lMHL9Uym3hwFi2zu0mqwZvxOAbGy7kfOPXkLYKOHTZLthzKj3PsdjeceWBfYIvPGKYcd6wDr36d1aXSYS4IWeApTS2AQ2lu0DUcgSefAvsA8NkgOklvJY1cjTMSg6j6cxQo48Bvl8RAWGLbr4h2S/8KwDGxwLsSv0Gop/gnFc3GzCsmL0EkEyHHWkCA8YRXCghfW80KLDV495ff7yF5oiwK56GniqowZ3RG9Jxp5MXoJQgsLV1VMQFMAmsY69yz8eoxRH3wl9L0dMyndLulhWWzNwPMQ2I0yAWdzA/pksVmwTJTFenB3MHCiWc5rEwJ3yofe6NZZnZQrYyL9r1TNnVwfTwRUiykPiLSk4x9Mi6DX7RamDAxc8u3gDVfjPsTOTagBOEGUWlGAL54KE/E6sgCQ5DEAt12chk8AxbjBFLPgV+/idrzS0lZHOL+IVBI9D0i3Bq1yZcSIqcjZB0M3IbxbPm4gLAYOWEiTUN2ecsEHHg9nt6rhgffVoqSbCCFPbpC0xf7WOC3+BQORIZECOCC7cUAciXq3xn+GuxpFE40RWRJeKAK7bBQ21X89ABIXlQFkFddZ9kRvlZ2Pnl0oeF+2pjnZu0Yc2czNfZEQF2P7BKIdLrgMgxG89snxAY8qAYTCKyQw6xTG87wkjDcpy1wzsZLP3WsOuO7cAm7b27xU0jRKq8Cw4d1hDoyRG+RdS53F8RFJzVMaNNYgxU2tfRwUvXpTRXiOheeRVvh25+YGVnjakUXjx/dSDnOw4ETHGHD+7styDkeSfc3BdSZxswzc6OehgMI+xsCxeeRym15QUm9hxvg8X7Bfz/0WulgFwgzrm11TVynZYOmvyHpiZKoqQyQyKahIrfhwuchCr7lMsZ4a+umIkNkKxCLZnI+T7jd+eGFMgKItjz3kTTxRl3IhaJG3LbPmwRUJynMxQKdMi4Uf0qy0U7+i8hIJ9m50QXc+3tw2bwDSbx22XYJ9Wf14gxx5G5SPTb1JVCbhe4fxNt91xIxCow2zk62tzbYfRe6dfmDmgYHkv2PIEtMJZK8iKLDjFfu2ZUxsKT2A5g1q17og6o9MeXeuFS3mzJXJYFQZd+3UzlFR9qwkFkby9mg5y4XSeMvRLOHPt/H/r5SpEqBE6a9MadZYt61FBV152CUEzd43ihXtrAa0XH9HdsiySBcWI1SpM3mv9rRP0DiLjMUzHw/K1D8TE2f07zW4t/9kvE11tFj/NpICixQAAAAA="
sdmf_old_cap = "URI:SSK:gmjgofw6gan57gwpsow6gtrz3e:5adm6fayxmu3e4lkmfvt6lkkfix34ai2wop2ioqr4bgvvhiol3kq"
sdmf_old_contents = "This is a test file.\n"
sdmf_old_shares[0] = b"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"
sdmf_old_shares[1] = b"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"
sdmf_old_shares[2] = b"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"
sdmf_old_shares[3] = b"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"
sdmf_old_shares[4] = b"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"
sdmf_old_shares[5] = b"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"
sdmf_old_shares[6] = b"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"
sdmf_old_shares[7] = b"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"
sdmf_old_shares[8] = b"VGFob2UgbXV0YWJsZSBjb250YWluZXIgdjEKdQlEA47ESLbTdKdpLJXCpBxd5OH239tl5hvAiz1dvGdE5rIOpf8cbfxbPcwNF+Y5dM92uBVbmV6KAAAAAAAAB/wAAAAAAAAJ0AAAAAFOWSw7jSx7WXzaMpdleJYXwYsRCV82jNA5oex9m2YhXSnb2POh+vvC1LE1NAfRc9GOb2zQG84Xdsx1Jub2brEeKkyt0sRIttN0p2kslcKkHF3k4fbf22XmAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABamJprL6ecrsOoFKdrXUmWveLq8nzEGDOjFnyK9detI3noX3uyK2MwSnFdAfyN0tuAwoAAAAAAAAAFQAAAAAAAAAVAAABjwAAAo8AAAMXAAADNwAAAAAAAAM+AAAAAAAAB/wwggEgMA0GCSqGSIb3DQEBAQUAA4IBDQAwggEIAoIBAQC1IkainlJF12IBXBQdpRK1zXB7a26vuEYqRmQM09YjC6sQjCs0F2ICk8n9m/2Kw4l16eIEboB2Au9pODCE+u/dEAakEFh4qidTMn61rbGUbsLK8xzuWNW22ezzz9/nPia0HDrulXt51/FYtfnnAuD1RJGXJv/8tDllE9FL/18TzlH4WuB6Fp8FTgv7QdbZAfWJHDGFIpVCJr1XxOCsSZNFJIqGwZnD2lsChiWw5OJDbKd8otqN1hIbfHyMyfMOJ/BzRzvZXaUt4Dv5nf93EmQDWClxShRwpuX/NkZ5B2K9OFonFTbOCexm/MjMAdCBqebKKaiHFkiknUCn9eJQpZ5bAgERgV50VKj+AVTDfgTpqfO2vfo4wrufi6ZBb8QV7hllhUFBjYogQ9C96dnS7skv0s+cqFuUjwMILr5/rsbEmEMGvl0T0ytyAbtlXuowEFVj/YORNknM4yjY72YUtEPTlMpk0Cis7aIgTvu5qWMPER26PMApZuRqiwRsGIkaJIvOVOTHHjFYe3/YzdMkc7OZtqRMfQLtwVl2/zKQQV8b/a9vaT6q3mRLRd4P3esaAFe/+7sR/t+9tmB+a8kxtKM6kmaVQJMbXJZ4aoHGfeLX0m35Rcvu2Bmph7QfSDjk/eaE3q55zYSoGWShmlhlw4Kwg84sMuhmcVhLvo0LovR8bKmbdgABUSzNKiMx0E91q51/WH6ASL0fDEOLef9oxuyBX5F5cpoABojmWkDX3k3FKfgNHIeptE3lxB8HHzxDfSD250psyfNCAAwGsKbMxbmI2NpdTozZ3SICrySwgGkatA1gsDOJmOnTzgAYmqKY7A9vQChuYa17fYSyKerIb3682jxiIneQvCMWCK5WcuI4PMeIsUAj8yxdxHvV+a9vtSCEsDVvymrrooDKX1GK98t37yoDE0Wua7Lx6Bnad5n91qmHAnwSEJE5YIhQM634omd6cq9Wk4seJCUIn+ucoknrpxp0IR9QMxpKSMRHRUg2K8ZegnY3YqFunRZKCfsq9ufQEKgjZN12AFqi551KPBdn4/3V5HK6xTv0P4robSsE/BvuIfByvRf/W7ZrDx+CFC4EEcsBOACOZCrkhhqd5TkYKbe9RA+vs56+9N5qZGurkxcoKviiyEncxvTuShD65DK/6x6kMDMgQv/EdZDI3x9GtHTnRBYXwDGnPJ19w+q2zC3e2XarbxTGYQIPEC5mYx0gAA0sbjf018NGfwBhl6SB54iGsa8uLvR3jHv6OSRJgwxL6j7P0Ts4Hv2EtO12P0Lv21pwi3JC1O/WviSrKCvrQD5lMHL9Uym3hwFi2zu0mqwZvxOAbGy7kfOPXkLYKOHTZLthzKj3PsdjeceWBfYIvPGKYcd6wDr36d1aXSYS4IWeApTS2AQ2lu0DUcgSefAvsA8NkgOklvJY1cjTMSg6j6cxQo48Bvl8RAWGLbr4h2S/8KwDGxwLsSv0Gop/gnFc3GzCsmL0EkEyHHWkCA8YRXCghfW80KLDV495ff7yF5oiwK56GniqowZ3RG9Jxp5MXoJQgsLV1VMQFMAmsY69yz8eoxRH3wl9L0dMyndLulhWWzNwPMQ2I0yAWdzA/pksVmwTJTFenB3MHCiWc5rEwJ3yofe6NZZnZQrYyL9r1TNnVwfTwRUiykPiLSk4x9Mi6DX7RamDAxc8u3gDVfjPsTOTagBOEGUWlGAL54KE/E6sgCQ5DEAt12chk8AxbjBFLPgV+/idrzS0lZHOL+IVBI9D0i3Bq1yZcSIqcjZB0M3IbxbPm4gLAYOWEiTUN2ecsEHHg9nt6rhgffVoqSbCCFPbpC0xf7WOC3+BQORIZECOCC7cUAciXq3xn+GuxpFE40RWRJeKAK7bBQ21X89ABIXlQFkFddZ9kRvlZ2Pnl0oeF+2pjnZu0Yc2czNfZEQF2P7BKIdLrgMgxG89snxAY8qAYTCKyQw6xTG87wkjDcpy1wzsZLP3WsOuO7cAm7b27xU0jRKq8Cw4d1hDoyRG+RdS53F8RFJzVMaNNYgxU2tfRwUvXpTRXiOheeRVvh25+YGVnjakUXjx/dSDnOw4ETHGHD+7styDkeSfc3BdSZxswzc6OehgMI+xsCxeeRym15QUm9hxvg8X7Bfz/0WulgFwgzrm11TVynZYOmvyHpiZKoqQyQyKahIrfhwuchCr7lMsZ4a+umIkNkKxCLZnI+T7jd+eGFMgKItjz3kTTxRl3IhaJG3LbPmwRUJynMxQKdMi4Uf0qy0U7+i8hIJ9m50QXc+3tw2bwDSbx22XYJ9Wf14gxx5G5SPTb1JVCbhe4fxNt91xIxCow2zk62tzbYfRe6dfmDmgYHkv2PIEtMJZK8iKLDjFfu2ZUxsKT2A5g1q17og6o9MeXeuFS3mzJXJYFQZd+3UzlFR9qwkFkby9mg5y4XSeMvRLOHPt/H/r5SpEqBE6a9MadZYt61FBV152CUEzd43ihXtrAa0XH9HdsiySBcWI1SpM3mv9rRP0DiLjMUzHw/K1D8TE2f07zW4t/9kvE11tFj/NpICixQAAAAA="
sdmf_old_shares[9] = b"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"
sdmf_old_cap = b"URI:SSK:gmjgofw6gan57gwpsow6gtrz3e:5adm6fayxmu3e4lkmfvt6lkkfix34ai2wop2ioqr4bgvvhiol3kq"
sdmf_old_contents = b"This is a test file.\n"
def copy_sdmf_shares(self):
# We'll basically be short-circuiting the upload process.
servernums = self.g.servers_by_number.keys()
servernums = list(self.g.servers_by_number.keys())
assert len(servernums) == 10
assignments = zip(self.sdmf_old_shares.keys(), servernums)
assignments = list(zip(self.sdmf_old_shares.keys(), servernums))
# Get the storage index.
cap = uri.from_string(self.sdmf_old_cap)
si = cap.get_storage_index()

20
src/allmydata/test/mutable/test_multiple_encodings.py
View File

@ -1,3 +1,15 @@
"""
Ported to Python 3.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from future.utils import PY2
if PY2:
from future.builtins import filter, map, zip, ascii, chr, hex, input, next, oct, open, pow, round, super, bytes, dict, list, object, range, str, max, min # noqa: F401
from twisted.trial import unittest
from allmydata.interfaces import SDMF_VERSION
from allmydata.monitor import Monitor
@ -10,7 +22,7 @@ from .util import FakeStorage, make_nodemaker
class MultipleEncodings(unittest.TestCase):
def setUp(self):
self.CONTENTS = "New contents go here"
self.CONTENTS = b"New contents go here"
self.uploadable = MutableData(self.CONTENTS)
self._storage = FakeStorage()
self._nodemaker = make_nodemaker(self._storage, num_peers=20)
@ -63,9 +75,9 @@ class MultipleEncodings(unittest.TestCase):
# then mix up the shares, to make sure that download survives seeing
# a variety of encodings. This is actually kind of tricky to set up.
contents1 = "Contents for encoding 1 (3-of-10) go here"*1000
contents2 = "Contents for encoding 2 (4-of-9) go here"*1000
contents3 = "Contents for encoding 3 (4-of-7) go here"*1000
contents1 = b"Contents for encoding 1 (3-of-10) go here"*1000
contents2 = b"Contents for encoding 2 (4-of-9) go here"*1000
contents3 = b"Contents for encoding 3 (4-of-7) go here"*1000
# we make a retrieval object that doesn't know what encoding
# parameters to use

18
src/allmydata/test/mutable/test_multiple_versions.py
View File

@ -1,3 +1,15 @@
"""
Ported to Python 3.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from future.utils import PY2
if PY2:
from future.builtins import filter, map, zip, ascii, chr, hex, input, next, oct, open, pow, round, super, bytes, dict, list, object, range, str, max, min # noqa: F401
from twisted.trial import unittest
from allmydata.monitor import Monitor
from allmydata.mutable.common import MODE_CHECK, MODE_READ
@ -36,7 +48,7 @@ class MultipleVersions(unittest.TestCase, PublishMixin, CheckerMixin):
self.failUnlessEqual(len(smap.unrecoverable_versions()), 1)
newer = smap.unrecoverable_newer_versions()
self.failUnlessEqual(len(newer), 1)
verinfo, health = newer.items()[0]
verinfo, health = list(newer.items())[0]
self.failUnlessEqual(verinfo[0], 4)
self.failUnlessEqual(health, (1,3))
self.failIf(smap.needs_merge())
@ -70,10 +82,10 @@ class MultipleVersions(unittest.TestCase, PublishMixin, CheckerMixin):
self._set_versions(target)
def _modify(oldversion, servermap, first_time):
return oldversion + " modified"
return oldversion + b" modified"
d = self._fn.modify(_modify)
d.addCallback(lambda res: self._fn.download_best_version())
expected = self.CONTENTS[2] + " modified"
expected = self.CONTENTS[2] + b" modified"
d.addCallback(lambda res: self.failUnlessEqual(res, expected))
# and the servermap should indicate that the outlier was replaced too
d.addCallback(lambda res: self._fn.get_servermap(MODE_CHECK))

60
src/allmydata/test/mutable/test_problems.py
View File

@ -1,4 +1,14 @@
"""
Ported to Python 3.
"""
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from future.utils import PY2
if PY2:
from future.builtins import filter, map, zip, ascii, chr, hex, input, next, oct, open, pow, round, super, bytes, dict, list, object, range, str, max, min # noqa: F401
import os, base64
from twisted.trial import unittest
@ -56,7 +66,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.basedir = "mutable/Problems/test_publish_surprise_%s" % version
self.set_up_grid()
nm = self.g.clients[0].nodemaker
d = nm.create_mutable_file(MutableData("contents 1"),
d = nm.create_mutable_file(MutableData(b"contents 1"),
version=version)
def _created(n):
d = defer.succeed(None)
@ -67,7 +77,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
d.addCallback(_got_smap1)
# then modify the file, leaving the old map untouched
d.addCallback(lambda res: log.msg("starting winning write"))
d.addCallback(lambda res: n.overwrite(MutableData("contents 2")))
d.addCallback(lambda res: n.overwrite(MutableData(b"contents 2")))
# now attempt to modify the file with the old servermap. This
# will look just like an uncoordinated write, in which every
# single share got updated between our mapupdate and our publish
@ -76,7 +86,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.shouldFail(UncoordinatedWriteError,
"test_publish_surprise", None,
n.upload,
MutableData("contents 2a"), self.old_map))
MutableData(b"contents 2a"), self.old_map))
return d
d.addCallback(_created)
return d
@ -91,7 +101,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.basedir = "mutable/Problems/test_retrieve_surprise"
self.set_up_grid()
nm = self.g.clients[0].nodemaker
d = nm.create_mutable_file(MutableData("contents 1"*4000))
d = nm.create_mutable_file(MutableData(b"contents 1"*4000))
def _created(n):
d = defer.succeed(None)
d.addCallback(lambda res: n.get_servermap(MODE_READ))
@ -101,7 +111,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
d.addCallback(_got_smap1)
# then modify the file, leaving the old map untouched
d.addCallback(lambda res: log.msg("starting winning write"))
d.addCallback(lambda res: n.overwrite(MutableData("contents 2")))
d.addCallback(lambda res: n.overwrite(MutableData(b"contents 2")))
# now attempt to retrieve the old version with the old servermap.
# This will look like someone has changed the file since we
# updated the servermap.
@ -128,7 +138,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.basedir = "mutable/Problems/test_unexpected_shares"
self.set_up_grid()
nm = self.g.clients[0].nodemaker
d = nm.create_mutable_file(MutableData("contents 1"))
d = nm.create_mutable_file(MutableData(b"contents 1"))
def _created(n):
d = defer.succeed(None)
d.addCallback(lambda res: n.get_servermap(MODE_WRITE))
@ -140,7 +150,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.g.remove_server(peer0)
# then modify the file, leaving the old map untouched
log.msg("starting winning write")
return n.overwrite(MutableData("contents 2"))
return n.overwrite(MutableData(b"contents 2"))
d.addCallback(_got_smap1)
# now attempt to modify the file with the old servermap. This
# will look just like an uncoordinated write, in which every
@ -150,7 +160,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.shouldFail(UncoordinatedWriteError,
"test_surprise", None,
n.upload,
MutableData("contents 2a"), self.old_map))
MutableData(b"contents 2a"), self.old_map))
return d
d.addCallback(_created)
return d
@ -159,7 +169,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.basedir = "mutable/Problems/test_multiply_placed_shares"
self.set_up_grid()
nm = self.g.clients[0].nodemaker
d = nm.create_mutable_file(MutableData("contents 1"))
d = nm.create_mutable_file(MutableData(b"contents 1"))
# remove one of the servers and reupload the file.
def _created(n):
self._node = n
@ -226,19 +236,19 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
d.addCallback(_break_peer0)
# now "create" the file, using the pre-established key, and let the
# initial publish finally happen
d.addCallback(lambda res: nm.create_mutable_file(MutableData("contents 1")))
d.addCallback(lambda res: nm.create_mutable_file(MutableData(b"contents 1")))
# that ought to work
def _got_node(n):
d = n.download_best_version()
d.addCallback(lambda res: self.failUnlessEqual(res, "contents 1"))
d.addCallback(lambda res: self.failUnlessEqual(res, b"contents 1"))
# now break the second peer
def _break_peer1(res):
self.g.break_server(self.server1.get_serverid())
d.addCallback(_break_peer1)
d.addCallback(lambda res: n.overwrite(MutableData("contents 2")))
d.addCallback(lambda res: n.overwrite(MutableData(b"contents 2")))
# that ought to work too
d.addCallback(lambda res: n.download_best_version())
d.addCallback(lambda res: self.failUnlessEqual(res, "contents 2"))
d.addCallback(lambda res: self.failUnlessEqual(res, b"contents 2"))
def _explain_error(f):
print(f)
if f.check(NotEnoughServersError):
@ -267,18 +277,18 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
peerids = [s.get_serverid() for s in sb.get_connected_servers()]
self.g.break_server(peerids[0])
d = nm.create_mutable_file(MutableData("contents 1"))
d = nm.create_mutable_file(MutableData(b"contents 1"))
def _created(n):
d = n.download_best_version()
d.addCallback(lambda res: self.failUnlessEqual(res, "contents 1"))
d.addCallback(lambda res: self.failUnlessEqual(res, b"contents 1"))
# now break one of the remaining servers
def _break_second_server(res):
self.g.break_server(peerids[1])
d.addCallback(_break_second_server)
d.addCallback(lambda res: n.overwrite(MutableData("contents 2")))
d.addCallback(lambda res: n.overwrite(MutableData(b"contents 2")))
# that ought to work too
d.addCallback(lambda res: n.download_best_version())
d.addCallback(lambda res: self.failUnlessEqual(res, "contents 2"))
d.addCallback(lambda res: self.failUnlessEqual(res, b"contents 2"))
return d
d.addCallback(_created)
return d
@ -294,7 +304,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
d = self.shouldFail(NotEnoughServersError,
"test_publish_all_servers_bad",
"ran out of good servers",
nm.create_mutable_file, MutableData("contents"))
nm.create_mutable_file, MutableData(b"contents"))
return d
def test_publish_no_servers(self):
@ -306,7 +316,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
d = self.shouldFail(NotEnoughServersError,
"test_publish_no_servers",
"Ran out of non-bad servers",
nm.create_mutable_file, MutableData("contents"))
nm.create_mutable_file, MutableData(b"contents"))
return d
@ -322,7 +332,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
# we need some contents that are large enough to push the privkey out
# of the early part of the file
LARGE = "These are Larger contents" * 2000 # about 50KB
LARGE = b"These are Larger contents" * 2000 # about 50KB
LARGE_uploadable = MutableData(LARGE)
d = nm.create_mutable_file(LARGE_uploadable)
def _created(n):
@ -359,7 +369,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.basedir = "mutable/Problems/test_privkey_query_missing"
self.set_up_grid(num_servers=20)
nm = self.g.clients[0].nodemaker
LARGE = "These are Larger contents" * 2000 # about 50KiB
LARGE = b"These are Larger contents" * 2000 # about 50KiB
LARGE_uploadable = MutableData(LARGE)
nm._node_cache = DevNullDictionary() # disable the nodecache
@ -385,7 +395,7 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
self.basedir = "mutable/Problems/test_block_and_hash_query_error"
self.set_up_grid(num_servers=20)
nm = self.g.clients[0].nodemaker
CONTENTS = "contents" * 2000
CONTENTS = b"contents" * 2000
CONTENTS_uploadable = MutableData(CONTENTS)
d = nm.create_mutable_file(CONTENTS_uploadable)
def _created(node):
@ -451,9 +461,9 @@ class Problems(GridTestMixin, unittest.TestCase, testutil.ShouldFailMixin):
return d
TEST_1654_CAP = "URI:SSK:6jthysgozssjnagqlcxjq7recm:yxawei54fmf2ijkrvs2shs6iey4kpdp6joi7brj2vrva6sp5nf3a"
TEST_1654_CAP = b"URI:SSK:6jthysgozssjnagqlcxjq7recm:yxawei54fmf2ijkrvs2shs6iey4kpdp6joi7brj2vrva6sp5nf3a"
TEST_1654_SH0 = """\
TEST_1654_SH0 = b"""\
VGFob2UgbXV0YWJsZSBjb250YWluZXIgdjEKdQlEA46m9s5j6lnzsOHytBTs2JOo
AkWe8058hyrDa8igfBSqZMKO3aDOrFuRVt0ySYZ6oihFqPJRAAAAAAAAB8YAAAAA
AAAJmgAAAAFPNgDkK8brSCzKz6n8HFqzbnAlALvnaB0Qpa1Bjo9jiZdmeMyneHR+
@ -507,7 +517,7 @@ TStXB+q0MndBXw5ADp/Jac1DVaSWruVAdjemQ+si1olk8xH+uTMXU7PgV9WkpIiy
bQHi/oRGA1aHSn84SIt+HpAfRoVdr4N90bYWmYQNqfKoyWCbEr+dge/GSD1nddAJ
72mXGlqyLyWYuAAAAAA="""
TEST_1654_SH1 = """\
TEST_1654_SH1 = b"""\
VGFob2UgbXV0YWJsZSBjb250YWluZXIgdjEKdQlEA45R4Y4kuV458rSTGDVTqdzz
9Fig3NQ3LermyD+0XLeqbC7KNgvv6cNzMZ9psQQ3FseYsIR1AAAAAAAAB8YAAAAA
AAAJmgAAAAFPNgDkd/Y9Z+cuKctZk9gjwF8thT+fkmNCsulILsJw5StGHAA1f7uL

4
src/allmydata/util/_python3.py
View File

@ -101,6 +101,10 @@ PORTED_TEST_MODULES = [
"allmydata.test.mutable.test_exceptions",
"allmydata.test.mutable.test_filehandle",
"allmydata.test.mutable.test_filenode",
"allmydata.test.mutable.test_interoperability",
"allmydata.test.mutable.test_multiple_encodings",
"allmydata.test.mutable.test_multiple_versions",
"allmydata.test.mutable.test_problems",
"allmydata.test.test_abbreviate",
"allmydata.test.test_base32",
"allmydata.test.test_base62",