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case 4, partitioned topic

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Oskar Thoren 2019-10-16 14:17:33 +08:00
parent e3bfad5ac2
commit 829edcdd3a
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GPG Key ID: B2ECCFD3BC2EF77E
1 changed files with 49 additions and 3 deletions
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52
whisper_scalability/whisper.py
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@ -12,7 +12,7 @@ class bcolors:
def sizeof_fmt(num):
for x in ['bytes','KB','MB','GB','TB']:
if num < 1024.0:
return "%3.0f%s" % (num, x)
return "%3.1f%s" % (num, x)
num /= 1024.0
def magnitude_fmt(num):
@ -134,8 +134,48 @@ def case3():
print "- A2. Envelopes / message (static): " + str(envelopes_per_message)
print "- A3. Received messages / day (static): " + str(received_messages_per_day)
print "- A4. Proportion of private messages (static): " + str(private_message_proportion)
print "- A5. All private messages are received by everyone (same topic) (static)"
print "- A6. Public messages only received by relevant recipients (static)"
print "- A5. Public messages only received by relevant recipients (static)"
print "- A6. All private messages are received by everyone (same topic) (static)"
print ""
print usage_str(100)
print usage_str(100 * 100)
print usage_str(100 * 100 * 100)
print ""
print("------------------------------------------------------------")
def case4():
# Case 4: all private messages are partitioned into shards
partitions = 5000
def load_users(n_users):
if n_users < partitions:
# Assume spread out, not colliding
factor_load = 1
else:
# Assume spread out evenly, collides proportional to users
factor_load = n_users / partitions
load_private = envelope_size * envelopes_per_message * \
received_messages_per_day * factor_load
load_public = envelope_size * envelopes_per_message * \
received_messages_per_day
total_load = load_private * private_message_proportion + \
load_public * (1 - private_message_proportion)
return total_load
def usage_str(n_users):
load = load_users(n_users)
return load_color_fmt(load, "For " + magnitude_fmt(n_users) + " users, receiving bandwidth is " + sizeof_fmt(load_users(n_users)) + "/day")
print bcolors.HEADER + "\nCase 4. All private messages are partitioned into shards" + bcolors.ENDC
print ""
print "Assumptions:"
print "- A1. Envelope size (static): " + str(envelope_size) + "kb"
print "- A2. Envelopes / message (static): " + str(envelopes_per_message)
print "- A3. Received messages / day (static): " + str(received_messages_per_day)
print "- A4. Proportion of private messages (static): " + str(private_message_proportion)
print "- A5. Public messages only received by relevant recipients (static)"
print "- A6. Private messages are partitioned evenly across partition shards (static), n=" + str(partitions)
print ""
print usage_str(100)
print usage_str(100 * 100)
@ -146,6 +186,7 @@ def case3():
case1()
case2()
case3()
case4()
# Ok, let's get serious. What assumptions do we need to encode?
@ -177,3 +218,8 @@ case3()
# Heavy user, and duplicate messages (peers), Envelope size?
# Say * 4 (size) * 2 (duplicates) * 2 (usage) then it is within x8-16.
# Also missing bloom filter here
# I am also assuming we are roughly 100 users today, is this accurate?
# How many unique public keys have we seen in common chats the last month?
# TODO: It'd be neat if you could encode assumptions set