為了tensorflow專案要求測試protobuf序列化/反序列化的效能，測試過程及測試結果如下：

一. 測試環境

python 2.7 + proto3

二. 測試方法

1. 自定義一個proto訊息（使用protobuf example裡的例子，進行修改）

message Person {
  string name = 1;
  int32 id = 2;  // Unique ID number for this person.
  string email = 3;

  enum PhoneType {
    MOBILE = 0;
    HOME = 1;
    WORK = 2;
  }

  message PhoneNumber {
    string number = 1;
    PhoneType type = 2;
  }

  repeated PhoneNumber phones = 4;
}

// Our address book file is just one of these.
message AddressBook {
  repeated Person people = 1;
}
 

protoc --python_out=. address.proto

得到 addressbook_pb2.py

3. 在測試檔案中，通過修改迴圈的大小，修改序列化內容的大小。並

for i in range(1024 * 1024):
  PromptForAddress(address_book.people.add())

4. 序列化

  begin = datetime.datetime.now()
  serialized = address_book.SerializeToString()
  end = datetime.datetime.now()
  print end-begin
  print len(serialized)
  f.write(serialized)
 

    book = f.read()
    parsebegin = datetime.datetime.now()
    address_book.ParseFromString(book)
    parseend = datetime.datetime.now()
    print parseend-parsebegin
    print len(book)

#! /usr/bin/env python

# See README.txt for information and build instructions.

import addressbook_pb2
import sys
import datetime

# This function fills in a Person message based on user input.
def PromptForAddress(person):
  person.id = 160824
  person.name = "xxxxx xxxxx"
  person.email = "[email protected]"
  phone_number = person.phones.add()
  phone_number.number = "12345678"
  phone_number.type = addressbook_pb2.Person.MOBILE

  phone_number = person.phones.add()
  phone_number.number = "23456789"
  phone_number.type = addressbook_pb2.Person.HOME

  phone_number = person.phones.add()
  phone_number.number = "34567890"
  phone_number.type = addressbook_pb2.Person.WORK

# Main procedure:  Reads the entire address book from a file,
#   adds one person based on user input, then writes it back out to the same
#   file.
if len(sys.argv) != 2:
  print "Usage:", sys.argv[0], "ADDRESS_BOOK_FILE"
  sys.exit(-1)
address_book = addressbook_pb2.AddressBook()

# Read the existing address book.
try:
  with open(sys.argv[1], "rb") as f:
    book = f.read()
    parsebegin = datetime.datetime.now()
    address_book.ParseFromString(book)
    parseend = datetime.datetime.now()
    print parseend-parsebegin
    print len(book)



#    address_book.ParseFromString(f.read())
except IOError:
  print sys.argv[1] + ": File not found.  Creating a new file."

# Add an address.
for i in range(1024 * 1024):
  PromptForAddress(address_book.people.add())

# Write the new address book back to disk.
with open(sys.argv[1], "wb") as f:
  begin = datetime.datetime.now()
  serialized = address_book.SerializeToString()
  end = datetime.datetime.now()
  print end-begin
  print len(serialized)
  
'''
address_book = addressbook_pb2.AddressBook()

# Read the existing address book.
try:
  with open(sys.argv[1], "rb") as f:
    book = f.read()
    parsebegin = datetime.datetime.now()
    address_book.ParseFromString(book)
    parseend = datetime.datetime.now()
    print parseend-parsebegin
    print len(book)
'''
 

6. 修改迴圈次數，記錄不同大小的protobuf序列化反序列的效能

三. 測試結果

注：表中位元組大小為序列化後得到的字串大小，即程式中的 len(serialized) 

四. 測試分析及問題

根據測試的結果看是基本成線性增長，位元組數越大，所用時間越多。當位元組數為243MB時，序列化耗時3s左右，反序列化耗時4s左右。在測試結果上有幾個問題如下：

1. 測試方法是否正確，我感覺應該是可行的，但是結果比我預期的要大。

2. 本次測試是用Python測試的，我在c++下進行測試，得到的結果比python好很多（C++部分參考FlatBuffers與protobuf效能比較）。

我只對比測試了小資料量（1KB）的，序列化及反序列化均迴圈100次，結果如下：（兩次測試的proto檔案為同一個，在C++中用的序列化/反序列化函式為ParseFromArray/SerializeToArray，python中用的序列化/反序列化函式是ParseFromString/SerializeToString）

3. 經查閱相關資料，序列化反序列化跟proto的結構也是有關係的（比如多層巢狀），所以建議在學習tensorflow之後結合tensorflow再進行一次測試，在訓練某一個模型時，將其中序列化反序列化的過程單獨計時。

以上兩個問題還需討論，也歡迎各位批評指正。

五. 參考及學習文章