ProtoBuf of the trial compared with JSON

　　Introduction
　　
　　ProtoBuf is google team development tool for efficiently storing and reading structured data. What is it structured data, as the expression literally, that is, with certain data structures. For example, there are many phone book data records, each record contains a name, ID, e-mail, phone, etc., this arrangement is repeated.
　　
　　Similar
　　
　　XML, JSON can also be used to store such structured data, but using data ProtoBuf representation can be more efficient, smaller and data compression.
　　
　　Principle
　　
　　ProtoBuf by ProtoBuf compiler-independent programming language-specific .proto suffix data structure documents translated into various programming languages (Java, C / C ++, Python) special class files, then by Google's various programming languages support library lib can call API. (On how to write proto structure, can be found on their own documentation)
　　
　　protobuf compiler install
　　
　　Mac: brew install protobuf
　　
　　example
　　
　　1. create a proto file
　　
　　message.proto
　　
　　syntax = "proto3";
　　
　　the Message the Person {
　　
　　Int32 the above mentioned id = 1;
　　
　　String 2 = name;
　　
　　REPEATED Phone = Phone. 4;
　　
　　enum {PhoneType
　　
　　MOBILE = 0;
　　
　　the HOME =. 1;
　　
　　WORK = 2;
　　
　　}
　　
　　Phone {Message
　　
　　String Number =. 1;
　　
　　PhoneType type = 2;
　　
　　}
　　
　　}
　　
　　2. Create a Java project
　　
　　and placed src / main proto file / folder proto
　　
　　3. proto compiled file to the Java version of
　　
　　the command line cd to src / main directory
　　
　　terminal execute the command:. protoc --java_out = / java ./proto/*.proto
　　
　　will find that has been generated in the corresponding Java classes in your src / main / java Lane
　　
　　4. depend on Java version of ProtoBuf support library
　　
　　here to cite a use-dependent chestnuts with Gradle
　　
　　Implementation 'com.google.protobuf: Protobuf Java-: 3.9.1'
　　
　　5. the Java objects into ProtoBuf data
　　
　　Message.Person.Phone.Builder phoneBuilder = Message.Person.Phone.newBuilder ();
　　
　　Message.Person.Phone phone1 = phoneBuilder
　　
　　.setNumber ( "100860")
　　
　　.setType (Message.Person.PhoneType.HOME)
　　
　　.build ();
　　
　　Message.Person.Phone phone2 = phoneBuilder
　　
　　.setNumber("100100")
　　
　　.setType(Message.Person.PhoneType.MOBILE)
　　
　　.build();
　　
　　Message.Person.Builder personBuilder = Message.Person.newBuilder();
　　
　　personBuilder.setId(1994);
　　
　　personBuilder.setName("XIAOLEI");
　　
　　personBuilder.addPhone(phone1);
　　
　　personBuilder.addPhone(phone2);
　　
　　Message.Person person = personBuilder.build();
　　
　　long old =http://www.senta7.net/content/?729.html System.currentTimeMillis();
　　
　　byte[] buff = person.toByteArray();
　　
　　System.out.println(www.jujinyule.com"ProtoBuf 编码耗时：" + (System.currentTimeMillis() - old));
　　
　　System.out.println(Arrays.toString(buff));
　　
　　System.out.println ( "ProtoBuf Data length:" + buff.length);
　　
　　6. The protobuf the data is converted back to Java objects
　　
　　System.out.println ( "- start decoding -");
　　
　　Old = System.currentTimeMillis ();
　　
　　personOut = Message.Person.parseFrom Message.Person (BUFF);
　　
　　System.out.println ( "protobuf decoding Processed:" + (System.currentTimeMillis () - Old));
　　
　　System.out.printf ( "Id:% D , the Name:% S \ n-", personOut.getId (), personOut.getName ());
　　
　　List <Message.Person.Phone> personOut.getPhoneList PhoneList = ();
　　
　　for (Message.Person.Phone Phone: PhoneList)
　　
　　{
　　
　　System.out.printf ( "phone number:% S (% S www.xcdeyiju.com) \ n-", phone.getNumber (), phone.getType ());
　　
　　}
　　
　　comparison
　　
　　In order to reflect the advantage ProtoBuf, I wrote a Java class the same structure, and convert Java objects to JSON data to be compared with ProtoBuf. JSON library compiled using GSON library provided by Google, part of the code of JSON is not posted, and the results show a direct
　　
　　result of the comparison results
　　
　　run once
　　
　　[JSON] start coding
　　
　　JSON-encoded once, time-consuming: 22ms
　　
　　JSON data Length: 106
　　
　　- Start decoding -
　　
　　JSON decoder 1, Processed: 1ms
　　
　　[] protobuf start encoding
　　
　　protobuf encoding 1, Processed: 32ms
　　
　　protobuf data length: 34
　　
　　- start decoding -
　　
　　protobuf decoder 1, Processed: 3ms
　　
　　10 runs
　　
　　[start coding JSON ]
　　
　　JSON encoder 10, Processed: 22ms
　　
　　JSON data length: 106
　　
　　- start decoding -
　　
　　JSON decoder 10, time-consuming: 4ms
　　
　　[] protobuf start encoding
　　
　　protobuf encoder 10, Processed: 29 ms
　　
　　protobuf data length: 34
　　
　　- start decoding -
　　
　　protobuf decoding 10 times, time-consuming: 3ms
　　
　　run 100 times
　　
　　[coding] JSON start
　　
　　100 JSON-encoded, time-consuming: 32ms
　　
　　JSON Data Length: 106
　　
　　- start decoding -
　　
　　JSON decoder 100, Processed: 8ms
　　
　　[] protobuf start encoding
　　
　　protobuf encoder 100, Processed: 31ms
　　
　　protobuf data length: 34
　　
　　- start decoding -
　　
　　ProtoBuf decoder 100, Processed: 4ms
　　
　　run 1000
　　
　　[JSON] start encoding
　　
　　JSON encoder 1000, Processed: 39ms
　　
　　JSON data length: 106
　　
　　- start decoding -
　　
　　JSON decoder 1000, Processed: 21ms
　　
　　[] ProtoBuf start coding
　　
　　ProtoBuf coding 1000, Processed: 37ms
　　
　　protobuf data length: 34
　　
　　- start decoding -
　　
　　protobuf decoder 1000, Processed: 8ms
　　
　　run 10,000
　　
　　[] JSON start encoding
　　
　　JSON encoding 10000, Processed: 126ms
　　
　　JSON data length: 106
　　
　　- start decoding -
　　
　　JSON decoding 10,000 times, time-consuming: 93ms
　　
　　[ProtoBuf start coding]
　　
　　ProtoBuf coded 10,000 times, time-consuming: 49ms
　　
　　Protobuf data length: 34
　　
　　- Start decoding -
　　
　　protobuf decoding 10,000, Processed: 23ms
　　
　　Run 100,000
　　
　　[] JSON start encoding
　　
　　JSON encoded 100,000, Processed: 248ms
　　
　　JSON Data Length: 106
　　
　　- Start decoding -
　　
　　JSON decoding 100,000 times, Processed: 180ms
　　
　　[] protobuf start encoding
　　
　　protobuf coding 100,000, Processed: 51ms
　　
　　protobuf data length: 34
　　
　　- start decoding -
　　
　　protobuf decoding 100,000, Processed: 58ms
　　
　　summarized
　　
　　codec performance of
　　
　　the above-described chestnut simply sampling, according to actually my experiments found that
　　
　　the number of times in a thousand less, protoBuf encoding and decoding performance, are comparable with JSON, JSON trend even than the poor.
　　
　　In more than 2,000 times, the encoding and decoding performance ProtoBuf, much higher than JSON.
　　
　　The number of 10 million or more, encoding and decoding performance ProtoBuf's quite clear, far higher than the performance of JSON.
　　
　　Memory footprint
　　
　　ProtoBuf memory 34, and JSON reach 106, ProtoBuf memory footprint JSON only 1/3
　　
　　the end of
　　
　　fact, the experiments can be optimized there are many places, even in this rough test, can also be seen in ProtoBuf Advantage.
　　
　　compatible
　　
　　New field
　　
　　added in the proto file nickname field
　　
　　generated Java file
　　
　　data in the old proto byte array, converted into objects
　　
　　Id: 1994, Name: XIAOLEI
　　
　　phone number: 100860 (HOME)
　　
　　phone number: 100100 (MOBILE)
　　
　　getNickname =
　　
　　result, that can be converted successfully.
　　
　　Delete field
　　
　　Delete the file name in the proto field
　　
　　generated Java file
　　
　　data with the old proto byte array, convert the object
　　
　　Id: 1994, Name: null
　　
　　phone number: 100860 (HOME)
　　
　　phone number: 100100 (MOBILE)