C++ simdjson from beginning

1.Download package

wget http://archive.ubuntu.com/ubuntu/pool/universe/s/simdjson/libsimdjson5_0.7.1-1_amd64.deb

2.Install simdjson package in Ubuntu

sudo dpkg -i libsimdjson5_0.7.1-1_amd64.deb

3.Check wether the simdjson is installed in Ubuntu

apt list |grep simdjson

C++ simdjson from beginning

 

 Confirmed that the simdjson is installed in Ubuntu successfully

 

4.Download simdjson.cpp simdjson.h and some json file for materials

wget https://raw.githubusercontent.com/simdjson/simdjson/master/singleheader/simdjson.h https://raw.githubusercontent.com/simdjson/simdjson/master/singleheader/simdjson.cpp  https://filesamples.com/samples/code/json/sample2.json 

C++ simdjson from beginning

 

 

When vim the material json file,it looks like below

{
   "firstName": "Joe",
   "lastName": "Jackson",
   "gender": "male",
   "age": 28,
   "address": {
       "streetAddress": "101",
       "city": "San Diego",
       "state": "CA"
   },
   "phoneNumbers": [
       { "type": "home", "number": "7349282382" }
   ]
}

 

5.Write the cpp test program 

#include <iostream>
#include <uuid/uuid.h>
#include <ostream>
#include <fstream>
#include "simdjson.h"

void simdjson6();

int main()
{
    simdjson6();
    return 0;
}

void simdjson6()
{
    ondemand::parser simdParser;
    padded_string jsonFile=padded_string::load("sample2.json");
    ondemand::document doc=simdParser.iterate(jsonFile); 
    auto firstName=doc["firstName"];
    cout<<"FirstName="<<firstName<<endl;

    auto city= doc["address"]["city"];
    cout<<"City ="<<city<<endl;
    
    auto streetAddress=doc["address"]["streetAddress"];
    cout<<"streetAddress="<<streetAddress<<endl; 
    cout<<"Finished in simdjson6() and now is "<<getTimeNow()<<endl;
}

 

6.Compile with simdjson.cpp via g++.

Be patient it will takes tens seconds.

g++ -g -std=c++2a -I. simdjson.cpp h1.cpp -o h1 -luuid

C++ simdjson from beginning

 

 

7.Run  ./h1

C++ simdjson from beginning

 

上一篇:【学习笔记】字符串—广义后缀自动机


下一篇:Optimizing radiotherapy plans for cancer treatment with Tensor Networks 公式推导解读