本文参考资料
一段废话
之前我已经介绍了这个方案的名称为JsonBuilder，这套方案最大的好处在于它的灵活可扩展性上，所以我可以很方便的对他进行优化和扩展！
性能优化
JsonBuilder第一版对一般对象的是进行实时反射的，所以性能不会很好，所以我首先想到的是优化他的性能
看我前几天发表过一篇《[源码]Literacy 快速反射读写对象属性,字段》的文章，这东西的效率不错，用来代替反射正好。
我把优化后的类取名QuickJsonBuilder
在继承JsonBuilder的基础上,我仅仅需要重写一个方法
public class QuickJsonBuilder : JsonBuilder { protected override void AppendOther(object obj) { Literacy lit = new Literacy(obj.GetType()); string fix = ""; Buff.Append('{'); foreach (var p in lit.Property) { Buff.Append(fix); AppendKey(p.Name, false); AppendObject(p.GetValue(obj));
fix = ','; } Buff.Append('}'); } }
像这样我很简单的将原来的实时反射改成了Literacy
但是这样显然并不能保证性能,所以我还要加一个缓存
static Dictionary<Type, Literacy> _LitCache = new Dictionary<Type, Literacy>(); protected override void AppendOther(object obj) { Literacy lit; Type type = obj.GetType(); if (_LitCache.TryGetValue(type, out lit) == false) { lock (_LitCache) { if (_LitCache.TryGetValue(type, out lit) == false) { lit = new Literacy(type); _LitCache.Add(type, lit); } } } string fix = ""; Buff.Append('{'); foreach (var p in lit.Property) { Buff.Append(fix); AppendKey(p.Name, false); AppendObject(p.GetValue(obj));
fix = ','; } Buff.Append('}'); }
缓存本身是全局静态的,所以为了防止多线程并发的问题,特别加了锁
再为了它能性能能够好上那么一点点(更多的是为了自己的强迫症吧....),再修改一些地方
static Dictionary<Type, Literacy> _LitCache = new Dictionary<Type, Literacy>(); protected override void AppendOther(object obj) { Literacy lit; Type type = obj.GetType(); if (_LitCache.TryGetValue(type, out lit) == false) { lock (_LitCache) { if (_LitCache.TryGetValue(type, out lit) == false) { lit = new Literacy(type); _LitCache.Add(type, lit); } } } Buff.Append('{'); var ee = lit.Property.GetEnumerator(); if (ee.MoveNext()) { AppendKey(ee.Current.Name, false); AppendObject(ee.Current.GetValue(obj)); while (ee.MoveNext()) { Buff.Append(','); AppendKey(ee.Current.Name, false); AppendObject(ee.Current.GetValue(obj)); } } Buff.Append('}'); }
好了来看看他和他父亲在性能上的差异:
测试代码和《几个常用Json组件的性能测试》中的是一样的,
I7-2630 4核8线程 主频2.0 内存 1600 4G*2 没有显卡 硬盘 闪迪 msata 128 r:500m/s w:350m/s
不过今天在自己的电脑上运行了,感觉和昨天的测试结果的差异还是有些大的,所以还是那句话:测试结果因配置不同会有所出入,此结果仅供参考
功能扩展
相比较性能优化来说 这个要更重要一些，随着现在硬件各种不值钱，性能上的问题绝对可以在硬件上解决，而功能不足却是大多数时候我们放弃一个现成类库的原因
在这个时候一个类库是否可扩展就显得尤为重要了。
先来看一个栗子
将DataSet转为Json对象
随便来个简单个DataSet
DataTable dt1 = new DataTable("User"); dt1.Columns.Add("UID", typeof(Guid)); dt1.Columns.Add("Name", typeof(string)); dt1.Columns.Add("Birthday", typeof(DateTime)); dt1.Columns.Add("Sex", typeof(int)); dt1.Columns.Add("IsDeleted", typeof(bool)); dt1.Rows.Add(Guid.NewGuid(), "blqw", DateTime.Parse("1986-10-29"), 1, false); dt1.Rows.Add(Guid.NewGuid(), "小明", DateTime.Parse("1990-1-1"), 1, false); dt1.Rows.Add(Guid.NewGuid(), "小华", DateTime.Parse("1990-2-2"), 0, false); DataTable dt2 = new DataTable("UserInfo"); dt2.Columns.Add("ID", typeof(int)); dt2.Columns.Add("UID", typeof(Guid)); dt2.Columns.Add("Address", typeof(string)); dt2.Columns.Add("ZipCode", typeof(int)); dt2.Rows.Add(1, dt1.Rows[0][0], "广州", 100000); dt2.Rows.Add(2, dt1.Rows[1][0], "上海", 200000); dt2.Rows.Add(3, dt1.Rows[2][0], "背景", 300000); DataSet ds = new DataSet(); ds.Tables.Add(dt1); ds.Tables.Add(dt2);
再来看看每个组件的转换结果
//JsonBuilder var a = {"User":{"columns":["UID","Name","Birthday","Sex","IsDeleted"],"rows":[["58cc6573-cb34-4d82-9343-166a3d23f518","blqw","1986-10-29 00:00:00",1,false],["c1ce 27aa-383c-4a90-b180-4f8827ef64a9","小明","1990-01-01 00:00:00",1,false],["79d24a02-7d90-4ade-80c5-93b57314f55b","小华","1990-02-02 00:00:00",0,false]]},"UserInf o":{"columns":["ID","UID","Address","ZipCode"],"rows":[[1,"58cc6573-cb34-4d82-9343-166a3d23f518","广州",100000],[2,"c1ce27aa-383c-4a90-b180-4f8827ef64a9","上海" ,200000],[3,"79d24a02-7d90-4ade-80c5-93b57314f55b","背景",300000]]}} //============================================== //QuickJsonBuilder var a = {"User":{"columns":["UID","Name","Birthday","Sex","IsDeleted"],"rows":[["58cc6573-cb34-4d82-9343-166a3d23f518","blqw","1986-10-29 00:00:00",1,false],["c1ce 27aa-383c-4a90-b180-4f8827ef64a9","小明","1990-01-01 00:00:00",1,false],["79d24a02-7d90-4ade-80c5-93b57314f55b","小华","1990-02-02 00:00:00",0,false]]},"UserInf o":{"columns":["ID","UID","Address","ZipCode"],"rows":[[1,"58cc6573-cb34-4d82-9343-166a3d23f518","广州",100000],[2,"c1ce27aa-383c-4a90-b180-4f8827ef64a9","上海" ,200000],[3,"79d24a02-7d90-4ade-80c5-93b57314f55b","背景",300000]]}} //============================================== //fastJSON.NET var a = {"User":[["f1a4f526-e723-4fb4-930e-0b5250ee3309","blqw","1986-10-29 00:00:00",1,false],["32b289a2-f116-47c3-99cc-0a7804706490","小明","1990-01-01 00:00:00" ,1,false],["a2ca668f-151e-4863-8979-d5a5e7c83a35","小华","1990-02-02 00:00:00",0,false]],"UserInfo":[[1,"f1a4f526-e723-4fb4-930e-0b5250ee3309","广州",100000],[2 ,"32b289a2-f116-47c3-99cc-0a7804706490","上海",200000],[3,"a2ca668f-151e-4863-8979-d5a5e7c83a35","背景",300000]]} ============================================== //Jayrock.Json var a = {"User":[{"UID":"58cc6573-cb34-4d82-9343-166a3d23f518","Name":"blqw","Birthday":"1986-10-29T00:00:00.0000000+08:00","Sex":1,"IsDeleted":false},{"UID":"c1ce 27aa-383c-4a90-b180-4f8827ef64a9","Name":"小明","Birthday":"1990-01-01T00:00:00.0000000+08:00","Sex":1,"IsDeleted":false},{"UID":"79d24a02-7d90-4ade-80c5-93b573 14f55b","Name":"小华","Birthday":"1990-02-02T00:00:00.0000000+08:00","Sex":0,"IsDeleted":false}],"UserInfo":[{"ID":1,"UID":"58cc6573-cb34-4d82-9343-166a3d23f518 ","Address":"广州","ZipCode":100000},{"ID":2,"UID":"c1ce27aa-383c-4a90-b180-4f8827ef64a9","Address":"上海","ZipCode":200000},{"ID":3,"UID":"79d24a02-7d90-4ade-8 0c5-93b57314f55b","Address":"背景","ZipCode":300000}]} //============================================== //Newtonsoft.Json var a = {"User":[{"UID":"58cc6573-cb34-4d82-9343-166a3d23f518","Name":"blqw","Birthday":"/Date(530899200000+0800)/","Sex":1,"IsDeleted":false},{"UID":"c1ce27aa-3 83c-4a90-b180-4f8827ef64a9","Name":"小明","Birthday":"/Date(631123200000+0800)/","Sex":1,"IsDeleted":false},{"UID":"79d24a02-7d90-4ade-80c5-93b57314f55b","Nam e":"小华","Birthday":"/Date(633888000000+0800)/","Sex":0,"IsDeleted":false}],"UserInfo":[{"ID":1,"UID":"58cc6573-cb34-4d82-9343-166a3d23f518","Address":"广州" ,"ZipCode":100000},{"ID":2,"UID":"c1ce27aa-383c-4a90-b180-4f8827ef64a9","Address":"上海","ZipCode":200000},{"ID":3,"UID":"79d24a02-7d90-4ade-80c5-93b57314f55b", "Address":"背景","ZipCode":300000}]} //============================================== //ServiceStack.Text 报错 //TestJavaScriptSerializer 报错
我们可以很清楚的发现每个组件都有各自的转换方式,结果都不相同
- fastJSON.NET 结果中每列的"列名"丢失了
- Jayrock.Json 算是比较标准的格式了
- Newtonsoft.Json 数据格式和Jayrock.Json类似,但是时间格式太蛋疼了，造成的结果就是这个字符串直接在网页js中运行是不可能的，需要2次转换
- TestJavaScriptSerializer 报错，无法转换
- TestServiceStack 报错，无法转换
- JsonBuilder,因为是我自己写的,所以有自己的格式,我习惯将Table转换为2个部分一个columns,一个rows，因为在行数多的情况下，列名每次都重复会增加数据大小
以上几个组件的结果 可能可以通过参数设置，我不确定 但即使是参数设定，也只不过是选择几种预置的格式罢了。
这时候JsonBuilder灵活性就显现出来了
假设我现在的项目有以下需求:
- DataTable转换需要可以在,Jayrock.Json和JsonBuilder格式之间切换
- DateTime可以在 Data,Time,DataTime 3种模式中切换
- Guid要去掉中间的连字符(-)
- true,false要转换为1,0
OK 现在我需要重写4个方法 AppendDataTable(如果有必要也一起重写AppendDataView),AppendDateTime,AppendGuid,AppendBoolean
using System; using System.Collections.Generic; using System.Data; using System.Linq; using System.Text; namespace blqw { class MyJsonBuilder : QuickJsonBuilder { /// <summary> 表风格 /// <para> 默认 { columns:[colname,colname2],rows:[[x,x],[y,y]] } </para> /// <para> 1 [{ colname:x,colname2:x },{ colname:y,colname2:y }] </para> /// </summary> public int TableStyle { get; set; } /// <summary> 时间风格 /// <para> 默认 yyyy-MM-dd HH:mm:ss </para> /// <para> 1 yyyy-MM-dd </para> /// <para> 2 HH:mm:ss </para> /// </summary> public int DateTimeStyle { get; set; } protected override void AppendDateTime(DateTime value) { switch (DateTimeStyle) { case 1: Buff.Append('"'); Buff.Append(value.ToString("yyyy-MM-dd")); Buff.Append('"'); break; case 2: Buff.Append('"'); Buff.Append(value.ToString("HH:mm:ss")); Buff.Append('"'); break; default: base.AppendDateTime(value); break; } } protected override void AppendGuid(Guid value) { Buff.Append('"'); Buff.Append(value.ToString("N")); Buff.Append('"'); } protected override void AppendBoolean(bool value) { base.AppendNumber(value.GetHashCode()); } protected override void AppendDataTable(System.Data.DataTable table) { if (TableStyle == 1) { Buff.Append('['); var fix = ""; foreach (DataRow row in table.Rows) { Buff.Append(fix); var fix2 = ""; Buff.Append('{'); foreach (DataColumn col in table.Columns) { Buff.Append(fix2); AppendKey(col.ColumnName,false); AppendObject(row[col]); fix2 = ","; } Buff.Append('}'); fix = ","; } Buff.Append(']'); } else { base.AppendDataTable(table); } } } }
然后就可以调用了
MyJsonBuilder jb = new MyJsonBuilder(); Console.WriteLine("TableStyle=1 ; DateTimeStyle=1"); jb.TableStyle = 1; jb.DateTimeStyle = 1; Console.WriteLine(jb.ToJsonString(ds)); Console.WriteLine(""); Console.WriteLine("TableStyle=0 ; DateTimeStyle=2"); jb.TableStyle = 0; jb.DateTimeStyle = 2; Console.WriteLine(jb.ToJsonString(ds));
结果
TableStyle=1 ; DateTimeStyle=1 {"User":[{"UID":"0f3f70f0939546d8803d8cd0ef4de8ec","Name":"blqw","Birthday":"1986-10-29","Sex":1,"IsDeleted":0},{"UID":"72489efcbc694801a358b697414892ee","Name":"小明","Birthday":"1990-01-01","Sex":1,"IsDeleted":0},{"UID":"865cddbb1f0c427e97a724844c35a83d","Name":"小华","Birthday":"1990-02-02","Sex":0,"IsDeleted":0}],"UserInfo":[{"ID":1,"UID":"0f3f70f0939546d8803d8cd0ef4de8ec","Address":"广州","ZipCode":100000},{"ID":2,"UID":"72489efcbc694801a358b697414892ee","Address":"上海","ZipCode":200000},{"ID":3,"UID":"865cddbb1f0c427e97a724844c35a83d","Address":"背景","ZipCode":300000}]} TableStyle=0 ; DateTimeStyle=2 {"User":{"columns":["UID","Name","Birthday","Sex","IsDeleted"],"rows":[["0f3f70f0939546d8803d8cd0ef4de8ec","blqw","00:00:00",1,0],["72489efcbc694801a358b697414892ee","小明","00:00:00",1,0],["865cddbb1f0c427e97a724844c35a83d","小华","00:00:00",0,0]]},"UserInfo":{"columns":["ID","UID","Address","ZipCode"],"rows":[[1,"0f3f70f0939546d8803d8cd0ef4de8ec","广州",100000],[2,"72489efcbc694801a358b697414892ee","上海",200000],[3,"865cddbb1f0c427e97a724844c35a83d","背景",300000]]}} 请按任意键继续. . .
比完整Demo还要完整的Demo下载
ps 此Demo中包含了一些我下一篇文章会写的内容,如果里面的代码和文章中的不完全一样,以Demo中可运行代码为准