反序列化DataTable后DateTime列类型变为String类型 [英] DateTime column type becomes String type after deserializing DataTable
问题描述
我有一个包含两列的 DataTable.ShipmentDate(DateTime) 和 Count(Int).在我反序列化字符串后，我注意到如果第一个 itemarray 值为 null，则 ShipmentDate 的类型变为字符串.
I have a DataTable having two columns. ShipmentDate(DateTime) and Count(Int). after I deserialize the string I noticed the type of ShipmentDate becomes string if the first itemarray value is null.
检查以下示例.除了第一个数组项之外，两个 json 字符串都具有相同的数据.
Check the below example. both the json string have same data except the first array item.
string jsonTable1 = "[{"ShipmentDate":null,"Count":3},{"ShipmentDate":"2015-05-13T00:00:00","Count":13},{"ShipmentDate":"2015-05-19T00:00:00","Count":1},{"ShipmentDate":"2015-05-26T00:00:00","Count":1},{"ShipmentDate":"2015-05-28T00:00:00","Count":2}]";
string jsonTable2 = "[{"ShipmentDate":"2015-05-13T00:00:00","Count":13},{"ShipmentDate":null,"Count":3},{"ShipmentDate":"2015-05-19T00:00:00","Count":1},{"ShipmentDate":"2015-05-26T00:00:00","Count":1},{"ShipmentDate":"2015-05-28T00:00:00","Count":2}]";
DataTable tbl1 = Newtonsoft.Json.JsonConvert.DeserializeObject<DataTable>(jsonTable1);
DataTable tbl2 = Newtonsoft.Json.JsonConvert.DeserializeObject<DataTable>(jsonTable2);
Console.WriteLine(tbl1.Columns["ShipmentDate"].DataType);
Console.WriteLine(tbl2.Columns["ShipmentDate"].DataType);
在我的场景中，第一项数组的 ShipmentDate 可以为 null，并且通过将其转换为字符串类型会产生问题.
In my scenario ShipmentDate of first item array can be null and it creates problem by converting it to string type.
我有一种情况，数据表的架构是动态的.我无法创建强类型类.
I have a situation where schema of datatable is dynamic. i can't create strongly typed class.
推荐答案
这里的基本问题是Json.NET的DataTableConverter 推断每个 DataColumn.DataType 通过查看仅第一行中存在的标记值.它以这种方式工作，因为它将表的 JSON 流式传输到而不是将整个加载到中间 JToken 层次结构.虽然流式传输在减少内存使用的情况下提供了更好的性能，但这意味着第一行中的 null 值可能会导致列类型错误.
The basic problem here is that Json.NET's DataTableConverter infers each DataColumn.DataType by looking at token values present in the first row only. It works this way because it streams the JSON for the table in rather than loading the entirety into an intermediate JToken hierarchy. While streaming gives better performance with reduced memory use, it means that null values in the first row may result in incorrectly typed columns.
这是 stackoverflow 上不时出现的问题，例如在问题 反序列化缺少第一列的数据表.在这种情况下，提问者预先知道列类型应该是double.在您的情况下，您已声明 数据表的架构是动态的，因此无法使用答案.但是，与那个问题一样，由于 Json.NET 在 MIT 下是开源的许可证，可以创建其DataTableConverter 带有必要的逻辑.
This is a problem that comes up from time to time on stackoverflow, for instance in the question deserialize a datatable with a missing first column. In that case, the questioner knew in advance that the column type should be double. In your case, you have stated schema of datatable is dynamic, so that answer cannot be used. However, as with that question, since Json.NET is open source under the MIT License, one can create a modified version of its DataTableConverter with the necessary logic.
事实证明，通过记住具有不明确数据类型的列，然后在可以确定正确类型时用正确类型的列替换这些列，可以在保持流行为的同时正确设置列类型:
As it turns out, it's possible to correctly set the column type while retaining the streaming behavior by remembering columns with ambiguous data types, then replacing those columns with correctly typed columns when a proper type can be determined:
/// <summary>
/// Converts a <see cref="DataTable"/> to and from JSON.
/// </summary>
public class TypeInferringDataTableConverter : Newtonsoft.Json.Converters.DataTableConverter
{
// Adapted from https://github.com/JamesNK/Newtonsoft.Json/blob/master/Src/Newtonsoft.Json/Converters/DataTableConverter.cs
// Original license: https://github.com/JamesNK/Newtonsoft.Json/blob/master/LICENSE.md
/// <summary>
/// Reads the JSON representation of the object.
/// </summary>
/// <param name="reader">The <see cref="JsonReader"/> to read from.</param>
/// <param name="objectType">Type of the object.</param>
/// <param name="existingValue">The existing value of object being read.</param>
/// <param name="serializer">The calling serializer.</param>
/// <returns>The object value.</returns>
public override object ReadJson(JsonReader reader, Type objectType, object existingValue, JsonSerializer serializer)
{
if (reader.TokenType == JsonToken.Null)
{
return null;
}
DataTable dt = existingValue as DataTable;
if (dt == null)
{
// handle typed datasets
dt = (objectType == typeof(DataTable))
? new DataTable()
: (DataTable)Activator.CreateInstance(objectType);
}
// DataTable is inside a DataSet
// populate the name from the property name
if (reader.TokenType == JsonToken.PropertyName)
{
dt.TableName = (string)reader.Value;
reader.ReadAndAssert();
if (reader.TokenType == JsonToken.Null)
{
return dt;
}
}
if (reader.TokenType != JsonToken.StartArray)
{
throw JsonSerializationExceptionHelper.Create(reader, "Unexpected JSON token when reading DataTable. Expected StartArray, got {0}.".FormatWith(CultureInfo.InvariantCulture, reader.TokenType));
}
reader.ReadAndAssert();
var ambiguousColumnTypes = new HashSet<string>();
while (reader.TokenType != JsonToken.EndArray)
{
CreateRow(reader, dt, serializer, ambiguousColumnTypes);
reader.ReadAndAssert();
}
return dt;
}
private static void CreateRow(JsonReader reader, DataTable dt, JsonSerializer serializer, HashSet<string> ambiguousColumnTypes)
{
DataRow dr = dt.NewRow();
reader.ReadAndAssert();
while (reader.TokenType == JsonToken.PropertyName)
{
string columnName = (string)reader.Value;
reader.ReadAndAssert();
DataColumn column = dt.Columns[columnName];
if (column == null)
{
bool isAmbiguousType;
Type columnType = GetColumnDataType(reader, out isAmbiguousType);
column = new DataColumn(columnName, columnType);
dt.Columns.Add(column);
if (isAmbiguousType)
ambiguousColumnTypes.Add(columnName);
}
else if (ambiguousColumnTypes.Contains(columnName))
{
bool isAmbiguousType;
Type newColumnType = GetColumnDataType(reader, out isAmbiguousType);
if (!isAmbiguousType)
ambiguousColumnTypes.Remove(columnName);
if (newColumnType != column.DataType)
{
column = ReplaceColumn(dt, column, newColumnType, serializer);
}
}
if (column.DataType == typeof(DataTable))
{
if (reader.TokenType == JsonToken.StartArray)
{
reader.ReadAndAssert();
}
DataTable nestedDt = new DataTable();
var nestedUnknownColumnTypes = new HashSet<string>();
while (reader.TokenType != JsonToken.EndArray)
{
CreateRow(reader, nestedDt, serializer, nestedUnknownColumnTypes);
reader.ReadAndAssert();
}
dr[columnName] = nestedDt;
}
else if (column.DataType.IsArray && column.DataType != typeof(byte[]))
{
if (reader.TokenType == JsonToken.StartArray)
{
reader.ReadAndAssert();
}
List<object> o = new List<object>();
while (reader.TokenType != JsonToken.EndArray)
{
o.Add(reader.Value);
reader.ReadAndAssert();
}
Array destinationArray = Array.CreateInstance(column.DataType.GetElementType(), o.Count);
Array.Copy(o.ToArray(), destinationArray, o.Count);
dr[columnName] = destinationArray;
}
else
{
object columnValue = (reader.Value != null)
? serializer.Deserialize(reader, column.DataType) ?? DBNull.Value
: DBNull.Value;
dr[columnName] = columnValue;
}
reader.ReadAndAssert();
}
dr.EndEdit();
dt.Rows.Add(dr);
}
static object RemapValue(object oldValue, Type newType, JsonSerializer serializer)
{
if (oldValue == null)
return null;
if (oldValue == DBNull.Value)
return oldValue;
return JToken.FromObject(oldValue, serializer).ToObject(newType, serializer);
}
private static DataColumn ReplaceColumn(DataTable dt, DataColumn column, Type newColumnType, JsonSerializer serializer)
{
var newValues = Enumerable.Range(0, dt.Rows.Count).Select(i => dt.Rows[i]).Select(r => RemapValue(r[column], newColumnType, serializer)).ToList();
var ordinal = column.Ordinal;
var name = column.ColumnName;
var @namespace = column.Namespace;
var newColumn = new DataColumn(name, newColumnType);
newColumn.Namespace = @namespace;
dt.Columns.Remove(column);
dt.Columns.Add(newColumn);
newColumn.SetOrdinal(ordinal);
for (int i = 0; i < dt.Rows.Count; i++)
dt.Rows[i][newColumn] = newValues[i];
return newColumn;
}
private static Type GetColumnDataType(JsonReader reader, out bool isAmbiguous)
{
JsonToken tokenType = reader.TokenType;
switch (tokenType)
{
case JsonToken.Integer:
case JsonToken.Boolean:
case JsonToken.Float:
case JsonToken.String:
case JsonToken.Date:
case JsonToken.Bytes:
isAmbiguous = false;
return reader.ValueType;
case JsonToken.Null:
case JsonToken.Undefined:
isAmbiguous = true;
return typeof(string);
case JsonToken.StartArray:
reader.ReadAndAssert();
if (reader.TokenType == JsonToken.StartObject)
{
isAmbiguous = false;
return typeof(DataTable); // nested datatable
}
else
{
isAmbiguous = false;
bool innerAmbiguous;
// Handling ambiguity in array entries is not yet implemented because the first non-ambiguous entry in the array
// might occur anywhere in the sequence, requiring us to scan the entire array to determine the type,
// e.g., given: [null, null, null, 314, null]
// we would need to scan until the 314 value, and do:
// return typeof(Nullable<>).MakeGenericType(new[] { reader.ValueType }).MakeArrayType();
Type arrayType = GetColumnDataType(reader, out innerAmbiguous);
return arrayType.MakeArrayType();
}
default:
throw JsonSerializationExceptionHelper.Create(reader, "Unexpected JSON token when reading DataTable: {0}".FormatWith(CultureInfo.InvariantCulture, tokenType));
}
}
}
internal static class JsonSerializationExceptionHelper
{
public static JsonSerializationException Create(this JsonReader reader, string format, params object[] args)
{
// Adapted from https://github.com/JamesNK/Newtonsoft.Json/blob/master/Src/Newtonsoft.Json/JsonPosition.cs
var lineInfo = reader as IJsonLineInfo;
var path = (reader == null ? null : reader.Path);
var message = string.Format(CultureInfo.InvariantCulture, format, args);
if (!message.EndsWith(Environment.NewLine, StringComparison.Ordinal))
{
message = message.Trim();
if (!message.EndsWith(".", StringComparison.Ordinal))
message += ".";
message += " ";
}
message += string.Format(CultureInfo.InvariantCulture, "Path '{0}'", path);
if (lineInfo != null && lineInfo.HasLineInfo())
message += string.Format(CultureInfo.InvariantCulture, ", line {0}, position {1}", lineInfo.LineNumber, lineInfo.LinePosition);
message += ".";
return new JsonSerializationException(message);
}
}
internal static class StringUtils
{
// Adapted from https://github.com/JamesNK/Newtonsoft.Json/blob/master/Src/Newtonsoft.Json/Utilities/StringUtils.cs
public static string FormatWith(this string format, IFormatProvider provider, object arg0)
{
return format.FormatWith(provider, new[] { arg0 });
}
private static string FormatWith(this string format, IFormatProvider provider, params object[] args)
{
return string.Format(provider, format, args);
}
}
internal static class JsonReaderExtensions
{
public static void ReadAndAssert(this JsonReader reader)
{
if (reader == null)
throw new ArgumentNullException("reader");
if (!reader.Read())
{
throw JsonSerializationExceptionHelper.Create(reader, "Unexpected end when reading JSON.");
}
}
}
然后像这样使用它:
var settings = new JsonSerializerSettings { Converters = new[] { new TypeInferringDataTableConverter() } };
DataTable tbl1 = Newtonsoft.Json.JsonConvert.DeserializeObject<DataTable>(jsonTable1, settings);
DataTable tbl2 = Newtonsoft.Json.JsonConvert.DeserializeObject<DataTable>(jsonTable2, settings);
不要设置 NullValueHandling = NullValueHandling.Ignore 因为现在可以正确处理空值.
Do not set NullValueHandling = NullValueHandling.Ignore since nulls are now handled correctly.
原型小提琴
请注意，虽然此类处理具有 null 值的列的重新键入，但它不处理包含第一个数组项为空的数组值的列的重新键入.例如，如果某列的第一行具有值
Note that, while this class handles re-typing of columns with null values, it doesn't handle re-typing of columns containing array values where the first array item is null. For instance, if the first row of some column has the value
[null, null, null, 314, null]
那么推断的列类型理想地是typeof(long?[])，但是这里没有实现.可能需要将 JSON 完全加载到 JToken 层次结构中才能做出决定.
Then the column type inferred would ideally be typeof( long? [] ), however that is not being implemented here. Likely it would be necessary to completely load the JSON into a JToken hierarchy to make that determination.
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