关于Jackson默认丢失Bigdecimal精度问题分析

Deserializing BigDecimal using JsonNode loses precision #2087

https://github.com/FasterXML/jackson-databind/issues/2087  

public enum JacksonUtils {
    INSTANCE;

    private static final Logger logger = LoggerFactory.getLogger(JacksonUtils.class);
    private final ObjectMapper objectMapper = new ObjectMapper();

    private JacksonUtils() {
        this.objectMapper.setNodeFactory(JsonNodeFactory.withExactBigDecimals(true));
        this.objectMapper.configure(DeserializationFeature.USE_BIG_DECIMAL_FOR_FLOATS, true);
    }

    public ObjectMapper getSingletonObjectMapper() {
        return this.objectMapper;
    }
}

 

【转】 

关于Jackson默认丢失Bigdecimal精度问题分析

protobuf

fastjson

jackson

发布于 2018-10-22

问题描述

最近在使用一个内部的RPC框架时,发现如果使用Object类型,实际类型为BigDecimal的时候,作为传输对象的时候,会出现丢失精度的问题;比如在序列化前为金额1.00,反序列化之后为1.0,本身值可能没有影响,但是在有些强依赖金额的地方,会出现问题;

问题分析

查看源码发现RPC框架默认使用的序列化框架为Jackson,那简单,看一下本地是否可以重现问题;

1.准备数据传输bean

public class Bean1 {
 
    private String p1;
    private BigDecimal p2;
     
    ...省略get/set...
}
 
public class Bean2 {
 
    private String p1;
    private Object p2;
     
    ...省略get/set...
}

为了更好的看出问题,分别准备了2个bean;

2.准备测试类

public class JKTest {
 
    public static void main(String[] args) throws IOException {
        ObjectMapper mapper = new ObjectMapper();
 
        Bean1 bean1 = new Bean1("haha1", new BigDecimal("1.00"));
        Bean2 bean2 = new Bean2("haha2", new BigDecimal("2.00"));
 
        String bs1 = mapper.writeValueAsString(bean1);
        String bs2 = mapper.writeValueAsString(bean2);
 
        System.out.println(bs1);
        System.out.println(bs2);
 
        Bean1 b1 = mapper.readValue(bs1, Bean1.class);
        System.out.println(b1.toString());
         
        Bean2 b22 = mapper.readValue(bs2, Bean2.class);
        System.out.println(b22.toString());
    }
}

分别对Bean1和Bean2进行序列化和反序列化操作,然后查看结果;

3.显示结果

{"p1":"haha1","p2":1.00}
{"p1":"haha2","p2":2.00}
Bean1 [p1=haha1, p2=1.00]
Bean2 [p1=haha2, p2=2.0]

4.结果分析

结果可以发现两个问题:
1.在序列化的时候2个bean都没有问题;
2.重现了问题,Bean2在反序列化时,p2出现了精度丢失的问题;

5.源码分析

通过一步一步查看Jackson源码,最终定位到UntypedObjectDeserializer的Vanilla内部类中,反序列方法如下:

public Object deserialize(JsonParser p, DeserializationContext ctxt) throws IOException
        {
            switch (p.getCurrentTokenId()) {
            case JsonTokenId.ID_START_OBJECT:
                {
                    JsonToken t = p.nextToken();
                    if (t == JsonToken.END_OBJECT) {
                        return new LinkedHashMap(2);
                    }
                }
            case JsonTokenId.ID_FIELD_NAME:
                return mapObject(p, ctxt);
            case JsonTokenId.ID_START_ARRAY:
                {
                    JsonToken t = p.nextToken();
                    if (t == JsonToken.END_ARRAY) { // and empty one too
                        if (ctxt.isEnabled(DeserializationFeature.USE_JAVA_ARRAY_FOR_JSON_ARRAY)) {
                            return NO_OBJECTS;
                        }
                        return new ArrayList(2);
                    }
                }
                if (ctxt.isEnabled(DeserializationFeature.USE_JAVA_ARRAY_FOR_JSON_ARRAY)) {
                    return mapArrayToArray(p, ctxt);
                }
                return mapArray(p, ctxt);
            case JsonTokenId.ID_EMBEDDED_OBJECT:
                return p.getEmbeddedObject();
            case JsonTokenId.ID_STRING:
                return p.getText();
 
            case JsonTokenId.ID_NUMBER_INT:
                if (ctxt.hasSomeOfFeatures(F_MASK_INT_COERCIONS)) {
                    return _coerceIntegral(p, ctxt);
                }
                return p.getNumberValue(); // should be optimal, whatever it is
 
            case JsonTokenId.ID_NUMBER_FLOAT:
                if (ctxt.isEnabled(DeserializationFeature.USE_BIG_DECIMAL_FOR_FLOATS)) {
                    return p.getDecimalValue();
                }
                return p.getNumberValue();
 
            case JsonTokenId.ID_TRUE:
                return Boolean.TRUE;
            case JsonTokenId.ID_FALSE:
                return Boolean.FALSE;
 
            case JsonTokenId.ID_END_OBJECT:
                // 28-Oct-2015, tatu: [databind#989] We may also be given END_OBJECT (similar to FIELD_NAME),
                //    if caller has advanced to the first token of Object, but for empty Object
                return new LinkedHashMap(2);
 
            case JsonTokenId.ID_NULL: // 08-Nov-2016, tatu: yes, occurs
                return null;
 
            //case JsonTokenId.ID_END_ARRAY: // invalid
            default:
            }
            return ctxt.handleUnexpectedToken(Object.class, p);
        } 
  

在Bean2中的p2是一个Object类型,所以Jackson中给定的反序列化类为UntypedObjectDeserializer,这个比较容易理解;然后根据具体的数据类型,调用不用的读取方法;因为json这种序列化方式,除了数据,本身并没有存放具体的数据类型,所有这里Jackson认定2.00为一个ID_NUMBER_FLOAT类型,在这个case下面有2个选择,默认是直接调用getNumberValue()方法,这种情况会丢失精度,返回结果为2.0;或者开启使用USE_BIG_DECIMAL_FOR_FLOATS特性,问题解决也很简单,使用此特性即可;

6.使用USE_BIG_DECIMAL_FOR_FLOATS特性

ObjectMapper mapper = new ObjectMapper();
mapper.enable(DeserializationFeature.USE_BIG_DECIMAL_FOR_FLOATS);

再次测试,可以发现结果如下:

{"p1":"haha1","p2":1.00}
{"p1":"haha2","p2":2.00}
Bean1 [p1=haha1, p2=1.00]
Bean2 [p1=haha2, p2=2.00]

7.反序列扩展

Jackson本身提供了对序列化和反序列扩展的功能,对应特殊的Bean可以自己定义反序列类,比如针对Bean2,可以实现Bean2Deserializer,然后在ObjectMapper进行注册

ObjectMapper mapper = new ObjectMapper();
SimpleModule desModule = new SimpleModule("testModule");
desModule.addDeserializer(Bean2.class, new Bean2Deserializer(Bean2.class));
mapper.registerModule(desModule);

扩展

Json本身并没有存放数据类型,只有数据本身,那应该类Json的序列化方式应该都存在此问题;

1.FastJson分析

准备测试代码如下:

public class FJTest {
 
    public static void main(String[] args) {
        Bean1 bean1 = new Bean1("haha1", new BigDecimal("1.00"));
        Bean2 bean2 = new Bean2("haha2", new BigDecimal("2.00"));
 
        String jsonString1 = JSON.toJSONString(bean1);
        String jsonString2 = JSON.toJSONString(bean2);
 
        System.out.println(jsonString1);
        System.out.println(jsonString2);
 
        Bean1 bean11 = JSON.parseObject(jsonString1, Bean1.class);
        Bean2 bean22 = JSON.parseObject(jsonString2, Bean2.class);
 
        System.out.println(bean11.toString());
        System.out.println(bean22.toString());
 
    }
 
}

结果如下:

{"p1":"haha1","p2":1.00}
{"p1":"haha2","p2":2.00}
Bean1 [p1=haha1, p2=1.00]
Bean2 [p1=haha2, p2=2.00]

可以发现FastJson并不存在此问题,查看源码,定位到DefaultJSONParser的parse方法,部分代码如下:

public Object parse(Object fieldName) {
        final JSONLexer lexer = this.lexer;
        switch (lexer.token()) {
            case SET:
                lexer.nextToken();
                HashSet set = new HashSet();
                parseArray(set, fieldName);
                return set;
            case TREE_SET:
                lexer.nextToken();
                TreeSet treeSet = new TreeSet();
                parseArray(treeSet, fieldName);
                return treeSet;
            case LBRACKET:
                JSONArray array = new JSONArray();
                parseArray(array, fieldName);
                if (lexer.isEnabled(Feature.UseObjectArray)) {
                    return array.toArray();
                }
                return array;
            case LBRACE:
                JSONObject object = new JSONObject(lexer.isEnabled(Feature.OrderedField));
                return parseObject(object, fieldName);
            case LITERAL_INT:
                Number intValue = lexer.integerValue();
                lexer.nextToken();
                return intValue;
            case LITERAL_FLOAT:
                Object value = lexer.decimalValue(lexer.isEnabled(Feature.UseBigDecimal));
                lexer.nextToken();
                return value;
            case LITERAL_STRING:
                String stringLiteral = lexer.stringVal();
                lexer.nextToken(JSONToken.COMMA);
 
                if (lexer.isEnabled(Feature.AllowISO8601DateFormat)) {
                    JSONScanner iso8601Lexer = new JSONScanner(stringLiteral);
                    try {
                        if (iso8601Lexer.scanISO8601DateIfMatch()) {
                            return iso8601Lexer.getCalendar().getTime();
                        }
                    } finally {
                        iso8601Lexer.close();
                    }
                }
 
                return stringLiteral;
            case NULL:
                lexer.nextToken();
                return null;
            case UNDEFINED:
                lexer.nextToken();
                return null;
            case TRUE:
                lexer.nextToken();
                return Boolean.TRUE;
            case FALSE:
                lexer.nextToken();
                return Boolean.FALSE;
            ...省略...
} 
  

类似jackson的方式,根据不同的类型做不同的数据处理,同样2.00也被认为是float类型,同样需要检测是否开启Feature.UseBigDecimal特性,只不过FastJson默认开启了此功能;

2.Protostuff分析

下面再来看一个非Json类序列化方式,看protostuff是如果处理此种问题的;
准备测试代码如下:

@SuppressWarnings("unchecked")
public class PBTest {
 
    public static void main(String[] args) {
        Bean1 bean1 = new Bean1("haha1", new BigDecimal("1.00"));
        Bean2 bean2 = new Bean2("haha2", new BigDecimal("2.00"));
 
        LinkedBuffer buffer1 = LinkedBuffer.allocate(LinkedBuffer.DEFAULT_BUFFER_SIZE);
        Schema schema1 = RuntimeSchema.createFrom(bean1.getClass());
        byte[] bytes1 = ProtostuffIOUtil.toByteArray(bean1, schema1, buffer1);
 
        Bean1 bean11 = new Bean1();
        ProtostuffIOUtil.mergeFrom(bytes1, bean11, schema1);
        System.out.println(bean11.toString());
 
        LinkedBuffer buffer2 = LinkedBuffer.allocate(LinkedBuffer.DEFAULT_BUFFER_SIZE);
        Schema schema2 = RuntimeSchema.createFrom(bean2.getClass());
        byte[] bytes2 = ProtostuffIOUtil.toByteArray(bean2, schema2, buffer2);
 
        Bean2 bean22 = new Bean2();
        ProtostuffIOUtil.mergeFrom(bytes2, bean22, schema2);
        System.out.println(bean22.toString());
 
    }
}

结果如下:

Bean1 [p1=haha1, p2=1.00]
Bean2 [p1=haha2, p2=2.00]

可以发现Protostuff也不存在此问题,原因是因为Protostuff在序列化的时候就将类型等信息存放在二进制中,不同的类型给定了不同的标识,RuntimeFieldFactory列出了所有标识:

public abstract class RuntimeFieldFactory implements Delegate
{
 
    static final int ID_BOOL = 1, ID_BYTE = 2, ID_CHAR = 3, ID_SHORT = 4,
            ID_INT32 = 5, ID_INT64 = 6, ID_FLOAT = 7,
            ID_DOUBLE = 8,
            ID_STRING = 9,
            ID_BYTES = 10,
            ID_BYTE_ARRAY = 11,
            ID_BIGDECIMAL = 12,
            ID_BIGINTEGER = 13,
            ID_DATE = 14,
            ID_ARRAY = 15, // 1-15 is encoded as 1 byte on protobuf and
            // protostuff format
            ID_OBJECT = 16, ID_ARRAY_MAPPED = 17, ID_CLASS = 18,
            ID_CLASS_MAPPED = 19, ID_CLASS_ARRAY = 20,
            ID_CLASS_ARRAY_MAPPED = 21,
 
            ID_ENUM_SET = 22, ID_ENUM_MAP = 23, ID_ENUM = 24,
            ID_COLLECTION = 25, ID_MAP = 26,
 
            ID_POLYMORPHIC_COLLECTION = 28, ID_POLYMORPHIC_MAP = 29,
            ID_DELEGATE = 30,
 
            ID_ARRAY_DELEGATE = 32, ID_ARRAY_SCALAR = 33, ID_ARRAY_ENUM = 34,
            ID_ARRAY_POJO = 35,
 
            ID_THROWABLE = 52,
 
            // pojo fields limited to 126 if not explicitly using @Tag
            // annotations
            ID_POJO = 127;
            ......
}

序列化的时候是已如下格式来存储数据的,如下图所示:
关于Jackson默认丢失Bigdecimal精度问题分析_第1张图片

tag里面包含了字段的位置标识,比如第一个字段,第二个字段…,以及类型信息,可以看一下两个bean序列化之后的二进制信息:

10 5 104 97 104 97 49 18 4 49 46 48 48
10 5 104 97 104 97 50 19 98 4 50 46 48 48 20

104 97 104 97 49和104 97 104 97 50分别是:haha1和haha2;49 46 48 48和50 46 48 48分别是1.00和2.00;
Bean2存储的数据量明细比Bean1大,因为Bean2中的p2作为Object存储,需要存储Object的起始标识和结束标识,还需要保存具体的类型信息;

更多可以参考:https://my.oschina.net/OutOfM...

总结

类Json序列化方式本身没有保存数据的类型,所以在反序列时有些类型不能区分,只能通过设置特性的方式来解决,但是json格式有更好的可读性;直接序列化为二进制的方式可读性差点,但是可以将很多信息保存进去,更加完善;

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