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最近项目中需要将业务对象直接序列化,然后存数据库;考虑到序列化、反序列化的时间以及生产文件的大小觉得Protobuf是一个很好的选择,但是Protobuf有的问题就是需要有一个.proto的描述文件,而且由Protobuf生成的对象用来作为业务对象并不是特别友好,往往业务对象和Protobuf对象存在一个互相转换的过程;考虑到我们仅仅是将业务对象直接序列化到数据库,发现Protobuf在这种情况下并不是特别的好;
这时候发现了Protostuff,protostuff不需要依赖.proto文件,可以直接对普通的javabean进行序列化、反序列化的操作,而效率上甚至比protobuf还快,生成的二进制数据库格式和Protobuf完全相同的,可以说是一个基于Protobuf的序列化工具。
简单测试
1.先测试一下Protostuff
提供一个简单的javabean
public class Person {
private int id;
private String name;
private String email;
// get/set方法省略
}
测试类PbStuff
public class PbStuff {
public static void main(String[] args) throws FileNotFoundException,
IOException {
Schema<Person> schema = RuntimeSchema.getSchema(Person.class);
Person person1 = new Person();
person1.setId(1);
person1.setName("zhaohui");
LinkedBuffer buffer = LinkedBuffer.allocate(1024);
byte[] data = ProtobufIOUtil.toByteArray(person1, schema, buffer);
System.out.println(data.length);
}
}
序列化之后二进制的大小为29字节
2.测试Protobuf
proto文件
option java_package = "protobuf.clazz";
option java_outer_classname = "PersonX";
message Person {
required int32 id = 1;
required string name = 2;
required string email = 3;
}
PBTest类
public class PBTest {
public static void main(String[] args) {
PersonX.Person.Builder builder = PersonX.Person.newBuilder();
builder.setId(1);
builder.setName("zhaohui");
builder.setEmail("xxxxxxxx@126.com");
PersonX.Person p = builder.build();
byte[] result = p.toByteArray();
System.out.println(result.length);
}
}
序列化之后二进制的大小同样也是29字节
经过简单的测试:发现Protobuf和Protostuff序列化相同的数据得到的结果是一样的
Protobuf的编码是尽其所能地将字段的元信息和字段的值压缩存储,并且字段的元信息中含有对这个字段描述的所有信息;既然Protostuff序列化之后的大小和Protobuf是一样的,那可以分析一下Protostuff的源码
源码分析
1.Schema
schema = RuntimeSchema.getSchema(Person.class); //获取业务对象Person的Schema
RuntimeSchema是一个包含业务对象所有信息的类,包括类信息、字段信息
/**
* Gets the schema that was either registered or lazily initialized at runtime.
* <p>
* Method overload for backwards compatibility.
*/
public static <T> Schema<T> getSchema(Class<T> typeClass)
{
return getSchema(typeClass, ID_STRATEGY);
}
/**
* Gets the schema that was either registered or lazily initialized at runtime.
*/
public static <T> Schema<T> getSchema(Class<T> typeClass,
IdStrategy strategy)
{
return strategy.getSchemaWrapper(typeClass, true).getSchema();
}
getSchema方法中指定了获取Schema的默认策略类ID_STRATEGY,ID_STRATEGY在类RuntimeEnv中进行了实例化:
ID_STRATEGY = new DefaultIdStrategy();
可以大致看一下DefaultIdStrategy类:
public final class DefaultIdStrategy extends IdStrategy
{
final ConcurrentHashMap<String, HasSchema<?>> pojoMapping = new ConcurrentHashMap<>();
final ConcurrentHashMap<String, EnumIO<?>> enumMapping = new ConcurrentHashMap<>();
final ConcurrentHashMap<String, CollectionSchema.MessageFactory> collectionMapping = new ConcurrentHashMap<>();
final ConcurrentHashMap<String, MapSchema.MessageFactory> mapMapping = new ConcurrentHashMap<>();
final ConcurrentHashMap<String, HasDelegate<?>> delegateMapping = new ConcurrentHashMap<>();
...
}
可以发现DefaultIdStrategy内存缓存了很多Schema信息,不难理解既然要或者业务对象的类和字段信息,必然用到反射机制,这是一个很耗时的过程,进行缓存很有必要,这样下次遇到相同的类就可以不用进行反射了
所以可以看到DefaultIdStrategy中有很多这种模式的方法:
public <T> HasSchema<T> getSchemaWrapper(Class<T> typeClass, boolean create)
{
HasSchema<T> hs = (HasSchema<T>) pojoMapping.get(typeClass.getName());
if (hs == null && create)
{
hs = new Lazy<>(typeClass, this);
final HasSchema<T> last = (HasSchema<T>) pojoMapping.putIfAbsent(
typeClass.getName(), hs);
if (last != null)
hs = last;
}
return hs;
}
先get,如果为null,就putIfAbsent
当业务对象的Schema还没被缓存,这时候就会去create,RuntimeSchema提供了createFrom方法:
public static <T> RuntimeSchema<T> createFrom(Class<T> typeClass,
Set<String> exclusions, IdStrategy strategy)
{
final Map<String, java.lang.reflect.Field> fieldMap = findInstanceFields(typeClass);
...省略
final Field<T> field = RuntimeFieldFactory.getFieldFactory(
f.getType(), strategy).create(fieldMapping, name, f,
strategy);
fields.add(field);
}
}
return new RuntimeSchema<>(typeClass, fields, RuntimeEnv.newInstantiator(typeClass));
}
主要就是对typeClass进行反射,然后进行封装;将字段类型封装成了RuntimeFieldFactory,最后通过RuntimeFieldFactory的create方法封装进入Field类中,RuntimeFieldFactory列举了所有支持的类型:
static final RuntimeFieldFactory<BigDecimal> BIGDECIMAL;
static final RuntimeFieldFactory<BigInteger> BIGINTEGER;
static final RuntimeFieldFactory<Boolean> BOOL;
static final RuntimeFieldFactory<Byte> BYTE;
static final RuntimeFieldFactory<ByteString> BYTES;
static final RuntimeFieldFactory<byte[]> BYTE_ARRAY;
static final RuntimeFieldFactory<Character> CHAR;
static final RuntimeFieldFactory<Date> DATE;
static final RuntimeFieldFactory<Double> DOUBLE;
static final RuntimeFieldFactory<Float> FLOAT;
static final RuntimeFieldFactory<Integer> INT32;
static final RuntimeFieldFactory<Long> INT64;
static final RuntimeFieldFactory<Short> SHORT;
static final RuntimeFieldFactory<String> STRING;
static final RuntimeFieldFactory<Integer> ENUM;
static final RuntimeFieldFactory<Object> OBJECT;
static final RuntimeFieldFactory<Object> POJO;
static final RuntimeFieldFactory<Object> POLYMORPHIC_POJO;
static final RuntimeFieldFactory<Collection<?>> COLLECTION =
new RuntimeFieldFactory<Collection<?>>(ID_COLLECTION)
当然还有常用的Map类型,在RuntimeMapFieldFactory中定义了
2.LinkedBuffer buffer = LinkedBuffer.allocate(1024);
开辟了1024字节缓存,用来存放业务对象序列化之后存放的地方,当然你可能会担心这个大小如果不够怎么办,后面的代码中可以看到,如果空间不足,会自动扩展的,所有这个大小要设置一个合适的值,设置大了浪费空间,设置小了会自动扩展浪费时间。
3.byte[] data = ProtobufIOUtil.toByteArray(person1, schema, buffer);
ProtobufIOUtil提供的就是以Protobuf编码的格式来序列化业务对象
public static <T> byte[] toByteArray(T message, Schema<T> schema, LinkedBuffer buffer)
{
if (buffer.start != buffer.offset)
throw new IllegalArgumentException("Buffer previously used and had not been reset.");
final ProtobufOutput output = new ProtobufOutput(buffer);
try
{
schema.writeTo(output, message);
}
catch (IOException e)
{
}
return output.toByteArray();
}
schema中调用writeTo方法,将message中的消息保存到ProtobufOutput中
public final void writeTo(Output output, T message) throws IOException
{
for (Field<T> f : getFields())
f.writeTo(output, message);
}
第一步中将业务对象的字段信息都封装到了Field中了,可以看一下Field类提供的几个方法:
/**
* Writes the value of a field to the {@code output}.
*/
protected abstract void writeTo(Output output, T message)
throws IOException;
/**
* Reads the field value into the {@code message}.
*/
protected abstract void mergeFrom(Input input, T message)
throws IOException;
/**
* Transfer the input field to the output field.
*/
protected abstract void transfer(Pipe pipe, Input input, Output output,
boolean repeated) throws IOException;
提供了三个抽象方法,分别是写数据,读数据和转移数据
下面已int类型为实例,看看实现:
public static final RuntimeFieldFactory<Integer> INT32 = new RuntimeFieldFactory<Integer>(
ID_INT32)
{
@Override
public <T> Field<T> create(int number, java.lang.String name,
final java.lang.reflect.Field f, IdStrategy strategy)
{
final boolean primitive = f.getType().isPrimitive();
final long offset = us.objectFieldOffset(f);
return new Field<T>(FieldType.INT32, number, name,
f.getAnnotation(Tag.class))
{
@Override
public void mergeFrom(Input input, T message)
throws IOException
{
if (primitive)
us.putInt(message, offset, input.readInt32());
else
us.putObject(message, offset,
Integer.valueOf(input.readInt32()));
}
@Override
public void writeTo(Output output, T message)
throws IOException
{
if (primitive)
output.writeInt32(number, us.getInt(message, offset),
false);
else
{
Integer value = (Integer) us.getObject(message, offset);
if (value != null)
output.writeInt32(number, value.intValue(), false);
}
}
...
};
}
上面这段代码可以在RuntimeUnsafeFieldFactory中找到,基本的数据类型都在此类中能找到,collection和map分别在RuntimeRepeatedFieldFactory和RuntimeMapFieldFactory中,writeTo方法调用了ProtobufOutput中的writeInt32方法:
public void writeInt32(int fieldNumber, int value, boolean repeated) throws IOException
{
...
tail = writeTagAndRawVarInt32(
makeTag(fieldNumber, WIRETYPE_VARINT),
value,
this,
tail);
...
}
写入field的Tag已经Value,Protobuf也是这种形式存放的,如下图所示:
public static LinkedBuffer writeTagAndRawVarInt32(int tag, int value,
final WriteSession session, LinkedBuffer lb)
{
final int tagSize = computeRawVarint32Size(tag);
final int size = computeRawVarint32Size(value);
final int totalSize = tagSize + size;
if (lb.offset + totalSize > lb.buffer.length)
lb = new LinkedBuffer(session.nextBufferSize, lb);
final byte[] buffer = lb.buffer;
int offset = lb.offset;
lb.offset += totalSize;
session.size += totalSize;
if (tagSize == 1)
buffer[offset++] = (byte) tag;
else
{
for (int i = 0, last = tagSize - 1; i < last; i++, tag >>>= 7)
buffer[offset++] = (byte) ((tag & 0x7F) | 0x80);
buffer[offset++] = (byte) tag;
}
if (size == 1)
buffer[offset] = (byte) value;
else
{
for (int i = 0, last = size - 1; i < last; i++, value >>>= 7)
buffer[offset++] = (byte) ((value & 0x7F) | 0x80);
buffer[offset] = (byte) value;
}
return lb;
}
tag是通过makeTag方法创建的:
public static int makeTag(final int fieldNumber, final int wireType)
{
return (fieldNumber << TAG_TYPE_BITS) | wireType;
}
fieldNumber每个字段的标号,wire_type是该字段的数据类型,所有如果我们改变了业务对象类中字段的顺序,或者改变了字段的类型,都会出现反序列化失败;
前面提到的数据压缩在方法computeRawVarint32Size中体现出来了:
public static int computeRawVarint32Size(final int value)
{
if ((value & (0xffffffff << 7)) == 0)
return 1;
if ((value & (0xffffffff << 14)) == 0)
return 2;
if ((value & (0xffffffff << 21)) == 0)
return 3;
if ((value & (0xffffffff << 28)) == 0)
return 4;
return 5;
}
根据value值的范围,返回不同的字节数;接下来的代码也可以看到检查LinkedBuffer的空间是否足够,不够进行扩充;接下来的代码就是用压缩的方式将tag和Value存入缓存中。
总结
大致了解了Protostuff对业务对象序列化的过程,不管是简单的测试还是通过查看源码,都可以发现Protostuff的序列化方式是完全借鉴Protobuf来实现的。
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