Which appears to be different from the serializer used by member

Serializers also provide deserialization, allowing parsed data to be converted back into complex types, after first validating the incoming data. It provides a Serializer class which gives you a powerful, generic way to control the output of your responses, as well as a ModelSerializer class which provides a useful shortcut for creating serializers that deal with model instances and querysets.

We'll declare a serializer that we can use to serialize and deserialize Comment objects. If we don't define this method, then deserializing data will simply return a dictionary of items. We can now use CommentSerializer to serialize a comment, or list of comments. Again, using the Serializer class looks a lot like using a Form class. At this point we've translated the model instance into Python native datatypes.

To finalise the serialization process we render the data into json. Sometimes when serializing objects, you may not want to represent everything exactly the way it is in your model.

For example if you needed to render some markdown from a text field:. By default, serializers must be passed values for all required fields or they will throw validation errors. You can use the partial argument in order to allow partial updates. If any validation errors occur, the.

For example:. Each key in the dictionary will be the field name, and the values will be lists of strings of any error messages corresponding to that field.

When deserializing a list of items, errors will be returned as a list of dictionaries representing each of the deserialized items. You can specify custom field-level validation by adding.

These are analogous to. They take a dictionary of deserialized attributes as a first argument, and the field name in that dictionary as a second argument which will be either the name of the field or the value of the source argument to the field, if one was provided.

To do any other validation that requires access to multiple fields, add a method called. This method takes a single argument, which is the attrs dictionary. It should raise a ValidationError if necessary, or just return attrs. The default behavior of the method is to simply call. You can override the default save behaviour by overriding the. The generic views provided by REST framework call the.

The previous examples are fine for dealing with objects that only have simple datatypes, but sometimes we also need to be able to represent more complex objects, where some of the attributes of an object might not be simple datatypes such as strings, dates or integers. The Serializer class is itself a type of Field , and can be used to represent relationships where one object type is nested inside another.

Validation of nested objects will work the same as before. Errors with nested objects will be nested under the field name of the nested object. You can then pass a queryset or list of objects to be serialized. This allows you to write views that create multiple items when a POST request is made.

You can also deserialize a list of objects as part of a bulk update of multiple existing items. In this case you need to supply both an existing list or queryset of items, as well as a list of data to update those items with. This allows you to write views that update or create multiple items when a PUT request is made.

By default bulk updates will be limited to updating instances that already exist in the provided queryset. When performing a bulk update you may want to allow new items to be created, and missing items to be deleted.

Serialization supports pointer and reference marshaling and demarshaling, i. Additionally this library has support for shared pointers only one copy of data pointed to is saved and objects with multiple inheritance also virtual inheritance. Unfortunately, the Boost.

Serialization has no forward compatibility. Portability between platforms is achieved only for text formats. In binary format only the data is stored, without additional information that allows the type identification. The numbers are stored as their memory image. The archive is very efficient in terms of size; processes of reading and writing are fast.

Unfortunately, there is a lack of portability of binary format. The backward compatibility is achieved by adding the versioning; therefore the library user can provide different solutions for each version. To add the serialization for user type, the programmer should implement method serialize , where one of its argument is archive and the second is the version number. Similarly to Boost. Serialization , cereal library [ 16 ] provides language-specific serialization capabilities.

It too makes developer responsible for writing explicit marshaling procedures and supports only backward compatibility. Library contains similar archive types as Boost. The major advantage of cereal is the presence of PortableBinary archive, which allows to store data efficiently in binary format in a cross-platform way— Boost.

Serialization does not support moving archive across platforms with various endianness, although it is currently being developed by Boost team. Developers can use some of cross-language tools, like Apache Thrift or Protocol Buffers, but those enforce data types used in application. Serialization as part of the most popular Boost library , whose binary archives are not portable.

Other solutions, like cereal , do not provide forward compatibility support. Use minimal size of saved data without hindering ability to evolve structure of serialized data. This is a similar solution to the Boost. Ongoing work [ 17 ] suggests that in the future such code could be significantly simplified.

Basic cereal serialization procedure. Figure 10 shows how structure can be serialized using selected archive type.

In the example BinaryArchive is used; cereal provides few more archive types, each with the same interface—choosing different archives does not require any changes on structure side.

Serializing structure in cereal. This makes it possible to load integers which have different sizes on writing and reading side. The attempt to load integer that cannot fit into type used for loading will result in thrown exception. For space-saving purpose, the size of the saved integer is determined as the minimal number of bytes needed to represent the number being stored. The size of array or string is saved using variable-length integer encoding—the most significant bit is used to indicate if the next byte is part of stored number.

This way for small arrays size is stored using only 1 byte. To speed up serialization and save archive space, all items in the array are assumed to have the same size—size information is stored only once per array. Serialization , for polymorphic pointers a unique type of identifier is saved to stream.

Identifier can be any string; by default fully qualified name of the type is used. During reading process identifier is a key in factory which helps in creating correct object and selecting proper deserialization function.

Identifier for specific class is saved only once in stream, for the first occurrence of given type, accompanied with corresponding ordinal number. For every next instance, just the ordinal number is saved. While reading data saved by newer application, it may happen that identifier of polymorphic type will be connected to field which is unknown and will not be normally read.

For that reason logic to process every occurrence of type identifier was added. During code evolution new derived types for existing base classes may be added and saved as polymorphic pointers in archive. In the original version of cereal library, the attempt to read unknown polymorphic type results in exception being thrown, consequently stopping reading process.

For pointers of unknown type, nullptr value is set, and reading process is continued without interruption. Existing cereal library supports saving several pointers pointing to the same object. In this situation only one copy of the data is saved into the stream.

For every other occurrence of the same object, only numerical identifier of previously saved data is stored. For saving process a dictionary of stored pointers and their identifiers is maintained. If address is found in the dictionary, only identifier is saved. For loading process similar mapping between identifiers and shared pointers is maintained. If during loading pointer identifier is already in the map, data is not loaded, and pointer is returned directly from the map.

Some initial assumptions and design decisions were challenged during implementation and are described below. Supporting possibility for adding fields in newer versions of application should be straightforward—old version of application should just ignore unknown fields. It is true, as long as shared objects are not concerned.

Class modification may lead to a new shared pointer field being added, which points to an object which is already used by a different class in the older version of the application. If the first occurrence of shared pointer is saved by field which is present only in the new version and the older version used for reading, reading such shared pointer may be difficult. An example of such situation is depicted in Figure Object saved in the archive is A class.

In the first version of the application, only one pointer to C class object is saved. In the second version, B::c field pointing to C class object is added. Fields A::c and B::c point to the same object. In the second version, B::c pointer, being part of b field of class A , is saved first. Next, c field of A class is saved. When data is read by the first version of the application, during reading of the B::c , the type of that field is not known, and the whole field could be skipped in basic situation.

However, data has to be read in some way to be available for A::c field restoration. Example of class evolution showing problem with saving shared pointers. One of the solutions to the described problem is saving stream position of each occurrence of shared pointers and restoring it in case data is needed to read the object by other pointers. This solution was rejected because it would introduce additional requirement on streams supported by the library, to handle rewinding reading position.

Such operation is not supported by all streams, i. Another rejected solution was partial interpretation of data and storing it in temporary objects of basic types supported by archive. If the object pointed by shared pointer from unknown field needs to be read by other pointers, instead of using main stream, data would be copied from temporary objects.

This approach would introduce computational overhead even if no other pointer to the same object was saved. The chosen solution copies binary data of pointed object of unknown field type to a temporary buffer, by default allocated on heap.

In case data needs to be read for earlier omitted pointer, it is read from helper stream created from buffer. Apart from additional stream, stack of reading positions is maintained. It is needed in case omitted object contains additional pointers which were also omitted.

An example of this case can be seen in Figure The main saved class is Outer. In the first version of application, two shared pointers Outer::q and Q::z are saved. In the second version, pointers are saved in the following order: Inner::z , Inner::q , Q::z , Outer::q , Q::z.

Only single object instances of Q and Z classes are stored; pointers point to the same objects. When data saved by the second version is read by the first one, data needed by Outer::q field can be found in Inner::q position.

Data for Q::z field can be found in Inner::z position. Example of nested shared pointers. Making copy of data may require a lot of memory.

In a worst case size of copied data may be close to the size of all data being read. Such memory allocations may not be acceptable in some applications. Because of that, option to limit maximal size of helper buffer has been added.

Trying to use more memory will result in exception being thrown. Apart from adding new fields, at some point of application evolution, it might be justified to remove no longer needed fields. Saving unnecessary fields results in size and computational overhead. Because in cereal order of saving fields is used for field identification, it is forbidden in that library to erase fields from saving and reading methods.

Using this type indicates position where there was a field but it is no longer used. The older version of application, when trying to read field, which in archive is marked using this tag, will just leave field value in the object unchanged.

In most situations it will leave that field with default value assigned in object constructor. Normally, when an object is serialized, the default name and namespace of the outermost XML element are determined according to the data contract name and namespace. However, you can customize the default name and namespace of the root element by passing the values of the rootName and rootNamespace parameters to the DataContractSerializer constructor.

Note that the rootNamespace does not affect the namespace of the contained elements that correspond to data members. It affects only the namespace of the outermost element. These values can be passed as strings or instances of the XmlDictionaryString class to allow for their optimization using the binary XML format.

This parameter determines the maximum number of objects the serializer serializes or deserializes in a single ReadObject method call. The method always reads one root object, but this object may have other objects in its data members. Those objects may have other objects, and so on. The default is Note that when serializing or deserializing arrays, every array entry counts as a separate object.

Also, note that some objects may have a large memory representation, and so this quota alone may not be sufficient to prevent a denial of service attack. For more information, see Security Considerations for Data. If you need to increase this quota beyond the default value, it is important to do so both on the sending serializing and receiving deserializing sides because it applies to both when reading and writing data. A round trip occurs when an object is deserialized and re-serialized in one operation.

Some DataContractSerializer constructor overloads have an ignoreExtensionDataObject parameter, which is set to false by default. In this default mode, data can be sent on a round trip from a newer version of a data contract through an older version and back to the newer version without loss, as long as the data contract implements the IExtensibleDataObject interface. For example, suppose version 1 of the Person data contract contains the Name and PhoneNumber data members, and version 2 adds a Nickname member.

If IExtensibleDataObject is implemented, when sending information from version 2 to version 1, the Nickname data is stored, and then re-emitted when the data is serialized again; therefore, no data is lost in the round trip. Round trips may have security implications.

For example, deserializing and storing large amounts of extraneous data may be a security risk. There may be security concerns about re-emitting this data that there is no way to verify, especially if digital signatures are involved. For example, in the previous scenario, the version 1 endpoint could be signing a Nickname value that contains malicious data. Finally, there may be schema validity concerns: an endpoint may want to always emit data that strictly adheres to its stated contract and not any extra values.

To turn off round trips, do not implement the IExtensibleDataObject interface. If you have no control over the types, set the ignoreExtensionDataObject parameter to true to achieve the same effect. Notice that billTo and shipTo fields are set to the same object instance.

Circular references. If objects refer to themselves, even through other objects, serializing by replication results in an infinite loop. The serializer throws a SerializationException if this happens. Sometimes it is important to preserve the fact that two references are to the same object, and not to two identical objects. For these reasons, some DataContractSerializer constructor overloads have a preserveObjectReferences parameter the default is false.

When this parameter is set to true , a special method of encoding object references, which only WCF understands, is used. When set to true , the XML code example now resembles the following. Each piece of data is serialized only once and given an ID number, and subsequent uses result in a reference to the already serialized data. If both "id" and "ref" attributes are present in the data contract XMLElement , then the "ref" attribute is honored and the "id" attribute is ignored.

The XML the DataContractSerializer produces with preserveObjectReferences set to true is not interoperable with any other technologies, and can be accessed only by another DataContractSerializer instance, also with preserveObjectReferences set to true. There is no metadata schema support for this feature.

The schema that is produced is valid only for the case when preserveObjectReferences is set to false. This feature may cause the serialization and deserialization process to run slower. Although data does not have to be replicated, extra object comparisons must be performed in this mode.

When the preserveObjectReferences mode is enabled, it is especially important to set the maxItemsInObjectGraph value to the correct quota. Due to the way arrays are handled in this mode, it is easy for an attacker to construct a small malicious message that results in large memory consumption limited only by the maxItemsInObjectGraph quota.

Some DataContractSerializer constructor overloads have a dataContractSurrogate parameter, which may be set to null.

Otherwise, you can use it to specify a data contract surrogate , which is a type that implements the IDataContractSurrogate interface.