REST Resource Representation Compression

In REST, compression, like encryption, happens to a representation in transit, and it must be undone via decryption before the client can use the representation.

REST APIs can return the resource representations in several formats such as XML, JSON, HTML, or even plain text. All such forms can be compressed to a lesser number of bytes to save bandwidth over the network.

Different protocols use different techniques to enable compression and notify the clients about the compression scheme – so that the client can decompress it before consuming the representations.

Compression, like encryption, is something that happens to the resource representation in transit and must be undone before the client can use the representation.

HTTP is the most widely used protocol for REST – so we are taking the example of HTTP-specific response compression.

1. Compression Related Request/Response Headers

1.1. Accept-Encoding

While requesting resource representations – along with an HTTP request, the client sends an Accept-Encoding header that says what kind of compression algorithms the client understands.

The two standard values for Accept-Encoding are compress and gzip.

A sample request with accept-encoding, the header looks like this :

GET        /employees         HTTP/1.1
Accept:     text/html
Accept-Encoding:     gzip,compress

Another possible usage of accept-encoding may be:

Accept-Encoding: compress, gzip
Accept-Encoding: *
Accept-Encoding: compress;q=0.5, gzip;q=1.0
Accept-Encoding: gzip;q=1.0, identity; q=0.5, *;q=0

If an Accept-Encoding the field is present in a request, and if the server cannot send a response that is acceptable according to the Accept-Encoding header, then the server SHOULD send an error response with the 406 (Not Acceptable) status code.

1.2. Content-Encoding

If the server understands one of the compression algorithms from Accept-Encoding, it can use that algorithm to compress the representation before serving it. When successfully compressed, the server lets know the client of encoding scheme by another HTTP header i.e. Content-Encoding.

200 OK
Content-Type:     text/html
Content-Encoding:     gzip

If the content-coding of an entity in a request message is not acceptable to the origin server, the server SHOULD respond with a status code of 415 (Unsupported Media Type). If multiple content encodings have been applied to an entity, all the encodings MUST be listed in the order in which they were used.

Please note that the original media type for request and response are not impacted whether compression is requested or not.

Compression can save a lot of bandwidth, with minimal cost without additional complexity. Also, you may know that most web browsers automatically request compressed representations from website host servers – using the above headers.



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If anyone is confused between 406 and 415…

406: when you can’t send what the client wants
415: when the client sends what you don’t want


Compressing data is vital for today’s information technology, so not surprisingly it is used in client-server communication but keep in mind that loss of important parts of the transmitted files is possible, especially when it comes to sending audio/video files. You should be aware of the disadvantages of the given compression method before using it, otherwise you’re risking ending up with inconsistent data.


All compression algorithms used by HTTP content encodings are lossless algorithms, so your statement is false. No loss of ‘important parts of the transmitted files’ is possible, whether those files are audio or video or not.

Yes, there are lossy algorithms used for audio and video, but that is not relevant here. Those apply to these files _before_ they are sent via HTTP, and are not applicable to the trasmision of the file.


In addition to Scott’s reply, check out for a better understanding of the distinct use cases for lossy vs lossless algorithms.

Lossy compression of images/videos/audio is usually unnoticeable to the end-user, depending on the degree of data reduction and the quality of the algorithm.