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Data Buffers and Codecs

Java NIO provides ByteBuffer but many libraries build their own byte buffer API on top, especially for network operations where reusing buffers and/or using direct buffers is beneficial for performance. For example Netty has the ByteBuf hierarchy, Undertow uses XNIO, Jetty uses pooled byte buffers with a callback to be released, and so on. The spring-core module provides a set of abstractions to work with various byte buffer APIs as follows:

DataBufferFactory

DataBufferFactory is used to create data buffers in one of two ways:

  1. Allocate a new data buffer, optionally specifying capacity upfront, if known, which is more efficient even though implementations of DataBuffer can grow and shrink on demand.

  2. Wrap an existing byte[] or java.nio.ByteBuffer, which decorates the given data with a DataBuffer implementation and that does not involve allocation.

Note that WebFlux applications do not create a DataBufferFactory directly but instead access it through the ServerHttpResponse or the ClientHttpRequest on the client side. The type of factory depends on the underlying client or server, for example, NettyDataBufferFactory for Reactor Netty, DefaultDataBufferFactory for others.

DataBuffer

The DataBuffer interface offers similar operations as java.nio.ByteBuffer but also brings a few additional benefits some of which are inspired by the Netty ByteBuf. Below is a partial list of benefits:

  • Read and write with independent positions, i.e. not requiring a call to flip() to alternate between read and write.

  • Capacity expanded on demand as with java.lang.StringBuilder.

  • Pooled buffers and reference counting via PooledDataBuffer.

  • View a buffer as java.nio.ByteBuffer, InputStream, or OutputStream.

  • Determine the index, or the last index, for a given byte.

PooledDataBuffer

As explained in the Javadoc for ByteBuffer, byte buffers can be direct or non-direct. Direct buffers may reside outside the Java heap which eliminates the need for copying for native I/O operations. That makes direct buffers particularly useful for receiving and sending data over a socket, but they’re also more expensive to create and release, which leads to the idea of pooling buffers.

PooledDataBuffer is an extension of DataBuffer that helps with reference counting which is essential for byte buffer pooling. How does it work? When a PooledDataBuffer is allocated the reference count is at 1. Calls to retain() increment the count, while calls to release() decrement it. As long as the count is above 0, the buffer is guaranteed not to be released. When the count is decreased to 0, the pooled buffer can be released, which in practice could mean the reserved memory for the buffer is returned to the memory pool.

Note that instead of operating on PooledDataBuffer directly, in most cases it’s better to use the convenience methods in DataBufferUtils that apply release or retain to a DataBuffer only if it is an instance of PooledDataBuffer.

DataBufferUtils

DataBufferUtils offers a number of utility methods to operate on data buffers:

  • Join a stream of data buffers into a single buffer possibly with zero copy, for example, via composite buffers, if that’s supported by the underlying byte buffer API.

  • Turn InputStream or NIO Channel into Flux<DataBuffer>, and vice versa a Publisher<DataBuffer> into OutputStream or NIO Channel.

  • Methods to release or retain a DataBuffer if the buffer is an instance of PooledDataBuffer.

  • Skip or take from a stream of bytes until a specific byte count.

Codecs

The org.springframework.core.codec package provides the following strategy interfaces:

  • Encoder to encode Publisher<T> into a stream of data buffers.

  • Decoder to decode Publisher<DataBuffer> into a stream of higher level objects.

The spring-core module provides byte[], ByteBuffer, DataBuffer, Resource, and String encoder and decoder implementations. The spring-web module adds Jackson JSON, Jackson Smile, JAXB2, Protocol Buffers and other encoders and decoders. See Codecs in the WebFlux section.

Using DataBuffer

When working with data buffers, special care must be taken to ensure buffers are released since they may be pooled. We’ll use codecs to illustrate how that works but the concepts apply more generally. Let’s see what codecs must do internally to manage data buffers.

A Decoder is the last to read input data buffers, before creating higher level objects, and therefore it must release them as follows:

  1. If a Decoder simply reads each input buffer and is ready to release it immediately, it can do so via DataBufferUtils.release(dataBuffer).

  2. If a Decoder is using Flux or Mono operators such as flatMap, reduce, and others that prefetch and cache data items internally, or is using operators such as filter, skip, and others that leave out items, then doOnDiscard(DataBuffer.class, DataBufferUtils::release) must be added to the composition chain to ensure such buffers are released prior to being discarded, possibly also as a result of an error or cancellation signal.

  3. If a Decoder holds on to one or more data buffers in any other way, it must ensure they are released when fully read, or in case of an error or cancellation signals that take place before the cached data buffers have been read and released.

Note that DataBufferUtils#join offers a safe and efficient way to aggregate a data buffer stream into a single data buffer. Likewise skipUntilByteCount and takeUntilByteCount are additional safe methods for decoders to use.

An Encoder allocates data buffers that others must read (and release). So an Encoder doesn’t have much to do. However an Encoder must take care to release a data buffer if a serialization error occurs while populating the buffer with data. For example:

  • Java

  • Kotlin

DataBuffer buffer = factory.allocateBuffer();
boolean release = true;
try {
	// serialize and populate buffer..
	release = false;
}
finally {
	if (release) {
		DataBufferUtils.release(buffer);
	}
}
return buffer;
val buffer = factory.allocateBuffer()
var release = true
try {
	// serialize and populate buffer..
	release = false
} finally {
	if (release) {
		DataBufferUtils.release(buffer)
	}
}
return buffer

The consumer of an Encoder is responsible for releasing the data buffers it receives. In a WebFlux application, the output of the Encoder is used to write to the HTTP server response, or to the client HTTP request, in which case releasing the data buffers is the responsibility of the code writing to the server response, or to the client request.

Note that when running on Netty, there are debugging options for troubleshooting buffer leaks.