Disruptor

The Google Code project does reference a technical paper on the implementation of the ring buffer, however it is a bit dry, academic and tough going for someone wanting to learn how it works. However there are some blog posts that have started to explain the internals in a more readable way. There anexplanation of ring buffer that is the core of the disruptor pattern, a description of the consumer barriers(the part related to reading from the disruptor) and some information on handling multiple producers available.

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The simplest description of the Disruptor is: It is a way of sending messages between threads in the most efficient manner possible. It can be used as an alternative to a queue, but it also shares a number of features with SEDA and Actors.

Compared to Queues:

The Disruptor provides the ability pass a message onto another threads, waking it up if required (similar to a BlockingQueue). However, there are 3 distinct differences.

1. The user of the Disruptor defines how messages are stored by extending Entry class and providing a factory to do the preallocation. This allows for either memory reuse (copying) or the Entry could contain a reference to another object. 
2. Putting messages into the Disruptor is a 2-phase process, first a slot is claimed in the ring buffer, which provides the user with the Entry that can be filled with the appropriate data. Then the entry must be committed, this 2-phase approach is necessary to allow for the flexible use of memory mentioned above. It is the commit that makes the message visible to the consumer threads. 
3. It is the responsibility of the consumer to keep track of the messages that have been consumed from the ring buffer. Moving this responsibility away from the ring buffer itself helped reduce the amount of write contention as each thread maintains its own counter.

Compared to Actors


The Actor model is closer the Disruptor than most other programming models, especially if you use the BatchConsumer/BatchHandler classes that are provided. These classes hide all of the complexities of maintaining the consumed sequence numbers and provide a set of simple callbacks when important events occur. However, there are a couple of subtle differences.

1. The Disruptor uses a 1 thread - 1 consumer model, where Actors use an N:M model i.e. you can have as many actors as you like and they will be distributed across a fixed numbers of threads (generally 1 per core). 
2. The BatchHandler interface provides an additional (and very important) callback onEndOfBatch(). This allows for slow consumers, e.g. those doing I/O to batch events together to improve throughput. It is possible to do batching in other Actor frameworks, however as nearly all other frameworks don't provide a callback at the end of the batch you need to use a timeout to determine the end of the batch, resulting in poor latency.

Compared to SEDA

LMAX built the Disruptor pattern to replace a SEDA based approach.

1. The main improvement that it provided over SEDA was the ability to do work in parallel. To do this the Disruptor supports multi-casting messages the same messages (in the same order) to multiple consumers. This avoids the need for fork stages in the pipeline. 
2. We also allow consumers to wait on the results of other consumers without having to put another queuing stage between them. A consumer can simply watch the sequence number of a consumer that it is dependent on. This avoids the need for join stages in pipelin

Compared to Memory Barriers

Another way to think about it is as a structured, ordered memory barrier. Where the producer barrier form the write barrier and the consumer barrier is the read barrier.
There are one or more writers. There are one or more readers. There is a line of entries, totally ordered from old to new (pictured as left to right). Writers can add new entries on the right end. Every reader reads entries sequentially from left to right. Readers can't read past writers, obviously.
There is no concept of entry deletion. I use "reader" instead of "consumer" to avoid the image of entries being consumed. However we understand that entries on the left of the last reader become useless.
Generally readers can read concurrently and independently. However we can declare dependencies among readers. Reader dependencies can be arbitrary acyclic graph. If reader B depends on reader A, reader B can't read past reader A.
Reader dependency arises because reader A can annotate an entry, and reader B depends on that annotation. For example, A does some calculation on an entry, and stores the result in field a in the entry. A then move on, and now B can read the entry, and the value of a A stored. If reader C does not depend on A, C should not attempt to read a.
This is indeed an interesting programming model. Regardless of the performance, the model alone can benefit lots of applications.
Of course, LMAX's main goal is performance. It uses a pre-allocated ring of entries. The ring is large enough, but it's bounded so that the system will not be loaded beyond design capacity. If the ring is full, writer(s) will wait until the slowest readers advance and make room.
Entry objects are pre-allocated and live forever, to reduce garbage collection cost. We don't insert new entry objects or delete old entry objects, instead, a writer asks for a pre-existing entry, populate its fields, and notify readers. This apparent 2-phase action is really simply an atomic action

setNewEntry(EntryPopulator);

interface EntryPopulator{ void populate(Entry existingEntry); }

Pre-allocating entries also means adjacent entries (very likely) locate in adjacent memory cells, and because readers read entries sequentially, this is important to utilize CPU caches.

And lots of efforts to avoid lock, CAS, even memory barrier (e.g. use a non-volatile sequence variable if there's only one writer)

For developers of readers: Different annotating readers should write to different fields, to avoid write contention. (Actually they should write to different cache lines.) An annotating reader should not touch anything that other non-dependent readers may read. This is why I say these readers annotate entries, instead of modify entries.

Martin Fowler has written an article about LMAX and the disruptor pattern, The LMAX Architecture, which may clarify it further.