Reactive Actors

I've been meaning to revisit reactive programming and the actor model for a while now. I first learned about them in the Principles of Reactive Programming Coursera class and then actors came up again in the Seven Concurrency Models in Seven Weeks book. The Scala I picked up is quickly being forgotten and I haven't done a post with code in a while, so here I'll get back into that and create a simple application using Akka and RxScala.

Actor model

The developerWorks article JVM Concurrency: Acting asynchronously with Akka gives a good introduction to the actor model:

The actor model for concurrent computations builds up systems based on primitives called actors. Actors take actions in response to inputs called messages. Actions can include changing the actor's own internal state as well as sending off other messages and even creating other actors. All messages are delivered asynchronously, thereby decoupling message senders from receivers. Because of this decoupling, actor systems are inherently concurrent: Any actors that have input messages available can be executed in parallel, without restriction.

Then JVM Concurrency: Building actor applications with Akka goes on to explain the advantages of this approach:

If you compose your actors and messages correctly, you end up with a system in which most things happen asynchronously. Asynchronous operation is harder to understand than a linear approach, but it pays off in scalability. Highly asynchronous programs are better able to use increased system resources (for example, memory and processors) either to accomplish a particular task more quickly or to handle more instances of the task in parallel. With Akka, you can even extend this scalability across multiple systems, by using remoting to work with distributed actors.

At first the actor model may sound the same as what I described in my Communicating Sequential Processes post because both involve message passing, but the two concurrency models have several differences:

• Actors have identities while CSPs are anonymous
• Actors transmit messages to named actors while CSPs transmit messages using channels
• Actors transmit messages asynchronously while CSPs can't transmit a message until the sender is ready to receive it

My impression, and I could be wrong, is that actors more naturally extend beyond a single machine to a distributed system since the sending and receiving of messages is decoupled. A quick search does turn up distributed channels in pycsp, though, so it seems that both can be distributed.

Reactive applications

The Reactive Manifesto details four qualities of reactive applications:

• responsive - the system responds in a timely manner if at all possible
• resilient - the system stays responsive in the face of failure
• elastic - the system stays responsive under varying workloads
• message driven - the system relies on asynchronous message passing between components

Where Akka describes itself as a toolkit and runtime, RxScala only claims to be a library for composing asynchronous and event-based programs using observable sequences. To me it's not clear how it helps us achieve all four qualities (or if it even intends to).  Nevertheless, the ReactiveX introduction explains their advantages:

The ReactiveX Observable model allows you to treat streams of asynchronous events with the same sort of simple, composable operations that you use for collections of data items like arrays. It frees you from tangled webs of callbacks, and thereby makes your code more readable and less prone to bugs.

This means is that the methods returning Observables can be implemented using thread pools, non-blocking I/O, actors, or anything else. This is how ReactiveX and Akka will be used together: Actors are the concurrency implementation for services communicating with asynchronous messages.

Combining Akka and RxScala

I came up with the following short example. First I wrote a couple of methods returning an Observable to get a feel for it, then added the stockQuote() method which also uses an actor in it's implementation:


Running it produces the expected output, something like:

6

8

broken service

GOOG: 253.22

I can really see the potential in the Observable model, especially after reading more about it at The Netflix Tech Blog. If you were already using actors maybe combining them like this could make sense. I also need to checkout Akka Streams which seems like a similar idea.

UPDATE

Ray is a framework for parallelizing ML workloads. They use the actor model as a way of coordinating work and maintaining state.

30 ideas sort of related to NLP

Over the past year or so, as I was trying to learn more about machine learning, one related topic I haven't gotten to is natural language processing (NLP). I've also had Matthew Russell's Mining the Social Web sitting unread on my bookshelf for a while. Even though it's a bit outdated at this point with references to Google Buzz (looks like there is an updated edition available though) I think it will be good for picking up some NLP basics. It's been described as a successor to Collective Intelligence, which I thought was a fantastic book, so I'm really been looking forward to having the time to finally get through it. This post is going to be lnotes of what I learn as I learn it.

• Even though lexical diversity (unique tokens / total number of tokens) and term frequency distributions are simple, they are still important and useful to start with
• The Natural Language Toolkit (NLTK) is a popular Python module for NLP
• Microformats and HTML 5's microdata are ways of decorating markup to expose structured information
• CouchDB can be used to build up indexes on data and perform frequency analysis through MapReduce operations
• Add Lucene to enable full-text searching of CouchDB documents
• I've known Redis as a key-value store or cache, but it's also known as a data structure server because it can contain lists, sets, hashes, etc.
• When analyzing a graph (like Twitter followers), a graph database can help by providing common operations like clique detection or breadth-first search
• There are many visualization tools besides matplotlib and Graphviz available from Python like Ubigraph, Protovis, and SIMILE Timeline
• Edit distance (aka Levenshtein distance) is a measure of how many changes it would take to convert one string to another 
• n-gram similarity is a measure of common n-grams between samples
• Jaccard index measures the similarity of two sets (|A ∩  B| / |A ∪ B|)
• Calculating the distance between every pair for clustering a large n can be impossible (I think the book could have gone into more detail here and mentioned an alternative approach like what I wrote about at Locality Sensitive Hashing) but k-means clustering at O(kn) can approximate well
• Two visualizations I recognized but didn't know by name: Dorling Cartograms and dendrograms
• New (to me) visualization for trees: radial trees and sunburst visualizations
• Natural language frequency analysis follows Zipf's Law (a power law and long tail distribution) meaning a word's frequency is inversely proportional to its rank in the frequency table 
• TF-IDF is one of the fundamental information retrieval techniques for retrieving documents from a corpus (I wrote about it at tf-idf)
• A common way to find similar documents is cosine similarity where the vectors are TF-IDF weights
• Document similarities can be visualized with arc and matrix diagrams
• Much information is gained when you can look at multiple tokens at a time, like bi-grams (2-grams)
• Collocations are sequences of words that occur together often
• Contingency tables are data structures for expressing frequencies associated with the terms of a bi-gram
• Dice's coefficient, likelihood ratio, chi-square, and Student's t-score, in addition to Jaccard index, are all statistical approaches that can be used for discovering collocations
• Stemming and lemmatization
• Stop-words
• A typical NLTK NLP pipeline is:
• end of sentence (EOS) detection
• tokenization
• part-of-speech tagging
• chunking - assembling compound tokens for logical concepts
• extraction - tagging chunks as named entities
• Filtering out sentences containing frequently occurring words appearing near each other is a basic way to summarize documents
• Extracting entities from documents can address some of the shortcomings of the bag-of-words approach TF-IDF (like homographs and different capitalizations), which n-grams don't completely solve
• Use the F1 score to measure accuracy against manually tagged documents
• Facebook's Open Graph Protocol enables you to turn any web page into a social graph by injecting RDFa metadata into the page
• The semantic web, if realized through standards like RDF and OWL, would be a domain-agnostic way to enable machines to understand and use web information

Communicating Sequential Processes: Goroutines and Channels

This post, like Software Transactional Memory: Dining Philosophers, is motivated by the book Seven Concurrency Models in Seven Weeks. One of the concurrency models discussed is communicating sequential processes (CSP). Even though it was completely new to me, the idea has been around for several decades. I've wanted to check out Go for a while now and since it's a language whose design was influenced by CSP, it seems like a good time to write a Go program.

For concurrent programming, Go encourages shared values to be passed around on channels instead of by sharing memory. The value can only be accessed by one goroutine at a time. Unbuffered channels combine communication with synchronization, and buffered channels can be used like a semaphore. A goroutine is a function executing concurrently with other goroutines in the same address space. It's multiplexed onto multiple OS threads so a blocking goroutine doesn't hold up other goroutines.

In addition to the Seven Concurrency Models in Seven Weeks book, I think the Clojure core.async Channels blog sums up the motivation for channels well. Basically, they are an alternative to using queues to communicate between different components and to using events/callbacks. You avoid avoid thread overhead and callback hell.

I wrote a short program for a vending machine where money deposits and soda dispenses are values passed on channels. It was more difficult than expected to think this way, but the two goroutines only communicate over channels which was the intent.