Lambda the Ultimate

A new mixed logic/imperative programming language by Bob Kowalski and Fariba Sadri:

“Logic-based Production System" is a new computer language that combines the characteristics of an imperative programming language with those of a declarative database and knowledge representation language. It is the result of over a decade of research led by Bob Kowalski and Fariba Sadri at Imperial College London.

The kernel of LPS consists of a database, together with reactive rules of the form if antecedent then consequent. The database changes destructively in response to actions and external events, according to a domain-specific causal theory. Computation consists in making the reactive rules true, by performing actions to make the consequent true whenever the antecedent becomes true. In addition, LPS includes Prolog-like logic programs both to recognise when antecedents become true and to generate plans of actions that make consequents true.

The page links to papers and slides that describe the language in more detail and there is an online playground to try it out.

kdb+ is a real-time time series database, known in the financial services universe as the fastest tick database on the market. It was first conceived by Arthur Whitney at Morgan Stanley as a prototype, and over the last 35+ years has grown to add many features. The database makes such aggressive usage of mmap() POSIX function for mapping file chunks into main memory, to the point where it has exposed issues with the implementation of mmap itself.

Recently, the company now behind kdb+ has also built Kx for DAAS (Data-as-a-Service), which is basically a cloud-based, massively clustered version of kdb+ that deals with the curious oddity that kdb+ is effectively entirely singly threaded. For those interested in reading more about kdb+'s unique cloud architecture (as compared to "big data" solutions like Hadoop), you can read the following whitepapers as suggestive guidelines for how the q community thinks about truly "big data" several orders of magnitude faster and larger than most Hadoop data sets:

• Query Routing: a kdb+ Framework for a Scalable, Load Balanced System (whitepaper) and Kevin Holsgrove: Query Routing, A kdb+ Framework for a Scalable, Load Balanced System (YouTube talk from Kx Meet-up last year)
• Common Design Principles for kdb+ Gateways
• A Natural Query Interface for Distributed Systems

While I don't suggest these papers are the blueprint for copying/mimicking the DAAS product, it does help the LtU reader imagine a "different world" of data processing than the often cited Map/Reduce paper and other more mainstream approaches. What is particularly striking is how tiny q.exe (the program that runs kdb+ and provides a CLI for q scripting) is. Language researchers are looking at provably correct C compilers, and it is not a huge leap to think about the world soon seeing provably correct real-time time series databases using kdb+ as an inspiration.

Another curiosity, relevant to us here at LtU, is that kdb+ has its own programming language, q. q is a variant of APL with a special library for statistics. Most "big data" solutions don't have native implementations for weighted average, which is a fairly important and frequently used function in quantitative finance, useful for computing volume weighted average price (VWAP) as well as tilt and weighted spread. q is itself implemented in another language, k. The whole language of each is just a couple lines of (terse) code.