Tagged as programming
Written on 2012-09-24 10:46:00
Though I didn't take notes on these, or today's keynotes, I have seen quite good coverage of ELC and Strange Loop talks here.
- Generative testing with contracts used /in tests/ (to avoid runtime overhead) seems a good compromise.
- Optional type systems that aren't part of the language are verification systems. Type systems must be part of the language by definition.
- We are doomed by Godel to inconsistency, never truly safe.
- My thought: How can we apply Paraconsistent Logics?
Functional Design Patterns
Design Patterns: Are they a sign of weakness in your language?
Graph of known Monad tutorials. Skyrocketing. :P
Monads are a great abstraction to capture/describe patterns, NOT explain them.
- A System of Patterns - book
- Architecture Pattern > Design Pattern > Idiom (idioms are least general)
- State/Event Pattern
- Store all the inputs and initial state. Rederive any point in execution.
- Can be troublesome to track tons of inputs/events.
- Consequences Pattern
- Input can trigger arbitrarily many events to fire/hooks.
- Returns the events as a sequence (of thunks, presumably).
- Don't let them recurse or you're turing complete!
- Data Building Patterns
- Accumulator Pattern - Reduce a sequence to a scalar value. Duh.
- If order matters, have to do it sequentially. Otherwise, divide!
- MapReduce about wringing data locality out of slow disk clusters. Already changing with SSDs.
- Reducers are nice when you have associativty. Easy to parallelize trees of operations.
- Recursive Expansion Pattern - Macroexpansion, Datmoic transaction, Are we done expanding? If not, keep going.
- Flow Control Patterns
- Pipeline pattern - Code may be longer but it's cleaner. Enforced composition through layering+encapsulation. No branching allowed!
- Wrapper pattern - Foundation for Ring. One main path with potential branches at each step. Just decorators/:before+:after+:around methods?
- Token pattern - May need to cancel and operation. Call Begin, returns a destructor, basically. Don't need direct access to resource to destroy it.
A Whole New World
Possibly the talk I'm most excited for today. DESTROY ALL SOFTWARE!
- Wrote "An Editor"
- Modal, Terminal Only, Neither VIM nor IDE
- Layers! Annotations on source. Diffs. Tracebacks from prod logs. YES!
- Crash - Have to parse logs+traceback, maybe use different checkout.
- Interactions - On-demand Class Hierarchy/CFG. Code navigation methods.
- No static analysis! Language-specific FIFO queues for program traces! Fork+render with graphviz. FUCK YEAHHHHH!
- Answer questions like: What code does a web request hit? More importantly, what code might have reached this crash point in our traceback?
- Wrote "An Terminal"
- DEC VT100 has determined terminal protocols for 30 YEARS. Powered by an 8080. @1978.
- Add raster graphics, 24-bit color, momentary keypresses, font styles.
- Use for more editor layers! Tag lines with profiling info. Bottom 95% grey, others yellow or red. Same thing for Type annotation. Record traces, remember?
- Do you want it?
- Wrote "An Lies". HAS BEEN LYING.
- All bullshit. C-c f t, flip all the tables.
- Takes a long time to fake all that shit.
- "Ship often. Ship lousy stuff, but ship. Ship constantly." -- bullshit
- I KNOW that all software sucks.
- Legacy & Paralysis? Legacy == Paralysis!
They will not merge our kernel patches. How do we move forward?
Our "Shipping Culture" is poisonous to infrastructure.
We just accrete low level infrastructure. Programmer Archaeologists are we.
INCREMENTAL DEVELOPMENT WILL NOT WORK FOR THIS.
Type-Driven Functional Design
- Basic overview of Haskell type syntax. Call to map. We all know this I hope.
- Currying + Partial Application trivial in ML family. Duh. Syntactic support.
- UML is garbage. Thinking of the /flow/ of types through your program gives insight.
- Moved to miniKanren talk.
- Growing benefit of compiling to JS. Lots of browser competition, obvio.
- "Lisp programmers know the value of everything and the cost of nothing."
- Hope to show that efficiency is important when getting richer semantics.
- Expression-based and value based semantics, vs statements.
- Go back to Robin Milner. Compiler figures out boolean inference.
- Functions aren't primitive in Clojure, unlike Scheme+CL, like T.
- Construct types that act like a fn, Collections are an instance of IFn.
- Invocation always emitted as obj.call. Expensive in JS engines though. Again helped by Compilation. Use Google Closure for DCE, aggressive inlining, etc.
- Whole Program Compilation allows propagation of args and type info.
- Bit of a dev/prod divide. Dynamic for devs, static compilation for perf to ship.
- arguments.slice is a big performance hit. Return a closure with a dispatch function for multiple arity defns.
- Performance competitive with hand-written JS. Based on Bagwell and Okasaki (since compare-and-swap isn't available, I presume).
- Digression on how persistent data structures don't suck. V8 handles them really well.
- Within 3x performance hit on Chrome 22. Opera and Safari a bit worse off. Firefox good at operations on data but slow creating it.
- But it won't be the bottleneck in your application. DOM traversal is vastly slower, for example, and probably dominates.
- Local type extension with protocols. \o/ Used internally for hashing.
- What about when you drop to JS? REPL connected to browser. Compiles in namespaces for you! Source maps should help with debugging.
- And it has macros, of course. Zebra problem demo. PAIP shoutout! 24 billion possible solutions. Runs in JS in 16ms, 1000x than Norvig's from 1993.
- Same broken numeric tower.
- Debugging much harder than CoffeeScript. Not necessarily readable.
- Needs Clojure. Not self-hosting. Not something they really want to fix.
- Multimethods and keywords still slow.
- Sure, the "runtime" looks big in generated code at first. But Google Closure compiles it down to a nice, small gzipped thing in the end.
- Clojurescript host compiler in clj is only 4000 lines of code! ClojureScript side is 7500.
Data Structures: The Code that isn't there
"A Data Structure is just a stupid programming language." - Bill Gosper
"A data structure is just a tiny virtual machine." - Scott Vokes
- Fundamentals: Lists, Arrays, Hash Tables, Trees
- Ruby 1.9.2 briefly used list instead of a hash-table or set for require. In big-oh this is O(#fail).
"The cheapest, fastest, and most reliable components are those that aren't there." - Gordon Bell
Good choice of data structure /subtracts/ code.
Data structures set the path of least resistance for interacting with your data.
You probably won't know the ideal DS up front. Don't paint yourself in a corner.
- Take an ordered, linked list. Add an express lane! Jump by 2. Add another! Jump by 4. More closely resembles a binary tree...
- But how do we balance it?
- Doesn't have to be perfect. Real trees aren't balanced! Just balanced enough.
- Use random probability distribution. Actually winds up balanced enough.
- Roundabouts, not traffic lights
- Traffic lights are SPOFs, bottle necks.
- Roundabout decentralized, delegates to cars, smart at the node. Global order from local decisions.
- Because only immediate neighbors are effected on insert, lock contention is low.
- Difference Lists
- Comes from Prolog.
?- uses(prolog, Person). no.
- Digression on Unification.
- Allows appending to immutable list.
- Closer to future/promises for new list elements than lazy evaluation.
- Rolling Hashes!
- Find matching/overlapping sequences in binary data. Rsync does this.
- Bioinformatics loves this stuff. Genome seqs.
- Hashing everything vs everything with traditional hashes (md5, sha) too slow.
- Drop a letter off the front, add another to the back, hash. Add to set/bloom filter or something.
- Break file into fixed width blocks + remainder.
- Send the hash for each block. If it's different on the server, update!
- Insert/delete shifts each block though...
- Use rolling hash for files that are already on remote. Otherwise, blocks.
- Can also be used for chunking data.
- A rolling hash: finds deterministic breaks, cheaply matches blocks
- Something new: A Jumprope
- Stores large binary strings (or files)
- Content Addressable Storage (reference by hash for easy distribution)
- Persistent and immutable so you can cache it anywhere
- ... so kind of like a git repo but much better for big files
- Three structural elements:
- Leaf - chunk of raw data
- Limb - Series of content hashes and their links, stored in an array
- Trunk - A limb with a big end node.
- Then you just back it with a key-value store.
- Obviously, choice implies things about performance.
- Somewhat like a skiplist that uses a hash as its probability function.
- Good for pipelining streaming content thanks to seeking properties.
- 2kb for limb nodes and 64kb overhead for leaf nodes.
- Trivial to fetch, stream, mirror.
- Using it for a distributed FS, scatterbrain!!
- Similar to Amazon Dynamo.
- lack of pointers, use Compare and Swap!
- Emergence from local behavior.
- Can tune bad performance to arbitrary guarantee. 1%, .1%, etc.
- Currently backed by C and Lua. Also a quick'n'dirty Erlang implementation.
All coming to a github near you soon.
Elided due to battery life.