Previously: Typing the Technical Interview.

Frigitt, vi danser
For et øyeblikk, vi leker
Vi tusen små bladskiper
Gleder oss, på det klare morgenlys

If you want to get a job as a software witch, you’re going to have to pass a whiteboard interview. We all do them, as engineers–often as a part of our morning ritual, along with arranging a beautiful grid of xterms across the astral plane, and compulsively running ls in every nearby directory–just in case things have shifted during the night–the incorporeal equivalent of rummaging through that drawer in the back of the kitchen where we stash odd flanges, screwdrivers, and the strangely specific plastic bits: the accessories, those long-estranged black sheep of the families of our household appliances, their original purpose now forgotten, perhaps never known, but which we are bound to care for nonetheless. I’d like to walk you through a common interview question: reversing a linked list.

First, we need a linked list. Clear your workspace of unwanted xterms, sprinkle salt into the protective form of two parentheses, and recurse. Summon a list from the void.

So there’s a blog post that advises every method should, when possible, return self. I’d like to suggest you do the opposite: wherever possible, return something other than self.

Mutation is hard

Previously: Modeling.

Writing software can be an exercise in frustration. Useless error messages, difficult-to-reproduce bugs, missing stacktrace information, obscure functions without documentation, and unmaintained libraries all stand in our way. As software engineers, our most useful skill isn’t so much knowing how to solve a problem as knowing how to explore a problem that we haven’t seen before. Experience is important, but even experienced engineers face unfamiliar bugs every day. When a problem doesn’t bear a resemblance to anything we’ve seen before, we fall back on general cognitive strategies to explore–and ultimately solve–the problem.

Earlier versions of Jepsen found glaring inconsistencies, but missed subtle ones. In particular, Jepsen was not well equipped to distinguish linearizable systems from sequentially or causally consistent ones. When people asked me to analyze systems which claimed to be linearizable, Jepsen could rule out obvious classes of behavior, like dropping writes, but couldn’t tell us much more than that. Since users and vendors are starting to rely on Jepsen as a basic check on correctness, it’s important that Jepsen be able to identify true linearization errors.

Previously: Logistics

Until this point in the book, we’ve dealt primarily in specific details: what an expression is, how math works, which functions apply to different data structures, and where code lives. But programming, like speaking a language, painting landscapes, or designing turbines, is about more than the nuts and bolts of the trade. It’s knowing how to combine those parts into a cohesive whole–and this is a skill which is difficult to describe formally. In this part of the book, I’d like to work with you on an integrative tour of one particular problem: modeling a rocket in flight.

Previously, we covered state and mutability.

Up until now, we’ve been programming primarily at the REPL. However, the REPL is a limited tool. While it lets us explore a problem interactively, that interactivity comes at a cost: changing an expression requires retyping the entire thing, editing multi-line expressions is awkward, and our work vanishes when we restart the REPL–so we can’t share our programs with others, or run them again later. Moreover, programs in the REPL are hard to organize. To solve large problems, we need a way of writing programs durably–so they can be read and evaluated later.

A few weeks ago I criticized a proposal by Antirez for a hypothetical linearizable system built on top of Redis WAIT and a strong coordinator. I showed that the coordinator he suggested was physically impossible to build, and that anybody who tried to actually implement that design would run into serious problems. I demonstrated those problems (and additional implementation-specific issues) in an experiment on Redis’ unstable branch.

Antirez’ principal objections, as I understand them, are:

Previously: Macros.

Most programs encompass change. People grow up, leave town, fall in love, and take new names. Engines burn through fuel while their parts wear out, and new ones are swapped in. Forests burn down and their logs become nurseries for new trees. Despite these changes, we say “She’s still Nguyen”, “That’s my motorcycle”, “The same woods I hiked through as a child.”

In Chapter 1, I asserted that the grammar of Lisp is uniform: every expression is a list, beginning with a verb, and followed by some arguments. Evaluation proceeds from left to right, and every element of the list must be evaluated before evaluating the list itself. Yet we just saw, at the end of Sequences, an expression which seemed to violate these rules.

Clearly, this is not the whole story.

In Chapter 3, we discovered functions as a way to abstract expressions; to rephrase a particular computation with some parts missing. We used functions to transform a single value. But what if we want to apply a function to more than one value at once? What about sequences?

For example, we know that (inc 2) increments the number 2. What if we wanted to increment every number in the vector [1 2 3], producing [2 3 4]?

We left off last chapter with a question: what are verbs, anyway? When you evaluate (type :mary-poppins), what really happens?

user=> (type :mary-poppins)
clojure.lang.Keyword

We’ve learned the basics of Clojure’s syntax and evaluation model. Now we’ll take a tour of the basic nouns in the language.

Types

This guide aims to introduce newcomers and experienced programmers alike to the beauty of functional programming, starting with the simplest building blocks of software. You’ll need a computer, basic proficiency in the command line, a text editor, and an internet connection. By the end of this series, you’ll have a thorough command of the Clojure programming language.

Who is this guide for?

Riemann 0.2.0 is ready. There’s so much left that I want to build, but this release includes a ton of changes that should improve usability for everyone, and I’m excited to announce its release.

Version 0.2.0 is a fairly major improvement in Riemann’s performance and capabilities. Many things have been solidified, expanded, or tuned, and there are a few completely new ideas as well. There are a few minor API changes, mostly to internal structure–but a few streams are involved as well. Most functions will continue to work normally, but log a deprecation notice when used.

The Netty redesign of riemann-java-client made it possible to expose an end-to-end asynchronous API for writes, which has a dramatic improvement on messages with a small number of events. By introducing a small queue of pipelined write promises, riemann-clojure-client can now push 65K events per second, as individual messages, over a single TCP socket. Works out to about 120 mbps of sustained traffic.

In the previous post, I described an approximation of Heroku’s Bamboo routing stack, based on their blog posts. Hacker News, as usual, is outraged that the difficulty of building fast, reliable distributed systems could prevent Heroku from building a magically optimal architecture. Coda Hale quips:

Really enjoying @RapGenius’s latest mix tape, “I Have No Idea How Distributed Systems Work”.

For more on Timelike and routing simulation, check out part 2 of this article: everything fails all the time. There’s also more discussion on Reddit.

RapGenius is upset about Heroku’s routing infrastructure. RapGenius, like many web sites, uses Rails, and Rails is notoriously difficult to operate in a multithreaded environment. Heroku operates at large scale, and made engineering tradeoffs which gave rise to high latencies–latencies with adverse effects on customers. I’d like to explore why Heroku’s Bamboo architecture behaves this way, and help readers reason about their own network infrastructure.

tl;dr Riemann is a monitoring system, so it emphasizes liveness over safety.

Riemann is aimed at high-throughput (millions of events/sec/node), partial-harvest event processing, where it is acceptable to trade completeness for throughput at low latencies. For instance, it’s probably fine to drop half of your request latency events on the floor, if you’re calculating a lossy histogram with sampling anyway. It’s also typically acceptable to have nondeterministic behavior with respect to time windows: if one node’s clock is skewed, it’s better to process it “soonish” rather than waiting an unbounded amount of time for it to check in.

I’ve been doing a lot of performance tuning in Riemann recently, especially in the clients–but I’d like to share a particularly spectacular improvement from yesterday.

The Riemann protocol

I’ve had two observations floating around in my head, looking for a way to connect with each other.

Many “architecture patterns” are scar tissue around the absence of higher-level language features.

I’ve been putting more work into riemann-java-client recently, since it’s definitely the bottleneck in performance testing Riemann itself. The existing RiemannTcpClient and RiemannRetryingTcpClient were threadsafe, but almost fully mutexed; using one essentially serialized all threads behind the client itself. For write-heavy workloads, I wanted to do better.

There are two logical optimizations I can make, in addition to choosing careful data structures, mucking with socket options, etc. The first is to bundle multiple events into a single Message, which the API supports. However, your code may not be structured in a way to efficiently bundle events, so where higher latencies are OK, the client can maintain a buffer of outbound events and flush it regularly.

Schadenfreude is a benchmarking tool I’m using to improve Riemann. Here’s a profile generated by the new riemann-bench, comparing a few recent releases in their single-threaded TCP server throughput. These results are dominated by loopback read latency–maxing out at about 8-9 kiloevents/sec. I’ll be using schadenfreude to improve client performance in high-volume and multicore scenarios.

I needed a tool to evaluate internal and network benchmarks of Riemann, to ask questions like

• Is parser function A or B more efficient?
• How many threads should I allocate to the worker threadpool?
• How did commit 2556 impact the latency distribution?

In response to Results of the 2012 State of Clojure Survey:

The idea of having a primary language honestly comes off to me as a sign that the developer hasn't spent much time programming yet: the real world has so many languages in it, and many times the practical choice is constrained by that of the platform or existing code to interoperate with.

More from Hacker News. I figure this might be of interest to folks working on parallel systems. I’ll let KirinDave kick us off with:

Go scales quite well across multiple cores iff you decompose the problem in a way that's amenable to Go's strategy. Same with Erlang.

No one is making “excuses”. It’s important to understand these problems. Not understanding concurrency, parallelism, their relationship, and Amdahl’s Law is what has Node.js in such trouble right now.

Most applications have configuration: how to open a connection to the database, what file to log to, the locations of key data files, etc.

Configuration is hard to express correctly. It’s dynamic because you don’t know the configuration at compile time–instead it comes from a file, the network, command arguments, etc. Config is almost always implicit, because it affects your functions without being passed in as an explicit parameter. Most languages address this in two ways: