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The Art of Incremental Stream Processing

This is the workshop material for the YOW LambdaJam 2014 Jam Session.

There are 5 choices (roughly in order of help you will be able to get):

  • scalaz-stream - a full project skeleton and build
  • pipes - a full project skeleton and build
  • conduit - a basic project structure
  • machines - a basic project structure
  • choose your own adventure - an empty directory

These are not easy libraries to get started with, so it is recommended that:

  • You work in groups - about 3 or 4 people working togther would be optimal
  • You (or at least someone in your group) is very familiar with the language you are using Haskell/Scala/Other.
  • Don't feel like you have to use the streaming libraries, feel free to sketch things out how ever you are comfortable - even loop school is ok - it will be good to compare.
  • Don't feel like you have to use Haskell or Scala, it will be great to see some clojure / erlang / F# comparisons.

This jam is about having some fun, doing some benchmarking, trying to understand how these libraries tick, and learning something. So don't get stuck, ask for help, rely heavily on the Hackage docs and Hoogle for the Haskell libraries, rely on the source code for scalaz-stream. Cheating is winning, because you will likely learn something on the way.

THERE WILL BE AUDIENCE PARTICIPATION, I will be strongly encouraging people around the room to jump up and show off there solutions.

A Description Language

A psuedo language for defining these problems:

  type In i         -- Something that produces elements of type i
  type Out o        -- Something that consumes elements of type o
  type Pipeline i o -- Something that turns elements of type i into elements of type o

Note where you see "String" that will be "String" in Scala but should be "Text" in Haskell. When you see "Bytes" that will be ByteString in Haskell, and ByteVector in Scala.

Pipelines

The first step in mastering stream processing is being able to build stream transducers - that is the Pipeline components that consume input and produce output. This components are often small and well defined, but it can still take some practice to identify the combinators required to tackle particular problems.

To practice this we will build the streaming equivalants of the following unix tools:

cat - Pipeline a a

The identity pipeline, it produces exactly the elements it consumes.

yes - In String

Continuously emits the string "y" while it is being asked for input.

fgrep - String -> Pipeline String String

A fixed grep search, process the line by line and only produce lines who contain the substring fgrep.

Note that you should not assume you will be handed chunks a line at a time, you need to make sure things are processed.

head - Pipeline String String

Process input line by line and only output the first 10 input elements, and drop the rest.

Note this is probably harder than it sounds!!!

tail - Pipeline String String

Like head, process input line by line and only output the last 10 lines, ignores the rest.

Note this is probably a lot harder than it sounds!!!

nl - Pipeline String (String, Int)

Line numbers, process input line by line, outputting each line with the line number.

wcl - In String -> Int

Count the number of lines processed by In String.

wc - In String -> Count { line :: Int, word :: Int, char }

Count the number of lines, words and characters processed by In String

cksum - In String -> Int

Produce a simple checkum. The checksum is calculated by xor'ing the ordinal value of each character (i.e. ascii code).

Input and Output

Next we want to build up some tools to hook our simple stream processors up to input and output (files and/or stdin & stdout).

tee - Pipeline a b -> Out b -> Pipeline a b

Such that all output goes to Out b as well as the output pipeline.

read - FileName -> In Text

Take a filename and produce a text input.

readBytes - FileName -> In Bytes

Take a filename and produce a byte input (Bytes will be language specific - ByteString in Haskell or ByteVector in Scala).

readStdIn - In Text

Read standard input as text.

readStdInBytes - In Bytes

Read standard input as bytes.

Test Driving

Build a runnable program so that you can perform some test runs from the command line.

We want to measure a number of things:

  • memory
  • cpu usage (multi-core?)
  • wall clock time
  • resource management

Generate a test file:

yes "this is a test line" | head -10000000 > testing.txt

time cat < testing > /tmp/ignore
time your-program cat < testing > /tmp/ignore

How do they compare?

time wc -l < testing
time your-program wcl < testing

time head < testing
time your-program head < testing

time tail < testing
time your-program tail < testing

How about, handling EOF and broken pipes:

your-program yes | head

Analysis

Now it is time to share:

  • What did you find difficult, did it get easier once you started to pick up the patterns?

  • How did you go at working which of your processes were going to be slow (tail for example has a good reason to be slower) - but were you able to predict?

  • How about composition with your library?

  • Error handline and effects?

  • How was memory usage when you were operating on lines?