Apache Beam and Google Cloud Dataflow on steroids Clojure. The walkthrough explains everything.

This is alpha software. Watch release notes carefully. API subject to mood swings.

• Quickstart
• Project Goals
• Documentation
• Demos
  • Word Count
  • Mobile Gaming Examples
• Make It Fast

1. Clone & cd into this repository.
2. lein repl
3. Copy & paste:

(ns try-thurber
  (:require [thurber :as th]
            [thurber.sugar :refer :all]))

(->
  (th/create-pipeline)

  (th/apply!
    (read-text-file
      "demo/word_count/lorem.txt")
    (th/fn* extract-words [sentence]
      (remove empty? (.split sentence "[^\\p{L}]+")))
    (count-per-element)
    (th/fn* format-as-text
      [[k v]] (format "%s: %d" k v))
    (log-sink))

  (th/run-pipeline!))

Output:

...
INFO thurber - extremely: 1
INFO thurber - undertakes: 1
INFO thurber - pleasure: 7
INFO thurber - you: 2
...

• Enable Clojure
  • Bring Clojure's powerful, expressive toolkit (destructuring, immutability, REPL, async tools, etc etc) to Apache Beam.
• REPL Oriented
  • Functions are idiomatic/pure Clojure functions by default. (E.g., lazy sequences are supported making iterative event output optional/unnecessary, etc.)
  • Develop and test pipelines incrementally from the REPL.
  • Evaluate/learn Beam semantics (windowing, triggering) interactively.
• Avoid Macros
  • Limit macro infection. Most thurber constructions are macro-less, use of any thurber macro constructions (like inline functions) is optional.
• AOT Nothing
  • Fully dynamic experience. Reload namespaces at whim. thurber's dependency on Beam, Clojure, etc versions are completely dynamic/floatable. No forced AOT'd dependencies, Etc.
• No Lock-in
  • Pipelines can be composed of Clojure and Java transforms. Incrementally refactor your pipeline to Clojure or back to Java.
• Not Afraid of Java Interop
  • Wherever Clojure's Java Interop is performant and works cleanly with Beam's fluent API, encourage it; facade/sugar functions are simple to create and left to your own domain-specific implementations.
• Completeness
  • Support all Beam capabilities (Transforms, State & Timers, Side Inputs, Output Tags, etc.)
• Performance
  • Be finely tuned for data streaming.

• Code Walkthrough
• Beam Tutorial

Each namespace in the demo/ source directory is a pipeline written in Clojure using thurber. Comments in the source highlight salient aspects of thurber usage.

Along with the code walkthrough these are the best way to learn thurber's API and serve as recipes for various scenarios (use of tags, side inputs, windowing, combining, Beam's State API, etc etc.)

To execute a demo, start a REPL and evaluate (demo!) from within the respective namespace.

The word_count package contains ports of Beam's Word Count Examples to Clojure/thurber.

Beam's Mobile Gaming Examples have been ported to Clojure using thurber.

These are fully functional ports. They require deployment to GCP Dataflow:

• How to Run Beam Mobile Gaming Examples

First make your pipeline work. Then make it fast.

Streaming/big data implies hot code paths. Use Clojure type hints liberally within your stream functions.

If deploying to GCP, use Dataflow profiling to zero in on areas to optimize.

• https://write.as/aaron-d/clojure-data-streaming-and-dodging-static-types

Copyright © 2020 Aaron Dixon

Like Clojure distributed under the Eclipse Public License.