Building Big Data Pipelines For Audio With Klio

Summary

Technologies for building data pipelines have been around for decades, with many mature options for a variety of workloads. However, most of those tools are focused on processing of text based data, both structured and unstructured. For projects that need to manage large numbers of binary and audio files the list of options is much shorter. In this episode Lynn Root shares the work that she and her team at Spotify have done on the Klio project to make that list a bit longer. She discusses the problems that are specific to working with binary data, how the Klio project is architected to allow for scalable and efficient processing of massive numbers of audio files, why it was released as open source, and how you can start using it today for your own projects. If you are struggling with ad-hoc infrastructure and a medley of tools that have been cobbled together for analyzing large or numerous binary assets then this is definitely a tool worth testing out.

Announcements

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• Your host as usual is Tobias Macey and today I’m interviewing Lynn Root about Klio, an open source pipeline for processing audio and binary data

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by describing what Klio is and how it got started?
• What are some of the challenges that are unique to processing audio data as compared to text?
• What use cases does Klio enable?
• What are some of the alternative options available for working with binary data?
  • What capabilities were lacking in other solutions that made it worthwhile to build a new system from scratch?
• Can you describe the design and architecture of Klio?
  • What was the motivation for implementing Klio as a Python framework, rather than building on top of the Scio project?
• How much of a challenge has it been to interface to the Beam framework from Python? (Java <-> Python impedance mismatch)
• One of the interesting optimizations in Klio is the option for bottom up execution of a job to avoid processing a given file unless absolutely necessary. What are some of the other useful or interesting capabilities that are built into Klio?
• What was the motivation and process for releasing Klio as open source?
• For someone who is building a pipeline with Klio, can you talk through the workflow?
  • What are the extension and integration points that are exposed?
  • How does Klio handle third party dependencies for a given job?
• What are some of the challenges, misunderstandings, or edge cases that users of Klio should be aware of?
• What are some of the most interesting, unexpected, or challenging lessons that you have learned while building and growing the Klio project?
• What are some of the most interesting, innovative, or unexpected ways that you have seen Klio used?
• What do you have planned for the future of the project?

Keep In Touch

• GitHub
• Twitter
• LinkedIn

Picks

• Tobias
  • PSF Fundraiser
• Lynn
  • Roam note-taking tool

Closing Announcements

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Links

• Klio
  • Announcement Blog Post
  • Docs
  • GitHub
• Spotify
• PyLadies SF
• Luigi
• RAML
• ramlfications
• Interrogate
• Apache Beam
• Librosa
• PyAudio
• Pillow
  • Podcast Episode
• FFMPeg
• ImageMagick
• Music Information Retrieval
• Machine Hearing
  • Data Engineering Podcast Episode
• Scio
• Microsoft Azure
• Google Cloud Platform
• Google Cloud Dataflow
• Protocol Buffers
• Apache Spark
• PySpark
• DAG == Directed Acyclic Graph
• ISMIR Conference
• Digital Signal Processing (DSP)
• Python Pickle
• Research paper on separating vocals from instrumentals of a song
• New York Times: Why songs of the summer sound the same
• Microsoft’s Rocket Platform for video analytics

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA