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kirby

Kirby slurps up the firehose of logs from Fastly and calculates daily counts for various Ruby ecosystem statistics, pretty quickly.

How fast is pretty quickly?

For an 80MB gzipped log file containing 915,427 JSON event objects (which is 1.02GB uncompressed):

  • 2.7 seconds total to read the entire file line by line
  • 5.0 seconds total to also parse every JSON object into a Rust struct
  • 7.8 seconds total to further parse every User Agent field for Bundler, RubyGems, and Ruby versions and other metrics

This is... very good. For comparison, a Python script that used AWS Glue to do something similar took about 30 minutes. My first approach of writing a nom parser-combinator to parse the User Agent field, instead of using a regex, took 18.7 seconds. Processing a gigabyte of almost a million JSON objects into useful histograms in less than 8 seconds just blows my mind. But then I figured out how to use Rayon, and now it can parse 8 gzipped log files in parallel on an 8-core MacBook Pro, and that's super fast.

Then Rust got more optimized and Apple released the M1, and it got still faster. Finally, and I found the profile-guided optimization docs, and it improved even more than I thought was still possible.

Wait, how fast?

    ~525 records/second/cpu in Python on Apache Spark in AWS Glue
 ~14,000 records/second/cpu in Ruby on a 2018 Intel MacBook Pro
~353,000 records/second/cpu in Rust on a 2018 Intel MacBook Pro
~550,000 records/second/cpu in Rust on a 2021 M1 MacBook Pro
~638,000 records/second/cpu in Rust on M1 with profile-guided optimization

Are you kidding me?

No. The latest version (which I am now benchmarking without also running cargo build 🤦🏻‍♂️) can parse records really, really fast.

    ~4,200 records/second in Python with 8 worker instances on AWS Glue
~1,085,000 records/second in Rust with rayon on an 8-core Intel MacBook Pro
~3,195,000 records/second in Rust with rayon on a 10-core M1 MacBook Pro
~3,583,000 records/second in Rust with rayon on M1 with profile-guided optimization

What does it calculate?

It counts Bundler, RubyGems, and Ruby versions, in hourly buckets, and prints those out as nested JSON to stdout.

Tell me more about how this happened.

Okay, I wrote a blog post with details about creating this library, and a follow up about more optimizations.