This work is supported by Continuum Analytics the XDATA Program and the Data Driven Discovery Initiative from the Moore Foundation.

## Summary

Performing benchmarks to compare software is surprisingly difficult to do fairly, even assuming the best intentions of the author. Technical developers can fall victim to a few natural human failings:

1. We judge other projects by our own objectives rather than the objectives under which that project was developed
2. We fail to use other projects with the same expertise that we have for our own
3. We naturally gravitate towards cases at which our project excels
4. We improve our software during the benchmarking process
5. We don’t release negative results

We discuss each of these failings in the context of current benchmarks I’m working on comparing Dask and Spark Dataframes.

## Introduction

Last week I started comparing performance between Dask Dataframes (a project that I maintain) and Spark Dataframes (the current standard). My initial results showed that Dask.dataframes were overall much faster, somewhere like 5x.

These results were wrong. They weren’t wrong in a factual sense, and the experiments that I ran were clear and reproducible, but there was so much bias in how I selected, set up, and ran those experiments that the end result was misleading. After checking results myself and then having other experts come in and check my results I now see much more sensible numbers. At the moment both projects are within a factor of two most of the time, with some interesting exceptions either way.

This blogpost outlines the ways in which library authors can fool themselves when performing benchmarks, using my recent experience as an anecdote. I hope that this encourages authors to check themselves, and encourages readers to be more critical of numbers that they see in the future.

This problem exists as well in academic research. For a pop-science rendition I recommend “The Experiment Experiment” on the Planet Money Podcast.

## Skewed Objectives

Feature X is so important. I wonder how the competition fares?

Every project is developed with different applications in mind and so has different strengths and weaknesses. If we approach a benchmark caring only about our original objectives and dismissing the objectives of the other projects then we’ll very likely trick ourselves.

For example consider reading CSV files. Dask’s CSV reader is based off of Pandas’ CSV reader, which was the target of great effort and love; this is because CSV was so important to the finance community where Pandas grew up. Spark’s CSV solution is less awesome, but that’s less about the quality of Spark and more a statement about how Spark users tend not to use CSV. When they use text-based formats they’re much more likely to use line-delimited JSON, which is typical in Spark’s common use cases (web diagnostics, click logs, and so on). Pandas/Dask came from the scientific and finance worlds where CSV is king while Spark came from the web world where JSON reigns.

Conversely, Dask.dataframe hasn’t bothered to hook up the pandas.read_json function to Dask.dataframe yet. Surprisingly it rarely comes up. Both projects can correctly say that the other project’s solution to what they consider the standard text-based file format is less-than-awesome. Comparing performance here either way will likely lead to misguided conclusions.

So when benchmarking data ingestion maybe we look around a bit, see that both claim to support Parquet well, and use that as the basis for comparison.

## Skewed Experience

Whoa, this other project has a lot of configuration parameters! Let’s just use the defaults.

Software is often easy to set up, but often requires experience to set up optimally. Authors are naturally more adept at setting up their own software than the software of their competition.

My original (and flawed) solution to this was to “just use the defaults” on both projects. Given my inability to tune Spark (there are several dozen parameters) I decided to also not tune Dask and run under default settings. I figured that this would be a good benchmark not only of the software, but also on choices for sane defaults, which is a good design principle in itself.

This failed spectacularly because I was making unconscious decisions like the size of machines that I was using for the experiment, CPU/memory ratios, and so on. It turns out that Spark’s defaults are optimized for very small machines (or more likely, small YARN containers) and use only 1GB of memory per executor by default while Dask is typically run on larger boxes or has the full use of a single machine in a single shared-memory process. My standard cluster configurations were biased towards Dask before I even considered running a benchmark.

Similarly the APIs of software projects are complex and for any given problem there is often both a fast way and a general-but-slow way. Authors naturally choose the fast way on their own system but inadvertently choose the general way that comes up first when reading documentation for the other project. It often takes months of hands-on experience to understand a project well enough to definitely say that you’re not doing things in a dumb way.

In both cases I think the only solution is to collaborate with someone that primarily uses the other system.

## Preference towards strengths

Oh hey, we’re doing really well here. This is great! Let’s dive into this a bit more.

It feels great to see your project doing well. This emotional pleasure response is powerful. It’s only natural that we pursue that feeling more, exploring different aspects of it. This can skew our writing as well. We’ll find that we’ve decided to devote 80% of the text to what originally seemed like a small set of features, but which now seems like the main point.

It’s important that we define a set of things we’re going to study ahead of time and then stick to those things. When we run into cases where our project fails we should take that as an opportunity to raise an issue for future (though not current) development.

## Tuning during experimentation

Oh, I know why this is slow. One sec, let me change something in the code.

I’m doing this right now. Dask dataframe shuffles are generally slower than Spark dataframe shuffles. On numeric data this used to be around a 2x difference, now it’s more like a 1.2x difference (at least on my current problem and machine). Overall this is great, seeing that another project was beating Dask motivated me to dive in (see dask/distributed #932) and this will result in a better experience for users in the future. As a developer this is also how I operate. I define a benchmark, profile my code, identify bottlenecks, and optimize. Business as usual.

However as an author of a comparative benchmark this is also somewhat dishonest; I’m not giving the Spark developers the same opportunity to find and fix similar performance issues in their software before I publish my results. I’m also giving a biased picture to my readers. I’ve made all of the pieces that I’m going to show off fast while neglecting the others. Picking benchmarks, optimizing the project to make them fast, and then publishing those results gives the incorrect impression that the entire project has been optimized to that level.

## Omission

So, this didn’t go as planned. Let’s wait a few months until the next release.

There is no motivation to publish negative results. Unless of course you’ve just written a blogpost announcing that you plan to release benchmarks in the near future. Then you’re really forced to release numbers, even if they’re mixed.

That’s ok. Mixed numbers can be informative. They build trust and community. And we all talk about open source community driven software, so these should be welcome.

## Straight up bias

Look, we’re not in grad-school any more. We’ve got to convince companies to actually use this stuff.

Everything we’ve discussed so far assumes best intentions, and that the author is acting in good faith, but falling victim to basic human failings.

However many developers today (including myself) are paid and work for for-profit companies that need to make money. To an increasing extent making this money depends on community mindshare, which means publishing benchmarks that sway users to our software. Authors have bosses that they’re trying to impress or the content and tone of an article may be influenced by people within the company other than the stated author.

I’ve been pretty lucky working with Continuum Analytics (my employer) in that they’ve been pretty hands-off with technical writing. For other employers that may be reading, we’ve actually had an easier time getting business because of the honest tone in these blogposts in some cases. Potential clients generally have the sense that we’re trustworthy.

Technical honesty goes a surprisingly long way towards implying technical proficiency.