Optimize CuDF Testing: Remove Redundant Tests

Hey everyone! Let's dive into a crucial discussion about optimizing our cuDF testing process. Currently, our classic test suite relies heavily on comparing cuDF results with those from pandas. But with the introduction of cudf.pandas, we're essentially doubling our workload by running the entire pandas test suite with cudf.pandas enabled. This raises a vital question: how can we streamline our testing efforts by removing redundant tests and even contributing some upstream to pandas?

The Challenge of Redundant Testing

Right now, redundant testing is a significant issue. Since cudf.pandas allows cuDF to function as a pandas-compatible backend, we're essentially running many of the same tests twice. This not only consumes valuable time and resources but also makes it harder to pinpoint the root cause of failures. Imagine running the same marathon twice – you'd definitely feel the burn!

Our current testing strategy involves comparing cuDF's outputs with pandas, which was a smart move initially. However, cudf.pandas changes the game. It means we're testing cuDF's pandas compatibility by, well, running pandas tests! It's like having two chefs independently create the same dish and then comparing them – a bit repetitive, isn't it?

So, what’s the solution? We need to carefully analyze our test suite and identify areas where we're duplicating efforts. This involves a deep dive into the tests themselves, understanding what they're verifying, and determining if they’re truly necessary in the context of cudf.pandas. It’s like decluttering your closet – you need to pull everything out, see what you have, and decide what to keep, donate, or toss. Speaking of edge cases, we should also explore whether some of our tests could actually benefit the broader pandas community. If our tests are uncovering issues that pandas itself doesn't catch, it's a win-win situation to contribute them upstream. This strengthens both libraries and reduces our maintenance burden. It’s like sharing your secret recipe with the world – everyone benefits!

The Importance of Maintaining Independent cuDF Functionality

It's crucial to remember that while cudf.pandas is a fantastic feature, we still intend to maintain cuDF's ability to run independently, without pandas compatibility mode enabled. This is vital for scenarios where performance is paramount, such as avoiding unnecessary sorting in join operations. Think of it like having a sports car and an SUV – both can get you from point A to point B, but the sports car offers a different level of performance and handling.

This means we can't simply eliminate all tests that overlap with pandas. We need to retain a subset of tests that specifically verify cuDF's core functionality and performance when running in its native mode. It’s like having a backup generator – you hope you never need it, but you're glad it's there when the power goes out.

So, how do we strike the right balance? We need to identify the key areas where cuDF diverges from pandas or offers unique optimizations. These areas should be the focus of our independent cuDF tests. It’s like focusing your training on your weak spots – you want to become well-rounded, not just strong in certain areas.

For example, consider operations like joins. In some cases, cuDF can perform joins more efficiently by skipping the sorting step, which is often required for pandas compatibility. We need to ensure that this optimization continues to work as expected, even when cudf.pandas is not in use. It’s like ensuring your car’s turbocharger is working – it gives you a performance boost when you need it.

Upstreaming Tests to Pandas: A Collaborative Approach

One of the most exciting aspects of this discussion is the potential to contribute our tests upstream to pandas. Our test suite has a reputation for covering many edge cases that the pandas test suite doesn't. By sharing these tests, we can help improve the robustness and reliability of pandas itself. It’s like open-source collaboration at its finest – everyone benefits from shared knowledge and effort.

Think about it: our tests are like carefully crafted puzzles designed to expose potential weaknesses in the code. If we share these puzzles with the pandas community, they can use them to strengthen their own codebase. This not only benefits pandas users but also indirectly benefits cuDF users, as pandas is a crucial dependency for many cuDF workflows. It’s like strengthening the foundation of a building – the entire structure becomes more resilient.

The challenge, of course, is to identify the tests that are most suitable for upstreaming. This requires a good understanding of the pandas codebase and testing philosophy. We need to ensure that our tests are well-written, well-documented, and aligned with pandas' coding standards. It’s like translating a book into another language – you need to capture the essence of the original while making it accessible to a new audience.

Moreover, we need to be mindful of the potential impact on pandas' test suite runtime. Adding a large number of new tests could significantly increase the time it takes to run the pandas test suite, which could be a barrier to adoption. It’s like adding new features to a software application – you need to weigh the benefits against the potential performance impact.

Addressing CI Time Concerns

Speaking of test suite runtime, this brings us to another critical consideration: our Continuous Integration (CI) times. Currently, running the pandas test suite with cudf.pandas enabled takes significantly longer than running the cuDF test suite (~50 minutes vs ~20 minutes). If we were to rely solely on the pandas test suite, we risk dramatically increasing our CI times, which could slow down our development process. It’s like hitting a traffic jam on your way to work – it can really throw off your schedule.

This is a valid concern, and we need to address it proactively. One approach is to explore parsimonious subselection of tests on different CI jobs. This means strategically selecting a subset of tests to run on each CI job, ensuring that we cover all critical areas without running the entire test suite every time. It’s like having a well-organized checklist – you prioritize the most important items and tackle them first.

For example, we could have separate CI jobs for core cuDF functionality, pandas compatibility, and performance testing. Each job would run a specific subset of tests tailored to its purpose. This would allow us to catch regressions quickly without bogging down our CI system. It’s like having different teams working on different parts of a project – you can make progress in parallel and avoid bottlenecks.

However, it's also important to recognize that our CI times are likely to fluctuate as we continue to develop cuDF and cudf.pandas. Recent efforts, such as #18659 and #19693, are expected to have a significant impact on the total runtime of the pandas test suite with cudf.pandas. It’s like riding a roller coaster – there will be ups and downs along the way.

Moving Forward: A Phased Approach

Given the complexities involved, it's wise to adopt a phased approach to optimizing our test suite. We can start by tackling the most obvious redundancies and gradually work our way towards more complex scenarios. It’s like climbing a mountain – you don’t try to reach the summit in one giant leap.

In the immediate future, we should focus on identifying and removing tests that are clearly duplicated between the cuDF and pandas test suites. This is the low-hanging fruit that will give us the most immediate benefit. It’s like weeding your garden – you start with the biggest, most obvious weeds.

In parallel, we can begin the process of evaluating our tests for potential upstreaming to pandas. This requires careful consideration of pandas' testing guidelines and a collaborative approach with the pandas community. It’s like building a bridge – you need to work with both sides to ensure a solid connection.

Finally, we should closely monitor our CI times and be prepared to adjust our testing strategy as needed. This is an ongoing process that requires continuous evaluation and adaptation. It’s like navigating a ship – you need to constantly adjust your course based on the changing winds and currents.

By taking a thoughtful and strategic approach, we can streamline our cuDF testing process, improve our CI times, and contribute to the broader open-source community. Let's work together to make cuDF even better!

Repair Input Keyword

• Can we remove or upstream some tests in the cuDF classic test suite, considering cudf.pandas and potential CI time increases?

Title

Optimize cuDF Testing: Remove Redundant Tests