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+---
+layout: post
+title: Improving GroupBy.map with Dask and Xarray
+author: Patrick Hoefler
+tags: [dask array, xarray]
+theme: twitter
+canonical_url: https://xarray.dev/blog/dask-detrending
+---
+
+{% include JB/setup %}
+
+_This post was originally published on the [Xarray blog](https://xarray.dev/blog/dask-detrending)._
+
+Running large-scale GroupBy-Map patterns with Xarray that are backed by [Dask arrays](https://docs.dask.org/en/stable/array.html) is
+an essential part of a lot of typical geospatial workloads. Detrending is a very common
+operation where this pattern is needed.
+
+In this post, we will explore how and why this caused so many pitfalls for Xarray users in
+the past and how we improved performance and scalability with a few changes to how Dask
+subselects data.
+
+## What is GroupBy.map?
+
+[`GroupBy.map`](https://docs.xarray.dev/en/stable/generated/xarray.core.groupby.DatasetGroupBy.map.html) lets you apply a User Defined Function (UDF)
+that accepts and returns Xarray objects. The UDF will receive an Xarray object (either a Dataset or a DataArray) containing Dask arrays corresponding to one single group.
+[`Groupby.reduce`](https://docs.xarray.dev/en/stable/generated/xarray.core.groupby.DatasetGroupBy.reduce.html) is quite similar
+in that it applies a UDF, but in this case the UDF will receive the underlying Dask arrays, _not_ Xarray objects.
+
+## The Application
+
+Consider a typical workflow where you want to apply a detrending step. You may want to smooth out
+the data by removing the trends over time. This is a common operation in climate science
+and normally looks roughly like this:
+
+```python
+def detrending_step(arr: DataArray) -> DataArray:
+ # important: the rolling operation is applied within a group
+ return arr - arr.rolling(time=30, min_periods=1).mean()
+
+data.groupby("time.dayofyear").map(detrending_step)
+```
+
+We are grouping by the day of the year and then are calculating the rolling average over
+30-year windows for a particular day.
+
+Our example will run on a 1 TiB array, 64 years worth of data and the following structure:
+
+
+
+
+
+The array isn't overly huge and the chunks are reasonably sized.
+
+## The Problem
+
+The general application seems straightforward. Group by the day of the year and apply a UDF
+to every group. There are a few pitfalls in this application that can make the result of
+this operation unusable. Our array is sorted by time, which means that we have to pick
+entries from many different areas in the array to create a single group (corresponding to a single day of the year).
+Picking the same day of every year is basically a slicing operation with a step size of 365.
+
+
+
+
+
+Our example has a year worth of data in a single chunk along the time axis. The general problem
+exists for any workload where you have to access random entries of data. This
+particular access pattern means that we have to pick one value per chunk, which is pretty
+inefficient. The right side shows the individual groups that we are operating on.
+
+One of the main issues with this pattern is that Dask will create a single output chunk per time
+entry, e.g. each group will consist of as many chunks as we have year.
+
+This results in a huge increase in the number of chunks:
+
+
+
+
+
+This simple operation increases the number of chunks from 5000 to close to 2 million. Each
+chunk only has a few hundred kilobytes of data. **This is pretty bad!**
+
+Dask computations generally scale along the number of chunks you have. Increasing the chunks by such
+a large factor is catastrophic. Each follow-up operation, as simple as `a-b` will create 2 million
+additional tasks.
+
+The only workaround for users was to rechunk to something more sensible afterward, but it
+still keeps the incredibly expensive indexing operation in the graph.
+
+Note this is the underlying problem that is [solved by flox](https://xarray.dev/blog/flox) for aggregations like `.mean()`
+using parallel-native algorithms to avoid the expense of indexing out each group.
+
+## Improvements to the Data Selection algorithm
+
+The method of how Dask selected the data was objectively pretty bad.
+A rewrite of the underlying algorithm enabled us to achieve a much more robust result. The new
+algorithm is a lot smarter about how to pick values from each individual chunk, but most importantly,
+it will try to preserve the input chunksize as closely as possible.
+
+For our initial example, it will put every group into a single chunk. This means that we will
+end up with the number of chunks along the time axis being equal to the number of groups, i.e. 365.
+
+
+
+
+
+The algorithm reduces the number of chunks from 2 million to roughly 30 thousand, which is a huge improvement
+and a scale that Dask can easily handle. The graph is now much smaller, and the follow-up operations
+will run a lot faster as well.
+
+This improvement will help every operation that we listed above and make the scale a lot more
+reliably than before. The algorithm is used very widely across Dask and Xarray and thus, influences
+many methods.
+
+## What's next?
+
+Xarray selects one group at a time for `groupby(...).map(...)`, i.e. this requires one operation
+per group. This will hurt scalability if the dataset has a very large number of groups, because
+the computation will create a very expensive graph. There is currently an effort to implement alternative
+APIs that are shuffle-based to circumvent that problem. A current PR is available [here](https://github.com/pydata/xarray/pull/9320).
+
+The fragmentation of the output chunks by indexing is something that will hurt every workflow that is selecting data in a random
+pattern. This also includes:
+
+- `.sel` if you aren't using slices explicitly
+- `.isel`
+- `.sortby`
+- `groupby(...).quantile()`
+- and many more.
+
+We expect all of these workloads to be substantially improved now.
+
+Additionally, [Dask improved a lot of things](https://docs.dask.org/en/stable/changelog.html#v2024-11-1) related to either increasing chunksizes or fragmentation
+of chunks over the cycle of a workload with more improvements to come. This will help a lot of
+users to get better and more reliable performance.
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