From 2c936d5d0e78d460e614f2169fd92c8e3734f147 Mon Sep 17 00:00:00 2001 From: Sarah Charlotte Johnson Date: Thu, 21 Nov 2024 12:53:47 -0800 Subject: [PATCH] Cross post dask detrending from xarray blog (#176) --- _posts/2024-11-21-dask-detrending.md | 135 ++++++++++++++++++ .../indexing-data-selection.png | Bin 0 -> 223830 bytes images/dask-detrending/input-array.png | Bin 0 -> 106711 bytes images/dask-detrending/output-array-new.png | Bin 0 -> 104908 bytes images/dask-detrending/output-array-old.png | Bin 0 -> 105429 bytes 5 files changed, 135 insertions(+) create mode 100644 _posts/2024-11-21-dask-detrending.md create mode 100644 images/dask-detrending/indexing-data-selection.png create mode 100644 images/dask-detrending/input-array.png create mode 100644 images/dask-detrending/output-array-new.png create mode 100644 images/dask-detrending/output-array-old.png diff --git a/_posts/2024-11-21-dask-detrending.md b/_posts/2024-11-21-dask-detrending.md new file mode 100644 index 0000000..2b5b5d1 --- /dev/null +++ b/_posts/2024-11-21-dask-detrending.md @@ -0,0 +1,135 @@ +--- +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: + +
+Python repr output of 1 TiB Dask array with shape (1801, 3600, 233376) split into 5460, 250 MiB chunks of (300, 300, 365) +
+ +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. + +
+Schematic showing an array sorted by time, where data is selected from many different areas in the array to create a single group (corresponding to a specific day of the year). +
+ +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: + +
+Python repr output of a 1 TiB Dask array with nearly 2 million, 700 kiB 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. + +
+Python repr output of a 1 TiB Dask array with 31164, 43 MiB chunks +
+ +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. 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