Shuffling Performance

Operations like groupby, join, and set_index have special performance considerations that are different from normal Pandas due to the parallel, larger-than-memory, and distributed nature of Dask DataFrame.

Easy Case

To start off, common groupby operations like df.groupby(columns).reduction() for known reductions like mean, sum, std, var, count, nunique are all quite fast and efficient, even if partitions are not cleanly divided with known divisions. This is the common case.

Additionally, if divisions are known, then applying an arbitrary function to groups is efficient when the grouping columns include the index.

Joins are also quite fast when joining a Dask DataFrame to a Pandas DataFrame or when joining two Dask DataFrames along their index. No special considerations need to be made when operating in these common cases.

So, if you’re doing common groupby and join operations, then you can stop reading this. Everything will scale nicely. Fortunately, this is true most of the time:

>>> ddf.groupby(columns).known_reduction()            # Fast and common case
>>> ddf.groupby(columns_with_index).apply(user_fn)    # Fast and common case
>>> ddf.join(pandas_df, on=column)                    # Fast and common case
>>> lhs.join(rhs)                                     # Fast and common case
>>> lhs.merge(rhs, on=columns_with_index)             # Fast and common case

Difficult Cases

In some cases, such as when applying an arbitrary function to groups (when not grouping on index with known divisions), when joining along non-index columns, or when explicitly setting an unsorted column to be the index, we may need to trigger a full dataset shuffle:

>>> ddf.groupby(columns_no_index).apply(user_fn)   # Requires shuffle
>>> lhs.join(rhs, on=columns_no_index)             # Requires shuffle
>>> ddf.set_index(column)                          # Requires shuffle

A shuffle is necessary when we need to re-sort our data along a new index. For example, if we have banking records that are organized by time and we now want to organize them by user ID, then we’ll need to move a lot of data around. In Pandas all of this data fits in memory, so this operation was easy. Now that we don’t assume that all data fits in memory, we must be a bit more careful.

Re-sorting the data can be avoided by restricting yourself to the easy cases mentioned above.

Shuffle Methods

There are currently two strategies to shuffle data depending on whether you are on a single machine or on a distributed cluster: shuffle on disk and shuffle over the network.

Shuffle on Disk

When operating on larger-than-memory data on a single machine, we shuffle by dumping intermediate results to disk. This is done using the partd project for on-disk shuffles.

Shuffle over the Network

When operating on a distributed cluster, the Dask workers may not have access to a shared hard drive. In this case, we shuffle data by breaking input partitions into many pieces based on where they will end up and moving these pieces throughout the network.

Selecting methods

Dask will use on-disk shuffling by default, but will switch to a distributed shuffling algorithm if the default scheduler is set to use a dask.distributed.Client, such as would be the case if the user sets the Client as default:

client = Client('scheduler:8786', set_as_default=True)

Alternatively, if you prefer to avoid defaults, you can configure the global shuffling method with the dataframe.shuffle.method configuration option. This can be done globally:

dask.config.set({"dataframe.shuffle.method": "p2p"})


or as a context manager:

with dask.config.set({"dataframe.shuffle.method": "p2p"}):

In addition, set_index also accepts a shuffle_method keyword argument that can be used to select either on-disk or task-based shuffling:

ddf.set_index(column, shuffle_method='disk')
ddf.set_index(column, shuffle_method='tasks')
ddf.set_index(column, shuffle_method='p2p')


Dask supports Pandas’ aggregate syntax to run multiple reductions on the same groups. Common reductions such as max, sum, list and mean are directly supported:

>>> ddf.groupby(columns).aggregate(['sum', 'mean', 'max', 'min', list])

Dask also supports user defined reductions. To ensure proper performance, the reduction has to be formulated in terms of three independent steps. The chunk step is applied to each partition independently and reduces the data within a partition. The aggregate combines the within partition results. The optional finalize step combines the results returned from the aggregate step and should return a single final column. For Dask to recognize the reduction, it has to be passed as an instance of dask.dataframe.Aggregation.

For example, sum could be implemented as:

custom_sum = dd.Aggregation('custom_sum', lambda s: s.sum(), lambda s0: s0.sum())

The name argument should be different from existing reductions to avoid data corruption. The arguments to each function are pre-grouped series objects, similar to df.groupby('g')['value'].

Many reductions can only be implemented with multiple temporaries. To implement these reductions, the steps should return tuples and expect multiple arguments. A mean function can be implemented as:

custom_mean = dd.Aggregation(
    lambda s: (s.count(), s.sum()),
    lambda count, sum: (count.sum(), sum.sum()),
    lambda count, sum: sum / count,

For example, let’s compute the group-wise extent (maximum - minimum) for a DataFrame.

>>> df = pd.DataFrame({
...   'a': ['a', 'b', 'a', 'a', 'b'],
...   'b': [0, 1, 0, 2, 5],
... })
>>> ddf = dd.from_pandas(df, 2)

We define the building blocks to find the maximum and minimum of each chunk, and then the maximum and minimum over all the chunks. We finalize by taking the difference between the Series with the maxima and minima

>>> def chunk(grouped):
...     return grouped.max(), grouped.min()

>>> def agg(chunk_maxes, chunk_mins):
...     return chunk_maxes.max(), chunk_mins.min()

>>> def finalize(maxima, minima):
...     return maxima - minima

Finally, we create and use the aggregation

>>> extent = dd.Aggregation('extent', chunk, agg, finalize=finalize)
>>> ddf.groupby('a').agg(extent).compute()
a  2
b  4

To apply dask.dataframe.groupby.SeriesGroupBy.nunique to more than one column you can use:

>>> df['c'] = [1, 2, 1, 1, 2]
>>> ddf = dd.from_pandas(df, 2)
>>> nunique = dd.Aggregation(
...     name="nunique",
...     chunk=lambda s: s.apply(lambda x: list(set(x))),
...     agg=lambda s0: s0.obj.groupby(level=list(range(s0.obj.index.nlevels))).sum(),
...     finalize=lambda s1: s1.apply(lambda final: len(set(final))),
... )
>>> ddf.groupby('a').agg({'b':nunique, 'c':nunique})

To access NumPy functions use apply with a lambda function such as .apply(lambda r: np.sum(r)). Here’s an example of how a sum of squares aggregation would look like:

>>> dd.Aggregation(name="sum_of_squares", chunk=lambda s: s.apply(lambda r: np.sum(np.power(r, 2))), agg=lambda s: s.sum())