Dask Best Practices

It is easy to get started with Dask’s APIs, but using them well requires some experience. This page contains suggestions for Dask best practices and includes solutions to common Dask problems.

This document specifically focuses on best practices that are shared among all of the Dask APIs. Readers may first want to investigate one of the API-specific Best Practices documents first.

Start Small

Parallelism brings extra complexity and overhead. Sometimes it’s necessary for larger problems, but often it’s not. Before adding a parallel computing system like Dask to your workload you may want to first try some alternatives:

  • Use better algorithms or data structures: NumPy, pandas, Scikit-learn may have faster functions for what you’re trying to do. It may be worth consulting with an expert or reading through their docs again to find a better pre-built algorithm.

  • Better file formats: Efficient binary formats that support random access can often help you manage larger-than-memory datasets efficiently and simply. See the Store Data Efficiently section below.

  • Compiled code: Compiling your Python code with Numba or Cython might make parallelism unnecessary. Or you might use the multi-core parallelism available within those libraries.

  • Sampling: Even if you have a lot of data, there might not be much advantage from using all of it. By sampling intelligently you might be able to derive the same insight from a much more manageable subset.

  • Profile: If you’re trying to speed up slow code it’s important that you first understand why it is slow. Modest time investments in profiling your code can help you to identify what is slowing you down. This information can help you make better decisions about if parallelism is likely to help, or if other approaches are likely to be more effective.

Use The Dashboard

Dask’s dashboard helps you to understand the state of your workers. This information can help to guide you to efficient solutions. In parallel and distributed computing there are new costs to be aware of and so your old intuition may no longer be true. Working with the dashboard can help you relearn about what is fast and slow and how to deal with it.

See Documentation on Dask’s dashboard for more information.

Avoid Very Large Partitions

Your chunks of data should be small enough so that many of them fit in a worker’s available memory at once. You often control this when you select partition size in Dask DataFrame (see DataFrame Partitions) or chunk size in Dask Array (see Array Chunks).

Dask will likely manipulate as many chunks in parallel on one machine as you have cores on that machine. So if you have 1 GB chunks and ten cores, then Dask is likely to use at least 10 GB of memory. Additionally, it’s common for Dask to have 2-3 times as many chunks available to work on so that it always has something to work on.

If you have a machine with 100 GB and 10 cores, then you might want to choose chunks in the 1GB range. You have space for ten chunks per core which gives Dask a healthy margin, without having tasks that are too small.

Note that you also want to avoid chunk sizes that are too small. See the next section for details. For a more detailed guide to choosing chunk sizes for Dask Arrays, see this blog post on Choosing good chunk sizes.

Avoid Very Large Graphs

Dask workloads are composed of tasks. A task is a Python function, like np.sum applied onto a Python object, like a pandas DataFrame or NumPy array. If you are working with Dask collections with many partitions, then every operation you do, like x + 1 likely generates many tasks, at least as many as partitions in your collection.

Every task comes with some overhead. This is somewhere between 200us and 1ms. If you have a computation with thousands of tasks this is fine, there will be about a second of overhead, and that may not trouble you.

However when you have very large graphs with millions of tasks then this may become troublesome, both because overhead is now in the 10 minutes to hours range, and also because the overhead of dealing with such a large graph can start to overwhelm the scheduler.

You can build smaller graphs by:

  • Increasing your chunk size: If you have a 1,000 GB of data and are using 10 MB chunks, then you have 100,000 partitions. Every operation on such a collection will generate at least 100,000 tasks.

    However if you increase your chunksize to 1 GB or even a few GB then you reduce the overhead by orders of magnitude. This requires that your workers have much more than 1 GB of memory, but that’s typical for larger workloads.

  • Fusing operations together: Dask will do a bit of this on its own, but you can help it. If you have a very complex operation with dozens of sub-operations, maybe you can pack that into a single Python function and use a function like da.map_blocks or dd.map_partitions.

    In general, the more administrative work you can move into your functions the better. That way the Dask scheduler doesn’t need to think about all of the fine-grained operations.

  • Breaking up your computation: For very large workloads you may also want to try sending smaller chunks to Dask at a time. For example if you’re processing a petabyte of data but find that Dask is only happy with 100 TB, maybe you can break up your computation into ten pieces and submit them one after the other.

Learn Techniques For Customization

The high level Dask collections (array, DataFrame, bag) include common operations that follow standard Python APIs from NumPy and pandas. However, many Python workloads are complex and may require operations that are not included in these high level APIs.

Fortunately, there are many options to support custom workloads:

  • All collections have a map_partitions or map_blocks function, that applies a user provided function across every pandas DataFrame or NumPy array in the collection. Because Dask collections are made up of normal Python objects, it’s often quite easy to map custom functions across partitions of a dataset without much modification.

  • More complex map_* functions. Sometimes your custom behavior isn’t embarrassingly parallel, but requires more advanced communication. For example maybe you need to communicate a little bit of information from one partition to the next, or maybe you want to build a custom aggregation.

    Dask collections include methods for these as well.

  • For even more complex workloads you can convert your collections into individual blocks, and arrange those blocks as you like using Dask Delayed. There is usually a to_delayed method on every collection.

map_partitions(func, *args[, meta, ...])

Apply Python function on each DataFrame partition.

map_overlap(func, df, before, after, *args)

Apply a function to each partition, sharing rows with adjacent partitions.

groupby.Aggregation(name, chunk, agg[, finalize])

User defined groupby-aggregation.

blockwise(func, out_ind, *args[, name, ...])

Tensor operation: Generalized inner and outer products

map_blocks(func, *args[, name, token, ...])

Map a function across all blocks of a dask array.

map_overlap(func, *args[, depth, boundary, ...])

Map a function over blocks of arrays with some overlap

reduction(x, chunk, aggregate[, axis, ...])

General version of reductions

Stop Using Dask When No Longer Needed

In many workloads it is common to use Dask to read in a large amount of data, reduce it down, and then iterate on a much smaller amount of data. For this latter stage on smaller data it may make sense to stop using Dask, and start using normal Python again.

df = dd.read_parquet("lots-of-data-*.parquet")
df = df.groupby('name').mean()  # reduce data significantly
df = df.compute()               # continue on with pandas/NumPy

Persist When You Can

Accessing data from RAM is often much faster than accessing it from disk. Once you have your dataset in a clean state that both:

  1. Fits in memory

  2. Is clean enough that you will want to try many different analyses

Then it is a good time to persist your data in RAM

df = dd.read_parquet("lots-of-data-*.parquet")
df = df.fillna(...)  # clean up things lazily
df = df[df.name == 'Alice']  # get down to a more reasonable size

df = df.persist()  # trigger computation, persist in distributed RAM


This is only relevant if you are on a distributed machine. On a local machine (using single-machine schedulers) persist just triggers immediate computation like compute.

Store Data Efficiently

As your ability to compute increases you will likely find that data access and I/O take up a larger portion of your total time. Additionally, parallel computing will often add new constraints to how your store your data, particularly around providing random access to blocks of your data that are in line with how you plan to compute on it.

For example:

  • For compression you’ll probably find that you drop gzip and bz2, and embrace newer systems like lz4, snappy, and Z-Standard that provide better performance and random access.

  • For storage formats you may find that you want self-describing formats that are optimized for random access, metadata storage, and binary encoding like Parquet, ORC, Zarr, HDF5, and GeoTIFF.

  • When working on the cloud you may find that some older formats like HDF5 may not work as well.

  • You may want to partition or chunk your data in ways that align well to common queries. In Dask DataFrame this might mean choosing a column to sort by for fast selection and joins. For Dask Array this might mean choosing chunk sizes that are aligned with your access patterns and algorithms.

Processes and Threads

If you’re doing mostly numeric work with Numpy, pandas, Scikit-learn, Numba, and other libraries that release the GIL, then use mostly threads. If you’re doing work on text data or Python collections like lists and dicts then use mostly processes.

If you’re on larger machines with a high thread count (greater than 10), then you should probably split things up into at least a few processes regardless. Python can be highly productive with 10 threads per process with numeric work, but not 50 threads.

For more information on threads, processes, and how to configure them in Dask, see the scheduler documentation.

Load Data with Dask

If you need to work with large Python objects, then please let Dask create them. A common anti-pattern we see is people creating large Python objects outside of Dask, then giving those objects to Dask and asking it to manage them. This works, but means that Dask needs to move around these very large objects with its metadata, rather than as normal Dask-controlled results.

Here are some common patterns to avoid and nicer alternatives:


# Don't

ddf = ... a dask dataframe ...
for fn in filenames:
    df = pandas.read_csv(fn)  # Read locally with pandas
    ddf = ddf.append(df)            # Give to Dask
# Do

ddf = dd.read_csv(filenames)


# Don't

f = h5py.File(...)
x = np.asarray(f["x"])  # Get data as a NumPy array locally

x = da.from_array(x)  # Hand NumPy array to Dask
# Do

f = h5py.File(...)
x = da.from_array(f["x"])  # Let Dask do the reading


# Don't

def process(a, b):

df = pandas.read_csv("some-large-file.csv")  # Create large object locally
results = []
for item in L:
    result = process(item, df)  # include df in every delayed call
# Do

def process(a, b):

df = dask.delayed(pandas.read_csv)("some-large-file.csv")  # Let Dask build object
results = []
for item in L:
    result = process(item, df)  # include pointer to df in every delayed call

Avoid calling compute repeatedly

Compute related results with shared computations in a single dask.compute() call

# Don't repeatedly call compute

df = dd.read_csv("...")
xmin = df.x.min().compute()
xmax = df.x.max().compute()
# Do compute multiple results at the same time

df = dd.read_csv("...")

xmin, xmax = dask.compute(df.x.min(), df.x.max())

This allows Dask to compute the shared parts of the computation (like the dd.read_csv call above) only once, rather than once per compute call.