Dask supports several user interfaces:
Each of these user interfaces employs the same underlying parallel computing machinery, and so has the same scaling, diagnostics, resilience, and so on, but each provides a different set of parallel algorithms and programming style.
This document helps you to decide which user interface best suits your needs, and gives some general information that applies to all interfaces. The pages linked above give more information about each interface in greater depth.
Many people who start using Dask are explicitly looking for a scalable version of NumPy, Pandas, or Scikit-Learn. For these situations, the starting point within Dask is usually fairly clear. If you want scalable NumPy arrays, then start with Dask array; if you want scalable Pandas DataFrames, then start with Dask DataFrame, and so on.
These high-level interfaces copy the standard interface with slight variations. These interfaces automatically parallelize over larger datasets for you for a large subset of the API from the original project.
# Arrays import dask.array as da x = da.random.uniform(low=0, high=10, size=(10000, 10000), # normal numpy code chunks=(1000, 1000)) # break into chunks of size 1000x1000 y = x + x.T - x.mean(axis=0) # Use normal syntax for high level algorithms # DataFrames import dask.dataframe as dd df = dd.read_csv('2018-*-*.csv', parse_dates='timestamp', # normal Pandas code blocksize=64000000) # break text into 64MB chunks s = df.groupby('name').balance.mean() # Use normal syntax for high level algorithms # Bags / lists import dask.bag as db b = db.read_text('*.json').map(json.loads) total = (b.filter(lambda d: d['name'] == 'Alice') .map(lambda d: d['balance']) .sum())
It is important to remember that, while APIs may be similar, some differences do exist. Additionally, the performance of some algorithms may differ from their in-memory counterparts due to the advantages and disadvantages of parallel programming. Some thought and attention is still required when using Dask.
Often when parallelizing existing code bases or building custom algorithms, you run into code that is parallelizable, but isn't just a big DataFrame or array. Consider the for-loopy code below:
results =  for a in A: for b in B: if a < b: c = f(a, b) else: c = g(a, b) results.append(c)
There is potential parallelism in this code (the many calls to
can be done in parallel), but it's not clear how to rewrite it into a big
array or DataFrame so that it can use a higher-level API. Even if you could
rewrite it into one of these paradigms, it's not clear that this would be a
good idea. Much of the meaning would likely be lost in translation, and this
process would become much more difficult for more complex systems.
Instead, Dask's lower-level APIs let you write parallel code one function call at a time within the context of your existing for loops. A common solution here is to use :doc:`Dask delayed <delayed>` to wrap individual function calls into a lazily constructed task graph:
import dask lazy_results =  for a in A: for b in B: if a < b: c = dask.delayed(f)(a, b) # add lazy task else: c = dask.delayed(g)(a, b) # add lazy task lazy_results.append(c) results = dask.compute(*lazy_results) # compute all in parallel
Combining High- and Low-Level Interfaces
It is common to combine high- and low-level interfaces. For example, you might use Dask array/bag/dataframe to load in data and do initial pre-processing, then switch to Dask delayed for a custom algorithm that is specific to your domain, then switch back to Dask array/dataframe to clean up and store results. Understanding both sets of user interfaces, and how to switch between them, can be a productive combination.
# Convert to a list of delayed Pandas dataframes delayed_values = df.to_delayed() # Manipulate delayed values arbitrarily as you like # Convert many delayed Pandas DataFrames back to a single Dask DataFrame df = dd.from_delayed(delayed_values)
Laziness and Computing
Most Dask user interfaces are lazy, meaning that they do not evaluate until
you explicitly ask for a result using the
# This array syntax doesn't cause computation y = x + x.T - x.mean(axis=0) # Trigger computation by explicitly calling the compute method y = y.compute()
If you have multiple results that you want to compute at the same time, use the
dask.compute function. This can share intermediate results and so be more
# compute multiple results at the same time with the compute function min, max = dask.compute(y.min(), y.max())
Note that the
compute() function returns in-memory results. It converts
Dask DataFrames to Pandas DataFrames, Dask arrays to NumPy arrays, and Dask
bags to lists. You should only call compute on results that will fit
comfortably in memory. If your result does not fit in memory, then you might
consider writing it to disk instead.
# Write larger results out to disk rather than store them in memory my_dask_dataframe.to_parquet('myfile.parquet') my_dask_array.to_hdf5('myfile.hdf5') my_dask_bag.to_textfiles('myfile.*.txt')
Persist into Distributed Memory
Alternatively, if you are on a cluster, then you may want to trigger a
computation and store the results in distributed memory. In this case you do
not want to call
compute, which would create a single Pandas, NumPy, or
list result. Instead, you want to call
persist, which returns a new Dask
object that points to actively computing, or already computed results spread
around your cluster's memory.
# Compute returns an in-memory non-Dask object y = y.compute() # Persist returns an in-memory Dask object that uses distributed storage if available y = y.persist()
This is common to see after data loading an preprocessing steps, but before rapid iteration, exploration, or complex algorithms. For example, we might read in a lot of data, filter down to a more manageable subset, and then persist data into memory so that we can iterate quickly.
import dask.dataframe as dd df = dd.read_parquet('...') df = df[df.name == 'Alice'] # select important subset of data df = df.persist() # trigger computation in the background # These are all relatively fast now that the relevant data is in memory df.groupby(df.id).balance.sum().compute() # explore data quickly df.groupby(df.id).balance.mean().compute() # explore data quickly df.id.nunique() # explore data quickly
Lazy vs Immediate
As mentioned above, most Dask workloads are lazy, that is, they don't start any
work until you explicitly trigger them with a call to
However, sometimes you do want to submit work as quickly as possible, track it
over time, submit new work or cancel work depending on partial results, and so
on. This can be useful when tracking or responding to real-time events,
handling streaming data, or when building complex and adaptive algorithms.
For these situations, people typically turn to the :doc:`futures interface <futures>` which is a low-level interface like Dask delayed, but operates immediately rather than lazily.
Here is the same example with Dask delayed and Dask futures to illustrate the difference.
@dask.delayed def inc(x): return x + 1 @dask.delayed def add(x, y): return x + y a = inc(1) # no work has happened yet b = inc(2) # no work has happened yet c = add(a, b) # no work has happened yet c = c.compute() # This triggers all of the above computations
from dask.distributed import Client client = Client() def inc(x): return x + 1 def add(x, y): return x + y a = client.submit(inc, 1) # work starts immediately b = client.submit(inc, 2) # work starts immediately c = client.submit(add, a, b) # work starts immediately c = c.result() # block until work finishes, then gather result
You can also trigger work with the high-level collections using the
persist function. This will cause work to happen in the background when
using the distributed scheduler.
There are established ways to combine the interfaces above:
The high-level interfaces (array, bag, dataframe) have a
to_delayedmethod that can convert to a sequence (or grid) of Dask delayed objects
delayeds = df.to_delayed()
The high-level interfaces (array, bag, dataframe) have a
from_delayedmethod that can convert from either Delayed or Future objects
df = dd.from_delayed(delayeds) df = dd.from_delayed(futures)
Client.computemethod converts Delayed objects into Futures
futures = client.compute(delayeds)
dask.distributed.futures_offunction gathers futures from persisted collections
from dask.distributed import futures_of df = df.persist() # start computation in the background futures = futures_of(df)
The Dask.delayed object converts Futures into delayed objects
delayed_value = dask.delayed(future)
The approaches above should suffice to convert any interface into any other. We often see some anti-patterns that do not work as well:
- Calling low-level APIs (delayed or futures) on high-level objects (like
Dask arrays or DataFrames). This downgrades those objects to their NumPy or
Pandas equivalents, which may not be desired.
Often people are looking for APIs like
compute()on Future objects. Often people want the
- Calling NumPy/Pandas functions on high-level Dask objects or high-level Dask functions on NumPy/Pandas objects
Most people who use Dask start with only one of the interfaces above but eventually learn how to use a few interfaces together. This helps them leverage the sophisticated algorithms in the high-level interfaces while also working around tricky problems with the low-level interfaces.
For more information, see the documentation for the particular user interfaces below: