Data Locality
Contents
Data Locality¶
Data movement often needlessly limits performance.
This is especially true for analytic computations. Dask.distributed minimizes data movement when possible and enables the user to take control when necessary. This document describes current scheduling policies and user API around data locality.
Current Policies¶
Task Submission¶
In the common case distributed runs tasks on workers that already hold
dependent data. If you have a task f(x)
that requires some data x
then
that task will very likely be run on the worker that already holds x
.
If a task requires data split among multiple workers, then the scheduler chooses
to run the task on the worker that requires the least data transfer to it.
The size of each data element is measured by the workers using the
sys.getsizeof
function, which depends on the __sizeof__
protocol
generally available on most relevant Python objects.
Data Scatter¶
When a user scatters data from their local process to the distributed network
this data is distributed in a round-robin fashion grouping by number of cores.
So for example If we have two workers Alice
and Bob
, each with two
cores and we scatter out the list range(10)
as follows:
futures = client.scatter(range(10))
Then Alice and Bob receive the following data
Alice:
[0, 1, 4, 5, 8, 9]
Bob:
[2, 3, 6, 7]
User Control¶
Complex algorithms may require more user control.
For example the existence of specialized hardware such as GPUs or database connections may restrict the set of valid workers for a particular task.
In these cases use the workers=
keyword argument to the submit
,
map
, or scatter
functions, providing a hostname, IP address, or alias
as follows:
future = client.submit(func, *args, workers=['Alice'])
Alice:
[0, 1, 4, 5, 8, 9, new_result]
Bob:
[2, 3, 6, 7]
Required data will always be moved to these workers, even if the volume of that
data is significant. If this restriction is only a preference and not a strict
requirement, then add the allow_other_workers
keyword argument to signal
that in extreme cases such as when no valid worker is present, another may be
used.
future = client.submit(func, *args, workers=['Alice'],
allow_other_workers=True)
Additionally the scatter
function supports a broadcast=
keyword
argument to enforce that all the data is sent to all workers rather than
round-robined. If new workers arrive they will not automatically receive this
data.
futures = client.scatter([1, 2, 3], broadcast=True) # send data to all workers
Alice:
[1, 2, 3]
Bob:
[1, 2, 3]
Valid arguments for workers=
include the following:
A single IP addresses, IP/Port pair, or hostname like the following:
192.168.1.100, 192.168.1.100:8989, alice, alice:8989
A list or set of the above:
['alice'], ['192.168.1.100', '192.168.1.101:9999']
If only a hostname or IP is given then any worker on that machine will be considered valid. Additionally, you can provide aliases to workers upon creation.:
$ dask worker scheduler_address:8786 --name worker_1
And then use this name when specifying workers instead.
client.map(func, sequence, workers='worker_1')
Specify workers with Compute/Persist¶
The workers=
keyword in scatter
, submit
, and map
is fairly
straightforward, taking either a worker hostname, host:port pair or a sequence
of those as valid inputs:
client.submit(f, x, workers='127.0.0.1')
client.submit(f, x, workers='127.0.0.1:55852')
client.submit(f, x, workers=['192.168.1.101', '192.168.1.100'])
For more complex computations, such as occur with dask collections like dask.dataframe or dask.delayed, we sometimes want to specify that certain parts of the computation run on certain workers while other parts run on other workers.
x = delayed(f)(1)
y = delayed(f)(2)
z = delayed(g)(x, y)
future = client.compute(z, workers={z: '127.0.0.1',
x: '192.168.0.1'})
Here the values of the dictionary are of the same form as before, a host, a
host:port pair, or a list of these. The keys in this case are either dask
collections or tuples of dask collections. All of the final keys of these
collections will run on the specified machines; dependencies can run anywhere
unless they are also listed in workers=
. We explore this through a set of
examples:
The computation z = f(x, y)
runs on the host 127.0.0.1
. The other
two computations for x
and y
can run anywhere.
future = client.compute(z, workers={z: '127.0.0.1'})
The computations for both z
and x
must run on 127.0.0.1
future = client.compute(z, workers={z: '127.0.0.1',
x: '127.0.0.1'})
Use a tuple to group collections. This is shorthand for the above.
future = client.compute(z, workers={(x, y): '127.0.0.1'})
Recall that all options for workers=
in scatter/submit/map
hold here as
well.
future = client.compute(z, workers={(x, y): ['192.168.1.100', '192.168.1.101:9999']})
Set allow_other_workers=True
to make these loose restrictions rather than
hard requirements.
future = client.compute(z, workers={(x, y): '127.0.0.1'},
allow_other_workers=True)
Provide a collection to allow_other_workers=[...]
to say that
the keys for only some of the collections are loose. In the case below z
must run on 127.0.0.1
while x
should run on 127.0.0.1
but can
run elsewhere if necessary:
future = client.compute(z, workers={(x, y): '127.0.0.1'},
allow_other_workers=[x])
This works fine with persist
and with any dask collection (any object with
a .__dask_graph__()
method):
df = dd.read_csv('s3://...')
df = client.persist(df, workers={df: ...})
See the efficiency page to learn about best practices.