Data Locality — Anaconda Enterprise 4.4 documentation

Anaconda Enterprise: Documentation

# 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 the all 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 `._keys()` method):

    df = dd.read_csv('s3://...')
    df = client.persist(df, workers={df: ...})

See the efficiency page to learn about best practices.