Managing Memory

Dask.distributed stores the results of tasks in the distributed memory of the worker nodes. The central scheduler tracks all data on the cluster and determines when data should be freed. Completed results are usually cleared from memory as quickly as possible in order to make room for more computation. The result of a task is kept in memory if either of the following conditions hold:

  1. A client holds a future pointing to this task. The data should stay in RAM so that the client can gather the data on demand.
  2. The task is necessary for ongoing computations that are working to produce the final results pointed to by futures. These tasks will be removed once no ongoing tasks require them.

When users hold Future objects or persisted collections (which contain many such Futures inside their .dask attribute) they pin those results to active memory. When the user deletes futures or collections from their local Python process the scheduler removes the associated data from distributed RAM. Because of this relationship, distributed memory reflects the state of local memory. A user may free distributed memory on the cluster by deleting persisted collections in the local session.

Creating Futures

The following functions produce Futures

Client.submit(func, *args[, key, workers, …]) Submit a function application to the scheduler, *iterables[, key, workers, …]) Map a function on a sequence of arguments
Client.compute(collections[, sync, …]) Compute dask collections on cluster
Client.persist(collections[, …]) Persist dask collections on cluster
Client.scatter(data[, workers, broadcast, …]) Scatter data into distributed memory

The submit and map methods handle raw Python functions. The compute and persist methods handle Dask collections like arrays, bags, delayed values, and dataframes. The scatter method sends data directly from the local process.

Persisting Collections

Calls to Client.compute or Client.persist submit task graphs to the cluster and return Future objects that point to particular output tasks.

Compute returns a single future per input, persist returns a copy of the collection with each block or partition replaced by a single future. In short, use persist to keep full collection on the cluster and use compute when you want a small result as a single future.

Persist is more common and is often used as follows with collections:

>>> # Construct dataframe, no work happens
>>> df = dd.read_csv(...)
>>> df = df[df.x > 0]
>>> df = df.assign(z = df.x + df.y)

>>> # Pin data in distributed ram, this triggers computation
>>> df = client.persist(df)

>>> # continue operating on df

Note for Spark users: this differs from what you’re accustomed to. Persist is an immediate action. However, you’ll get control back immediately as computation occurs in the background.

In this example we build a computation by parsing CSV data, filtering rows, and then adding a new column. Up until this point all work is lazy; we’ve just built up a recipe to perform the work as a graph in the df object.

When we call df = client.persist(df) we cut this graph off of the df object, send it up to the scheduler, receive Future objects in return and create a new dataframe with a very shallow graph that points directly to these futures. This happens more or less immediately (as long as it takes to serialize and send the graph) and we can continue working on our new df object while the cluster works to evaluate the graph in the background.

Difference with dask.compute

The operations client.persist(df) and client.compute(df) are asynchronous and so differ from the traditional df.compute() method or dask.compute function, which blocks until a result is available. The .compute() method does not persist any data on the cluster. The .compute() method also brings the entire result back to the local machine, so it is unwise to use it on large datasets. However, .compute() is very convenient for smaller results particularly because it does return concrete results in a way that most other tools expect.

Typically we use asynchronous methods like client.persist to set up large collections and then use df.compute() for fast analyses.

>>> # df.compute()  # This is bad and would likely flood local memory
>>> df = client.persist(df)    # This is good and asynchronously pins df
>>> df.x.sum().compute()  # This is good because the result is small
>>> future = client.compute(df.x.sum())  # This is also good but less intuitive

Clearing data

We remove data from distributed ram by removing the collection from our local process. Remote data is removed once all Futures pointing to that data are removed from all client machines.

>>> del df  # Deleting local data often deletes remote data

If this is the only copy then this will likely trigger the cluster to delete the data as well.

However if we have multiple copies or other collections based on this one then we’ll have to delete them all.

>>> df2 = df[df.x < 10]
>>> del df  # would not delete data, because df2 still tracks the futures

Aggressively Clearing Data

To definitely remove a computation and all computations that depend on it you can always cancel the futures/collection.

>>> client.cancel(df)  # kills df, df2, and every other dependent computation

Alternatively, if you want a clean slate, you can restart the cluster. This clears all state and does a hard restart of all worker processes. It generally completes in around a second.

>>> client.restart()


Results are not intentionally copied unless necessary for computations on other worker nodes. Resilience is achieved through recomputation by maintaining the provenance of any result. If a worker node goes down the scheduler is able to recompute all of its results. The complete graph for any desired Future is maintained until no references to that future exist.

For more information see Resilience.

Advanced techniques

At first the result of a task is not intentionally copied, but only persists on the node where it was originally computed or scattered. However result may be copied to another worker node in the course of normal computation if that result is required by another task that is intended to by run by a different worker. This occurs if a task requires two pieces of data on different machines (at least one must move) or through work stealing. In these cases it is the policy for the second machine to maintain its redundant copy of the data. This helps to organically spread around data that is in high demand.

However, advanced users may want to control the location, replication, and balancing of data more directly throughout the cluster. They may know ahead of time that certain data should be broadcast throughout the network or that their data has become particularly imbalanced, or that they want certain pieces of data to live on certain parts of their network. These considerations are not usually necessary.

Client.rebalance([futures, workers]) Rebalance data within network
Client.replicate(futures[, n, workers, …]) Set replication of futures within network
Client.scatter(data[, workers, broadcast, …]) Scatter data into distributed memory