# Dask.distributed

Dask.distributed is a lightweight library for distributed computing in Python. It extends both the `concurrent.futures` and `dask` APIs to moderate sized clusters.

See the quickstart to get started.

## Motivation

`Distributed` serves to complement the existing PyData analysis stack. In particular it meets the following needs:

- Low latency: Each task suffers about 1ms of overhead. A small computation and network roundtrip can complete in less than 10ms.

- Peer-to-peer data sharing: Workers communicate with each other to share data. This removes central bottlenecks for data transfer.

- Complex Scheduling: Supports complex workflows (not just map/filter/reduce) which are necessary for sophisticated algorithms used in nd-arrays, machine learning, image processing, and statistics.

- Pure Python: Built in Python using well-known technologies. This eases installation, improves efficiency (for Python users), and simplifies debugging.

- Data Locality: Scheduling algorithms cleverly execute computations where data lives. This minimizes network traffic and improves efficiency.

- Familiar APIs: Compatible with the concurrent.futures API in the Python standard library. Compatible with dask API for parallel algorithms

- Easy Setup: As a Pure Python package distributed is `pip` installable and easy to set up on your own cluster.

## Architecture

Dask.distributed is a centrally managed, distributed, dynamic task scheduler. The central `dask scheduler` process coordinates the actions of several `dask worker` processes spread across multiple machines and the concurrent requests of several clients.

The scheduler is asynchronous and event driven, simultaneously responding to requests for computation from multiple clients and tracking the progress of multiple workers. The event-driven and asynchronous nature makes it flexible to concurrently handle a variety of workloads coming from multiple users at the same time while also handling a fluid worker population with failures and additions. Workers communicate amongst each other for bulk data transfer over TCP.

Internally the scheduler tracks all work as a constantly changing directed acyclic graph of tasks. A task is a Python function operating on Python objects, which can be the results of other tasks. This graph of tasks grows as users submit more computations, fills out as workers complete tasks, and shrinks as users leave or become disinterested in previous results.

Users interact by connecting a local Python session to the scheduler and submitting work, either by individual calls to the simple interface `client.submit(function, *args, **kwargs)` or by using the large data collections and parallel algorithms of the parent `dask` library. The collections in the dask library like dask.array and dask.dataframe provide easy access to sophisticated algorithms and familiar APIs like NumPy and Pandas, while the simple `client.submit` interface provides users with custom control when they want to break out of canned “big data” abstractions and submit fully custom workloads.

## Contents

Getting Started

- Install Dask.Distributed
- Quickstart
- Setup
- Client
- API
- Examples
- Frequently Asked Questions

Build Understanding

- Diagnosing Performance
- Efficiency
- Limitations
- Data Locality
- Logging
- Managing Computation
- Managing Memory
- Prioritizing Work
- Related Work
- Resilience
- Scheduling Policies
- Scheduler State Machine
- Worker
- Worker State Machine
- Worker Memory Management
- Work Stealing
- Why did my worker die?

Additional Features

- Actors
- Asynchronous Operation
- HTTP endpoints
- Publish Datasets
- Worker Resources
- Launch Tasks from Tasks
- TLS/SSL
- Active Memory Manager
- Prometheus monitoring
- Fine Performance Metrics
- Spans

Developer Documentation

- Changelog
- Communications
- Development Guidelines
- Foundations
- Journey of a Task
- Protocol
- Serialization
- Plugins