Dask vs Spark

#Apache Spark ?is a popular distributed computing tool for tabular datasets that is growing to become a dominant name in Big Data analysis today. #Dask has several elements that appear to intersect this space and we are often asked, “How does Dask compare with Spark?”

Answering such comparison questions in an unbiased and informed way is hard, particularly when the differences can be somewhat technical. This document tries to do this.

Generally, Dask is smaller and lighter weight than Spark. This means that it has fewer features and, instead, is used in conjunction with other libraries, particularly those in the numeric Python ecosystem. It couples with libraries like Pandas or Scikit-Learn to achieve high-level functionality.

Language

• Spark is written in Scala with some support for Python and R. It interoperates well with other JVM code.
• Dask is written in Python and only really supports Python. It interoperates well with C/C++/Fortran/LLVM or other natively compiled code linked through Python.

Ecosystem

• Spark is an all-in-one project that has inspired its own ecosystem. It integrates well with many other Apache projects.
• Dask is a component of the larger Python ecosystem. It couples with and enhances other libraries like NumPy, Pandas, and Scikit-Learn.

Age and Trust

• Spark is older (since 2010) and has become a dominant and well-trusted tool in the Big Data enterprise world.
• Dask is younger (since 2014) and is an extension of the well trusted NumPy/Pandas/Scikit-learn/Jupyter stack.

Scope

• Spark is more focused on traditional business intelligence operations like SQL and lightweight machine learning.
• Dask is applied more generally both to business intelligence applications, as well as a number of scientific and custom situations.

Internal Design

• Spark’s internal model is higher level, providing good high level optimizations on uniformly applied computations, but lacking flexibility for more complex algorithms or ad-hoc systems. It is fundamentally an extension of the Map-Shuffle-Reduce paradigm.
• Dask’s internal model is lower level, and so lacks high level optimizations, but is able to implement more sophisticated algorithms and build more complex bespoke systems. It is fundamentally based on generic task scheduling.

Scale

• Spark scales from a single node to thousand-node clusters.
• Dask scales from a single node to thousand-node clusters.

APIs

DataFrames

• Spark DataFrame has its own API and memory model. It also implements a large subset of the SQL language. Spark includes a high-level query optimizer for complex queries.
• Dask DataFrame reuses the Pandas API and memory model. It implements neither SQL nor a query optimizer. It is able to do random access, efficient time series operations, and other Pandas-style indexed operations.

Machine Learning

• Spark MLLib is a cohesive project with support for common operations that are easy to implement with Spark’s Map-Shuffle-Reduce style system. People considering MLLib might also want to consider?other?JVM-based machine learning libraries like H2O, which may have better performance.
• Dask relies on and interoperates with existing libraries like Scikit-Learn and XGBoost. These can be more familiar or higher performance, but generally results in a less-cohesive whole. See the?dask-ml ?project for integrations.

Arrays

• Spark does not include support for multi-dimensional arrays natively (this would be challenging given their computation model), although some support for two-dimensional matrices may be found in MLLib. People may also want to look at the?Thunder ?project, which combines Apache Spark with NumPy arrays.
• Dask fully supports the NumPy model for?scalable multi-dimensional arrays .

Streaming

• Spark’s support for streaming data is first-class and integrates well into their other APIs. It follows a mini-batch approach. This provides decent performance on large uniform streaming operations.
• Dask provides a?real-time futures interface ?that is lower-level than Spark streaming. This enables more creative and complex use-cases, but requires more work than Spark streaming.

Graphs / complex networks

• Spark provides GraphX, a library for graph processing.
• Dask provides no such library.

Custom parallelism

• Spark generally expects users to compose computations out of their high-level primitives (map, reduce, groupby, join, …). It is also possible to extend Spark through subclassing RDDs, although this is rarely done.
• Dask allows you to specify arbitrary task graphs for more complex and custom systems that are not part of the standard set of collections.

Reasons you might choose Spark

• You prefer Scala or the SQL language
• You have mostly JVM infrastructure and legacy systems
• You want an established and trusted solution for business
• You are mostly doing business analytics with some lightweight machine learning
• You want an all-in-one solution

Reasons you might choose Dask

• You prefer Python or native code, or have large legacy code bases that you do not want to entirely rewrite
• Your use case is complex or does not cleanly fit the Spark computing model
• You want a lighter-weight transition from local computing to cluster computing
• You want to interoperate with other technologies and don’t mind installing multiple packages

Reasons to choose both

It is easy to use both Dask and Spark on the same data and on the same cluster.

They can both read and write common formats, like CSV, JSON, ORC, and Parquet, making it easy to hand results off between Dask and Spark workflows.

They can both deploy on the same clusters. Most clusters are designed to support many different distributed systems at the same time, using resource managers like Kubernetes and YARN. If you already have a cluster on which you run Spark workloads, it’s likely easy to also run Dask workloads on your current infrastructure and vice versa.

In particular, for users coming from traditional Hadoop/Spark clusters (such as those sold by Cloudera/Hortonworks) you are using the Yarn resource manager. You can deploy Dask on these systems using the?Dask Yarn ?project, as well as other projects, like?JupyterHub on Hadoop .