Software · head to head
Dask vs Apache Spark MLlib
The short version
- Each has a real cost: Dask each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
- They diverge on capability: Dask covers Parallel computing, Apache Spark MLlib covers Classification.
Where they differ
Only the attributes on which Dask and Apache Spark MLlib actually diverge.
| Attribute | Dask | Apache Spark MLlib |
|---|---|---|
| Platforms | Linux, Mac, Windows | Linux, macOS, Windows |
| Founded | 2015 | 1999 |
Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), user rating (Not yet rated), category (Unknown).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Dask
- Parallel computing
- Distributed DataFrames
- Lazy evaluation
- Dynamic task scheduling
- Dashboard
- NumPy
- Pandas
- scikit-learn
Only in Apache Spark MLlib
- Classification
- Regression
- Clustering
- Collaborative filtering
- Feature engineering
- Apache Spark
- Hadoop
- Kafka
Both cover
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
Dask
- Scaling pandas and NumPy workloads beyond a single machine's memorynot Apache Spark MLlib
- Parallelising custom Python task graphsnot Apache Spark MLlib
- Processing larger than memory arrays and dataframes on a clusternot Apache Spark MLlib
Apache Spark MLlib
- Large-scale distributed machine learning on Spark clustersnot Dask
- Classification and regression with decision trees, random forests, gradient-boosted treesnot Dask
- Clustering with K-means and Gaussian Mixture Modelsnot Dask
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Dask
- Each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead
- Partition sizing is left to the user: chunks must fit several times over in worker memory, and both oversized and undersized chunks are documented failure modes
- Embedding large locally created DataFrames or Arrays into a Dask computation is documented as a practice to avoid because of network overhead
- Calling compute repeatedly in a loop rather than batching prevents parallelisation of queries
- The documentation itself advises trying better algorithms, file formats or sampling before adopting Dask
Apache Spark MLlib
- Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
Pricing, plan by plan
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Which should you pick?
Choose Dask if
- You need parallel computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want distributed dataframes.
Choose Apache Spark MLlib if
- You need classification.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want regression.
Questions people ask
- Is Dask or Apache Spark MLlib better?
- Neither clearly leads. Dask starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dask or Apache Spark MLlib?
- Dask starts at Free and Apache Spark MLlib at Free.
- Does Dask or Apache Spark MLlib run on more platforms?
- Dask runs on Linux, Mac, Windows. Apache Spark MLlib runs on Linux, macOS, Windows.
- Can I use Dask for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Dask best used for?
- Dask is most often used for scaling pandas and numpy workloads beyond a single machine's memory, parallelising custom python task graphs, processing larger than memory arrays and dataframes on a cluster. Of those, scaling pandas and numpy workloads beyond a single machine's memory and parallelising custom python task graphs are not what Apache Spark MLlib is typically brought in for.
- What can Dask do that Apache Spark MLlib cannot?
- Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Both handle Linux support, Mac support, Windows support.