MLLib (Spark) engine

MLLib is Spark’s machine learning library. DSS can use it to train prediction or clustering models on your large datasets that don’t fit into memory.


Spark’s overhead is non-negligible and its support is limited (see Limitations).
If your data fits into memory, you should use regular in-memory ML instead for faster learning and more extensive options and algorithms.


When you create a new machine learning in an Analysis, you can select the backend. By default it’s Python in memory, but if you have Spark correctly set up, you can also see Spark MLLib. Select it and your model will be trained on Spark, using algorithms available in MLLib or your custom MLLib-compatible models.

You can then fine-tune your model, deploy it in the Flow as a retrainable model and apply it in a scoring recipe to perform prediction on unlabelled datasets. Clustering models may also be retrained on new datasets through the cluster recipe.

In the model’s settings and the training, scoring and cluster recipes, there is an additional Spark config section, in which you can:

  • Change the base Spark configuration
  • Add / override Spark configuration options
  • Select the storage level of the dataset for caching once the data is loaded and prepared
  • Select the number of Spark RDD partitions to split non-HDFS input datasets

See DSS and Spark for more information about Spark in Data Science Studio.

Prediction Algorithms

DSS 4.0.0 supports the following algorithms on MLLib:

  • Logistic Regression (classification)
  • Linear Regression (regression)
  • Decision Trees (classification & regression)
  • Random Forest (classification & regression)
  • Gradient Boosted Trees (binary classification & regression)
  • Naive Bayes (multiclass classification)
  • Custom models

Clustering algorithms

DSS 4.0.0 supports the following algorithms on MLLib:

  • KMeans (clustering)
  • Gaussian Mixtures (clustering)
  • Custom models

Custom Models

Models using custom code may be trained with the MLLib backend. To train such a model,

  • Implement classes extending the Estimator and Model classes of the package. These will be the classes used to train your model: DSS will call the fit(DataFrame) method of your Estimator and the transform(DataFrame) method of your Model.
  • Package your classes and all necessary classes in a jar, and place it in the lib/java folder of your data directory.
  • In DSS, open your MLLib model settings and add a new custom algorithm in the algorithm list.
  • Place the initialization (scala) code for your Estimator into the code editor, together with any necessary import statements. The initialization statement should be the last to be called. Note that declaring classes (including anonymous classes) in the editor is not recommended, as it may cause serialization errors. They should therefore be compiled and put in the jar.


On top of the general Spark limitations in DSS, MLLib has specific limitations:

  • Gradient Boosted Trees in MLLib does not output per-class probabilities, so there is no threshold to set, and some metrics (AUC, Log loss, Lift) are not available, as are some report sections (variable importance, decision & lift charts, ROC curve).
  • Some feature preprocessing options are not available (although most can be achieved by other means):
    • Feature combinations
    • Numerical handling other than regular
    • Categorical handling other than dummy encoding
    • Text handling other than Tokenize, hash & count
    • Dimensionality-reduction for clustering
  • If test dataset is larger than 1 million rows, it will be subsampled to ~1M rows for performance and memory consumption reasons, since some scoring operations require sorting and collecting the whole data.
  • K-fold cross-test and hyperparameter optimization (grid search) are not supported.