DSS 2.0 Relase notes

Version 2.0.4 - August 31th, 2015


For migration from DSS 1.X, please see the DSS 2.0.0 release notes

DSS 2.0.4 contains bug fixes


Fix layout of SQL and R recipe editors


  • Flag login cookies as HTTP-only

  • Fix missing access control on export internal API

  • Fix path traversal in “logs” internal API (accessible only to admin)

  • Fix a few GET/POST mismatches

  • Add new security-related options

    • Option to force usage of Secure cookies

    • Option to disable error stacks

    • Option to disable version strings

Version 2.0.3 - August 20th, 2015


For migration from DSS 1.X, please see the DSS 2.0.0 release notes

DSS 2.0.3 contains both bug fixes and new features

New Features

  • DSS can now read and perform advanced extraction on XML files. Please see XML for more information.

  • DSS is now compatible with MongoDB 3.0

Bug Fixes


  • It is now possible to read and write from S3 buckets without the permission to list the buckets on the account.

  • Small UI fixes


  • Preparation recipe: fixed some corner cases with cross-project recipes

  • Outer join is not possible with the “DSS internal” engine and is therefore not suggested anymore

  • Fixed some issues with Oracle on visual recipes

  • Fixed mass actions in Grouping recipe

  • Several fixes with the filter editor

  • Fixed some small UI issues

  • Since Oracle identifiers are limited to 30 characters, DSS will now try to limit the size of column names it generates in visual recipes

  • Fixed a display bug in “Stack” recipe


  • Fixed Hive recipes when “TextFile” is not the default Hive storage format

Machine learning

  • Fixed regression with H2O models

  • Fixed an issue with computation of RMSLE measure which could break models

  • Fixed the “Keep my settings” button

  • Fixed the filters on the “Predicted data” view

  • Fixed failure of scoring recipes in some cases with date columns

  • Fixed important issue with boolean variables that could be wrongly handled, leading to invalid results

  • Fixed issue with large number of clusters (>100)

  • Fixed regression on random forest with manually-entered multiple number of trees


  • Fixed UI issues in scheduler

  • Fixed saving of allowed groups in connections

Version 2.0.2 - June 23rd, 2015


For migrations from DSS 1.X, please see the DSS 2.0.0 release notes

DSS 2.0.2 contains both bug fixes and new features

New Features

  • New “Fold multiple columns by prefix” processor. See Reshaping for more information

  • You can now “redeploy” a training recipe and a saved model from an analysis. This allows you to change the settings of the model without having to “replug” the Flow to a new saved model.

  • The “Geo-Join” processor can now output distance as miles

  • Minor UX improvements

Important change: MySQL column names

The behaviour of MySQL datasets has been changed. The MySQL connector will now automatically use column names specified by “AS” aliases in SQL queries.

So for example, “SELECT a AS b FROM table” will now yield a dataset with a column named “b”, while it was previously named “a”.

To revert to the old behaviour, go to the settings of the MySQL connection, and add the property: “useOldAliasMetadataBehavior” = “false”.

This change only affects versions 5.1 and above of the MySQL JDBC driver. For more information, please see: http://dev.mysql.com/doc/connector-j/en/connector-j-installing-upgrading-5-1.html

Bug fixes


  • Empty Zip files are now properly handled

  • Fixed an issue with multi-file JSON datasets


  • Fixed some data parsing issues in the Grouping recipe

  • Fixed handling of booleans in the Grouping recipe on PostgreSQL

  • Fixed SQL recipes on custom JDBC connections


  • Improved the behavior of the Hive integration with Sentry. Authorizing file:/// URIs is not required anymore, and integration with the HDFS ACL synchronization now works properly

  • Fixes for exotic Hive options (“fixed-metastore” DataNucleus mode)

  • Fixed validation of some Hive recipes on MapR


  • It’s now possible to disable probability columns in multi-class classification recipes

  • Fixed features hashing

  • Fixed notebook export for Spectral Clustering models

  • Updated URI for the IUS repository

  • Various small UI fixes

Version 2.0.1 - June 10th, 2015


For migrations from DSS 1.X, please see the DSS 2.0.0 release notes

DSS 2.0.1 is a bugfix release

Recipes and Flow

  • Fixed bad initial settings for partitioned recipes

  • Small UI improvements

  • DSS now includes its own version of the Graphviz tool: fixes Flow layout on CentOS 6


  • Fixed the “Advanced” settings display for filesystem datasets

  • Fixed the “Explore” view for datasets imported from other projects

  • Fixed reading multiple JSON files with root path for elements.

Machine Learning

  • Text handling: fixed display of the vocabulary for the Count and TF/IDF vectorizers

  • Avoid doing grid search when not needed in various alogrihtms

  • Fixed custom scoring function for regression problems

  • Fixed error when trying several number of trees in Random Forest

  • Fixed wrong results in scoring recipes when “drop rows” is selected for missing values handling

Dashboard and insights

  • Fixed loading of nvd3.js

  • Fixed issues with settings of the insights miniatures

  • Fixed an issue with the hexagonal binning parameter (was not saved)

Version 2.0.0 - May 19th, 2015

DSS 2.0.0 is a major upgrade that brings new exciting features and a redesigned user experience.

Migration notes


Migration to DSS 2.0 from a previous DSS 1.X instance requires some attention.

To migrate to DSS 2.0, you must first upgrade your instance to the latest 1.4 version. See DSS 1.4 Relase notes

Automatic migration from Data Science Studio 1.4.X is supported, with the following restrictions and warnings:

  • Previously trained machine learning models must be retrained

  • As a consequence, machine learning models deployed directly in Flow without a retraining recipe won’t be usable anymore for scoring. You will need to retrain the model in an Analysis, redeploy it to Flow, and replug a scoring recipe.

  • If you use cross-projects recipes, you need to perform some adjustements detailed below

How to update ML models in Flow

If you have ML models in Flow, you need to retrain them before they are usable again.

How to update cross-projects recipes

In DSS 1.X, if you had access to projects A and B, then all datasets from project A could be directly used in project B. However, you had to create the recipe “manually”.

In DSS 2.X, the default behaviour has changed: only datasets from project A that are explicitly “exposed to project B” can be used, and they directly appear on the Flow of project B.

You can either:

  • Go to the project settings of project A and “expose” the required datasets to project B

  • Go to Administration > Settings > Misc and change the “Cross-projects access to datasets” behaviour.

Furthermore, by default, recursive builds now “stop” at project boundaries. You can change this behaviour on a per-dataset basis, and you can also change the default global behaviour in Administration > Settings > Misc.

Preparation and machine learning

On upgrade, all previous preparation scripts and machine learning model benches will be converted to the new Analysis component

How to upgrade

It is strongly recommended that you perform a full backup of your Data Science Studio data directory prior to starting the upgrade procedure.

Hadoop support

This release removes support for CDH 4

External libraries upgrades

Several external libraries bundled with DSS have been bumped to major revisions. Some of these libraries (most notably Pandas include some backwards-incompatible changes). You might need to upgrade your code.

Notable upgrades:

New features

User experience

The user experience of DSS has been redesigned based on the feedbacks from our users.

  • Thanks to the organization in universes, you’ll always find what you need at your fingertips.

  • The new sidebar gives you immediate access to all actions in context.

  • A redesigned search that gives you immediate access to your recent items and contextually-relevant objects

  • The streamlined Flow lets you focus on what matters most and reduces visual clutter

  • Use checklists to organize your collaborative work in projects

Analysis and data preparation

The new “Analysis” module is where you’ll perform all visual analysis on a dataset. It combines the power of visual data preparation, drag-and-drop visualizations and guided machine learning.

You can now create new features using visual data preparation and immediately use them in machine learning models.

Data preparation now features a “Column-oriented view” for immediate glances on your dataset and easy mass actions.

New processor: currency converter (supports 40 currencies with historical data)

Machine learning

The machine learning component has been completely rehauled. It now features:

  • Advanced cross-validation policies:

    • K-Fold cross validation

    • Explicit train and test sets

  • Completely redesigned model assessment pages, with much deeper insight into the performance your models

  • Parallel grid search for semi-automatic optimization of models

  • New feature generation options

  • Text processing options: count, TF-IDF and hashing vectorizers, with support for stop words and n-grams

  • Binarization and quantization of numerical variables

  • Models in Flow are now versioned and you can choose how to switch to new versions

  • Built-in data preparation without prior materialization of the prepared datasets

Visual recipes

Several new visual recipes let you do more and more advanced data manipulation without writing a single line of code:

  • “Join” recipe (with multi-dataset, multi-key joins, fuzzy joins, case-insensitive joins, …)

  • “Split” recipe

  • “Union” recipe to concatenate datasets

  • Redesigned “Grouping” recipe

Easter eggs

Will you find all our new easter eggs ?