DSS 5.1 Release notes¶

Migration notes
Version 5.1.3 - April 11th, 2019
Version 5.1.2 - March 1st, 2019
- Machine Learning
- Collaboration
- Coding
- Spark & Hadoop
- Flow
- Data visualization
- Scenarios
- Datasets
- Recipes
- Containers
- Security
Version 5.1.1 - February 13th, 2019
- Machine learning
- Datasets
- Webapps
- Hadoop & Spark
- Wiki
- API node
- Code
- Collaboration
- Security
- Misc
Version 5.1.0 - January 29th, 2019

Migration notes ¶

Migration paths to DSS 5.1 ¶

From DSS 5.0: Automatic migration is supported, with the restrictions and warnings described in Limitations and warnings

From DSS 4.3: In addition to the restrictions and warnings described in Limitations and warnings, you need to pay attention to the restrictions and warnings applying to your previous versions. See 4.2 -> 4.3 and 4.3 -> 5.0

From DSS 4.2: In addition to the restrictions and warnings described in Limitations and warnings, you need to pay attention to the restrictions and warnings applying to your previous versions. See 4.2 -> 4.3 and 4.3 -> 5.0

From DSS 4.1: In addition to the restrictions and warnings described in Limitations and warnings, you need to pay attention to the restrictions and warnings applying to your previous versions. See 4.1 -> 4.2 and 4.2 -> 4.3 and 4.3 -> 5.0

From DSS 4.0: In addition to the restrictions and warnings described in Limitations and warnings, you need to pay attention to the restrictions and warnings applying to your previous versions. See 4.0 -> 4.1, 4.1 -> 4.2 and 4.2 -> 4.3 and 4.3 -> 5.0

Migration from DSS 3.1 and below is not supported. You must first upgrade to 5.0. See DSS 5.0 Release notes

How to upgrade ¶

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

For automatic upgrade information, see Upgrading a DSS instance.

Pay attention to the warnings described in Limitations and warnings.

Limitations and warnings ¶

Automatic migration from previous versions (see above) is supported, but there are a few points that need manual attention.

Upgrade of Python packages ¶

The following Python packages have been upgraded in the builtin environment:

pandas (0.20 -> 0.23)
numpy (1.13 -> 1.15)
scikit-learn (0.19 -> 0.20)
xgboost (0.72 -> 0.80)

The pandas dependency is also upgraded in code environments.

Importantly, the dataiku Python package is not compatible with pandas 0.20 anymore. You must upgrade to pandas 0.23.

Rebuild of code environments ¶

Due to the upgraded dependency on pandas, it is necessary to update all previous Python code environments.

In most cases, you simply need, for each code environment, to go to the code environment page and click on the “Update” button (since the pandas 0.23 requirement is part of the base packages).

Retraining of machine-learning models ¶

XGBoost models trained with prior versions of DSS must be retrained when upgrading to 5.1. This includes models deployed to the flow (re-run the training recipe), models in analysis (retrain them before deploying) and API package models (retrain the flow saved model and build a new package)
“Isolation forests” models trained with prior versions of DSS and using “In-memory” engine must be retrained when upgrading to 5.1. This includes models deployed to the flow (re-run the training recipe), models in analysis (retrain them before deploying) and API package models (retrain the flow saved model and build a new package)

Multi-user-security configuration file move ¶

For improved security, the security module configuration file for Multi-User-Security has been moved from DATADIR/security/security-config.ini to /etc/dataiku-security/INSTALL_ID/security-config.ini.

DSS will automatically move the file upon upgrade, so you don’t need to perform any operation. However, any further update must be done on the /etc/dataiku-security/INSTALL_ID/security-config.ini. For more information, and details about INSTALL_ID, see the MUS setup documentation.

Dashboard exports ¶

Like with any upgrade, the Dashboards export feature must be reinstalled after upgrade. For more details, on how to reinstall this feature please see Setting up Dashboards and Flow export to PDF or images

Support removal notice ¶

DSS 5.1 removes support for Python 3.4. This constraint is caused by external libraries (notably pandas) that have removed the support for Python 3.4. Python 3.4 is also out of support upstream.

It is not possible to switch the version of Python used by code environments. If you have code environments using Python 3.4, you’ll need to (for each Python 3.4 code env):

Install Python 3.6
Delete the Python 3.4 code env
Create a Python 3.6 code env with the same name and same packages

Deprecation notice ¶

DSS 5.1 deprecates support for some features and versions. Support for these will be removed in a later release.

The prepare recipe running on the Hadoop Mapreduce engine is deprecated. We strongly advise you to use the Spark engine instead.

Version 5.1.3 - April 11th, 2019 ¶

DSS 5.1.3 is a minor release. For a summary of major changes in 5.1, see below

Machine learning ¶

New feature: Subpopulation analysis - Try it in the results screen of prediction models
New feature: Ability to retain settings when changing the target variable or prediction type
New feature: Ability to copy feature handling settings across ML tasks
Significant performance improvements for scoring of deep learning models
Fixed support of recent Keras versions
Fixed race conditions that could cause issues with large grids
Fixed wrongful train and test set record counts in presence of multiline records
Fixed display of best hyperparameters for logistic regression
Fixed scoring of XGBoost models with multiclass classification
Fixed ability to disable a custom model
Fixed possible training failure when using PCA on very small datasets
Added support for Python 3 for tensorboard
Added the ability to use custom Keras objects in deep learning models
Faster random forest training when “skip expensive reports” is enabled
Fixed scoring discrepancies in XGBoost regression when using Optimized engine

Hadoop & Spark ¶

New feature: Added compatibility with Hortonworks HDP 3.1
New feature: Experimental support for ADLS gen2
New feature: Experimental support for Spark-on-Kubernetes with multi-user-security
Fixed ability to use Spark-over-S3 with a S3 dataset when the S3 connection specifies a mandatory bucket
Added ability to perform variable expansion in Spark configurations
Fixed support of ORC files with dates on Hortonworks HDP 3
Fixed missing “Additional JARs” field in Spark configuration UI

Flow ¶

Fixed several bugs in the “Propagate schema” tool
Made the “Propagate schema” tool more efficient using mass actions
Fixed “Check consistency” tools in presence of visual recipes running on SQL engine
Fixed “new webapp” dialog from the Flow
Fixed “Spark pipelines” Flow view with Split recipes

Data preparation ¶

Fixed “concatenate” step when ujsing prepeare-on-SQL with PostgreSQL
Fixed timestamp-related issues when using prepare-on-SQL with Vertica
Fixed failure with “String transformation” processor when using native Spark implementation
Fixed “cell content” popup sticking around
Fixed Geographical join processor when the joined dataset has missing values

Visual recipes ¶

New feature: Snowflake to S3 fast sync
Fixed issue with Pivot recipe and scientific notation
Fixed display of generated SQL query for split recipe
Fixed display issues with custom aggregations in grouping and window recipes
Fixed issue when removing filters in split recipe
Fixed HTTP/HTTPS URL at domain root in Download recipe

Datasets & Connections ¶

New feature: Added ability to detect charset of text files (notably UTF-16)
Fixed simultaneous computation of quantile and min/max metrics on Vertica
Fixed display of some “Column statistics” metrics
Improved feedback when clearing a dataset
Fixed “substring” filter reverting to “full string” mode

SQL Notebooks ¶

Improved auto-save to make it faster and more resilient to various operations
Fixed bug in error tracking across cells
Added more information about previous runs in cell history and cell display
Fixed reloading of previous run metadata when coming back to a notebook

Coding and APIs ¶

Fixed various bugs with RStudio integration
Fixed bug that could cause conda to destroy the Jupyter notebook server
Fixed warning when using Python 3.6.6 or higher
Fixed listing of external Git branches for Git references mistakenly requiring admin privileges
Added the ability to get and set the “short description” in a project’s metadata through the API
Added an API to create a managed dataset
Fixed get_status on DSSCluster in Python API client
Fixed ability to create conda-powered R code environments without Jupyter support

Automation ¶

Fixed attaching folder contents to a scenario email

Security ¶

Fixed CSRF issue in image and attachment uploads
Fixed reflected XSS issue in image upload

API designer & API deployer ¶

Fixed duplicate data when using “bundled” enrichment or lookup and multiple enrichments on the same dataset
Improved performance of enrichments and lookups when the project contains many datasets with many columns

Misc ¶

Improved webapp creation user experience
Fixed deep-linking in Wiki articles
Fixed saving of “Palette type” in charts
Added sort of groups list when granting access to a project
Added sort of connections list in “Run SQL” scenario step
Fixed detection of OpenJDK 11
Fixed full-screen mode on dashboard exports
Improved performance and scalability when deleting datasets
Improved performance for notebooks listing with many notebooks
Don’t make the navigation bar disappear when the license is expired
Fixed error message when entering an invalid license

Version 5.1.2 - March 1st, 2019 ¶

DSS 5.1.2 is a minor release. For a summary of major changes in 5.1, see below

Machine Learning ¶

New feature: Partial dependency plots are now available for all algorithms, computable on test set
New feature: Partial dependency plots for categorical variables showing all categories at once
New feature: Ability to view distribution on partial dependency plots
Numerous other improvements on partial dependency plots
Fixed machine learning in projects importing libraries from other projects
Fixed edge cases leading to scoring discrepancies between engines with doubles as categories
Fixed display of L1/L2 regularization controls on multiclass logistic regression
Fixed UI bug in sample weight controls
Fixed UI bug in endpoints tuning controls
Isolation forest: added ability to set contamination parameter at a finer-grained level
Fixed optimized scoring of XGBoost models with gamma objective function
Fixed wrong grid search scores display with class weights

Collaboration ¶

New feature: Added ability to always go back to the last home screen (home, all projects, all dashboards, …)
Improved error reporting when reading datasets with wrong cross-project permissions
Added delay before sending Wiki notifications to Slack/… to avoid sending too many notifications
Fixed registration of Enterprise trial
Fixed “list branches” button for non-admins

Coding ¶

Fixed various issues with new “using Python API outside of DSS” capabilities

Spark & Hadoop ¶

Fixed cross-project Spark pipelines
Fixed class loading issue on CDH 6.0.1

Flow ¶

Strongly improved “computing dependencies” performance for forced recursive build of complex flows

Data visualization ¶

Added remembering of zoom level in map charts

Scenarios ¶

Fixed computation issue in temporal triggers that could cause scenarios to stop triggering
Added a warning when leaving a scenario page with unsaved changes

Datasets ¶

Added support for Greenplum 5.0
Fixed percentile metrics with SparkSQL engine

Recipes ¶

Fixed failures in prepare recipe in some specific formula cases
Fixed issue with external Teradata datasets containing dates in visual recipes
Added support for single-file inputs in S3-to-Snowflake fast path

Containers ¶

Fixed containerized Python kernels on Python 3 code environments
Fixed containerized Python recipe execution when code contains non-ASCII string literals
Fixed interrupt of containerized Jupyter kernels
Fixed support for EKS ingress for API node deployments with load balancer

Security ¶

Added protection against filesystem access through SQLite or H2 connections
Fixed possible leak of dataset data in frontend log files
Fixed possible code execution through malicious project imports

Version 5.1.1 - February 13th, 2019 ¶

DSS 5.1.1 is a minor release. For a summary of major changes in 5.1, see below

Machine learning ¶

Fixed error in Isolation forest when no anomaly was found
Fixed support for calibration in K-Fold cross-test mode
Fixed training recipes in “train on 100% of the data” mode
Fixed possible error when training on containers

Datasets ¶

Fixed a display issue in metrics
A metrics dataset showing checks will now be named “_checks”
Fixed computation of percentile metrics on Spark

Webapps ¶

Improved the default code sample for standard webapps
Fixed Shiny plugin webapps
Fixed permissions when copying a webapp

Hadoop & Spark ¶

**New feature*: Added support for CDH 6.1
**New feature*: Added experimental support for Spark 2.4
Added special option to handle cases where the Hive staging dir is in a non-standard location

Wiki ¶

**New feature*: Added automatic generation of a table of contents to Wiki articles
Fixed contributor tooltips
Improved Git commit messages for Wiki actions
Improved notifications for article renamings
Added ability to remove attachments in folder view
Added automatic scroll in the taxonomy when opening a Wiki page
Fixed update of the timeline after a save
Fixed links to items when changing the key of a project (through export/import)

API node ¶

Fixed issue with enrichments in custom R prediction endpoints

Code ¶

Fixed container execution that could fail depending on the number of cores of the machine / number of recipes being run
Fixed container execution of recipes when running on a code environment without Jupyter support
Fixed container execution with code environments on automation node
Fixed warnings when reading datasets in R
Fixed notebooks when importing libraries from other projects
Improved default package sets for conda, no more requiring external repositories
Added missing exported functions in the R package

Collaboration ¶

Added default template to the Slack reporter
Fixed error appearing after pushing a project to Git remotes
Improved highlighting in the new Jobs UI
Fixed timer in Jobs UI

Security ¶

Enforced connections permissions on “Execute SQL” scenario steps
Fixed XSS vulnerabilities
Fixed possible file tampering through visual recipes
Fixed SQL injection in Metrics datasets
Fixed vulnerability in license registration workflow

Misc ¶

Improved auto-generated titles on some charts
Fixed version number on login page
Fixed dead documentation links

Version 5.1.0 - January 29th, 2019 ¶

DSS 5.1.0 is a very major upgrade to DSS with major new features.

New features ¶

Git integration for plugins editor ¶

The plugin editor now features full Git integration, allowing you to view the history of a plugin, revert changes, and to push and pull changes from a remote Git repository.

For more details, please see our tutorial and reference.

Import code libraries from Git ¶

In the library editor of each project, you can now import code from external Git repositories. For example, if you have code that has been developed outside of DSS and is available in a Git repository (for example, a library created by another team), you can import this repository (or a part of it) in the project libraries, and use it in any code capability of DSS (recipes, notebooks, web apps, …).

This code can then be updated from the external Git repository, either manually or automatically.

For more details, please see our tutorial and reference.

More code reuse capabilities ¶

Combined with the ability to import code libraries from Git, new features for code reuse have been added:

R code can now use per-project libraries, just like Python code.
For both Python and R code, you can now have multiple libraries folders per project
For both Python and R code, you can now use the libraries of one project in another project

For more details, please see reusing Python code and reusing R code

Prepare recipe in-database (SQL)¶

A subset of preparation processors can now be translated to SQL queries. When a prepare recipe contains translatable processors, it can be executed fully in-database, which can provide speed-ups up to hundreds of times.

For more details, please see Execution engines.

Lightning-fast prepare recipe on Spark ¶

DSS now includes a new engine for data preparation on Spark that can provide significant performance boosts.

A subset of preparation processors are compatible with the optimized Spark engine, which will be used automatically whenever possible. When non-compatible processors are present, DSS automatically falls back to the previous engine.

For more details, please see Execution engines.

Containerized execution of notebooks ¶

Notebooks (Python and R) can now be run in Docker and Kubernetes

For more details, please see Containerized notebooks.

Databricks ¶

DSS now features an experimental integration with Databricks to leverage Databricks as a Spark execution engine.

For more details, please see Databricks integration.

Web apps as plugins ¶

Web apps can now be turned into plugins. This allows you to have reusable and instantiable web apps.

Some use cases notably include making custom visualizations for datasets.

For more details, please see our tutorial and reference.

Use Dataiku libs and develop code outside of DSS ¶

You can now use the Dataiku Python and R libraries outside of DSS in order to develop code for DSS (recipes, webapps, …) outside of DSS and in your favorite IDE.

For more details, please see using Python API outside of DSS and using R API outside of DSS

Folding the Flow view ¶

You can now hide parts of the Flow in order to improve the readability of very large flows. You can easily hide all parts of a flow upstream/downstream of a single node.

External hosting of runtime databases ¶

DSS maintains a number of databases, called the “runtime databases” that store some additional information, which is mostly “non-primary” information (i.e. which can be rebuilt), like history of jobs, metrics, state of datasets, timelines, discussions, …

By default, the runtime databases are hosted internally by DSS, using an embedded database engine (called H2). You can also move the runtime databases to an external PostgreSQL server. Moving the runtime databases to an external PostgreSQL server improves resilience, scalability and backup capabilities.

For more details, please see The runtime databases.

Exporting the Flow as an image ¶

You can now export the Flow of a project as an image or a PDF.

For more details, please see Exporting the Flow to PDF or images.

Probability calibration ¶

When training a classification model, you can now choose to apply a calibration of the predicted probabilities.

The purpose of calibrating probabilities is to bring the actual frequency of classes occurrence as close as possible to the predicted probability of such occurrence.

For more details, please see Prediction settings.

Models export as PMML and POJO ¶

You can now export a trained model as a PMML file for scoring with any PMML-compatible scorer.

You can also export trained models as a set of Java classes for extremely efficient scoring in any JVM application.

For more details, please see Exporting models.

Duplicate projects ¶

You can now easily duplicate a DSS project, optionally duplicating the content of some datasets.

RStudio integration ¶

In addition to the ability to use the DSS R API outside of DSS, DSS now features several integration points with RStudio:

Ability to develop code for DSS (recipes, …) directly in RStudio
RStudio Desktop/Server addins for easy connection to DSS and download/upload of recipes
Embedding of the RStudio Server UI in DSS
Easy configuration of RStudio Server for connection with DSS

For more details, please see our tutorial and reference.

Other notable enhancements ¶

Copy-paste preparation steps ¶

You can now copy and paste preparation steps, either within a single preparation recipe or across preparation recipes, or even across DSS instances.

Copy-paste scenario steps ¶

You can now copy and paste scenario steps, either within a single scenario or across scenarios, or even across DSS instances.

Support for CDH 6 ¶

DSS now supports CDH 6.0 and 6.1

For more details, please see Cloudera CDH

New capabilities for Snowflake ¶

DSS now supports a fast-path to sync from S3 to Snowflake.

For more details, please see Snowflake.

Setting distribution / primary index on Teradata, Redshift and Greenplum ¶

Additional options are now available for these databases:

Ability to control the primary index for Teradata datasets
Ability to control the distribution keys for Greenplum datasets
Ability to control the distribution and sort keys for Redshift datasets

Support for impersonation on Teradata ¶

You can now use the “proxyuser” mechanism of Teradata to impersonate end-users for all database access.

For more details, please see Teradata

Support for custom query banding on Teradata ¶

In order to provide for better audit, it can be interesting to add in the Query band of your Teradata queries information about the queries that are being performed.

DSS now lets you easily do that and track which users and jobs, … perform Teradata queries.

For more details, please see Teradata.

The join recipe now supports < and > operators (in addition to <= and >=)

Datasets ¶

A potential memory overrun when listing too many partitions has been fixed
GCS: Fixed issue with datasets whose size was a multiple of 4MB
“Cell value” metric now works properly even in the presence of other metrics
Reduced the number of “getBucketLocation” AWS API calls
Added support for XLSM files

Code ¶

It is now possible to use datasets in a Python or R recipe, even if they are not declared as inputs or outputs. For example:

dataset = dataiku.Dataset("mydataset_that_is_not_in_input", ignore_flow=True)
df = dataset.get_dataframe()

Various bugs on SQL code formatter have been fixed

Data preparation ¶

CJK characters can now be used as literals in the Python processor

Dashboards ¶

It is now possible to export dashboards on a machine without outgoing Internet connection (after initial setup)

Machine learning ¶

Don’t try to use Optimized scoring when a custom text preprocessing is in effect
It is now possible to tune the scoring batch size when using the Local (Python) engine for scoring

Misc ¶

Clear job logs macro now removes the job folders instead of just emptying them
It is now possible to run a DSS UI that does zero connections to the outside world