DSS 6.0 Release notes¶

Migration notes ¶

Migration paths to DSS 6.0 ¶

From DSS 5.1: Automatic migration is supported, with the restrictions and warnings described in Limitations and warnings

From DSS 5.0: 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 5.0 -> 5.1

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 4.3 -> 5.0 and 5.0 -> 5.1

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, 4.3 -> 5.0 and 5.0 -> 5.1

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, 4.2 -> 4.3, 4.3 -> 5.0 and 5.0 -> 5.1

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, 4.2 -> 4.3, 4.3 -> 5.0 and 5.0 -> 5.1

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.

Like with any upgrade, the graphics export feature (exporting Flow or dashboards) must be reinstalled after upgrade. For more details, on how to reinstall this feature please see Setting up DSS item exports to PDF or images

Models grid search behavior change ¶

DSS 6.0 introduces a minor upgrade to scikit-learn which fixes a bug in the model selection feature. In some rare cases, this can cause grid searching to select a different hyperparameter value when retraining a model on the same data. For more details, please see https://scikit-learn.org/stable/whats_new/v0.20.html#sklearn-model-selection

Support removal notice ¶

As previously announced, DSS 6.0 removes support for the prepare recipe running on the Hadoop Mapreduce engine. We strongly advise you to use the Spark engine instead.

Deprecation notice ¶

DSS 6.0 deprecates support for some features and versions. Support for these will be removed in a later release.

Pig support is deprecated. We strongly advise you to migrate to Spark.
Support for Spark 1 (1.6) is deprecated. We strongly advise you to migrate to Spark 2. All Hadoop distributions can use Spark 2.
Conditional outputs on binary classification models are deprecated.

Version 6.0.5 - February 25th, 2020 ¶

DSS 6.0.5 is a bugfix release. For a summary of major changes in 6.0, see below

Automation ¶

Fixed “Triggers” view

Collaboration ¶

Fixed display of object types in catalog

Security ¶

Fixed CVE-2020-8817: Ability to tamper with creation and ownership metadata
Fixed CVE-2020-9378: Directory traversal vulnerability in Shapefile parser

Version 6.0.4 - February 4th, 2020 ¶

DSS 6.0.4 is a minor release. For a summary of major changes in 6.0, see below

Datasets ¶

Fixed metrics computation on SQL query datasets

Version 6.0.3 - January 21th, 2020 ¶

DSS 6.0.3 is a minor release. For a summary of major changes in 6.0, see below

Datasets ¶

New Feature Support for creating natively partitioned BigQuery datasets
Better support of uploaded dataset with very large number of files
Fixed browsing of exposed managed folders (Now properly redirects to the target project)
Fixed unpartitioning of Elasticsearch datasets
Fixed bad meaning detection of very low numbers (wrongfully detected as “Longitude”)
Added support for STS credentials for S3 connections with EMRFS interface

Recipes ¶

Fixed possible error on window recipe when using date range and DSS engine on datasets with empty cells
Removed DSS engine on prepare recipe when input and output are on BigQuery
Fixed DSS formula “modulo” function on BigQuery engine
Made dataiku.get_connection() API usable in Python recipes
Fixed find and replace shortcut on code editors

Plugins ¶

New feature Improved support of python-code based ‘SELECT’ plugin parameter
Fixed progress reporting of macros provided by plugins
Fixed auto-generated plugin description file when converting a code recipe to a plugin
Fixed ‘MANAGED_FOLDER’ and ‘SAVED_MODEL’ plugins parameter types
Fixed plugin store URL in the ‘New recipe’ drop down
Add possibility for plugin recipes targeting folder to be visible in the right panel
Fixed search in the plugin store page

ML ¶

Fixed possible race condition when running scoring recipes inside containers
Fixed usage of ensemble model on the automation node
Fixed migration of train recipes that made the underlying model unusable by evaluate recipes
Fixed partitioned models when all targets in the test set are equal to 0
Clarified some help messages on calibration and time ordering
SQL pipeline can now be used for partitioned models scoring
Fixed Javascript error when opening a train recipe that has been run before 6.0
Improved variable choice UI for time-based ordering in visual ML

Collaboration ¶

New Feature Added a Microsoft Teams integration for project events
New Feature Add mathematical formula support in the wiki (using Mathjax)
Fixed possibility to reference DSS object in To do lists

Performance ¶

Improved catalog performance and fixed possible instance hang for certain “killer queries”
Fixed race condition when using sync recipe on uploaded datasets with underlying cloud-based storage

Code environment and container execution ¶

Fixed creation of R code environments with Jupyter when using Python 3 for builtin env
Fixed build of Docker base image
Fixed “Remove old container images” macro when builtin env uses Python 3

Flow ¶

Smarter display in the right panel of available plugin recipes when datasets are selected

Cluster ¶

Fixed possible error when setting default cluster for a project
Administrator can now add/attach clusters from administration settings on a remote API Deployer

SSO ¶

New Feature Added possibility to redirect to a custom page after logout
Added support for IdP metadata with multiple signing certificate

Misc ¶

New Feature Contextual right panel is now available on most DSS components
Fixed “safe mode” edition of webapps
Fixed drag-and-drop reordering of dashboard slides
S3 and HDFS managed folder are now usable on scenario reporter attachments

Version 6.0.2 - December 5th, 2019 ¶

DSS 6.0.2 is a minor release. For a summary of major changes in 6.0, see below

Kubernetes ¶

New feature Added support of managed Kubernetes clusters for use with Model API Deployer

Charts ¶

Fixed usage of ‘day of week’ when using SparkSQL chart engine on old Spark versions (<2.3)

Dataset ¶

Fixed wrong display of the tooltip for the “count of records” metric
Fixed a bug on MongoDB and Cassandra datasets that could not be easily unpartitioned after being partitioned
Fixed “Files from folder” datasets when the underlying folder targets a cloud-based connection (Azure Blob, S3, GCS)
Fixed dataset mass import when hadoop standalone integration has been run

Security ¶

Make the SSL ciphers recommended option available on the API node

Flow ¶

Improved update of flow after mass importing datasets

Administration ¶

Fixed displayed value of maximum mail attachments size in SMTP messaging channel settings

Coding recipes ¶

Fixed issue on python recipe when using docker execution and libraries importing the ‘code’ python builtin module

Visual recipes ¶

Fixed split recipe random subsets mode when using splitting proportions below 10%
Improved timezone management when using date formatter preparation step on SQLServer

Dashboards ¶

Fixed migration of wiki tiles in dashboards
Fixed display issue of metric tooltips on Firefox
Fixed current displayed label on animated charts

Machine learning ¶

Improved reproducibility of results using feature reduction preprocessing with python 3
Improved reproducibility of results of DBSCAN and Isolation Forest clustering algorithms
Improved feature handling copy capabilities when working on a copied analysis
Fixed possible non display of grid search curves
Fixed non java compatible models deployment when model partitioning is enabled
Fixed metric computation on partitioned models when the ‘pearson’ metric is not available for one of the trained models
Fixed creation of non-partitioned datasets when creating scoring recipes based on partitioned input datasets
Fixed scoring recipe when the dataset to score only contains 1 row

Plugins ¶

New feature make ‘DATASET_COLUMN’ and ‘DATASET_COLUMNS’ plugin parameter types available for checks and metrics
Fixed possible error when uploading an update for a plugin which does not exist

Web apps ¶

Put back the ‘run as user’ settings on non User Isolation Framework (previously MUS) installations

Version 6.0.1 - November 6th, 2019 ¶

DSS 6.0.1 is a minor release. For a summary of major changes in 6.0, see below

Collaboration ¶

Fixed non visible discussions on articles after migration

Visual recipes ¶

Add ability to rename columns when using SQL pipelines
Fixed S3 to Redshift fast path on S3 partitioned datasets
Improved support of customized metastore table name of non HDFS datasets when using Spark engine

Coding ¶

Make the dkuManagedFolderCopyToLocal R function recursive
Fixed dkuManagedFolderCopyFromLocal R function which ignored beginning of each copied file

Webapps ¶

Fixed Bokeh webapps that always reused the same port

Misc ¶

Fixed possible issue when accessing a non existing table using the DSS internal metastore

Plugins ¶

Fixed plugin recipes using dynamically-filled dropdowns

Version 6.0.0 - October, 24th, 2019 ¶

DSS 6.0.0 is a major upgrade to DSS with major new features.

New features ¶

Managed Kubernetes clusters ¶

DSS can automatically start, stop and manage for you multiple clusters running on the major cloud providers. This makes it very seamless to deploy Kubernetes clusters with very low setup and administration work.

DSS provides managed Kubernetes capabilities on:

Amazon Web Services through EKS
Azure through AKS
Google Cloud Platform through GKE

For more details, please see Managed Kubernetes clusters and DSS in the cloud

Managed Spark on Kubernetes ¶

DSS can now automatically manage deployment of Spark jobs on Kubernetes. This includes automatically setting up connectivity to cloud storages, building container images, handling multiple code environments, providing security and isolation.

Thanks to this feature, you can now deploy Spark jobs on a unified Kubernetes infrastructure, handling both Spark and non-Spark jobs. Multiple Kubernetes clusters are supported.

For more details, please see DSS and Spark and DSS in the cloud

Partitioned models ¶

DSS can now build partitioned models, that is, train a separate model for each partition of an input dataset. Training separate models (also sometimes referred to as “stratified models”) is useful when you expect data to be significantly different between partitions, or when you need incrementality. For example, you may want to train one model per country, per business unit, per factory, …

Once trained, partitioned models can be used to score other partitioned data, or unpartitioned data containing partition identifiers. For more information, see Partitioned Models.

Time series visualization ¶

DSS now includes a dynamically zoomable line chart for time series.

For more details, please see Time Series

New plugins experience ¶

The plugins store has a brand new look, allowing you to find plugins much more easily.

We have also strongly improved the plugin installation experience, with guided steps to install plugins, code envs and other dependencies.

The plugin development experience has been overhauled for better productivity.

Plugins now feature a predefined parameters system, which allows you to reuse parameters between plugins, and to have sensitive information for plugins managed by the administrator.

For more details, please see Plugins

Support for AWS Athena and Glue metastore ¶

DSS now supports experimental connection with AWS Athena. This connection provides the following capabilities:

Running interactive SQL notebooks on Athena based on previously-built S3 datasets
Using Athena as charts engine for S3 datasets
Running SQL queries on Athena based on previously-built S3 datasets (execution and data read through Athena, write through DSS)

DSS also adds support for leveraging AWS Glue as a metastore catalog.

For more details, please see DSS in AWS (overview, reference architecture), AWS Athena and Glue metastore (details).

SQL pipelines ¶

DSS provides pipeline functionality for a flow that uses a SQL engine and consists of consecutive recipes sharing the same connection. SQL pipelines can minimize or avoid unnecessary writes and reads of intermediate datasets in a flow, thereby boosting workflow performance.

For more details, please see SQL pipelines in DSS.

Pluggable algorithms ¶

You can now add custom algorithms for the in-memory Visual ML component as plugins, making them available without any code.

For more details, please see Component: Prediction algorithm.

Pluggable webapps ¶

Webapps can now be packaged as plugins, shared and reused.

For more details, please see Component: Web Apps.

Pluggable chart types ¶

New chart types can now be packages as plugins, shared and reused.

Pluggable custom view for folders and models ¶

Managed folders and models now support a concept of pluggable custom views. Use cases can include:

A custom view representing the content of a folder (for example, a neural network visualizer)
A custom view on the results of a saved model (for example, to display interpretability results)

Time series preparation ¶

DSS provides a preparation plugin that includes visual recipes for performing the following operations on time series data:

Resampling into equispaced time intervals
Performing analytics functions over a moving window
Extracting aggregations around a global extremum
Extracting intervals where values lie within an acceptable range

This plugin is fully supported by Dataiku. For more details, please see Time series preparation.

Native Python processor in preparation ¶

The Python processor in data preparation can now use a real Python process, which allows usage of Python 3 and of any additional package through the usage of the DSS code environments feature.

The Python processor now supports vectorized operation using Pandas for fast operation.

For more details, please see Python function

Scenario reporting to Microsoft Teams ¶

Scenarios can now report on completion and custom events to Microsoft Teams.

Other notable enhancements ¶

Improved project folders ¶

The project folders feature has been strongly enhanced with the following capabilities:

Drag & drop to add folders in projects on the “projects list” page
Ability to view project folders on the personalized home page
Security on project folders
Ability to have empty project folders
Per-folder view of the graph of projects

Time-aware cross-validation and evaluation ¶

When running prediction tasks on time-oriented datasets (for example, a daily sales dataset), it is useful to use time-aware cross-validation for optimizing and evaluating your model. This allows you to ensure that by only looking at past data, your model is able to adequately predict future data.

For more details, please see Advanced models optimization.

Enhanced Snowflake integration ¶

Thanks to the new native Spark integration, you can now directly access Snowflake datasets in any Spark-powered recipe (either visual or code). This leverages the native Spark Snowflake connector for optimal performance.

In addition, the Sync recipe can now perform fast synchronization between Azure Blob and Snowflake datasets.

For more details, please see Snowflake

Added ability to access shared datasets in Pyspark notebooks
Added ability to select Hive runtime configuration for exploration and direct read through DSS

Datasets & file formats ¶

Added support for ElasticSearch 7
Added ability to support ElasticSearch mapping type _doc
Added ability to rename columns when importing an Excel file
Fixed Snowflake synchronization failure with special characters
Fixed Excel export when running on Java 11
Fixed reading of booleans in Excel files
Fixed “click to configure” button on “Analyze on full data”

Data preparation ¶

Added SQL compatibility for the “Round” processor

Flow ¶

Added support for Spark engine on SQL input datasets

Visual recipes ¶

Split recipe: fixed “drop data” in random dispatch mode on Spark engine
Sort recipe: fixed on MS SQL Server
Sync recipe: improved S3 to Redshift fast-path on partitioned datasets

Notebooks ¶

Automatically install by default Jupyter kernels for containerized execution when updating a code env

Machine learning ¶

Fixed UI of prediction and clustering recipes when running on HDFS datasets
Better variables ordering for Partial Dependencies Plot
Added subsampling on Partial Dependencies Plot and Subpopulation Analysis for faster results
Improved performance of Deep Learning training
Added support for Partial dependencies and subpopulation analysis on containers
Fixed possible non-stability across trainings when using Python 3
Added error percentage as a metric that can be output as part of the evaluation recipe

Webapps ¶

Fixed issues when exporting/importing projects containing webapps

Automation ¶

Added support for variables expansion in SQL triggers
Added ability to execute or not, and to create new exports or not when attaching Jupyter notebooks to mails

Collaboration ¶

Fixed sending of Slack notifications on job builds
Added back “description icon” on Flow

Reliability & Scalability ¶

Improved Oracle insertion performance in presence of NULL values
Fixed potential issues while reading enormous log files

Security ¶

Fixed and clarified issues with code env permissions

API ¶

Added ability to terminate a cluster through Python API
Fixed ability to update R code environments through API