DSS 8.0 Release notes¶
From DSS 7.0: Automatic migration is supported, with the restrictions and warnings described in Limitations and warnings
From DSS 6.0: Automatic migration is supported. 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 6.0 -> 7.0
From DSS 5.1: Automatic migration is supported. 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 5.1 -> 6.0, 6.0 -> 7.0
From DSS 5.0: Automatic migration is supported. 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, 5.1 -> 6.0, 6.0 -> 7.0
From DSS 4.3: Automatic migration is supported. 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, 5.0 -> 5.1, 5.1 -> 6.0, 6.0 -> 7.0
From DSS 4.2: Automatic migration is supported. 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, 5.0 -> 5.1, 5.1 -> 6.0, 6.0 -> 7.0
From DSS 4.1: Automatic migration is supported. 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, 5.0 -> 5.1, 5.1 -> 6.0, 6.0 -> 7.0
From DSS 4.0: Automatic migration is supported. 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, 5.0 -> 5.1, 5.1 -> 6.0, 6.0 -> 7.0
Migration from DSS 3.1 and below is not supported. You must first upgrade to 5.0. See DSS 5.0 Release notes
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.
Automatic migration from previous versions (see above) is supported, but there are a few points that need manual attention.
The commands to build base images for container execution and API deployer have changed. All base images are now built using options of
The legacy “Hadoop 2” standalone packages for Hadoop and Spark integration have been removed. Please use the universal
Some features that were previously deprecated are now removed or unsupported.
Support for Spark 1 (1.6) is removed. We strongly advise you to migrate to Spark 2. All Hadoop distributions can use Spark 2.
DSS 8.0 deprecates support for some features and versions. Support for these will be removed in a later release.
As a reminder from DSS 7.0, support for “Hive CLI” execution modes for Hive is deprecated and will be removed in a future release. We recommend that you switch to HiveServer2. Please note that “Hive CLI” execution modes are already incompatible with User Isolation Framework.
As a reminder from DSS 7.0, Support for Microsoft HDInsight is now deprecated and will be removed in a future release. We recommend that users plan a migration toward a Kubernetes-based infrastructure.
As a reminder from DSS 7.0, Support for Machine Learning through Vertica Advanced Analytics is now deprecated and will be removed in a future release. We recommend that you switch to In-memory based machine learning models. In-database scoring of in-memory-trained machine learnings will remain available.
As a reminder from DSS 7.0, Support for Hive SequenceFile and RCFile formats is deprecated and will be removed in a future release.
As a reminder from DSS 6.0, support for Pig is deprecated. We strongly advise you to migrate to Spark.
Fixed variable expansions on the “catalog”/”database” field of SQL datasets
Fixed table lookup when the “catalog”/”databasse” field is empty
Improved robustness of the driver/database version detection when the driver returns an error
Fixed possible hang when exporting a dashboard to PDF when there are charts that can’t be rendered
Fixed handling of failed jobs in “Build items” scenario step that appeared with an internal error
Improved performance of the “get project metadata” API call, especially when called large number of times
Improved performance for rendering of flow with flow zones
Improved caching of flow rendering, leading to improved overall flow visualization experience
Strongly improved performance for “scenario edition” and “scenario last runs” pages in the case of scenarios that ran a very large number of times
Stack recipe: Fixed “Replace input” button in
Pivot recipe: Fixed race condition which could drop aggregations on DSS engine when a filter is set
Prepare recipe: Fixed a bug with Python step in “real Python process” mode when running high-concurrency jobs on Spark
Prepare recipe: Added some missing methods on the row object in Python step
Python API: Fixed set_code_env recipe method
Python API: Fixed run of training recipes
Python API: Added catalog and schema support in query builder API
In SQL Notebook, refresh table button is now displayed by default
Fixed attachments in scenario reports
Fixed monthly trigger that stopped working after the first month
Fixed export of notebooks from scenario when project libs are involved
Fixed possible failure in “Build” step caused by a race condition
Fixed the assigned Flow zone of a dataset created from the split recipe
Fixed schema consistency errors not showing when using Flow zones
Fixed “building” indicator that did not properly refresh when using Flow zones
Fixed ability to create plugin datasets when using a free license
Improved dynamic select implementation in Project Creation Macro for usage by users with restricted permissions
Fixed error message not displayed when performing an unauthorized action
Fixed performance issue caused by daily maintenance tasks
Fixed required permissions to delete empty project folders that were not top-level folders
API node: Fixed possible failure of R function endpoints due to bad type in “timing”
Make the “BigQuery Project Id” field at dataset level optional
Pinned the pip version in docker base images to keep Python 2.7 compatibility
Note: DSS 8.0.4 has a known issue regarding dss objects attachment in scenario reports. Contact Dataiku support if you need the available hotfix.
Fixed Cassandra dataset
Fixed handling of SQL, Hive, Impala and SparkSQL notebooks in datasets’ ‘Lab’ tab
Allow to specify a default catalog at the connection level for Snowflake and BigQuery connections
Fixed Snowflake/Spark native integration when schema is not specified as a Snowflake connection property
When importing a new dataset, add it to the currently open zone instead of the default zone
Join recipe: Fixed prefilter with DSS engine when joining with the same dataset
Use ‘days’ as default unit when using the date ‘diff’ formula function
Reduced excezsive logging when using ‘variables.<variable_name>’ to access variables in a formula step
Sync recipe: Fixed Azure Blob / Synapse sync for tables in non-default schemas
Improved reliability for Kubernetes usage with extreme partitioning concurrency on a single coding recipe
Fixed listing of Hive and Impala tables in notebooks
In UIF mode, fixed Jupyter notebook export if you have never previously visited the “Library Editor” of the project
Fixed schema propagation in python API
Fixed issue in Wiki API when getting article by its name
Fixed HiveExecutor/ImpalaExecutor when used in a project with specific user impersonation rules
Fixed plugin credentials settings API
Fixed Model Document Generation ‘design.train_set.image’ placeholder when the train set has a filter defined
Fixed the “Write your own estimator” link when creating a model
Fixed threshold setting after a training recipe run on a partitioned binary classification model
Fixed PMML export when both impact coding and impute of missing with a constant are used while there is no missing value
Allowed display of linear coefficients for custom linear classification algorithms
Fixed scoring of clustering models trained with very old versions of DSS (4 and below)
Fixed prediction not shown in API Deployer test query for services with several endpoints
Fixed display of complex result types in API Deployer
Fixed email reporting when a job fails without an error message
Added support for checking GDPR export flags when bundling a project
Fixed errors when switching between modes on “Set scenario variables” step
Added ability to bypass proxy for webhook reporter
Fixed custom multi-select type when removing a previously set option
Fixed dynamic choices field when used in cluster action macros
Fixed display of adminParams section in actions modal for cluster action macros
Don’t send digest emails to users who have been removed from a project
Fixed bogus ability to write custom cross-validation in hyperparameter searches while not allowed to write code
New feature: Support for cross-database operations. This covers importing from catalog, datasets, visual recipes and SQL recipes
New feature: Added ability to dynamically switch Warehouse and Role, at the project and recipe level (using variables expansion)
New feature: Added fast load from Parquet files on S3 to Snowflake
New feature: Added support of concat in window & group recipes
New feature: Added support for ignoring null values in ‘first value’ and ‘last value’ retrievals in window recipe
Managed Snowflake tables are now created upper-case by default
The ‘Assumed time zone’ option is now usable on DATETIME and TIMESTAMP_NTZ Snowflake types
Made all connection parameters optional, since they can be inherited from user default
Fixed “list table fields” in SQL notebook in case catalog is not set in the connection
Added ability to read Snowflake streams
New feature: Support for cross-project operations. This covers importing from catalog, datasets, visual recipes and SQL recipes
New feature: Added support for Bokeh 2
For Kubernetes based webapps, the start backend now waits for the webapp backend to be fully started
Fixed vanity URL option of public webapps for plugin webapps
New feature: Added ability to expand JDBC connection variables using per-project and per-recipe variables
Fixed sampling ordering on SQL partitioned datasets when used with filtering
Restored the delete icon of items in the “sort order” list of the sampling panel
Fixed wrongful “you have unsaved changes” in Teradata dataset settings
Fixed dashboard tiles display on personal home page
Fixed issues with display of dashboards on home page
Prevented saving of empty global tags
Fixed reassignment of a global tags when deleting it
Fixed the catalog search for the project owner
PDF exports of Flow and Dashboard: fixed display of license expiration warnings
Redshift/S3 fast sync: Allowed different spelling for UTF8 charset (utf8, utf-8, UTF8 …)
Fixed display of “Merge folders” recipe icon
Fixed possible UI misalignment of the error message in join recipe
Prevent immediate error message from being shown in prepare recipe while setting date format in the date parser processor
Fixed toString(format) formula function when working on DateTime
Fixed job status in case of input reading error when using DSS engine on group, topn, distinct, pivot or split recipe
New feature: Variables expansion is now supported for the “Explicit values” partition dependency function
Fixed the ability to cancel a job preview
Improved error message when automatic schema propagation fails
Fixed smart rebuild option on editable datasets
Improved dataset contextual menu UI to be consistent with the user’s authorization
Relaxed required access rights to move a shared item to a flow zone
Fixed update of flow zones after a dataset renaming
Improved flow zone user experience by coloring them even when not selected
Tags can now be added to a flow zone
Fixed reset of job list filter when a new job notification popup appears
Fixed display of complex types results of test queries in API designer
Fixed explanation computation on Python 2 when the features contain non-ASCII characters
New feature Project creation macros now have access to the current project folder
Project creations macros can now use a Python-based choices field
Fixed display of multi lines data in query results of SQL notebooks
Fixed dataframe export to dataset from a Jupyter notebook when a column contains non-ASCII characters
Fixed insight creation from a shared Jupyter notebook
Creating recipe from notebook now removes spaces in the recipe name for safety purpose
Fixed display of error message details on notebook upload
New feature Added PKCE support for OAuth2 credential requests
Improved switching from list to raw edition for the MAP parameter
Fixed detection of outdated plugin settings when using a MAP parameter
Fixed getChoicesFromPython parameter on webapp plugin component with a DATASET role
Fixed column completion not showing for DATASET_COLUMNS parameter
Fixed truncated read of custom Python filesystem provider
New feature: “Dataset change” trigger can now trigger only when multiple datasets have changed
Fixed possibility to share scenario across projects
Added an additional check in the UI to make URL mandatory on Webhook notifications
New feature: Added support for Cloudera HDP 3.1.5
Fixed rescaling cluster macro on EKS
Added support for Spark jobs from ADLS gen2 to ADLS gen2 with different input and output accounts
Various improvements in the model list filtering
Prevent raw level interpretation from failing when there is lot of rejected features
Model document generation: prevented license expiration warning from appearing in the screenshots
Model document generation: Prevented possible loading spinner from appearing in screenshots
Fix clustering rescoring on saved model
Fixed harmless migration failure message of tree visualisations
DSS 8.0.2 is a bugfix release. For a summary of major changes in 8.0, see below
Fixed the “Pattern” column selection mode of prepare recipe processors
Fixed pivot recipe on Redshift when column names contain uppercase letters
Added support of timezone in DatePart formula function
Improved support of unfold prepare recipe processor when run on Hive 3
Fixed window visual recipe on Spark and Hive when a column renaming is set and concat operation is used
Added ability to globally disable some prepare recipe processors
Updated cross validation strategy samples when using custom code
Fixed custom cross validation on Python 3
Fixed custom evaluation metric on binary prediction using probabilities
Better warning display in the train modal in case of code environment incompatibility
Prevent creating an evaluation recipe on a saved model with weights if the dataset does not have the weights column
Fixed random-search when a plugin algorithm is enabled
Fixed multiple partitioned models training when trained in the same job
Fixed the displayed number of models in summary of stratified models
Fixed wrongful deployment of failed partitions to Saved Models
Fixed creation of SparkSQL recipe from a SparkSQL notebook cell
write_jsonon managed folders with Python 3
Added ability to download a plugin using the API
Exposed forceRebuildEnv option in code environments API
Prevented creation of plugin recipe with required input or input if they are not provided
get_items_in_traversal_orderPython method when Flow contains saved models or managed folders
Added support for global tags with a semicolon in their names
Improved tags UI in “Search DSS” screen
Fixed autocompletion for tag creation from the project summary on Chrome
Fixed error at project import if one of the default code environments of the imported project is not remapped
Fixed DynamoDB connection when secret key has been encrypted
Added ability to configure STS token duration for S3 connections when using ‘STS with AssumeRole’
Improved default settings when row level interpretation is asked at query time
Improved error in API Node when scoring images with base64 encoding
API Deployer: Fixed ability to disable K8S deployments on non-builtin cluster
Fixed possible error when using custom ui field in application tiles
Fixed the download dataset tile when using a custom exporter
Fixed ‘–dockerfile-append’ and ‘–dockerfile-prepend’ image building options
Improved usability of ‘–docker-build-opt’ image building option
Fixed Cuda 10.1 docker image build
Fixed ability for “numerical-only” DSS user names to use Kubernetes
Fixed ‘Add to scenario’ dataset action which created non relocatable scenarios
Fixed missing refresh of list of flow zones just after creation of a new flow zone
Fixed saving of scenario using “stop cluster” step
Fixed “Run scenario” button in dashboard when targeting a foreign scenario
Fixed wrong permission checks for shared scenario tiles
Improved behavior of the “Restart backend” button when run on Kubernetes
Fixed resource leak when running webapps on Kubernetes with “port-forwarding” exposition
Fixed “port-forwarding” exposition when number of pods is greater than 1
Fixed Bokeh webapps on macOS
Fixed plugin uninstall when a Dataiku Application has been deleted
Added a filter and a search field in the macro list screen
Disabled ability for users that have no rights to create active WebContent from uploading notebooks
Fixed CVE-2020-25822: Incorrect access control allows users to edit discussions
DSS 8.0.1 is a bugfix release. For a summary of major changes in 8.0, see below
Prepare recipe: Fixed autocomplete of column name when using “multiple columns” step mode
Prepare recipe: Improved error handling of the “Rename columns” processor when the step has just been created
Fixed display of “File in folder” dataset when using Flow zones
Fixed display of “Metrics” dataset when using Flow zones
Fixed behaviour of the “Create prediction model” inside an analysis.
Fixed display of the AutoML dialog images on chrome
Fixed the “View original analysis” button of saved models when the analysis has been deleted
Prevent silent failure when clicking on the ‘Lab’ button while user does not have the right user profile
Fixed creation of the DSS Core Designer tutorials
Fixed remapping of code environments when importing projects
DSS 8.0.0 is a major upgrade to DSS with major new features.
Dataiku Applications allow Dataiku designers to make their projects reusable and consumable by business users. Once a designer has made a project available as an application, business users can create their own instances of the application, set parameters, upload data, run the applications, and directly obtain results.
For more details, please see Dataiku Applications.
In regulated industries, data-scientists have to document ML models, at creation and after every change for traceability. This is often tedious. DSS now features the ability to automatically generate a DOCX document from a machine learning model.
Designers can upload their own DOCX template with placeholders that will be automatically be replaced by information, explanations and charts from the ML model. Model Document Generation has an extensive coverage of the advanced result screens of DSS Visual ML, allowing creation of rich documents.
For more details, please see Model Document Generator.
Data Science projects tend to quickly become complex, with large number of recipes and datasets in the Flow. This can make the Flow complex to read and navigate.
Flow Zones are a completely new way to organize bigger flows into more manageable sub-parts, called zones.
You can now define your zones in the Flow, and assign each dataset,recipe, … to a zone. The zones are automatically laid out in a graph, like super-sized nodes. You can work within a single zone or the whole flow, and collapse zones to create a simplified view of the flow.
For more details, please see Flow zones.
In addition to the already-existing grid searching for hyperparameters, DSS can now perform Random search and Bayesian search for faster and more thorough search for the best set of hyperparameters.
For more details, please see Advanced models optimization.
DSS 7.0 added support for row-level interpretability for Machine Learning models. This allows you to get a detailed explanation of why a Dataiku model made a given prediction, even when said model is a “black-box” model.
In DSS 7.0, Row-level interpretations were available in the UI, and as the output of the scoring recipe.
DSS 8.0 adds the ability to programmatically obtain explanations through the API node, and also through the Saved Model Python API.
For more details, please see Exposing a visual prediction model.
In addition to their “Visual re-use by business users” usage, Dataiku Applications can also be used to reuse an entire flow as if it was a single recipe. This allows designers to quickly design complex flows while making usage of “building blocks” built by other designers, without having to maintain the complexity of the underlying reused flow.
For more details, please see Application-as-recipe
Dataiku now supports Pandas 1.0 (in addition to maintained support for the legacy 0.23 version).
Support for Pandas 1.0 is only available when using a code env. Pandas 1.0 is only compatible with Python >= 3.6.1, so only code envs using Python 3.6.1 (and above) will get the ability to use Pandas 1.0
There are multiple use cases for centralizing audit logs from multiple DSS nodes in a single system.
Some of these use cases include:
Customers with multiple instances want a centralized audit log in order to grab information like “when did each user last do something”.
Customers with multiple instances want a centralized audit log in order to have a global view on the usage of their different audit nodes, and compliance with license
Compute Resource Usage reporting capabilities use the audit trail, and make more sense if fully centralized. You may want to cross that information with HR resources, department assignments, …
Most MLOps use case require centralized analysis of API node audit logs
DSS now features a complete routing dispatch mechanism for these use cases, with the ability to centralize audit log from multiple machines to a central location, and enhanced capabilities for analyzing audit logs within DSS.
For more details, please see Audit trail.
Building on audit log centralization, you can now also centralize API node query logs. This allows you to setup a feedback loop for your ML Ops strategy, in order to analyze the predictions made by the API node, either to detect input data drift or model performance drift.
For more details, please see Configuration for API nodes.
DSS acts as the central orchestrator of many computation resources, from SQL databases to Kubernetes. Through DSS, users can leverage these elastic computation resources and consume them. It is thus very important to be able to monitor and report on the usage of computation resources, for total governance and cost control of your Elastic AI stack.
DSS now includes a complete stack for reporting and tagging compute resources. For more details, please see Compute resource usage reporting.
It is now possible to uninstall plugins, both from the UI and API. Trying to uninstall a plugin will automatically warn you if the plugin is still in use.
Two new features reinforce the ability to serve webapps to large number of users:
Webapps can now be shared to users who are not DSS users and do not have a DSS account. This allows you to share webapps widely to the whole company. For more details, please see Public webapps
Webapp backend code can now perform API calls to the Dataiku API on behalf of the end-user viewing the webapp, with full traceability of the end-user identity. This allows better governance and tracability of actions performed on behalf of users. For more details, please see Webapps and security.
Administrators can now define tag categories. Tag categories allow you to create custom “fields” in the form of tags, and have predefined set of values.
Categorized tags can then be set easily by the end user with validation on the values.
For example, you could create a tag category for the responsible team, one for the department, one for the brand that you’re working on, …
Tag categories can be created and managed by the administrator from Administration > Settings > Tag categories.
The Visual ML user experienced has been enhanced to streamline the creation of models and understanding of the Dataiku Lab:
Find the Lab associated to each dataset directly from the dataset’s right panel
Faster creation of ML models, with streamlined workflow. You can now create a ML model in 3 clicks from a dataset
Ability to create ML models directly from a column in the dataset’s Explore view
Better explanations in-product for the various cross-validation strategies
The API for users and authentication management have been greatly enhanced with:
Ability to set user secrets through API, either for end users or admins
Ability to set per-user-credentials through API, either for end users or admins
Ability to impersonate end-users using admin credentials
Ability to manipulate user and admin properties through API
Many APIs have seen vast improvements, especially regarding the ability to entirely build and control Flows via the API:
Ability to detect dataset settings (See Datasets (other operations))
Much easier ability to create recipes (See Flow creation and management)
Ability to traverse the Flow graph (See Flow creation and management)
Ability to compute and set output schema for recipes (See Recipes)
Ability to propagate schema across entire flows (See Flow creation and management)
Ability to manage Flow zones (See Flow creation and management)
And many other, please see Python APIs for a complete index of the Python API.
All three kinds of container images (containerized execution, Spark-on-Kubernetes and API deployer) are now built on a single CentOS 7 base.
This release brings the following enhancement:
Support for CUDA 10.0 and 10.1 in containers
Full support for Python-3-only containers
Far enhanced customization capabilities, including ability to use a proxy
Ability to use prebuilt images for faster images build
DSS 8.0 adds experimental for Openshift as a Kubernetes runtime
For more details, please see Using Openshift.
DSS can now automatically create Kubernetes namespaces for both containerized execution and Spark-on-Kubernetes. Namespaces can be defined using variable expansion, in order to create namespaces per user/team/project/…
DSS can automatically apply policies to the dynamic namespaces, notably resource quotas (in order to limit the total amount of computation/memory available to a namespace/user/team/project/…) and limit ranges (in order to set default resource control for computations running in the dynamic namespace).
For more details, please see Dynamic namespace management.
You can now add custom Kubernetes tolerations, affinity statements or node selectors in order to control more precisely the placement of your pods on Kubernetes.
For more details, please see Dynamic namespace management.
API node audit logging now includes project key / saved model id / saved model version for prediction endpoints.
In addition, you can ask DSS to dump and/or audit the post-enrich data, when using queries enrichments.
For more details, please see Exposing a visual prediction model.
It is now possible to disable users instead of outright deleting them. Disabled users cannot login, cannot run scenarios, and don’t consume licenses.
Disabling/enabling users can be done through the UI and API.
You can now select a default code env which will be applied by default across all projects.
You can now select a default containerized execution config which will be applied by default across all projects.
Fixed wrong value in partitioning “Test dependencies” function
Fixed navigation issue with cross-project datasets leading to loss of flow centering
Fixed issue when copying a subflow containing HDFS datasets to a new project
Fixed icons display issues for plugin recipes
Fixed wrongful attempt to write BigQuery datasets when importing a project
Project duplication will now only duplicate uploaded datasets by default
Fixed dynamic select widget for custom exporters
Python plugin recipes can now accept BigQuery datasets as outputs
Fixed issue when removing values from a “Remove rows on value” processor
Extract Date components processor: Extracting minutes,seconds and milliseconds can now run in SQL databases
Fixed support for Kubernetes > 1.16
Spark install can now setup better defaults tuned for Kubernetes
Cost matrix gain was added to the list of metrics displayed in the all metrics screen
“Max feature proportion” on tree ensemble algorithms is now hyperparameter-searchable
PMML export now outputs probabilities and can now use the model-specified threshold
API node: Fixed wrongful scoring of rows that were removed by the preparation script
Add more parameters to the Isolation Forest algorithm
Fixed issues with empty columns with unicode column names
Fixed clustering scoring when outliers detection is enabled and dataset to score is very small
Code of custom models is now displayed in results
Fixed issue when DSS is installed with base Python 3.6 environment
Properly show the Python version in the notebooks list
Added ability to duplicate wiki articles
Improved Slack integration with Slack Blocks
Enhanced API for project folders - see Project folders
Fixed API for pushing container base images