Updated build environment to use Databricks Runtime 13.3 LTS as baseline

.github/workflows/push.yml

@@ -31,10 +31,10 @@ jobs:
           sudo update-alternatives --set java /usr/lib/jvm/temurin-8-jdk-amd64/bin/java
           java -version
 
-      - name: Set up Python 3.9.21
+      - name: Set up Python 3.10.12
         uses: actions/setup-python@v5
         with:
-          python-version: '3.9.21'
+          python-version: '3.10.12'
           cache: 'pipenv'
 
       - name: Check Python version

.github/workflows/release.yml

@@ -24,10 +24,10 @@ jobs:
           sudo update-alternatives --set java /usr/lib/jvm/temurin-8-jdk-amd64/bin/java
           java -version
 
-      - name: Set up Python 3.9.21
+      - name: Set up Python 3.10.12
         uses: actions/setup-python@v5
         with:
-          python-version: '3.9.21'
+          python-version: '3.10.12'
           cache: 'pipenv'
 
       - name: Check Python version

CHANGELOG.md

@@ -9,7 +9,8 @@ All notable changes to the Databricks Labs Data Generator will be documented in
 * Updated build scripts to use Ubuntu 22.04 to correspond to environment in Databricks runtime
 
 #### Changed
-* Changed base Databricks runtime version to DBR 11.3 LTS (based on Apache Spark 3.3.0)
+* Changed base Databricks runtime version to DBR 13.3 LTS (based on Apache Spark 3.4.1) - minimum supported version
+  of Python is now 3.10.12
 
 #### Added
 * Added support for serialization to/from JSON format

CONTRIBUTING.md

@@ -43,7 +43,7 @@ Our recommended mechanism for building the code is to use a `conda` or `pipenv`
 But it can be built with any Python virtualization environment.
 
 ### Spark dependencies
-The builds have been tested against Spark 3.3.0. This requires the OpenJDK 1.8.56 or later version of Java 8.
+The builds have been tested against Apache Spark 3.4.1. 
 The Databricks runtimes use the Azul Zulu version of OpenJDK 8 and we have used these in local testing.
 These are not installed automatically by the build process, so you will need to install them separately.
 
@@ -72,7 +72,7 @@ To build with `pipenv`, perform the following commands:
     - Run `make dist` from the main project directory
   - The resulting wheel file will be placed in the `dist` subdirectory
 
-The resulting build has been tested against Spark 3.3.0
+The resulting build has been tested against Spark 3.4.1
 
 ## Creating the HTML documentation
 
@@ -158,19 +158,19 @@ See https://legacy.python.org/dev/peps/pep-0008/
 
 # Github expectations
 When running the unit tests on Github, the environment should use the same environment as the latest Databricks
-runtime latest LTS release. While compatibility is preserved on LTS releases from Databricks runtime 11.3 onwards, 
+runtime latest LTS release. While compatibility is preserved on LTS releases from Databricks runtime 13.3 LTS onwards, 
 unit tests will be run on the environment corresponding to the latest LTS release. 
 
-Libraries will use the same versions as the earliest supported LTS release - currently 11.3 LTS
+Libraries will use the same versions as the earliest supported LTS release - currently 13.3 LTS
 
 This means for the current build:
 
-- Use of Ubuntu 22.04 for the test runner
+- Use of Ubuntu 22.04.2 LTS for the test runner
 - Use of Java 8
-- Use of Python 3.9.21 when testing / building the image
+- Use of Python 3.10.12 when testing / building the image
 
 See the following resources for more information
 = https://docs.databricks.com/en/release-notes/runtime/15.4lts.html
-- https://docs.databricks.com/en/release-notes/runtime/11.3lts.html
+- https://docs.databricks.com/aws/en/release-notes/runtime/13.3lts
 - https://github.com/actions/runner-images/issues/10636
 

Pipfile

@@ -10,7 +10,7 @@ sphinx = ">=2.0.0,<3.1.0"
 nbsphinx = "*"
 numpydoc = "==0.8"
 pypandoc = "*"
-ipython = "==7.32.0"
+ipython = "==8.10.0"
 pydata-sphinx-theme = "*"
 recommonmark = "*"
 sphinx-markdown-builder = "*"
@@ -19,13 +19,13 @@ prospector = "*"
 
 [packages]
 numpy = "==1.22.0"
-pyspark = "==3.3.0"
-pyarrow = "==7.0.0"
-wheel = "==0.37.0"
-pandas = "==1.3.4"
-setuptools = "==58.0.4"
-pyparsing = "==3.0.4"
+pyspark = "==3.4.1"
+pyarrow = "==8.0.0"
+wheel = "==0.37.1"
+pandas = "==1.4.4"
+setuptools = "==63.4.1"
+pyparsing = "==3.0.9"
 jmespath = "==0.10.0"
 
 [requires]
-python_version = "3.9.21"
+python_version = "3.10.12"

README.md

@@ -83,23 +83,26 @@ The documentation [installation notes](https://databrickslabs.github.io/dbldatag
 contains details of installation using alternative mechanisms.
 
 ## Compatibility 
-The Databricks Labs Data Generator framework can be used with Pyspark 3.3.0 and Python 3.9.21 or later. These are 
-compatible with the Databricks runtime 11.3 LTS and later releases. For full Unity Catalog support, 
-we recommend using Databricks runtime 13.2 or later (Databricks 13.3 LTS or above preferred)
+The Databricks Labs Data Generator framework can be used with Pyspark 3.4.1 and Python 3.10.12 or later. These are 
+compatible with the Databricks runtime 13.3 LTS and later releases. This version also provides Unity Catalog
+compatibily.
 
 For full library compatibility for a specific Databricks Spark release, see the Databricks 
 release notes for library compatibility
 
 - https://docs.databricks.com/release-notes/runtime/releases.html
 
-When using the Databricks Labs Data Generator on "Unity Catalog" enabled Databricks environments, 
+In older releases, when using the Databricks Labs Data Generator on "Unity Catalog" enabled Databricks environments, 
 the Data Generator requires the use of `Single User` or `No Isolation Shared` access modes when using Databricks 
 runtimes prior to release 13.2. This is because some needed features are not available in `Shared` 
 mode (for example, use of 3rd party libraries, use of Python UDFs) in these releases. 
-Depending on settings, the `Custom` access mode may be supported.
+Depending on settings, the `Custom` access mode may be supported for those releases.
 
 The use of Unity Catalog `Shared` access mode is supported in Databricks runtimes from Databricks runtime release 13.2
-onwards.
+onwards. 
+
+*This version of the data generator uses the Databricks runtime 13.3 LTS as the minimum supported 
+version and alleviates these issues.*
 
 See the following documentation for more information:
 
@@ -155,7 +158,7 @@ The GitHub repository also contains further examples in the examples directory.
 
 ## Spark and Databricks Runtime Compatibility
 The `dbldatagen` package is intended to be compatible with recent LTS versions of the Databricks runtime, including 
-older LTS versions at least from 11.3 LTS and later. It also aims to be compatible with Delta Live Table runtimes, 
+older LTS versions at least from 13.3 LTS and later. It also aims to be compatible with Delta Live Table runtimes, 
 including `current` and `preview`. 
 
 While we don't specifically drop support for older runtimes, changes in Pyspark APIs or

makefile

@@ -27,11 +27,11 @@ prepare: clean
 
 create-dev-env:
 	@echo "$(OK_COLOR)=> making conda dev environment$(NO_COLOR)"
-	conda create -n $(ENV_NAME) python=3.9.21
+	conda create -n $(ENV_NAME) python=3.10.12
 
 create-github-build-env:
 	@echo "$(OK_COLOR)=> making conda dev environment$(NO_COLOR)"
-	conda create -n pip_$(ENV_NAME) python=3.9.21
+	conda create -n pip_$(ENV_NAME) python=3.10.12
 
 install-dev-dependencies:
 	@echo "$(OK_COLOR)=> installing dev environment requirements$(NO_COLOR)"

python/dev_require.txt

@@ -1,19 +1,19 @@
 # The following packages are used in building the test data generator framework.
 # All packages used are already installed in the Databricks runtime environment for version 6.5 or later
 numpy==1.22.0
-pandas==1.3.4
+pandas==1.4.4
 pickleshare==0.7.5
 py4j>=0.10.9.3
-pyarrow==7.0.0
-pyspark==3.3.0
+pyarrow==8.0.0
+pyspark==3.4.1
 python-dateutil==2.8.2
 six==1.16.0
-pyparsing==3.0.4
+pyparsing==3.0.9
 jmespath==0.10.0
 
 # The following packages are required for development only
-wheel==0.37.0
-setuptools==58.0.4
+wheel==0.37.1
+setuptools==63.4.1
 bumpversion
 pytest
 pytest-cov
@@ -28,9 +28,9 @@ sphinx_rtd_theme
 nbsphinx
 numpydoc==0.8
 pypandoc
-ipython==7.32.0
+ipython==8.10.0
 recommonmark
 sphinx-markdown-builder
-Jinja2 < 3.1
+Jinja2 < 3.1, >= 2.11.3
 sphinx-copybutton
 

python/require.txt

@@ -1,19 +1,19 @@
 # The following packages are used in building the test data generator framework.
 # All packages used are already installed in the Databricks runtime environment for version 6.5 or later
 numpy==1.22.0
-pandas==1.3.4
+pandas==1.4.4
 pickleshare==0.7.5
 py4j==0.10.9
-pyarrow==7.0.0
-pyspark==3.3.0
+pyarrow==8.0.0
+pyspark==3.4.1
 python-dateutil==2.8.2
 six==1.16.0
-pyparsing==3.0.4
+pyparsing==3.0.9
 jmespath==0.10.0
 
 # The following packages are required for development only
-wheel==0.37.0
-setuptools==58.0.4
+wheel==0.37.1
+setuptools==63.4.1
 bumpversion
 pytest
 pytest-cov
@@ -27,9 +27,9 @@ sphinx_rtd_theme
 nbsphinx
 numpydoc==0.8
 pypandoc
-ipython==7.32.0
+ipython==8.10.0
 recommonmark
 sphinx-markdown-builder
-Jinja2 < 3.1
+Jinja2 < 3.1, >= 2.11.3
 sphinx-copybutton
 

setup.py

@@ -55,5 +55,5 @@
         "Intended Audience :: Developers",
         "Intended Audience :: System Administrators"
     ],
-    python_requires='>=3.9.21',
+    python_requires='>=3.10.12',
 )

tests/test_generation_from_data.py

@@ -49,7 +49,6 @@ def generation_spec(self):
             .withColumn("short_value", "short", max=32767, percentNulls=0.1)
             .withColumn("byte_value", "tinyint", max=127)
             .withColumn("decimal_value", "decimal(10,2)", max=1000000)
-            .withColumn("decimal_value", "decimal(10,2)", max=1000000)
             .withColumn("date_value", "date", expr="current_date()", random=True)
             .withColumn("binary_value", "binary", expr="cast('spark' as binary)", random=True)