pysparklyr 0.1.5

Improvements

Adds support for I() in tbl()
Ensures arrow is installed by adding it to Imports (#116)
If the cluster version is higher than the available Python library, it will either use, or offer to install the available Python library

Fixes

Fixes issues with having multiple line functions in spark_apply()

pysparklyr 0.1.4

New

Adds support for spark_apply() via the rpy2 Python library
- It will not automatically distribute packages, it will assume that the necessary packages are already installed in each node. This also means that the packages argument is not supported
- As in its original implementation, schema inferring works, and as with the original implementation, it has a performance cost. Unlike the original, the Databricks, and Spark, Connect version will return a ‘columns’ specification that you can use for the next time you run the call.

Improvements

At connection time, it enables Arrow by default. It does this by setting these two configuration settings to true:
- spark.sql.execution.arrow.pyspark.enabled
- spark.sql.execution.arrow.pyspark.fallback.enabled

pysparklyr 0.1.3

New

Adds support for sdf_schema()
Adds support for spark_write_table()
Adds deploy_databricks() function. It will simplify publishing to Posit Connect by automating much of the needed setup, and triggers the publication.
Adds requirements_write() function. It will inventory the Python libraries loaded in a given Python environment and create the ‘requirements.txt’. This is in an effort to make it easier to republish deployed content.

Improvements

Improvements to the RStudio connections snippet. It now adapts for when the host and, or the token, are not available to verify the cluster’s DBR version. If missing, then the snippet will hide the host and token sections, and display a cluster DBR section so that the user can enter it manually. After entering, the snippet will verify the installed environment.
Improves how it process host, token and cluster ID. If it doesn’t find a token, it no longer fails. It will pass nothing for that argument, letting ‘databricks.connect’ find the token. This allows for Databricks configurations files to work.
Prevents failure when the latest ‘databricks.connect’ version is lower than the DBR version of the cluster. It will not prompt to install, but rather alert the user that they will be on a lower version of the library.
Simplifies to spark_connect() connection output.

pysparklyr 0.1.2

Improvements

When connecting,spark_connect(), it will automatically prompt the user to install a Python Environment a viable one is not not found. This way, the R user will not have to run install_databricks()/ install_pyspark() manually when using the package for the first time. (#69)
Instead of simply warning that RETICULATE_PYTHON is set, it will now un-set the variable. This allows pysparklyr to select the correct Python environment. It will output a console message to the user when the variable is un-set. (#65). Because of how Posit Connect manages reticulate Python environments, pysparklyr will force the use of the Python environment under that particular published content’s RETICULATE_PYTHON.
Adds enhanced RStudio Snippet for Databricks connections. It will automatically check the cluster’s version by pooling the Databricks REST API with the cluster’s ID. This to check if there is a pre-installed Python environment that will suport the cluster’s version. All these generate notifications in the snippet’s UI. It also adds integration with Posit Workbench’s new ‘Databricks’ pane. The snippet looks for a specific environment variable that Posit Workbench temporarily sets with the value of the cluster ID, and initializes the snippet with that value. (#53)
Adds install_ml argument to install_databricks() and install_pyspark(). The ML related Python libraries are very large, and take a long time to install. In most cases, the user will not need these to interact with the cluster. The install_ml argument is a flag that will control if the ML libraries will be installed. It defaults to FALSE. The first time the R user runs an ML related function, then pysparklyr will prompt them to install the needed libraries at that time.(#63, #78)
Adds support for Databricks OAuth by adding a handler to the Posit Connect integration. Internally, it centralizes the authentication processing into one un-exported function. (#68)
General improvements to all of console outputs

Machine Learning

Adds support for:
- ft_standard_scaler()
- ft_max_abs_scaler()
- ml_logistic_regression()
- ml_pipeline()
- ml_save()
- ml_predict()
- ml_transform()
Adds ml_prepare_dataset() in lieu of a Vector Assembler transformer

Fixes

Fixes error in use_envname() - No environment name provided, and no environment was automatically identified (#71)

pysparklyr 0.1.1

Improvements

Adds URL sanitation routine for the Databricks Host. It will remove trailing forward slashes, and add scheme (https) if missing. The Host sanitation can be skipped by passing host_sanitize = FALSE to spark_connect().
Suppresses targeted warning messages coming from Python. Specifically, deprecation warnings given to PySpark by Pandas for two variable types: is_datetime64tz_dtype, and is_categorical_dtype
Defaults Python environment creation and installation to run as an RStudio job if the user is within the RStudio IDE. This feature can be overriden using the new as_job argument inside install_databricks(), and install_pyspark() functions
Uses SQL to pull the tree structure that populates the RStudio Connections Pane. This avoids fixing the current catalog and database multiple times, which causes delays. With SQL, we can just pass the Catalog and/or Database directly in the query.

Diagnostics

installed_components() now displays the current version of reticulate in the R session
Adds handling of RETICULATE_PYTHON flag
Fixes Error: Unable to find conda binary. Is Anaconda installed? error (#48)
Improves error messages when installing, and connecting to Databricks (#44 #45)

pysparklyr 0.1.0

Initial CRAN submission.

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