docs/sparkr-migration-guide.md

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title: "Migration Guide: SparkR (R on Spark)"
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* Table of contents
{:toc}

Note that this migration guide describes the items specific to SparkR.
Many items of SQL migration can be applied when migrating SparkR to higher versions.
Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.html).

## Upgrading from SparkR 3.5 to 4.0

 - In Spark 4.0, SparkR is deprecated and will be removed in a future version.

## Upgrading from SparkR 3.1 to 3.2

 - Previously, SparkR automatically downloaded and installed the Spark distribution in user's cache directory to complete SparkR installation when SparkR runs in a plain R shell or Rscript, and the Spark distribution cannot be found. Now, it asks if users want to download and install or not. To restore the previous behavior, set `SPARKR_ASK_INSTALLATION` environment variable to `FALSE`.

## Upgrading from SparkR 2.4 to 3.0

 - The deprecated methods `parquetFile`, `saveAsParquetFile`, `jsonFile`, `jsonRDD` have been removed. Use `read.parquet`, `write.parquet`, `read.json` instead.

## Upgrading from SparkR 2.3 to 2.4

 - Previously, we don't check the validity of the size of the last layer in `spark.mlp`. For example, if the training data only has two labels, a `layers` param like `c(1, 3)` doesn't cause an error previously, now it does.

## Upgrading from SparkR 2.3 to 2.3.1 and above

 - In SparkR 2.3.0 and earlier, the `start` parameter of `substr` method was wrongly subtracted by one and considered as 0-based. This can lead to inconsistent substring results and also does not match with the behaviour with `substr` in R. In version 2.3.1 and later, it has been fixed so the `start` parameter of `substr` method is now 1-based. As an example, `substr(lit('abcdef'), 2, 4))` would result to `abc` in SparkR 2.3.0, and the result would be `bcd` in SparkR 2.3.1.

## Upgrading from SparkR 2.2 to 2.3

 - The `stringsAsFactors` parameter was previously ignored with `collect`, for example, in `collect(createDataFrame(iris), stringsAsFactors = TRUE))`. It has been corrected.
 - For `summary`, option for statistics to compute has been added. Its output is changed from that from `describe`.
 - A warning can be raised if versions of SparkR package and the Spark JVM do not match.

## Upgrading from SparkR 2.1 to 2.2

 - A `numPartitions` parameter has been added to `createDataFrame` and `as.DataFrame`. When splitting the data, the partition position calculation has been made to match the one in Scala.
 - The method `createExternalTable` has been deprecated to be replaced by `createTable`. Either methods can be called to create external or managed table. Additional catalog methods have also been added.
 - By default, derby.log is now saved to `tempdir()`. This will be created when instantiating the SparkSession with `enableHiveSupport` set to `TRUE`.
 - `spark.lda` was not setting the optimizer correctly. It has been corrected.
 - Several model summary outputs are updated to have `coefficients` as `matrix`. This includes `spark.logit`, `spark.kmeans`, `spark.glm`. Model summary outputs for `spark.gaussianMixture` have added log-likelihood as `loglik`.

## Upgrading from SparkR 2.0 to 3.1

 - `join` no longer performs Cartesian Product by default, use `crossJoin` instead.


## Upgrading from SparkR 1.6 to 2.0

 - The method `table` has been removed and replaced by `tableToDF`.
 - The class `DataFrame` has been renamed to `SparkDataFrame` to avoid name conflicts.
 - Spark's `SQLContext` and `HiveContext` have been deprecated to be replaced by `SparkSession`. Instead of `sparkR.init()`, call `sparkR.session()` in its place to instantiate the SparkSession. Once that is done, that currently active SparkSession will be used for SparkDataFrame operations.
 - The parameter `sparkExecutorEnv` is not supported by `sparkR.session`. To set environment for the executors, set Spark config properties with the prefix "spark.executorEnv.VAR_NAME", for example, "spark.executorEnv.PATH"
 - The `sqlContext` parameter is no longer required for these functions: `createDataFrame`, `as.DataFrame`, `read.json`, `jsonFile`, `read.parquet`, `parquetFile`, `read.text`, `sql`, `tables`, `tableNames`, `cacheTable`, `uncacheTable`, `clearCache`, `dropTempTable`, `read.df`, `loadDF`, `createExternalTable`.
 - The method `registerTempTable` has been deprecated to be replaced by `createOrReplaceTempView`.
 - The method `dropTempTable` has been deprecated to be replaced by `dropTempView`.
 - The `sc` SparkContext parameter is no longer required for these functions: `setJobGroup`, `clearJobGroup`, `cancelJobGroup`

## Upgrading from SparkR 1.5 to 1.6

 - Before Spark 1.6.0, the default mode for writes was `append`. It was changed in Spark 1.6.0 to `error` to match the Scala API.
 - SparkSQL converts `NA` in R to `null` and vice-versa.
 - Since 1.6.1, withColumn method in SparkR supports adding a new column to or replacing existing columns
   of the same name of a DataFrame.