Migration Guide: SparkR (R on Spark)
- Upgrading from SparkR 2.4 to 3.0
- Upgrading from SparkR 2.3 to 2.4
- Upgrading from SparkR 2.3 to 2.3.1 and above
- Upgrading from SparkR 2.2 to 2.3
- Upgrading from SparkR 2.1 to 2.2
- Upgrading from SparkR 2.0 to 3.1
- Upgrading from SparkR 1.6 to 2.0
- Upgrading from SparkR 1.5 to 1.6
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.
Upgrading from SparkR 2.4 to 3.0
- The deprecated methods
parquetFile
,saveAsParquetFile
,jsonFile
,jsonRDD
have been removed. Useread.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, alayers
param likec(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 ofsubstr
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 withsubstr
in R. In version 2.3.1 and later, it has been fixed so thestart
parameter ofsubstr
method is now 1-based. As an example,substr(lit('abcdef'), 2, 4))
would result toabc
in SparkR 2.3.0, and the result would bebcd
in SparkR 2.3.1.
Upgrading from SparkR 2.2 to 2.3
- The
stringsAsFactors
parameter was previously ignored withcollect
, for example, incollect(createDataFrame(iris), stringsAsFactors = TRUE))
. It has been corrected. - For
summary
, option for statistics to compute has been added. Its output is changed from that fromdescribe
. - 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 tocreateDataFrame
andas.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 bycreateTable
. 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 withenableHiveSupport
set toTRUE
. spark.lda
was not setting the optimizer correctly. It has been corrected.- Several model summary outputs are updated to have
coefficients
asmatrix
. This includesspark.logit
,spark.kmeans
,spark.glm
. Model summary outputs forspark.gaussianMixture
have added log-likelihood asloglik
.
Upgrading from SparkR 2.0 to 3.1
join
no longer performs Cartesian Product by default, usecrossJoin
instead.
Upgrading from SparkR 1.6 to 2.0
- The method
table
has been removed and replaced bytableToDF
. - The class
DataFrame
has been renamed toSparkDataFrame
to avoid name conflicts. - Spark’s
SQLContext
andHiveContext
have been deprecated to be replaced bySparkSession
. Instead ofsparkR.init()
, callsparkR.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 bysparkR.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 bycreateOrReplaceTempView
. - The method
dropTempTable
has been deprecated to be replaced bydropTempView
. - 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 toerror
to match the Scala API. - SparkSQL converts
NA
in R tonull
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.