This guide covers migration for SparkR, the R API for Apache Spark. For general SQL and DataFrame changes that also affect SparkR, review the SQL Migration Guide .
SparkR is deprecated as of Spark 4.0 and will be removed in a future version. Consider migrating to alternative solutions for R-based big data processing.
Upgrading from SparkR 3.5 to 4.0 SparkR Deprecation Notice As of Spark 4.0, SparkR is officially deprecated. While it continues to work in Spark 4.0, you should plan to migrate away from SparkR.
Migration Alternatives Consider these alternatives for R-based big data processing:
1. sparklyr Package sparklyr provides a dplyr-compatible interface to Spark:# Install sparklyr install.packages ( "sparklyr" ) library (sparklyr) # Connect to Spark sc <- spark_connect( master = "local" ) # Use dplyr syntax library (dplyr) data <- spark_read_csv(sc, "data" , "path/to/data.csv" ) result <- data %>% filter (value > 100 ) %>% group_by(category) %>% summarize( avg_value = mean (value), count = n() ) %>% collect()
2. Arrow and DuckDB For smaller-scale data processing:
library (arrow) library (duckdb) # Read data with Arrow df <- read_parquet( "data.parquet" ) # Process with DuckDB for SQL operations con <- dbConnect(duckdb::duckdb()) dbWriteTable(con, "data" , df) result <- dbGetQuery(con, " SELECT category, AVG(value) as avg_value, COUNT(*) as count FROM data WHERE value > 100 GROUP BY category " )
Continuing to Use SparkR in Spark 4.0 If you must continue using SparkR in Spark 4.0:
library (SparkR) # Initialize SparkSession sparkR.session( master = "local[*]" ) # Note: You may see deprecation warnings df <- read.df( "data.parquet" , source = "parquet" ) result <- SparkR::filter(df, df $ value > 100 )
Upgrading from SparkR 3.1 to 3.2 Automatic Installation Prompt SparkR now prompts before downloading and installing the Spark distribution.
Before (SparkR 3.1): # Automatically downloaded Spark if not found library (SparkR) sparkR.session() # Silent automatic download
After (SparkR 3.2): # Prompts user for confirmation library (SparkR) sparkR.session() # Asks: "Download Spark? (y/n)" # Restore automatic behavior via environment variable Sys.setenv ( SPARKR_ASK_INSTALLATION = "FALSE" ) library (SparkR) sparkR.session() # No prompt
Upgrading from SparkR 2.4 to 3.0 Deprecated Methods Removed Several deprecated methods have been removed:
Removed methods:
parquetFile() → Use read.parquet()saveAsParquetFile() → Use write.parquet()jsonFile() → Use read.json()jsonRDD() → Use read.json()
Migration example: # Before (SparkR 2.4) df <- parquetFile( "data.parquet" ) # Deprecated saveAsParquetFile(df, "output.parquet" ) # Deprecated # After (SparkR 3.0) df <- read.parquet( "data.parquet" ) write.parquet(df, "output.parquet" )
Upgrading from SparkR 2.3 to 2.4 Neural Network Layer Validation The spark.mlp function now validates layer sizes.
Before (SparkR 2.3): # Invalid layer configuration silently accepted model <- spark.mlp( data = df, label = "label" , layers = c ( 1 , 3 ) # Only 3 output neurons for multiple labels )
After (SparkR 2.4): # Throws error for invalid configuration model <- spark.mlp( data = df, label = "label" , layers = c ( 10 , 5 , 2 ) # Must match number of labels ) # Error if output layer size doesn't match label count
Upgrading from SparkR 2.3.0 to 2.3.1 substr Function Fix This is a breaking change that affects substring operations.
The substr function now correctly uses 1-based indexing, matching R’s behavior.
Before (SparkR 2.3.0): # Incorrectly used 0-based indexing df <- createDataFrame( data.frame ( text = "abcdef" )) result <- select(df, substr (df $ text, 2 , 4 )) collect(result) # Result: "abc" (positions 1-3, incorrectly)
After (SparkR 2.3.1+): # Correctly uses 1-based indexing df <- createDataFrame( data.frame ( text = "abcdef" )) result <- select(df, substr (df $ text, 2 , 4 )) collect(result) # Result: "bcd" (positions 2-4, correctly)
Upgrading from SparkR 2.2 to 2.3 stringsAsFactors Parameter Fix The stringsAsFactors parameter is now properly respected in collect().
# Before (SparkR 2.2): Parameter ignored df <- createDataFrame(iris) result <- collect(df, stringsAsFactors = TRUE ) # Result: Strings not converted to factors # After (SparkR 2.3): Parameter works correctly result <- collect(df, stringsAsFactors = TRUE ) # Result: Strings properly converted to factors
summary Function Output Change The summary() function output has changed to provide more statistical options.
# SparkR 2.3: Can specify statistics to compute summary (df, statistics = c ( "mean" , "stddev" , "min" , "max" )) # Different from describe() output describe(df) # Basic statistics only
Version Mismatch Warning SparkR now warns when package and JVM versions don’t match.
library (SparkR) sparkR.session() # Warning: SparkR package version 2.3.0 does not match Spark JVM 2.2.0
Upgrading from SparkR 2.1 to 2.2 numPartitions Parameter New numPartitions parameter added to data frame creation functions.
# SparkR 2.2: Can control partitioning df <- createDataFrame( data = iris, numPartitions = 4 # Explicitly set partition count ) df2 <- as.DataFrame( data = iris, numPartitions = 8 )
createExternalTable Deprecation createExternalTable() deprecated in favor of createTable().Migration: # Before (SparkR 2.1) createExternalTable( "table_name" , path = "data" , source = "parquet" ) # After (SparkR 2.2) createTable( "table_name" , path = "data" , source = "parquet" )
Derby Log Location Derby log now saved to temporary directory.
# SparkR 2.2: Derby log location sparkR.session( enableHiveSupport = TRUE ) # derby.log saved to tempdir()
LDA Optimizer Fix The spark.lda function now correctly sets the optimizer.
# SparkR 2.2: Optimizer parameter now works correctly model <- spark.lda( data = df, optimizer = "online" , # Now properly applied k = 10 )
Model summaries now return coefficients as matrices.
# SparkR 2.2: Coefficients as matrix model <- spark.logit(label ~ features, data = df) summary_obj <- summary (model) coefs <- summary_obj $ coefficients # Now a matrix # Apply to multiple functions: # - spark.logit # - spark.kmeans # - spark.glm # - spark.gaussianMixture (added loglik)
Upgrading from SparkR 2.0 to 2.1 Cartesian Product Changes join() no longer performs Cartesian products by default.Migration: # Before (SparkR 2.0) df1 <- createDataFrame( data.frame ( a = 1 : 3 )) df2 <- createDataFrame( data.frame ( b = 1 : 3 )) result <- join(df1, df2) # Cartesian product # After (SparkR 2.1) result <- crossJoin(df1, df2) # Explicit cross join
Upgrading from SparkR 1.6 to 2.0 Major API Changes SparkR 2.0 introduced significant API changes:
DataFrame Renamed to SparkDataFrame # Before (SparkR 1.6) df <- createDataFrame(iris) class (df) # "DataFrame" # After (SparkR 2.0) df <- createDataFrame(iris) class (df) # "SparkDataFrame"
SparkSession Replaces SQLContext # Before (SparkR 1.6) sparkR.init() sqlContext <- sparkRSQL.init(sc) # After (SparkR 2.0) spark <- sparkR.session() # No separate SQLContext needed
Simplified Function Signatures Many functions no longer require sqlContext parameter:
# Before (SparkR 1.6) df <- read.json(sqlContext, "data.json" ) tables(sqlContext) cacheTable(sqlContext, "tableName" ) # After (SparkR 2.0) df <- read.json( "data.json" ) tables() cacheTable( "tableName" )
Temp Table Functions Renamed # Before (SparkR 1.6) registerTempTable(df, "temp" ) dropTempTable( "temp" ) # After (SparkR 2.0) createOrReplaceTempView(df, "temp" ) dropTempView( "temp" )
Executor Environment Configuration # Before (SparkR 1.6) sparkR.init( sparkExecutorEnv = list ( PATH = "/custom/path" )) # After (SparkR 2.0) sparkR.session( sparkConfig = list ( "spark.executorEnv.PATH" = "/custom/path" ) )
Best Practices for Migration Testing Strategy # Create test suite for migration library (testthat) test_that( "DataFrame operations work correctly" , { df <- createDataFrame(iris) result <- SparkR::filter(df, df $ Sepal_Length > 5.0 ) expect_true( nrow (collect(result)) > 0 ) }) test_that( "Read/write operations function" , { df <- createDataFrame(iris) write.parquet(df, "test_output.parquet" ) df2 <- read.parquet( "test_output.parquet" ) expect_equal( nrow (collect(df)), nrow (collect(df2))) })
Version Checking # Check SparkR version check_sparkr_version <- function ( min_version ) { current <- packageVersion ( "SparkR" ) if (current < min_version) { stop ( paste ( "SparkR version" , min_version, "or higher required." , "Current version:" , current )) } } check_sparkr_version( "3.0.0" )
Logging Configuration # Enable verbose logging for debugging sparkR.session( sparkConfig = list ( "spark.sql.shuffle.partitions" = "4" , "spark.logConf" = "true" ) )
Alternative: migrating to sparklyr For new R projects, consider starting with sparklyr:
# Install sparklyr install.packages ( "sparklyr" ) library (sparklyr) # Install Spark spark_install( version = "3.5.0" ) # Connect to Spark sc <- spark_connect( master = "local" ) # Use familiar dplyr syntax library (dplyr) df <- copy_to(sc, iris, "iris" ) result <- df %>% filter (Sepal_Length > 5.0 ) %>% group_by(Species) %>% summarize( avg_sepal = mean (Sepal_Length), count = n() ) %>% collect() print (result) # Disconnect spark_disconnect(sc)
Additional Resources
sparklyr Recommended alternative for R users
SparkR Documentation Official SparkR programming guide
See Also 