repartition {SparkR} | R Documentation |
The following options for repartition are possible:
"Option 1" Return a new SparkDataFrame partitioned by the given columns into 'numPartitions'.
"Option 2" Return a new SparkDataFrame that has exactly 'numPartitions'.
"Option 3" Return a new SparkDataFrame partitioned by the given column(s), using 'spark.sql.shuffle.partitions' as number of partitions.
## S4 method for signature 'SparkDataFrame' repartition(x, numPartitions = NULL, col = NULL, ...)
x |
A SparkDataFrame |
numPartitions |
The number of partitions to use. |
col |
The column by which the partitioning will be performed. |
Other SparkDataFrame functions: $
,
$<-
, select
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select
,
select,SparkDataFrame,Column-method
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select,SparkDataFrame,list-method
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selectExpr
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SparkDataFrame-class
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dataFrame
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[[
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agg
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count,GroupedData-method
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summarize
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arrange
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arrange
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orderBy
, orderBy
,
orderBy
; as.data.frame
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as.data.frame,SparkDataFrame-method
;
attach
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attach,SparkDataFrame-method
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cache
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colnames
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colnames<-
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columns
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names<-
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coltypes
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coltypes<-
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dtypes
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schema
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count
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dapply
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dapplyCollect
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describe
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describe
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summary
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summary,AFTSurvivalRegressionModel-method
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summary,GeneralizedLinearRegressionModel-method
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summary,KMeansModel-method
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summary,NaiveBayesModel-method
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dim
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unique
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dropDuplicates
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dropna
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fillna
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na.omit
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drop
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except
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explain
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filter
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where
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first
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groupBy
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group_by
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head
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insertInto
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intersect
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isLocal
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join
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merge
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mutate
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transform
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persist
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rbind
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unionAll
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registerTempTable
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registerTempTable
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rename
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withColumnRenamed
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sample
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sample_frac
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saveAsParquetFile
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saveAsParquetFile
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write.parquet
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saveAsTable
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saveDF
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write.df
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showDF
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show
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show,GroupedData-method
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show,WindowSpec-method
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take
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withColumn
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write.jdbc
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;
write.json
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write.text
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## Not run: sc <- sparkR.init() sqlContext <- sparkRSQL.init(sc) path <- "path/to/file.json" df <- read.json(sqlContext, path) newDF <- repartition(df, 2L) newDF <- repartition(df, numPartitions = 2L) newDF <- repartition(df, col = df$"col1", df$"col2") newDF <- repartition(df, 3L, col = df$"col1", df$"col2") ## End(Not run)