repartitionByRange {SparkR}

R Documentation

Repartition by range

Description

The following options for repartition by range are possible:

• 1. Return a new SparkDataFrame range partitioned by the given columns into numPartitions.

• 2. Return a new SparkDataFrame range partitioned by the given column(s), using spark.sql.shuffle.partitions as number of partitions.

At least one partition-by expression must be specified. When no explicit sort order is specified, "ascending nulls first" is assumed.

Usage

repartitionByRange(x, ...)

## S4 method for signature 'SparkDataFrame'
repartitionByRange(x, numPartitions = NULL, col = NULL, ...)

Arguments

x	a SparkDataFrame.
...	additional column(s) to be used in the range partitioning.
numPartitions	the number of partitions to use.
col	the column by which the range partitioning will be performed.

Details

Note that due to performance reasons this method uses sampling to estimate the ranges. Hence, the output may not be consistent, since sampling can return different values. The sample size can be controlled by the config spark.sql.execution.rangeExchange.sampleSizePerPartition.

Note

repartitionByRange since 2.4.0

See Also

repartition, coalesce

Other SparkDataFrame functions: SparkDataFrame-class, agg(), alias(), arrange(), as.data.frame(), attach,SparkDataFrame-method, broadcast(), cache(), checkpoint(), coalesce(), collect(), colnames(), coltypes(), createOrReplaceTempView(), crossJoin(), cube(), dapplyCollect(), dapply(), describe(), dim(), distinct(), dropDuplicates(), dropna(), drop(), dtypes(), exceptAll(), except(), explain(), filter(), first(), gapplyCollect(), gapply(), getNumPartitions(), group_by(), head(), hint(), histogram(), insertInto(), intersectAll(), intersect(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), randomSplit(), rbind(), rename(), repartition(), rollup(), sample(), saveAsTable(), schema(), selectExpr(), select(), showDF(), show(), storageLevel(), str(), subset(), summary(), take(), toJSON(), unionAll(), unionByName(), union(), unpersist(), withColumn(), withWatermark(), with(), write.df(), write.jdbc(), write.json(), write.orc(), write.parquet(), write.stream(), write.text()

Examples

## Not run: 
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D newDF <- repartitionByRange(df, col = df$col1, df$col2)
##D newDF <- repartitionByRange(df, 3L, col = df$col1, df$col2)
## End(Not run)

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