SparkR (version 2.1.2)

join: Join

Description

Joins two SparkDataFrames based on the given join expression.

Usage

# S4 method for SparkDataFrame,SparkDataFrame
join(x, y, joinExpr = NULL,
  joinType = NULL)

Arguments

x

A SparkDataFrame

y

A SparkDataFrame

joinExpr

(Optional) The expression used to perform the join. joinExpr must be a Column expression. If joinExpr is omitted, the default, inner join is attempted and an error is thrown if it would be a Cartesian Product. For Cartesian join, use crossJoin instead.

joinType

The type of join to perform, default 'inner'. Must be one of: 'inner', 'cross', 'outer', 'full', 'full_outer', 'left', 'left_outer', 'right', 'right_outer', 'left_semi', or 'left_anti'.

Value

A SparkDataFrame containing the result of the join operation.

See Also

merge crossJoin

Other SparkDataFrame functions: SparkDataFrame-class, agg, arrange, as.data.frame, attach, cache, coalesce, collect, colnames, coltypes, createOrReplaceTempView, crossJoin, dapplyCollect, dapply, describe, dim, distinct, dropDuplicates, dropna, drop, dtypes, except, explain, filter, first, gapplyCollect, gapply, getNumPartitions, group_by, head, histogram, insertInto, intersect, isLocal, limit, merge, mutate, ncol, nrow, persist, printSchema, randomSplit, rbind, registerTempTable, rename, repartition, sample, saveAsTable, schema, selectExpr, select, showDF, show, storageLevel, str, subset, take, union, unpersist, withColumn, with, write.df, write.jdbc, write.json, write.orc, write.parquet, write.text

Examples

# NOT RUN {
sparkR.session()
df1 <- read.json(path)
df2 <- read.json(path2)
join(df1, df2, df1$col1 == df2$col2) # Performs an inner join based on expression
join(df1, df2, df1$col1 == df2$col2, "right_outer")
join(df1, df2) # Attempts an inner join
# }