Intersect

Return a new SparkDataFrame containing rows only in both this SparkDataFrame and another SparkDataFrame. This is equivalent to INTERSECT in SQL.

Usage

intersect(x, y)

# S4 method for class 'SparkDataFrame,SparkDataFrame'
intersect(x, y)

Arguments

x

A SparkDataFrame

y

A SparkDataFrame

Value

A SparkDataFrame containing the result of the intersect.

Note

intersect since 1.4.0

See also

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

Examples

if (FALSE) { # \dontrun{
sparkR.session()
df1 <- read.json(path)
df2 <- read.json(path2)
intersectDF <- intersect(df, df2)
} # }