SparkR (version 3.1.2)

intersect: Intersect

Description

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 SparkDataFrame,SparkDataFrame
intersect(x, y)

Arguments

x

A SparkDataFrame

y

A SparkDataFrame

Value

A SparkDataFrame containing the result of the intersect.

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(), dapplyCollect(), dapply(), describe(), dim(), distinct(), dropDuplicates(), dropna(), drop(), dtypes(), exceptAll(), except(), explain(), filter(), first(), gapplyCollect(), gapply(), getNumPartitions(), group_by(), head(), hint(), histogram(), insertInto(), intersectAll(), isLocal(), isStreaming(), join(), limit(), localCheckpoint(), merge(), mutate(), ncol(), nrow(), persist(), printSchema(), randomSplit(), rbind(), rename(), repartitionByRange(), 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 {
sparkR.session()
df1 <- read.json(path)
df2 <- read.json(path2)
intersectDF <- intersect(df, df2)
# }