Return a new SparkDataFrame containing the union of rows

Return a new SparkDataFrame containing the union of rows in this SparkDataFrame and another SparkDataFrame. This is equivalent to UNION ALL in SQL. Input SparkDataFrames can have different schemas (names and data types).

Usage

union(x, y)

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

Arguments

x

A SparkDataFrame

y

A SparkDataFrame

Value

A SparkDataFrame containing the result of the union.

Details

Note: This does not remove duplicate rows across the two SparkDataFrames. Also as standard in SQL, this function resolves columns by position (not by name).

Note

union since 2.0.0

See also

rbind unionByName

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(), intersect(), 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(), 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)
unioned <- union(df, df2)
unions <- rbind(df, df2, df3, df4)
} # }