版本說明：Spark-2.3.0
使用Spark SQL在對數據進行處理的過程中，可能會遇到對一列數據拆分為多列，或者把多列數據合並為一列。這里記錄一下目前想到的對DataFrame列數據進行合並和拆分的幾種方法。
1 DataFrame列數據的合並
例如：我們有如下數據，想要將三列數據合並為一列，並以“,”分割
+----+---+-----------+
|name|age| phone|
+----+---+-----------+
|Ming| 20|15552211521|
|hong| 19|13287994007|
| zhi| 21|15552211523|
+----+---+-----------+
1.1 使用map方法重寫
使用map方法重寫就是將DataFrame使用map取值之后，然后使用toSeq方法轉成Seq格式，最后使用Seq的foldLeft方法拼接數據，並返回，如下所示：
//方法1：利用map重寫
val separator = "," df.map(_.toSeq.foldLeft("")(_ + separator + _).substring(1)).show() /** * +-------------------+ * | value| * +-------------------+ * |Ming,20,15552211521| * |hong,19,13287994007| * | zhi,21,15552211523| * +-------------------+ */
1.2 使用內置函數concat_ws
合並多列數據也可以使用SparkSQL的內置函數concat_ws()
//方法2： 使用內置函數 concat_ws
import org.apache.spark.sql.functions._ df.select(concat_ws(separator, $"name", $"age", $"phone").cast(StringType).as("value")).show() /** * +-------------------+ * | value| * +-------------------+ * |Ming,20,15552211521| * |hong,19,13287994007| * | zhi,21,15552211523| * +-------------------+ */
1.3 使用自定義UDF函數
自己編寫UDF函數，實現多列合並
//方法3：使用自定義UDF函數 // 編寫udf函數
def mergeCols(row: Row): String = { row.toSeq.foldLeft("")(_ + separator + _).substring(1) } val mergeColsUDF = udf(mergeCols _) df.select(mergeColsUDF(struct($"name", $"age", $"phone")).as("value")).show()
完整代碼：
import org.apache.spark.sql.{Row, SparkSession} import org.apache.spark.sql.types.StringType /** * Created by shirukai on 2018/9/12 * DataFrame 合並列 */
object MergeColsTest { def main(args: Array[String]): Unit = { val spark = SparkSession .builder() .appName(this.getClass.getSimpleName) .master("local") .getOrCreate() //從內存創建一組DataFrame數據
import spark.implicits._ val df = Seq(("Ming", 20, 15552211521L), ("hong", 19, 13287994007L), ("zhi", 21, 15552211523L)) .toDF("name", "age", "phone") df.show() /** * +----+---+-----------+ * |name|age| phone| * +----+---+-----------+ * |Ming| 20|15552211521| * |hong| 19|13287994007| * | zhi| 21|15552211523| * +----+---+-----------+ */
//方法1：利用map重寫
val separator = "," df.map(_.toSeq.foldLeft("")(_ + separator + _).substring(1)).show() /** * +-------------------+ * | value| * +-------------------+ * |Ming,20,15552211521| * |hong,19,13287994007| * | zhi,21,15552211523| * +-------------------+ */
//方法2： 使用內置函數 concat_ws
import org.apache.spark.sql.functions._ df.select(concat_ws(separator, $"name", $"age", $"phone").cast(StringType).as("value")).show() /** * +-------------------+ * | value| * +-------------------+ * |Ming,20,15552211521| * |hong,19,13287994007| * | zhi,21,15552211523| * +-------------------+ */
//方法3：使用自定義UDF函數 // 編寫udf函數
def mergeCols(row: Row): String = { row.toSeq.foldLeft("")(_ + separator + _).substring(1) } val mergeColsUDF = udf(mergeCols _) df.select(mergeColsUDF(struct($"name", $"age", $"phone")).as("value")).show() /** * /** * * +-------------------+ * * | value| * * +-------------------+ * * |Ming,20,15552211521| * * |hong,19,13287994007| * * | zhi,21,15552211523| * * +-------------------+ **/
*/ } }
2 DataFrame列數據的拆分
上面我們將DataFrame的多列數據合並為一列如下所示，有時候我們也需要將單列數據，以某種拆分規則，拆分為多列。下面提供幾種將一列拆分為多列的方法。
+-------------------+
| value|
+-------------------+
|Ming,20,15552211521|
|hong,19,13287994007|
| zhi,21,15552211523|
+-------------------+
2.1 使用內置函數split，然后遍歷添加列
該方法，先利用內置函數split將單列的數據拆分，然后遍歷使用getItem(角標)方法獲取拆分后的數據，依次使用withColumn方法添加新列，代碼如下所示：
//方法1： 使用內置函數split，然后遍歷添加列
val separator = "," lazy val first = df.first() val numAttrs = first.toString().split(separator).length val attrs = Array.tabulate(numAttrs)(n => "col_" + n) //按指定分隔符拆分value列，生成splitCols列
var newDF = df.withColumn("splitCols", split($"value", separator)) attrs.zipWithIndex.foreach(x => { newDF = newDF.withColumn(x._1, $"splitCols".getItem(x._2)) }) newDF.show() /** * +-------------------+--------------------+-----+-----+-----------+ * | value| splitCols|col_0|col_1| col_2| * +-------------------+--------------------+-----+-----+-----------+ * |Ming,20,15552211521|[Ming, 20, 155522...| Ming| 20|15552211521| * |hong,19,13287994007|[hong, 19, 132879...| hong| 19|13287994007| * | zhi,21,15552211523|[zhi, 21, 1555221...| zhi| 21|15552211523| * +-------------------+--------------------+-----+-----+-----------+
2.2 使用UDF函數創建多列數據，然后合並
該方法是使用udf函數，生成多個列，然后合並到原來的數據。該方法參考了VectorDisassembler（與spark ml官網提供的VectorAssembler相反），這是一個第三方的spark ml向量拆分算法，該方法github地址：https://github.com/jamesbconner/VectorDisassembler。代碼如下所示：
//方法2：使用udf函數創建多列，然后合並
val attributes: Array[Attribute] = { val numAttrs = first.toString().split(separator).length //生成attributes
Array.tabulate(numAttrs)(i => NumericAttribute.defaultAttr.withName("value" + "_" + i)) } //創建多列數據
val fieldCols = attributes.zipWithIndex.map(x => { val assembleFunc = udf { str: String => str.split(separator)(x._2) } assembleFunc(df("value").cast(StringType)).as(x._1.name.get, x._1.toMetadata()) }) //合並數據
df.select(col("*") +: fieldCols: _*).show() /** * +-------------------+-------+-------+-----------+ * | value|value_0|value_1| value_2| * +-------------------+-------+-------+-----------+ * |Ming,20,15552211521| Ming| 20|15552211521| * |hong,19,13287994007| hong| 19|13287994007| * | zhi,21,15552211523| zhi| 21|15552211523| * +-------------------+-------+-------+-----------+ */
完整代碼：
import org.apache.spark.ml.attribute.{Attribute, NumericAttribute} import org.apache.spark.sql.SparkSession import org.apache.spark.sql.types.StringType /** * Created by shirukai on 2018/9/12 * 拆分列 */
object SplitColTest { def main(args: Array[String]): Unit = { val spark = SparkSession .builder() .appName(this.getClass.getSimpleName) .master("local") .getOrCreate() //從內存中創建DataFrame
import spark.implicits._ val df = Seq("Ming,20,15552211521", "hong,19,13287994007", "zhi,21,15552211523") .toDF("value") df.show() /** * +-------------------+ * | value| * +-------------------+ * |Ming,20,15552211521| * |hong,19,13287994007| * | zhi,21,15552211523| * +-------------------+ */ import org.apache.spark.sql.functions._ //方法1： 使用內置函數split，然后遍歷添加列
val separator = "," lazy val first = df.first() val numAttrs = first.toString().split(separator).length val attrs = Array.tabulate(numAttrs)(n => "col_" + n) //按指定分隔符拆分value列，生成splitCols列
var newDF = df.withColumn("splitCols", split($"value", separator)) attrs.zipWithIndex.foreach(x => { newDF = newDF.withColumn(x._1, $"splitCols".getItem(x._2)) }) newDF.show() /** * +-------------------+--------------------+-----+-----+-----------+ * | value| splitCols|col_0|col_1| col_2| * +-------------------+--------------------+-----+-----+-----------+ * |Ming,20,15552211521|[Ming, 20, 155522...| Ming| 20|15552211521| * |hong,19,13287994007|[hong, 19, 132879...| hong| 19|13287994007| * | zhi,21,15552211523|[zhi, 21, 1555221...| zhi| 21|15552211523| * +-------------------+--------------------+-----+-----+-----------+ */
//方法2：使用udf函數創建多列，然后合並
val attributes: Array[Attribute] = { val numAttrs = first.toString().split(separator).length //生成attributes
Array.tabulate(numAttrs)(i => NumericAttribute.defaultAttr.withName("value" + "_" + i)) } //創建多列數據
val fieldCols = attributes.zipWithIndex.map(x => { val assembleFunc = udf { str: String => str.split(separator)(x._2) } assembleFunc(df("value").cast(StringType)).as(x._1.name.get, x._1.toMetadata()) }) //合並數據
df.select(col("*") +: fieldCols: _*).show() /** * +-------------------+-------+-------+-----------+ * | value|value_0|value_1| value_2| * +-------------------+-------+-------+-----------+ * |Ming,20,15552211521| Ming| 20|15552211521| * |hong,19,13287994007| hong| 19|13287994007| * | zhi,21,15552211523| zhi| 21|15552211523| * +-------------------+-------+-------+-----------+ */ } }