import org.apache.spark.sql.functions.{col, lit, row_number} import org.apache.spark.sql.types.DataTypes val df = spark.createDataFrame(Seq( ("A", "20200501"), ("B", "20211121"), ("C", "20151230") )).toDF("BAI", "Date") df.withColumn("AAB", to_date(col("Date"),"yyyyMMdd"...
val record: RDD[Row] = tmpRdd.map(x => { Row(x._1.get(0), x._1.get(1), x._2) }) val schema = new StructType().add("name", "string") .add("age", "string") .add("id", "long") spark.createDataFrame(record, schema).show() 1. 2. 3. 4. 5. 6. 7. 8. 结果:...
// 在原Schema信息的基础上添加一列 “id”信息 val schema: StructType = dataframe.schema.add(StructField("id", LongType)) // DataFrame转RDD 然后调用 zipWithIndex val dfRDD: RDD[(Row, Long)] = dataframe.rdd.zipWithIndex() val rowRDD: RDD[Row] = dfRDD.map(tp => Row.merge(tp._1,...
val df2= df.withColumn("id", row_number().over(w)) println(df2.rdd.getNumPartitions 方案三:将DataFrame转成RDD,使用RDD的方法zipWithIndex()/zipWithUniqueId(),分区数不变。 val df1: DataFrame = spark.range(0,1000000).toDF("col1")//转成rdd并使用zipWithIndex()vartempRDD: RDD[(Row, Lo...
Exception in thread "main" org.apache.spark.sql.AnalysisException: Window function row_number() requires window to be ordered, please add ORDER BY clause. For example SELECT row_number()(value_expr) OVER (PARTITION BY window_partition ORDER BY window_ordering) from table; ...
select(add_months(df.d,1).alias('d')).collect() [Row(d=datetime.date(2015, 5, 8))] 4.pyspark.sql.functions.array_contains(col, value) 集合函数:如果数组包含给定值,则返回True。收集元素和值必须是相同的类型。 >>> df = sqlContext.createDataFrame([(["a", "b", "c"],), ([]...
# 读取一个文件转化每一行为Row对象 lines = sc.textFile("file:///export/pyfolder1/pyspark-chapter03_3.8/data/sql/people.txt") parts = lines.map(lambda l: l.split(",")) people = parts.map(lambda p: Row(name=p[0], age=int(p[1]))) # 推断Schema,并且将DataFrame注册为Table schema...
Java:如何基于对象列表向dataframe添加列 result = myDf.withColumn(val1, expr(val2)); should be result = result.withColumn(val1, expr(val2)); 否则,您将在每次迭代中丢弃result。 Iterator<Myclass> iterator = cols.iterator();Dataset<Row> result=myDf;while (iterator.hasNext()) { Myclass res ...
Row对象 DataFrame中每条数据封装在Row中,Row表示每行数据,具体哪些字段位置,获取DataFrame中第一条数据。 如何构建Row对象:传递value即可,官方实例代码: frompyspark.sqlimportRow//Create a Rowfromvalues.Row(value1,value2,value3,...) 如何获取Row中每个字段的值呢?
sql.{DataFrame, Row, SparkSession} case class Person(name: String, age: Int) object SparkRDDtoDF { def main(agrs: Array[String]): Unit = { val conf = new SparkConf().setMaster("local[2]") conf.set("spark.sql.warehouse.dir", "file:D:\\learn\\JetBrains\\workspace_idea3\\commerce...