CountDistinct(String, String[]) 傳回群組中相異專案的數目。 C# 複製 public static Microsoft.Spark.Sql.Column CountDistinct (string columnName, params string[] columnNames); 參數 columnName String 資料行名稱 columnNames String[] 其他資料行名稱 傳回 Column Column 物件 適用於 Microsoft.Spark...
Spark SQL approx_count_distinct Window Function as a Count Distinct Alternative Theapprox_count_distinctwindows function returns the estimated number of distinct values in a column within the group. Following Spark SQL example uses theapprox_count_distinctwindows function to return distinct count. SELECT...
countDistinct()is a SQL function that could be used to get the count distinct of the selected multiple columns. Let’s see these two ways with examples. Before we start, first let’screate a DataFramewith some duplicate rows and duplicate values in a column. # Create SparkSession and Prepa...
我们将使用distinct()来获取不重复的值,并且使用collect_set来收集这些值。 frompyspark.sqlimportfunctionsasF# 统计distinct数量distinct_count=data.select(target_column).distinct().count()# 使用collect_set收集所有唯一值unique_values=data.select(F.collect_set(target_column)).first()[0]# 输出结果print(f...
countDistinct( Column expr, Column... exprs) 返回一列数据或一组数据中不重复项的个数。expr 为返回 column 的表达式。 avg( Column e) 返回e 列的平均数。 count( Column e) 返回e 列的行数。 max( Column e) 返回e 中的最大值 sum( Column e) 返回e 中所有数据之和 skewness( Column e) 返回...
You will notice that approx_count_distinct took another parameter with which you can specify the maximum estimation error allowed.这样有很大性能提升。 first、last This will be based on the rows in the DataFrame, not on the values in the DataFrame ...
GroupedData对象是一个特殊的DataFrame数据集,GroupedData对象也有很多API,比如count、min、max、avg、sum等等 3.DataFrame之SQL 如果想使用SQL风格的语法,需要将DataFrame注册成表,采用如下的方式: 4.pyspark.sql.functions 包 里的功能函数, 返回值多数都是Column对象.例: 5.SparkSQL Shuffle 分区数目 在SparkSQL中当...
arrays_overlap(a1: Column, a2: Column): Column array_distinct() In Spark, thearray_distinct()function is used to return an array with distinct elements from the input array. It removes duplicate elements and returns only unique elements in the resulting array. ...
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the aggregation at all for those columns. So like if we are doing a count on a column where there are only nulls in the batch, we don't do the count at all, we just insert in a column of zeros after doing the other aggregations, or whatever it is that Spark would have put in....