Let us say I have a dataframe df in PySpark (an interface I'm completely new to) with two columns, one with the label 'sports' that takes only 3 values ('soccer', 'basketball', 'volleyball') and another one with the label 'player_names' which can take any string...
In case anyone else is interested, this is how I do it in python: from pyspark.sql import DataFrame from pyspark.sql import functions a f partition_cols = spark.sql(f'describe detail delta.`{path}`').select('partitionColumns').collect()[0][0] JDeltaLog = spark._jvm.org.apache.spark...
partition_by_columns = ['id'] desired_rows_per_output_file = 10 partition_count = skewed_data.groupBy(partition_by_columns).count() partition_balanced_data = ( skewed_data .join(partition_count, on=partition_by_columns) .withColumn( 'repartition_seed', ( rand() * partition_count['count'...
# 需要导入模块: from pyspark.sql import functions [as 别名]# 或者: from pyspark.sql.functions importrow_number[as 别名]defcompile_row_number(t, expr, scope, *, window, **kwargs):returnF.row_number().over(window).cast('long') -1# --- Temporal Operations ---# Ibis value to PySpark...
The data can be ordered within each partition based on one or more columns. Ordering defines the sequence of rows that window functions operate on. Window functions require a window specifying the data’s partitioning and ordering. This is created using the Window class from pyspark.sql.window....