%pythonĭf_with_consecutive_increasing_id.withColumn("cnsecutiv_increase", col("increasing_id") + lit(previous_max_value)). For this example, we are going to define it as 1000. You would normally do this by fetching the value from your existing output table. We’re going to build on the example code that we just ran.įirst, we need to define the value of previous_max_value. If you need to increment based on the last updated maximum value, you can define a previous maximum value and then start counting from there. | Name|Age|monotonically_increasing_id|increasing_id| Run the example code and we get the following results: +-+-+-+-+ Window = Window.orderBy(col('monotonically_increasing_id'))ĭf_with_consecutive_increasing_id = df_with_increasing_id.withColumn('increasing_id', row_number().over(window)) We are going to use the following example code to add monotonically increasing id numbers and row numbers to a basic table with two entries. The row_number() function generates numbers that are consecutive.Ĭombine this with monotonically_increasing_id() to generate two columns of numbers that can be used to identify data entries. +-+-+-+ Combine monotonically_increasing_id() with row_number() for two columns %pythonĭf_with_increasing_id = df.withColumn("monotonically_increasing_id", monotonically_increasing_id()) We are going to use the following example code to add monotonically increasing id numbers to a basic table with two entries. The generated id numbers are guaranteed to be increasing and unique, but they are not guaranteed to be consecutive. The monotonically_increasing_id() function generates monotonically increasing 64-bit integers. +-+-+-+ Use monotonically_increasing_id() for unique, but not consecutive numbers Run the example code and we get the following results: +-+-+-+ We are going to use the following example code to add unique id numbers to a basic table with two entries. You cannot use it directly on a DataFrame.Ĭonvert your DataFrame to a RDD, apply zipWithIndex() to your data, and then convert the RDD back to a DataFrame. The zipWithIndex() function is only available within RDDs. view df.createOrReplaceTempView('view') 2. Use zipWithIndex() in a Resilient Distributed Dataset (RDD) You should select the method that works best with your use case. First released in 1991 by Guido van Rossum, Python makes use of indentation and whitespace to delimit different blocks of code. df2 spark.sql('select UUID from view') 2. Python is a very popular, interpreted, dynamically typed, object-oriented programming language. Now I create two new dataframes that take data from the view, both dataframes will use the original UUID column. We review three different methods to use. view df.createOrReplaceTempView('view') 2. This article shows you how to use Apache Spark functions to generate unique increasing numeric values in a column.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |