databrickslabs / dbldatagen

Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, POCs, and other uses in Databricks environments including in Delta Live Tables pipelines
https://databrickslabs.github.io/dbldatagen
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When generating array valued column generation spec, use different random seed for each element #178

Closed ronanstokes-db closed 1 year ago

ronanstokes-db commented 1 year ago

Expected Behavior

When generating array valued column generation spec, use different random seed for each element

Current Behavior

When generating multiple values for array elements, current default random seed produces same value for each array element:

For example:

import dbldatagen as dg
from pyspark.sql.types import ArrayType, StringType

dataspec = dg.DataGenerator(spark, rows=10 * 1000000)

dataspec = (dataspec
           .withColumn("name", "string", percentNulls=0.01, template=r'\\w \\w|\\w A. \\w|test')                                       
           .withColumn("serial_number", "string", minValue=1000000, maxValue=10000000, 
                                 prefix="dr", random=True) 
           .withColumn("email", "string", template=r'\\w.\\w@\\w.com', random=True, numColumns=5, structType="array",
                           omit=True) 
            .withColumn("emails", ArrayType(StringType()), expr="slice(email, 1, (abs(hash(id)) % 4)+1)", 
                           baseColumns=["email"]) 
            .withColumn("license_plate", "string", template=r'\\n-\\n')
           )
dfTestData = dataspec.build()

display(dfTestData)

Workaround

Add randomSeed option of -1 to array valued column - however the data generation is then not-repeatable.

import dbldatagen as dg
from pyspark.sql.types import ArrayType, StringType

dataspec = dg.DataGenerator(spark, rows=10 * 1000000)

dataspec = (dataspec
           .withColumn("name", "string", percentNulls=0.01, template=r'\\w \\w|\\w A. \\w|test')                                       
           .withColumn("serial_number", "string", minValue=1000000, maxValue=10000000, 
                                 prefix="dr", random=True) 
           .withColumn("email", "string", template=r'\\w.\\w@\\w.com', random=True, numColumns=5, structType="array",
                           omit=True, randomSeed=-1) 
            .withColumn("emails", ArrayType(StringType()), expr="slice(email, 1, (abs(hash(id)) % 4)+1)", 
                           baseColumns=["email"]) 
            .withColumn("license_plate", "string", template=r'\\n-\\n')
           )
dfTestData = dataspec.build()

display(dfTestData)

Context

Your Environment