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The method for selecting parquet partitions according to a specific range of dates may vary based on the specific tool or programming language being used. However, a commonly used method in Apache Spark is to use the filter() function.
Assuming the parquet file has a 'date' column, here is an example code snippet in pyspark that selects parquet partitions for a specific date range:
from pyspark.sql import SparkSession
# create a spark session
spark = SparkSession.builder.appName("parquet_partition_selection").getOrCreate()
# read the parquet file
df = spark.read.parquet("<path_to_parquet_file>")
# select partitions for a specific date range
start_date = "2022-01-01"
end_date = "2022-01-31"
selected_partitions = df.filter((df.date >= start_date) & (df.date <= end_date))
In this example, the filter() function is used to select the partitions with a 'date' value within the specified range using the greater than or equals to (>=) and less than or equals to (<=) operators. The resultant 'selected_partitions' dataframe will contain the selected partitions based on the specified date range.