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index a dataframe from a csv file based on the file's original order (not based on any specific column, based on the entire row) using spark
how to guarantee the index is always following the file's original order no matter what. Currently, I'm using val df = spark.read.options(Map("header"-> "true", "inferSchema" -> "true")).csv("filePath").withColumn("index", monotonically_increasing...
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monotonically_increasing_id will not as it is to guarantee that every partition has separate ids. What is the whole code? Do you load directory with a lot of CSVs? What "original order" means? Is it csvs ordered by file creation date, by file name? o...
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Resolved! How to get all the tables name with a specific column or columns in a database?
let's say there is a database db in which 700 tables are there, and we need to find all the tables name in which column "project_id" is present.just an example for ubderstanding the questions.
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databaseName = "db" desiredColumn = "project_id" database = spark.sql(f"show tables in {databaseName} ").collect() tablenames = [] for row in database: cols = spark.table(row.tableName).columns if desiredColumn in cols: tablenames.append(row....
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