How do I get unique values in the hive?
The DISTINCT keyword is used in the SELECT statement in HIVE to get only unique rows. Row does not mean a complete row in the table, but it does mean “row” based on the column listed in the SELECT statement. If the SELECT has 3 columns in the list, SELECT DISTINCT will get a single row for only those 3 column values.
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Does it work differently in the hive?
ALL and DISTINCT Clauses Note that Hive supports SELECT DISTINCT * as of version 1.1. 0 (Hive-9194). ALL and DISTINCT can also be used in a UNION clause; see Join syntax for more information.
How is unique id counted after groupBy in PySpark?
How to count unique ID after groupBy in PySpark Dataframe?
- To do this, we will use two different methods:
- Production:
- groupBy() – Used to group the data based on the column name.
- distinguished() .count() : Used to count and display the various rows of the data frame.
- Example 1:
How to get number of unique values in pyspark column?
I have a PySpark dataframe with a column URL. All I want to know is how many different values there are. I just need the number of total distinct values.
When to use the DISTINCT keyword in Hive?
The DISTINCT keyword is used in the SELECT statement in HIVE to get only unique rows. Row does not mean a complete row in the table, but it does mean “row” based on the column listed in the SELECT statement. If the SELECT has 3 columns in the list, SELECT DISTINCT will get a single row for only those 3 column values.
Is there a different way to drop rows in pyspark?
PySpark does not have a separate method that takes columns that should be executed differently (drop duplicate rows to multiple selected columns), however it does provide another signature of the dropDuplicates() function that takes multiple columns to remove duplicates.
How to create a table with hive in spark?
Find the complete example code at “examples/src/main/python/sql/hive.py” in the Spark repository. When working with Hive, you must create a SparkSession instance with Hive support. This adds support for looking up tables in the MetaStore and writing queries using HiveQL. Find the complete example code at “examples/src/main/r/RSparkSQLExample.R” in the Spark repository.