Spark sql distinct.
This is second part of PySpark Tutorial series.
Spark sql distinct Let us see its example. It allows developers to seamlessly integrate SQL queries with Spark programs, making it easier to work with structured data using the familiar SQL language. In this article, we will discuss how to select distinct rows or values in a column of a pyspark dataframe using three different ways. The method resolves columns by position (not by name), following the standard behavior in SQL. . agg(fn. countDistinct() is a SQL function that could be used to get the count distinct of the selected multiple columns. count () etc. show() Counting PySpark unique values - PySpark Unique Values in Column - PySpark show This returns a single row with the count of unique StudentName. Apr 26, 2024 · Spark with Scala provides several built-in SQL standard array functions, also known as collection functions in DataFrame API. functions import col import pyspark. The whole intention Sep 1, 2020 · As you can see in the source code pyspark. Mar 27, 2024 · How to get distinct values from a Spark RDD? We are often required to get the distinct values from the Spark RDD, you can use the distinct () function of RDD to achieve this. Jun 20, 2015 · I'm a newbie to Apache Spark and was learning basic functionalities. I want to agregate the students by year, count the total number of student by year and avoid the repetition of ID's. Let's create a sample dataframe for demonstration: Apr 24, 2024 · In this Spark SQL tutorial, you will learn different ways to count the distinct values in every column or selected columns of rows in a DataFrame using Jun 6, 2021 · In this article, we are going to display the distinct column values from dataframe using pyspark in Python. groupby ('column'). Is there an efficient method to also show the number of times these distinct values occur in the data frame? (count for each distinct value) Jan 19, 2024 · In this video, You will get to know the differences between Distinct () and DropDuplicates () functions in Apache Spark. sql("SELECT COUNT(DISTINCT StudentName) AS unique_count FROM students_table") unique_count. Had a small doubt. pyspark. Let’s see these two ways with examples. So I have a table "rep" with a column "id" and when I execute select count (distinct id) from rep; and select count (*) from (select distinct (id) from rep);, the number of entries is the same. 0. What is the Distinct Operation in PySpark? The distinct method in PySpark DataFrames removes duplicate rows from a dataset, returning a new DataFrame with only unique entries. approxCountDistinct simply calls pyspark. We can use distinct () and count () functions of DataFrame to get the count distinct of PySpark DataFrame. SELECT Description Spark supports a SELECT statement and conforms to the ANSI SQL standard. These come in handy when we need to perform operations on an array (ArrayType) column. countDistinct("a","b","c")). The choice of operation to remove pyspark. The grouping expressions and Now I want to count distinct number of DEMO_DATE but also reserve every columns' data in each row. Apr 6, 2022 · In this article, we will discuss how to count distinct values present in the Pyspark DataFrame. functions as F df. EXCEPT EXCEPT and EXCEPT ALL return the rows that are found in one relation but not the other Mar 27, 2024 · PySpark distinct() transformation is used to drop/remove the duplicate rows (all columns) from DataFrame and dropDuplicates() is used to drop rows based on selected (one or multiple) columns. In the previous post, we covered following points and if you haven’t read it I will strongly Notes This method performs a SQL-style set union of the rows from both DataFrame objects, with no automatic deduplication of elements. Select distinct rows in Spark DataFrame - Scala The distinct () method in Apache Spark DataFrame is used to return a new DataFrame with unique rows based on all columns. See full list on sparkbyexamples. I want to list out all the unique values in a pyspark dataframe column. Apr 24, 2024 · In this Spark SQL tutorial, you will learn different ways to get the distinct values in every column or selected multiple columns in a DataFrame using Jul 30, 2009 · Functions ! != % & * + - / < << <= <=> <> = == > >= >> >>> ^ abs acos acosh add_months aes_decrypt aes_encrypt aggregate and any any_value approx_count_distinct approx_percentile array array_agg array_append array_compact array_contains array_distinct array_except array_insert array_intersect array_join array_max array_min array_position array_prepend array_remove array_repeat array_size array Set Operators Description Set operators are used to combine two input relations into a single one. distinct() and dropDuplicates() returns a new DataFrame. This function doesn’t take any argument and by default applies distinct on all columns. An alias of count_distinct(), and it is encouraged to use count_distinct() directly. sql. In this post, we will talk about : Fetch unique values from dataframe in PySpark Use Filter to select few records from Dataframe in PySpark AND OR LIKE IN BETWEEN NULL How to SORT data on basis of one or more columns in ascending or descending order. Also, still according to the source code, approx_count_distinct is based on the HyperLogLog++ algorithm. I use distinct() For instance, to count unique StudentName: unique_count = spark. show() 1 It seems that the way F. select("x"). May 16, 2024 · By using countDistinct () PySpark SQL function you can get the count distinct of the DataFrame that resulted from PySpark groupBy (). Suppose I have an RDD of tuples (key, value) and wanted to obtain some unique ones out of them. Introduction to the array_distinct function The array_distinct function in PySpark is a powerful tool that allows you to remove duplicate elements from an array column in a DataFrame. When processing data, we need to a lot of different functions so it is a good thing Spark has provided us many in built functions. DataFrame. groupby(['Year']) df_grouped = gr. The column contains more than 50 million records and can grow larger. Oct 29, 2019 · 在EMR Spark中通过Relational Cache支持了Count Distinct的预聚合和重聚合，提供了pre_count_distinct和re_count_distinct函数的实现，还提供了自定义的优化规则，将pre_count_distinct函数自动转化为基于Global Dictionary和bit_mapping的执行计划，不需要用户手工拼写复杂的预聚合SQL逻辑。 pyspark. Mar 16, 2017 · I have a data in a file in the following format: 1,32 1,33 1,44 2,21 2,56 1,23 The code I am executing is following: val sqlContext = new org. It returns a new array column with distinct elements, eliminating any duplicates present in the original array. The following section describes the overall query syntax and the sub-sections cover different constructs of a query along with examples. dataframe. Column ¶ Returns a new Column for distinct count of col or cols. Examples Jul 17, 2023 · When using a pyspark dataframe, we sometimes need to select unique rows or unique values from a particular column. Dec 19, 2023 · apache-spark pyspark apache-spark-sql count distinct edited Dec 19, 2023 at 14:04 ZygD 24. 8k 41 106 144 Nov 4, 2023 · In simple terms, distinct () removes duplicate rows from a Spark DataFrame and returns only unique data. To do this: Setup a Spark SQL context Read your file into a dataframe Register your dataframe as a temp table Query it directly using SQL syntax Save results as objects, output to files. cols Column or column name other columns to compute on. 1, Spark offers an equivalent to countDistinct function, approx_count_distinct which is more efficient to use and most importantly, supports counting distinct over a window. Column selection: The distinct function considers all columns of a DataFrame to determine uniqueness. Not the SQL type way (registertemplate the Parameters col Column or str name of column or expression Returns Column A new column that is an array of unique values from the input column. It‘s an essential tool for deduplicating messy data by discarding repeating, redundant rows in your distributed datasets. com Mar 21, 2016 · For PySPark; I come from an R/Pandas background, so I'm actually finding Spark Dataframes a little easier to work with. But I failed to understand the reason behind it. agg(F. Another way is to use SQL countDistinct () function which will provide the distinct value count of all the selected columns Since then, Spark version 2. show() shows the distinct values that are present in x column of edf DataFrame. Column [source] ¶ Returns a new Column for distinct count of col or cols. In Pyspark, there are two ways to get the count of distinct values. 2. May 19, 2022 · Are those queries executed at all? I never used apache-spark or azure Databricks, but usually, you can't do distinct without providing a column name. distinct () is Jul 4, 2021 · In this article, we will discuss how to find distinct values of multiple columns in PySpark dataframe. Learn about the distinct () method in Apache PySpark DataFrame and its usage for data deduplication and analysis. 2. count(col('Student_ID')). GROUP BY Clause Description The GROUP BY clause is used to group the rows based on a set of specified grouping expressions and compute aggregations on the group of rows based on one or more specified aggregate functions. unique(). Let's create a sample dataframe. approx_count_distinct, nothing more except giving you a warning. Returns a new DataFrame containing the distinct rows in this DataFrame Jul 24, 2023 · While handling data in pyspark, we often need to find the count of distinct values in one or multiple columns in a pyspark dataframe. By chaining these you can get the count distinct of PySpark DataFrame. 4) introduces IS DISTINCT FROM, which treats NULLs properly! 👀 Before: The Old, Confusing Way 💡 Suppose you have a Students table: Parameters col Column or column name first column to compute on. distinct # DataFrame. g. Syntax Aug 2, 2024 · Understanding the differences between distinct () and dropDuplicates () in PySpark allows you to choose the right method for removing duplicates based on your specific use case. Getting distinct values from columns or rows is one of the most used operations. Mar 30, 2021 · Spark sql distinct count over window function [duplicate] Asked 4 years, 7 months ago Modified 4 years, 7 months ago Viewed 3k times Nov 19, 2025 · In this article, I’ve consolidated and listed all PySpark Aggregate functions with Python examples and also learned the benefits of using PySpark SQL functions. In this blog, we are going to learn aggregation functions in Spark. df. SQLContext(sc) import spark. Spark SQL supports three types of set operators: EXCEPT or MINUS INTERSECT UNION Note that input relations must have the same number of columns and compatible data types for the respective columns. Here are five key points about distinct (): Feb 21, 2021 · What's the difference between distinct() and dropDuplicates() in Spark? Oct 31, 2016 · import pyspark. Apr 5, 2025 · That’s why Spark SQL (starting from version 3. select ('column'). If the order of rows is important, it is recommended to use additional sorting operations after applying the distinct function. These are very important and frequently used function in Raw Data Cleaning Jan 1, 2022 · Versions: Apache Spark 3. Examples Example 1: Removing duplicate values from a simple array Feb 27, 2016 · The main difference is the consideration of the subset of columns which is great! When using distinct you need a prior . The order of rows may change due to the distributed nature of Spark processing and the shuffling of data. Step 9—Find Unique Values in Multiple Aug 13, 2022 · Of the various ways that you've tried, e. functions. Use the distinct () method to perform deduplication of rows. Returns Column distinct values of these two column values. This is second part of PySpark Tutorial series. Jun 21, 2016 · 40 edf. from pyspark. If you want to deduplicate data based on a set of compatible columns you should use dropDuplicates: Nov 29, 2023 · distinct() eliminates duplicate records (matching all columns of a Row) from DataFrame, count () returns the count of records on DataFrame. 1 distinct Syntax Following is the syntax on PySpark distinct. DataFrame ¶ Returns a new DataFrame containing the distinct rows in this DataFrame. In this article, you will learn how to use distinct () and dropDuplicates () functions with PySpark example. column. Oct 6, 2023 · This tutorial explains how to find unique values in a column of a PySpark DataFrame, including several examples. spark. Changed in version 3. functions as fn gr = Df2. distinct. Count This is one of basic function where we count number of records or specify column to count. Return a new SparkDataFrame containing the distinct rows in this SparkDataFrame. 1 version I need to fetch distinct values on a column and then perform some specific transformation on top of it. apache. Is it true for Apache Spark SQL? Mar 10, 2021 · bcogrel changed the title DISTINCT with ORDER BY DISTINCT with ORDER BY (Spark SQL) on Mar 10, 2021 bcogrel added w: db support Nov 29, 2022 · Spark SQL approx_count_distinct Window Function as a Count Distinct Alternative The approx_count_distinct windows function returns the estimated number of distinct values in a column within the group. All these array functions accept input as an array column and several other arguments based on the function. New in version 1. 0: Supports Spark Connect. distinct ¶ DataFrame. 4. Examples Example 1: Combining two DataFrames with the same schema Jul 10, 2025 · PySpark SQL is a very important and most used module that is used for structured data processing. countDistinct deals with the null value is not intuitive for me. PySpark SQL provides a DataFrame API for manipulating data in a distributed and fault-tolerant manner. Oct 15, 2025 · Learn how to use the EXCEPT, MINUS, INTERSECT, and UNION set operators of the SQL language in Databricks SQL and Databricks Runtime. countDistinct ¶ pyspark. select to select the columns on which you want to apply the duplication and the returned Dataframe contains only these selected columns while dropDuplicates(colNames) will return all the columns of the initial dataframe after removing duplicated rows as per the columns. So regardless the one you use, the very same code runs in the end. Sep 3, 2025 · Learn the syntax of the collect\\_list function of the SQL language in Databricks SQL and Databricks Runtime. For this, we are using distinct () and dropDuplicates () functions along with select () function. Using Spark 1. Mar 27, 2024 · PySpark distinct () pyspark. , what is the most efficient way to extract distinct values from a column? With pyspark dataframe, how do you do the equivalent of Pandas df['col']. do your thing Here's a class I created Oct 10, 2023 · This tutorial explains how to select distinct rows in a PySpark DataFrame, including several examples. Row consists of columns, if you are selecting only one column then output will be unique values for that specific column. 3. Spark also supports advanced aggregations to do multiple aggregations for the same input record set via GROUPING SETS, CUBE, ROLLUP clauses. This function is particularly useful when working with large datasets that may contain redundant or Nov 25, 2024 · Aggregation Functions are important part of big data analytics. 6. alias('total_student_by_year')) The problem that I discovered that so many ID's are repeated, so the result is wrong and huge. countDistinct () is used to get the count of unique values of the specified column. Queries are used to retrieve result sets from one or more tables. Sep 11, 2018 · I have seen a lot of performance improvement in my pyspark code when I replaced distinct() on a spark data frame with groupBy(). Does it looks a bug or normal for you ? And if it is normal, how I can write something that output exactly the result of the first approach but in the same spirit than the second Method. In this article, we will discuss how to count distinct values in one or multiple columns in pyspark. Jan 20, 2024 · Removing duplicate rows or data using Apache Spark (or PySpark), can be achieved in multiple ways by using operations like drop_duplicate, distinct and groupBy. distinct (), df. distinct() is used to get the unique rows from all the columns from DataFrame. It’s a transformation operation, meaning it’s lazy—Spark plans the deduplication but waits for an action like show to execute it. pyspark. So I use COUNT (DISTINCT) window function (which is also common in other mainstream databases like Oracle) in Hive beeline and it work: Spark DISTINCT or spark drop duplicates is used to remove duplicate rows in the Dataframe. 0 I've heard an opinion that using DISTINCT can have a negative impact on big data workloads, and that the queries with GROUP BY were more performant. We will learn how to get distinct values & count of distinct values. countDistinct(col: ColumnOrName, *cols: ColumnOrName) → pyspark. distinct() → pyspark. distinct() [source] # Returns a new DataFrame containing the distinct rows in this DataFrame. Examples Example 1: Counting distinct values of a single column Mar 6, 2019 · Your take on SQL solution is not logically equivalent to distinct on Dataset.