Spark sql distinct. functions import col import pyspark.
Spark sql distinct Also, still according to the source code, approx_count_distinct is based on the HyperLogLog++ algorithm. Jun 20, 2015 · I'm a newbie to Apache Spark and was learning basic functionalities. Let us see its example. 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 Set Operators Description Set operators are used to combine two input relations into a single one. Let’s see these two ways with examples. parser. Oct 6, 2023 · This tutorial explains how to find unique values in a column of a PySpark DataFrame, including several examples. Changed in version 3. 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. 2. Preferably I'd like the syntax to be in PySpark, rather than SQL. 31 hours to run. It allows developers to seamlessly integrate SQL queries with Spark programs, making it easier to work with structured data using the familiar SQL language. Queries are used to retrieve result sets from one or more tables. 6 behavior regarding string literal parsing. distinct (), df. This is second part of PySpark Tutorial series. Syntax Jul 4, 2021 · In this article, we will discuss how to find distinct values of multiple columns in PySpark dataframe. This guide for analysts explains syntax, examples, performance tips, and best practices for removing duplicates and counting unique records. How does it work? What are the tradeoffs if it is increased or decreased? I guess for this one should understand how Mar 11, 2020 · Spark's native distinct counting runs faster for a number of reasons, the main one being that it doesn't have to produce all the counted data in an array. This function doesn’t take any argument and by default applies distinct on all columns. 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. For example, if the config is enabled, the pattern to match "\abc" should be "\abc". Not the SQL type way (registertemplate the Aug 13, 2022 · Of the various ways that you've tried, e. Column [source] ¶ Returns a new Column for distinct count of col or cols. approx_count_distinct, nothing more except giving you a warning. In Pyspark, there are two ways to get the count of distinct values. Sep 15, 2022 · I would like to count the distinct number of emails of the current month and the previous 2 months. distinct(). g. dataframe. functions as fn gr = Df2. 8k 41 106 144 Parameters col Column or column name first column to compute on. pyspark. count(col('Student_ID')). But I failed to understand the reason behind it. agg(F. 4. This function is particularly useful when working with large datasets that may contain redundant or Now I want to count distinct number of DEMO_DATE but also reserve every columns' data in each row. So, your assumption regarding shuffles happening over at the executors to process distinct is correct. countDistinct () is used to get the count of unique values of the specified column. We will learn how to get distinct values & count of distinct values. groupby(['Year']) df_grouped = gr. pyspark. countDistinct() is a SQL function that could be used to get the count distinct of the selected multiple columns. Oct 10, 2023 · Learn the syntax of the array\\_distinct function of the SQL language in Databricks SQL and Databricks Runtime. In this article, we will discuss how to count distinct values in one or multiple columns in pyspark. Return a new SparkDataFrame containing the distinct rows in this SparkDataFrame. The order of rows may change due to the distributed nature of Spark processing and the shuffling of data. distinct method is a valuable tool in the toolkit of data engineers and data teams when working with large datasets in Apache Spark. It allows you to efficiently filter out duplicate rows, leaving you with only the unique records. For Scala Spark developers, Apache Spark’s DataFrame API provides powerful tools to remove duplicate rows, with the distinct and dropDuplicates methods serving as the primary Mar 20, 2019 · apache-spark apache-spark-sql edited Mar 20, 2019 at 19:46 abiratsis 7,341 4 31 49 Feb 6, 2023 · In the pyspark's approx_count_distinct function there is a precision argument rsd. When SQL config 'spark. New in version 1. df. countDistinct("a","b","c")). Examples Example 1: Removing duplicate values from a simple array For instance, to count unique StudentName: unique_count = spark. alias('total_student_by_year')) The problem that I discovered that so many ID's are repeated, so the result is wrong and huge. distinct ¶ DataFrame. Oct 19, 2020 · The main difference is the consideration of the subset of columns which is great! When using distinct you need a prior . approxCountDistinct simply calls pyspark. , matching all the columns of the Row) from the DataFrame, and the count () returns the count of the records on the Oct 1, 2022 · df = spark. 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. Had a small doubt. Another way is to use SQL countDistinct () function which will provide the distinct value count of all the selected columns Jan 1, 2022 · Versions: Apache Spark 3. Apr 29, 2019 · 同事告诉我，当然有区别，前者相当于Spark中的 groupByKey，而后者相当于 reduceByKey。 本着怀疑态度，查看了一下这两条SQL的执行计划。 Jun 13, 2021 · sql count apache-spark-sql distinct distinct-values edited Jun 13, 2021 at 19:47 Serg 22. e. Learn how to select distinct rows in a Spark DataFrame with clear explanations and code examples in Scala and Python. distinct() → pyspark. Nov 4, 2023 · In simple terms, distinct () removes duplicate rows from a Spark DataFrame and returns only unique data. 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 pyspark. 0. Jun 20, 2014 · visitors. SQLContext(sc) import spark. Use the distinct () method to perform deduplication of rows. spark. Aug 26, 2024 · Difference between distinct () and dropDuplicates () In PySpark, both distinct () and dropDuplicates () are used to remove duplicate rows from a DataFrame. functions as F df. 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(). column. agg(fn. The column contains more than 50 million records and can grow larger. All these array functions accept input as an array column and several other arguments based on the function. Nov 21, 2025 · Learn the difference between SELECT DISTINCT and SELECT UNIQUE in SQL. In this blog, we are going to learn aggregation functions in Spark. Learn about the distinct () method in Apache PySpark DataFrame and its usage for data deduplication and analysis. show() Counting PySpark unique values - PySpark Unique Values in Column - PySpark show This returns a single row with the count of unique StudentName. Mar 4, 2018 · Spark SQL does NOT use predicate pushdown for distinct queries; meaning that the processing to filter out duplicate records happens at the executors, rather than at the database. cols Column or column name other columns to compute on. In this article, you will learn how to use distinct () and dropDuplicates () functions with PySpark example. In the previous post, we covered following points and if you haven’t read it I will strongly 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. Spark also supports advanced aggregations to do multiple aggregations for the same input record set via GROUPING SETS, CUBE, ROLLUP clauses. If you want to deduplicate data based on a set of compatible columns you should use dropDuplicates: Apr 6, 2022 · In this article, we will discuss how to count distinct values present in the Pyspark DataFrame. show() shows the distinct values that are present in x column of edf DataFrame. sql("SELECT COUNT(DISTINCT StudentName) AS unique_count FROM students_table") unique_count. Is it true for Apache Spark SQL? Jan 14, 2019 · The question is pretty much in the title: Is there an efficient way to count the distinct values in every column in a DataFrame? The describe method provides only the count but not the distinct co pyspark. select("x"). sql. 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. Let's create a sample dataframe. Example input: df = spark. An alias of count_distinct(), and it is encouraged to use count_distinct() directly. 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. Returns a new DataFrame containing the distinct rows in this DataFrame Oct 15, 2023 · Expand Spark transforms COUNT DISTINCT calculation into COUNT, and the first step is to expand the input rows by generating a new row for every distinct aggregation on different columns (product and category in our example) as well as 1 row for all non-distinct aggregations as follows: 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. Am I doing something wrong here that such a simple query is taking so long? Oct 25, 2024 · Introduction In this tutorial, we want to count the distinct values of a PySpark DataFrame column. It‘s an essential tool for deduplicating messy data by discarding repeating, redundant rows in your distributed datasets. 3. Nov 25, 2024 · Aggregation Functions are important part of big data analytics. 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. Inspite of this, I would still advise you to go ahead and perform the de-duplication on Spark, rather than build a Mastering the Distinct Operation in Scala Spark DataFrames: A Comprehensive Guide In the domain of distributed data processing, ensuring data uniqueness is a critical task for producing accurate and reliable results. Notes This method performs a SQL-style set union of the rows from both DataFrame objects, with no automatic deduplication of elements. Examples Example 1: Counting distinct values of a single column Nov 29, 2023 · distinct() eliminates duplicate records (matching all columns of a Row) from DataFrame, count () returns the count of records on DataFrame. Jun 4, 2024 · Learn the syntax of the approx\\_count\\_distinct aggregate function of the SQL language in Databricks SQL and Databricks Runtime. Jul 10, 2024 · Learn the syntax of the is distinct operator of the SQL language in Databricks SQL. count () method and the countDistinct () function of PySpark. count() would be the obvious ways, with the first way in distinct you can specify the level of parallelism and also see improvement in the speed. If it is possible to set up visitors as a stream and use D-streams, that would do the count in realtime. The grouping expressions and Nov 8, 2023 · This tutorial explains how to perform a union between two PySpark DataFrames and only return distinct rows, including an example. 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. Returns Column distinct values of these two column values. 6, Spark implements approximate algorithms for some common tasks: counting the number of distinct elements in a set, finding if an element belongs to a set, computing some basic statistical information 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。 Sep 26, 2020 · 0 I ran this SQL query in databricks to check the distinct values of a column in a parquet file: SELECT distinct country FROM parquet_table This took 1. groupby ('column'). See full list on sparkbyexamples. Aug 8, 2017 · I'm trying to get the distinct values of a column in a dataframe in Pyspark, to them save them in a list, at the moment the list contains "Row (no_children=0)" but I need only the value as I will use it for another part of my code. 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. 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) Oct 16, 2023 · This tutorial explains how to count distinct values in a PySpark DataFrame, including several examples. distinct () is 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 Jul 17, 2023 · When using a pyspark dataframe, we sometimes need to select unique rows or unique values from a particular column. 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 Optimized by Spark’s Spark SQL engine and Catalyst, it scales seamlessly across distributed environments. sql import SparkSession from pyspark. show() 1 It seems that the way F. from pyspark. May 16, 2024 · By using countDistinct () PySpark SQL function you can get the count distinct of the DataFrame that resulted from PySpark groupBy (). 1 version I need to fetch distinct values on a column and then perform some specific transformation on top of it. The choice of operation to remove 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. Column ¶ Returns a new Column for distinct count of col or cols. So I use COUNT (DISTINCT) window function (which is also common in other mainstream databases like Oracle) in Hive beeline and it work: The pyspark. Step 9—Find Unique Values in Multiple With pyspark dataframe, how do you do the equivalent of Pandas df['col']. Jan 19, 2024 · In this video, You will get to know the differences between Distinct () and DropDuplicates () functions in Apache Spark. Import Libraries First, we import the following python modules: from pyspark. sql("""select distinct name, details from table_name""") AnalysisException: Cannot have map type columns in DataFrame which calls set operations (intersect, except, etc. Examples Example 1: Combining two DataFrames with the same schema Oct 31, 2016 · import pyspark. Spark 4. I want to agregate the students by year, count the total number of student by year and avoid the repetition of ID's. You can stream directly from a directory and use the same methods as on the RDD like: val file = ssc. Feb 21, 2021 · What's the difference between distinct() and dropDuplicates() in Spark? Mar 27, 2024 · PySpark distinct () pyspark. Using functions defined here provides a little bit more compile-time safety to make sure the function exists. 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. SELECT Description Spark supports a SELECT statement and conforms to the ANSI SQL standard. Using Spark 1. DataFrame ¶ Returns a new DataFrame containing the distinct rows in this DataFrame. Oct 28, 2021 · In the above image, is it possible to run a DISTINCT on name2 and name whilst still returning all 3 columns? Example above with expected output returning row 2 and row 3. expr("_FUNC_()"). We can use distinct () and count () functions of DataFrame to 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: 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. countDistinct(col: ColumnOrName, *cols: ColumnOrName) → pyspark. As an example, regr_count is a function that is defined here. 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. 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. 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. . distinct() and dropDuplicates() returns a new DataFrame. Jul 10, 2025 · PySpark SQL is a very important and most used module that is used for structured data processing. In order to do this, we use the distinct (). unique(). 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. You can use In PySpark, the distinct() function is used to retrieve unique rows from a Dataframe. You can call the functions defined here by two ways: _FUNC_() and functions. One of its fundamental operations is the union method, which allows you to combine rows from two DataFrames with compatible schemas, stacking them A new column that is an array of unique values from the input column. select ('column'). The whole intention Sep 1, 2020 · As you can see in the source code pyspark. It returns a new Dataframe with distinct rows based on all the columns of the original Dataframe. Here are five key points about distinct (): Apr 5, 2025 · That’s why Spark SQL (starting from version 3. 0: Supports Spark Connect. 2. 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. In this article, we will discuss how to select distinct rows or values in a column of a pyspark dataframe using three different ways. distinct. distinct() [source] # Returns a new DataFrame containing the distinct rows in this DataFrame. ), but the type of column details is map<string,string>; May 19, 2016 · Introduction Apache Spark is fast, but applications such as preliminary data exploration need to be even faster and are willing to sacrifice some accuracy for a faster result. countDistinct deals with the null value is not intuitive for me. Row consists of columns, if you are selecting only one column then output will be unique values for that specific column. Apr 26, 2024 · Spark with Scala provides several built-in SQL standard array functions, also known as collection functions in DataFrame API. Jun 21, 2016 · 40 edf. If the order of rows is important, it is recommended to use additional sorting operations after applying the distinct function. apache. Spark DISTINCT or spark drop duplicates is used to remove duplicate rows in the Dataframe. 8k 5 23 50 Jan 19, 2023 · Recipe Objective - Explain Count Distinct from Dataframe in PySpark in Databricks? The distinct (). Count This is one of basic function where we count number of records or specify column to count. However, there are some differences in Combining Datasets with Spark DataFrame Union: A Comprehensive Guide Apache Spark’s DataFrame API is a robust framework for processing large-scale datasets, offering a structured and efficient way to perform complex data transformations. Jun 6, 2021 · In this article, we are going to display the distinct column values from dataframe using pyspark in Python. count () etc. Column selection: The distinct function considers all columns of a DataFrame to determine uniqueness. functions. I use distinct() 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. DataFrame. distinct() is used to get the unique rows from all the columns from DataFrame. Let's create a sample dataframe for demonstration: Dec 19, 2023 · apache-spark pyspark apache-spark-sql count distinct edited Dec 19, 2023 at 14:04 ZygD 24. 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. PySpark SQL provides a DataFrame API for manipulating data in a distributed and fault-tolerant manner. countDistinct ¶ pyspark. Since version 1. approx_count_distinct # pyspark. Assuming I can select MAX Oct 30, 2023 · This tutorial explains how to use groupBy with count distinct in PySpark, including several examples. , what is the most efficient way to extract distinct values from a column? Mar 6, 2019 · Your take on SQL solution is not logically equivalent to distinct on Dataset. 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. 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. The method resolves columns by position (not by name), following the standard behavior in SQL. 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. distinct # DataFrame. textFileStream Jul 10, 2024 · Learn the syntax of the is distinct operator of the SQL language in Databricks SQL. The Distinct () is defined to eliminate the duplicate records (i. This guide covers what distinct does, how to use it, and its practical applications, with examples to illustrate each step. So regardless the one you use, the very same code runs in the end. 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. I want to list out all the unique values in a pyspark dataframe column. approx_count_distinct(col, rsd=None) [source] # This aggregate function returns a new Column, which estimates the approximate distinct count of elements in a specified column or a group of columns. The typical approach to solving this problem is with a self-join. escapedStringLiterals' is enabled, it falls back to Spark 1. The following section describes the overall query syntax and the sub-sections cover different constructs of a query along with examples. 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. functions import col import pyspark. count () of DataFrame or countDistinct () SQL function in Apache Spark are popularly used to get count distinct. 1 distinct Syntax Following is the syntax on PySpark distinct. These come in handy when we need to perform operations on an array (ArrayType) column. functionsCommonly used functions available for DataFrame operations. It returns a new array column with distinct elements, eliminating any duplicates present in the original array. 1 ScalaDoc - org. Examples Getting distinct values from columns or rows is one of the most used operations. Suppose I have an RDD of tuples (key, value) and wanted to obtain some unique ones out of them. These are very important and frequently used function in Raw Data Cleaning Since then, Spark version 2. 6. By chaining these you can get the count distinct of PySpark DataFrame. For this, we are using distinct () and dropDuplicates () functions along with select () function. sxprjvximqqrmwxkbpqivgtpnkxkxumozhvyawvfamltxfuwqvuwcsasesmtelpxwxpbxtanomdedzsj