Spark scala dataframe left outer join multiple columns Mar 27, 2024 · Similar to SQL, Spark also supports Inner join to join two DataFrame tables, In this article, you will learn how to use an Inner Join on DataFrame with Scala example. Sep 7, 2022 · I'm trying to do a left-outer join below. Apr 16, 2025 · Diving Straight into Spark’s Join Powerhouse Joining datasets is the backbone of relational analytics, and Apache Spark’s join operation in the DataFrame API is your key to combining data with precision and scale. Step-by-step guide with examples and explanations. Oct 27, 2023 · This tutorial explains how to join two DataFrames in PySpark based on different column names, including an example. Building Sample DataFrames Let us build two sample DataFrame to perform join upon in Scala. For non-matching rows, the corresponding columns will contain null values. DataFrames can be joined together to combine data from multiple tables. May 8, 2024 · a quick walkthru of spark sql dataframe code showing joining scenarios when both tables have columns with the same name; this includes when they are used in the join condition as well as when they … Learn how to use the right join function in Spark with Scala to combine DataFrames based on common columns. Like using subtract, except etc. join(right, on=None, how='left', lsuffix='', rsuffix='') [source] # Join columns of another DataFrame. Thus, we have explained in this article, how to rename duplicated columns after join in Pyspark data frame. Spark doesn't include rows with null by default. If there is no matching key in the left data frame, Spark will insert null. Aug 18, 2018 · My requirement is I have to join both the dataframes so as to get the additional information for each login Id from DataFrame 2. You can join two datasets using the join operators with an optional join condition. column_name == dataframe2. In this article, we will discuss how to Mar 12, 2019 · I have a file A and B which are exactly the same. Outer join on a single column with implicit join condition using column name When you provide the column name directly as the join condition, Spark will treat both name columns as one, and will not produce separate columns for df. Alternatively, you could rename these columns too. For example I want to run the following : val Lead_all = Leads. If a row in one table has no corresponding match in the other table, null values are filled in for the missing columns. 3 and would like to join on multiple columns using python interface (SparkSQL) The following works: I first register them as temp tables. Use explain operator to see the underlying logical and physical plans. If there is no equivalent row in the right DataFrame, Spark will insert null: Jun 16, 2016 · The reason for such a goodie is that the Spark optimizer will join (no pun intended) consecutive where s into one with join. When both of the columns doesn't match I want null Aug 2, 2016 · You should use leftsemi join which is similar to inner join difference being leftsemi join returns all columns from the left dataset and ignores all columns from the right dataset. Dec 19, 2021 · In this article, we will discuss how to join multiple columns in PySpark Dataframe using Python. Joining on multiple columns required to perform multiple conditions using & and | operators. Here is the default Spark behavior. The simple answer (from the Databricks FAQ on this matter) is to perform the join where the joined columns are expressed as an array of strings (or one string) instead of a predicate. The following performs a full outer join between df1 and df2. Jul 23, 2025 · For unstructured data, we need to modify it to fit into the data frame. As a data engineering veteran with a decade of experience crafting scalable ETL pipelines, you’ve likely faced nulls disrupting joins, and Spark’s join operation offers robust ways to manage them Nov 1, 2017 · The method should return the result of a left join between these two frames using the two columns provided for each dataframe (ignoring their case sensitivity). DataFrame. Also, you will learn different ways to provide Join condition. join (dataframe2,dataframe1. Unlike single-column joins, multi-column joins allow Jul 24, 2023 · Join operations are fundamental to data integration and analysis, allowing us to combine data from multiple sources based on common attributes. Here is the left dataframe: Apache Spark is a powerful distributed data processing framework that allows you to perform large-scale data processing tasks. I am trying to perform inner and outer joins on these two dataframes. rightColName, how='left') The left & right column names are known before runtime so the column names can be hard coded. It will help you to understand, how join works in spark scala. In the world of big data processing, Spark and Delta Jul 23, 2025 · The merge or join can be inner, outer, left, right, etc. The syntax is: dataframe1. s = sqlCtx. The code performs outer join using only one column 'ID'. Queries can access multiple tables at once, or access the same table in such a way that multiple rows of the table are being processed at the same time. columns("LeadSource","Utm_Source"," Oct 6, 2025 · In this article, I will explain how to do PySpark join on multiple columns of DataFrames by using join () and SQL, and I will also explain how to eliminate duplicate columns after join. Common types include inner, left, right, full outer, left semi and left anti joins. Parameters right: DataFrame, Series on: str, list of str, or array-like, optional Column or index Dec 15, 2018 · Requirement You have two table named as A and B. May 12, 2024 · PySpark Join is used to combine two DataFrames and by chaining these you can join multiple DataFrames; it supports all basic join type operations available in traditional SQL like INNER, LEFT OUTER, RIGHT OUTER, LEFT ANTI, LEFT SEMI, CROSS, SELF JOIN. This avoids having duplicate columns in the output. The outer join operation returns all the rows from both DataFrame, along with the matching rows. sql('select * from symptom_type where created_year = 2016') Jul 23, 2025 · To perform an outer join on the two DataFrame, we will use the "join" function in PySpark. leftsemi join will select the data from left side dataframe from a joined dataframe. You just need to rename one of the columns before joining. also, you will learn how to eliminate the duplicate columns on the result DataFrame. leftColName == tb. can you please help me alter the code to include two more columns: 'date' and 'location' in the join Nov 5, 2025 · In this Spark article, I will explain how to do Left Outer Join (left, leftouter, left_outer) on two DataFrames with Scala Example. Below, we discuss methods to avoid these duplicate columns. com Multiple joins in Spark involve sequentially or iteratively combining a DataFrame with two or more other DataFrames, using the join method repeatedly to build a unified dataset. I would expect the second uuid column to be null only. Is there a way to replicate the following command: sqlCo Apr 23, 2016 · All these Spark Join methods available in the Dataset class and these methods return DataFrame (note DataFrame = Dataset [Row]) All these methods take first arguments as a Dataset [_] meaning it also takes DataFrame. Apr 17, 2025 · How to Join DataFrames on Multiple Columns in a PySpark DataFrame: The Ultimate Guide Diving Straight into Joining DataFrames on Multiple Columns in a PySpark DataFrame Joining DataFrames on multiple columns is a critical operation for data engineers and analysts working with Apache Spark in ETL pipelines, data integration, or analytics. Nov 25, 2024 · Like left outer join, spark will only pick records from right data frame while doing right outer join. Either login_Id1 or login_Id2 will have data (in most of the cases). name and df2. how to do a left outer join correctly? === Additional information == If I using dataframe to do left outer join i got correct result. Dec 28, 2017 · This happens because when spark combines the columns from the two DataFrames it doesn't do any automatic renaming for you. Here we join two dataframes df1 and df2 based on column col1. Mar 17, 2024 · A left anti join returns all rows from the left DataFrame where there is no match in the right DataFrame based on the specified join condition. What I noticed drop works for inner join but the same is not working for left join , like here in this case I want drop duplicate join column from right . PySpark Joins are wider transformations that involve data shuffling across the network. drop("join_id") Jan 30, 2025 · Learn how to use the JOIN syntax of the SQL language in Databricks SQL and Databricks Runtime. Where Names is a table with columns ['Id', 'Name', 'DateId', 'Description'] and Dates is a table with columns ['Id', 'Date', 'Description'], the columns Id and Description will Mar 27, 2024 · PySpark DataFrame has a join() operation which is used to combine fields from two or multiple DataFrames (by chaining join ()), in this article, you will learn how to do a PySpark Join on Two or Multiple DataFrames by applying conditions on the same or different columns. Nov 4, 2016 · I got same result either using LEFT JOIN or LEFT OUTER JOIN (the second uuid is not null). Oct 9, 2023 · This tutorial explains how to perform a left join with two DataFrames in PySpark, including a complete example. It only returns columns from the left DataFrame. Sep 30, 2024 · PySpark SQL Left Outer Join, also known as a left join, combines rows from two DataFrames based on a related column. Let's create the first dataframe: Oct 26, 2017 · After I've joined multiple tables together, I run them through a simple function to drop columns in the DF if it encounters duplicates while walking from left to right. here, column emp_id is unique on emp and dept_id is unique on the dept datasets and emp_dept_id from emp has a See full list on baeldung. join(Utm_Master, Leaddetails. Jul 3, 2018 · Extending upon use case given here: How to avoid duplicate columns after join? I have two dataframes with the 100s of columns. After the join you can drop the renamed column. Feb 21, 2023 · Introduction to PySpark Join on Multiple Columns PySpark Join on multiple columns contains join operation, which combines the fields from two or more data frames. One common operation in data processing is joining two DataFrames based on a common key or column. In that case I want to use login_Id1 to perform join. 4: How to Filter Data Introduction If you’ve spent any time writing SQL, or Structured Query Language, you’re familiar with the JOIN. Two fundamental operations in data analysis are grouping, which aggregates data based on common Apr 24, 2024 · Spark DataFrame supports all basic SQL Join Types like INNER, LEFT OUTER, RIGHT OUTER, LEFT ANTI, LEFT SEMI, CROSS, SELF JOIN. Mar 3, 2024 · In this Spark article, I will explain how to do Full Outer Join (outer, full,fullouter, full_outer) on two DataFrames with Scala Example and Spark SQL. pandas. This is particularly relevant when performing self-joins or joins on multiple columns. It is also referred to as a left outer join. and you want to perform all types of join in spark using scala. I would like to include null values in an Apache Spark join. join(df2, $"id" === $"join_id", "left"). Before we jump into Spark Left Outer Join examples, first, let’s create an emp and dept DataFrames. join(tb, ta. Following are some samples with join columns: df1. Dec 2, 2020 · And I get this final = ta. Since I have all the columns as duplicate columns, the existing answers were Jun 13, 2016 · We can also do it with leftsemi join. Grouping and Joining Multiple Datasets in Spark DataFrames: A Comprehensive Guide Apache Spark’s DataFrame API is a robust framework for processing large-scale datasets, offering a structured and distributed environment for executing complex data transformations with efficiency and scalability. Nov 20, 2020 · Photo by Nick Fewings on Unsplash Previous post: Spark Starter Guide 4. Apr 16, 2025 · Diving Straight into Spark’s Join with Null Handling Joining datasets while handling null values is a critical skill in Apache Spark, where mismatches or missing data can derail your analytics. We can eliminate the duplicate column from the data frame result using it. However, if the DataFrames contain columns with the same name (that aren't used as join keys), the resulting DataFrame can have duplicate columns. join # DataFrame. With your decade of data engineering expertise and a passion for scalable ETL pipelines, you’ve likely tackled joins in countless scenarios, but Spark’s nuances can still Jul 7, 2015 · How to give more column conditions when joining two dataframes. There are four main types of DataFrame joins: Inner join returns rows from both DataFrames that have matching values in the join columns. I want to perform a full outer join on these two data frames. Now how can we have one Dataframe Mar 21, 2016 · Let's say I have a spark data frame df1, with several columns (among which the column id) and data frame df2 with two columns, id and other. A query that accesses multiple rows of the same or different tables at one time is called a join query. The "join" function accepts the two DataFrame and the join column name as arguments. May 8, 2018 · I have created two data frames in pyspark like below. Outer join returns all rows from the left A left join returns all values from the left relation and the matched values from the right relation, or appends NULL if there is no match. All rows from the left DataFrame (the “left” side) are included in the result DataFrame, regardless of whether there is a matching row in the right DataFrame (the “right” side). name. In these data frames I have column id. Jun 16, 2025 · In PySpark, joins combine rows from two DataFrames using a common key. Master handling duplicate column names in Spark join operations with this detailed guide Learn syntax parameters and advanced techniques in Scala Sep 5, 2024 · When working with PySpark, it's common to join two DataFrames. Parameters: other – Right side of the join on – a string for join column name, a list of column names, , a join expression (Column) or a list of Columns. I am using Spark 1. Learn how to use the left join function in Spark with Scala to combine DataFrames based on common columns. Spark SQL Joins are wider Sep 25, 2024 · A full outer join in PySpark SQL combines rows from two tables based on a matching condition, including all rows from both tables. Oct 31, 2016 · I have constructed two dataframes. withColumnRenamed("id", "join_id") val joined = df1. With your ETL and optimization expertise, these techniques should slot right into your pipelines, boosting efficiency and clarity. Apr 16, 2025 · The join operation in Spark’s DataFrame API is a cornerstone, and Scala’s syntax—from basic to complex joins—empowers you to merge data with finesse. column_name,"type") where, dataframe1 is the first dataframe pyspark. Oct 9, 2023 · This tutorial explains how to perform a left join in PySpark using multiple columns, including a complete example. val df2join = df2. Overview of DataFrame Joins In Scala, a DataFrame is a tabular data structure that can be used to represent a relational database table. We are doing PySpark join of various conditions by applying the condition on different or same columns. May 12, 2015 · How do I drop duplicate column after left_outer/left join . If not, then the short explanation is that you can use it in SQL to combine two or more data tables together, leveraging a column (s) of data that is shared or related between them Feb 3, 2023 · A left semi join in Spark SQL is a type of join operation that returns only the columns from the left dataframe that have matching values in the right dataframe. columns // Array(. Dataframes are built on the core API of Spark called RDDs to provide type-safety, optimization, and other things. Each type serves a different purpose for handling matched or unmatched data during merges. Oct 7, 2016 · Continue to help good content that is interesting, well-researched, and useful, rise to the top! To gain full voting privileges, join (other, on=None, how=None) Joins with another DataFrame, using the given join expression. Below is an example adapted from the Databricks FAQ but with two join columns in order to answer the original poster's question. Spark provides a method for this. Left Outer Joins Left outer joins evaluate the keys in both of the DataFrames or tables and includes all rows from the left DataFrame as well as any rows in the right DataFrame that have a match in the left DataFrame. Apr 4, 2017 · How to make this type of join in Spark efficiently? I'm looking for an SQL query because I need to be able to specify columns which to compare between two tables, not just compare row by row like it is done in other recommended questions. Efficiently join multiple DataFrame objects by index at once by passing a list. A left join returns all values from the left relation and the matched values from the right relation, or appends NULL if there is no match. Join columns with right DataFrame either on index or on a key column. How can we join multiple Spark dataframes ? For Example : PersonDf, ProfileDf with a common column as personId as (key). This ensures that no data is excluded from the result set. At times both the columns may also have data. In this article, we will explore how to join two DataFrames in Scala Spark using various types of joins. , but after join, if we observe that some of the columns are duplicates in the data frame, then we will get stuck and not be able to apply functions on the joined data frame. zspl jrpaz kyrmwv aneur rtyxq eed wwkqci jmxq rmam qecjmk zzzn oqvx cjwf grr ypljoacc