Spark Dataframe Join Multiple Columns Scala


Apache Spark Started in UC Berkeley ~ 2010 Most popular and de facto standard framework in big data One of the largest OSS projects written in Scala (but with user-facing APIs in Scala, Java, Python, R, SQL) Many companies introduced to Scala due to Spark. Assuming having some knowledge on Dataframes and basics of Python and Scala. To support a wide variety of data sources and analytics workloads in Spark SQL, we designed an extensible query optimizer called Catalyst. Inner equi-join with another DataFrame using the given column. Explore careers to become a Big Data Developer or Architect!. I am facing an issue here that I have a dataframe with 2 columns, "ID" and "Amount". Spark SQl is a Spark module for structured data processing. Dataframe in Apache Spark is a distributed collection of data, organized in the form of columns. similar to SQL's JOIN USING syntax. Combine several columns into single column of sequence of values. Today, I will show you a very simple way to join two csv files in Spark. groupby (colname). x example updated in another answer with full set of join operations supported by spark 2. Data frame A PIs usually supports elaborate methods for slicing-and-dicing the data. scala - Spark - DataFrameとしてcsvファイルを読み込む方法? メモリ内のJSON文字列をSpark DataFrameに読み込む方法; scala - Spark DataFrame - SQLを使ってパイプ区切りのファイルを読み込む; apache-spark - PySpark CSVをデータフレームに読み込んで操作する方法. similar to SQL's `JOIN USING` syntax. let’s take all entries with A > 3: >>> a_df. How can I return only the details of the student that h. Catalyst uses features of the Scala programming language,. pyspark sort dataframe by multiple columns 0. The same concept will be applied to Scala as well. In the upcoming 1. I could have rename >> the columns on the right data frame, as described in the following code. x with examples + result. We can also perform aggregation on some specific columns which is equivalent to GROUP BY clause we have in typical SQL. A cross join with a predicate * is specified as an inner join. This topic demonstrates a number of common Spark DataFrame functions using Python. It is an aggregation where one of the grouping columns values transposed into individual columns with distinct data. You can run, but you can't hide! Native Spark code. Running into an issue trying to perform a simple join of two DataFrames created from two different parquet files on HDFS. This means that if you are joining to the same DataFrame many times (by the same expressions each time), Spark will be doing the repartitioning of this DataFrame each time. Let's discuss all possible ways to rename column with Scala examples. More than a year later, Spark's DataFrame API provides a rich set of operations for data munging, SQL queries, and analytics. multiple Perform a typed join in Scala with Spark Datasets this functions to provide me with the right column name as a String. Lets create DataFrame with sample data Employee. We explored a lot of techniques and finally came upon this one which we found was the easiest. Since each DataFrame object is a collection of Series. fill("e",Seq("blank")) DataFrames are immutable structures. (Scala-specific) Returns a new DataFrame where each row has been expanded to zero or more rows by the provided function. except(dataframe2) but the comparison happens at a row level and not at specific column level. Lets see how to select multiple columns from a spark data frame. How to Update Spark DataFrame Column Values using Pyspark? The Spark dataFrame is one of the widely used features in Apache Spark. Throughout this Spark 2. Model loading can be backwards-compatible with Apache Spark 1. This post will give an overview of all the major features of Spark's DataFrame API, focusing on the Scala API in 1. Create a spark dataframe from sample data; Load spark dataframe into non existing hive table; How to add new column in Spark Dataframe; How to read JSON file in Spark; How to execute Scala script in Spark without creating Jar; Spark-Scala Quiz-1; Hive Quiz - 1; Join in hive with example; Trending now. Apache Spark Started in UC Berkeley ~ 2010 Most popular and de facto standard framework in big data One of the largest OSS projects written in Scala (but with user-facing APIs in Scala, Java, Python, R, SQL) Many companies introduced to Scala due to Spark. We provide programs to kids like Play Group, Nursery, Sanjary Junior, Sanjary Senior and Teacher training Program. Split DataFrame Array column. join method is equivalent to SQL join like this. This topic demonstrates a number of common Spark DataFrame functions using Scala. I have 3dataframes generated from 3 different processes. Then Spark SQL will scan only required columns and will automatically tune compression to minimize memory usage and GC pressure. My code looks very ugly because of the multiple when condition. Prevent Duplicated Columns when Joining Two DataFrames. To support a wide variety of data sources and analytics workloads in Spark SQL, we designed an extensible query optimizer called Catalyst. // Joining df1 and df2 using the column "user_id" df1. Spark SQL is a Spark module for structured data processing. Explore careers to become a Big Data Developer or Architect!. With the recent changes in Spark 2. perform join on multiple DataFrame in spark. In this post, we will see how to replace nulls in a DataFrame with Python and Scala. Next, we specify the " on" of our join. Many existing Spark developers will be wondering whether to jump from RDDs directly to the Dataset API, or whether to first move to the DataFrame API. setLogLevel(newLevel). Initially I was unaware that Spark RDD functions cannot be applied on Spark Dataframe. This Running Queries Using Apache Spark SQL tutorial provides in-depth knowledge about spark sql, spark query, dataframe, json data, parquet files, hive queries Running SQL Queries Using Spark SQL lesson provides you with in-depth tutorial online as a part of Apache Spark & Scala course. Updating Dataframe Column name in Spark - Scala while performing Joins. No requirement to add CASE keyword though. If you want to ignore duplicate columns just drop them or select columns of interest afterwards. join with different partitioners), to avoid recomputing the input Dataset should be cached first. we can do something like it with "Purrr" package,but not sure how to. For every row custom function is applied of the dataframe. get specific row from spark dataframe apache-spark apache-spark-sql Is there any alternative for df[100, c(“column”)] in scala spark data frames. I am trying to implement a sample as explained below, I am quite new to this spark/scala, so need some inputs as to how this can be implemented in an efficient way. 5, with more than 100 built-in functions introduced in Spark 1. Each dataframe has a "value" column, so when I join them I rename the second table's value column to "Df2 value" let's say. To give your kid a best environment and learning it is the right way to join in play and pre school were kids can build there physically, emotionally and mentally skills developed. Simple join of two Spark DataFrame failing with “org. load to spark scala dataframe and merge the two files. The foldLeft way is quite popular (and elegant) but recently I came across an issue regarding its performance when the number of columns to add is not trivial. The data frame will identify the type of columns. In Java API, the user uses Dataset to represent a DataFrame. Encode and assemble multiple features in PySpark. Create a spark dataframe from sample data; Load spark dataframe into non existing hive table; How to add new column in Spark Dataframe; How to read JSON file in Spark; How to execute Scala script in Spark without creating Jar; Spark-Scala Quiz-1; Hive Quiz - 1; Join in hive with example; Trending now. It simply MERGEs the data without removing. 22 January 2018. Can somebody please help me simplify my code? Here is my existing code. Published 2017-03-28. If this not desired, use `as` with explicitly empty metadata. Can pass an array as the join key if it is not already contained in the calling DataFrame. Spark SQL 是 Spark 的结构化数据处理模块。. The additional information is used for optimization. I need to concatenate two columns in a dataframe. I want to select specific row from a column of spark data frame. Multiple Joins. in Dataframe in Apache Spark is a distributed collections of data , organized in form of columns. A way to Merge Columns of DataFrames in Spark with no Common Column Key March 22, 2017 Made post at Databricks forum, thinking about how to take two DataFrames of the same number of rows and combine, merge, all columns into one DataFrame. Create a spark dataframe from sample data; Load spark dataframe into non existing hive table; How to add new column in Spark Dataframe; How to read JSON file in Spark; How to execute Scala script in Spark without creating Jar; Spark-Scala Quiz-1; Hive Quiz – 1; Join in hive with example; Trending now. Spark SQL functions take org. [email protected] Out of these 3 dataframes, i want to create two dataframes, (final and consolidated). The class has been named PythonHelper. Next, we specify the " on" of our join. * * @param right Right side of the join operation. In one of our Big Data / Hadoop projects, we needed to find an easy way to join two csv file in spark. withColumn('age2', sample. For In conclusion, I need to cast type of multiple columns manually:. A cross join with a predicate * is specified as an inner join. Let’s discuss all possible ways to rename columns with Scala examples. But look at what happens if we try to take, say, entries with A > 3 and A < 9:. [/code]The one that has usingColumns (Seq[String]) as second parameter works best, as the columns that you join on won't be duplicate. How to select multiple columns from a spark data frame using List[Column] Let us create Example DataFrame to explain how to select List of columns of type "Column" from a dataframe spark-shell --queue= *; To adjust logging level use sc. You have learned multiple ways to add a constant literal value to DataFrame using Spark SQL lit() function and have learned the difference between lit and typedLit functions. 5, with more than 100 built-in functions introduced in Spark 1. scala Skip to content All gists Back to GitHub. join(df2, usingColumns=Seq("col1", …), joinType="left"). Spark generate multiple rows based on column value anonfun$1 cannot be cast to scala. Lets see how to select multiple columns from a spark data frame. Refer to SPARK-7990: Add methods to facilitate equi-join on multiple join keys. NET MVC with Entity Framework. filter(a_df. We are using inferSchema is True for telling sqlContext to automatically detect the data type of each column in data frame. To the udf “addColumnUDF” we pass 2 columns of the DataFrame “inputDataFrame”. sql( "select * from t1, t2 where t1. Producer sends messages to Kafka topics in the form of records, a record is a key-value pair along with topic name and consumer receives a messages from a topic. There seems to be no 'add_columns' in spark, and add_column while allowing for a user-defined function doesn't seem to allow multiple return values - so does anyone have a recommendation how I would. 4 release, DataFrames in Apache Spark provides improved support for statistical and mathematical functions, including random data generation, summary and descriptive statistics, sample covariance and correlation, cross tabulation, frequent items, and mathematical functions. In SQL, if we have to check multiple conditions for any column value then we use case statament. Best Play and Pre School for kids in Hyderabad,India. The Apache Spark DataFrame API provides a rich set of functions (select columns, filter, join, aggregate, and so on) that allow you to solve common data analysis problems efficiently. Native Spark code handles null gracefully. Spark SQL is a Spark module for structured data processing. SELECT*FROM a JOIN b ON joinExprs. Drop one or more than one columns from a DataFrame can be achieved in multiple ways. Both in Scala and Java, we represent DataFrame as Dataset of rows. Now how can we have one Dataframe. The class has been named PythonHelper. Apache Spark is a cluster computing system. For In conclusion, I need to cast type of multiple columns manually:. Join GitHub today. Earlier versions of spark extensively used RDD for data operations. Spark automatically removes duplicated "DepartmentID" column, so column names are unique and one does not need to use table prefix to address them. scala> val df1p1 = df1. jdbc, mysql, Spark, spark dataframe, spark sql, spark with scala Top Big Data Courses on Udemy You should Take When i was newbie , I used to take so many courses on Udemy and other platforms to learn. id val1 val2 val3 val4 1 null null null null 2 A2 A21 A31 A41 id val1 val2 val3 val4 1 B1 B21 B31 B41 2 null null null null id val1 val2 val3 val4 1 C1 C2 C3 C4 2 C11 C12 C13 C14. Next, we specify the " on" of our join. Dataframes are similar to traditional database tables, which are structured and concise. spark-shell --queue= *; To adjust logging level use sc. Different from other join functions, the join column will only appear once in the output, i. We can use the dataframe1. We explored a lot of techniques and finally came upon this one which we found was the easiest. spark data frame. I can write a function something like. Hi Ankit, Thanks i found the article quite informative. • "Opening" a data source works pretty much the same way, no matter what. 4 release, DataFrames in Apache Spark provides improved support for statistical and mathematical functions, including random data generation, summary and descriptive statistics, sample covariance and correlation, cross tabulation, frequent items, and mathematical functions. In our example, we’re telling our join to compare the “name” column of customersDF to the “customer. Let’s discuss all possible ways to rename column with Scala examples. In general, Spark DataFrames are quite efficient in terms of performance as shown in Fig. Remember that the main advantage to using Spark DataFrames vs those other programs is that Spark can handle data across many RDDs, huge data sets that would never fit on a single computer. select multiple columns given a Sequence of column names joe Asked on January 12, 2019 in Apache-spark. Spark DataFrame UDFs: Examples using Scala and Python Last updated: 11 Nov 2015. You'll need to create a new DataFrame. AnalysisException: Cannot resolve column name”. Upon going through the data file, I observed that some of the rows have empty rating and runtime values. This topic contains examples of a UDAF and how to register them for use in Spark SQL. Spark Tutorials. In this specific case collect and join can be completely avoided. A Spark DataFrame is a distributed collection of data organized into named columns that provides operations. Let’s try a simple filter operation in our Spark dataframe, e. I am trying to implement a sample as explained below, I am quite new to this spark/scala, so need some inputs as to how this can be implemented in an efficient way. registerTempTable("tempDfTable") Use Jquery Datatable Implement Pagination,Searching and Sorting by Server Side Code in ASP. Values must be of the same type. Introduction to DataFrames - Scala. We often need to rename one or multiple columns on Spark DataFrames, especially when columns are nested it becomes complicated. For every row custom function is applied of the dataframe. A software engineer gives a quick tutorial on how to work with Apache Spark in order to convert data from RDD format to a DataFrames format using Scala. [email protected]950f As you may have noticed, spark in Spark shell is actually a org. …I'm going to just clear the screen. join(df2, "user_id"). Producer sends messages to Kafka topics in the form of records, a record is a key-value pair along with topic name and consumer receives a messages from a topic. For In conclusion, I need to cast type of multiple columns manually:. repartition(1) scala> val df2p1 = df2. Encode and assemble multiple features in PySpark. multiple columns stored from a List to Spark Dataframe,apache spark, scala, dataframe, List, foldLeft, lit, spark-shell, withcoumn in spark,example Here is Something !: How to add multiple withColumn to Spark Dataframe. Spark DataFrames are also compatible with R's built-in data frame support. Introduction to Datasets The Datasets API provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine. We can also perform aggregation on some specific columns which is equivalent to GROUP BY clause we have in typical SQL. Let’s discuss how to drop one or multiple columns in Pandas Dataframe. 4 release, DataFrames in Apache Spark provides improved support for statistical and mathematical functions, including random data generation, summary and descriptive statistics, sample covariance and correlation, cross tabulation, frequent items, and mathematical functions. Column arguments whereas vanilla Scala functions take native Scala data type arguments like Int or String. The first element in a tuple is the name of a column and the second element is the data type of that column. Converting Spark RDDs to DataFrames - DZone. in Dataframe in Apache Spark is a distributed collections of data , organized in form of columns. Native Spark code handles null gracefully. Left outer join. // Joining df1 and df2 using the column "user_id" df1. Apache Spark. sql import DataFrame from pyspark. …And just as a refresher I'm going to show the contents…of a DataFrame called emps. A software engineer gives a quick tutorial on how to work with Apache Spark in order to convert data from RDD format to a DataFrames format using Scala. Series object. We can also perform aggregation on some specific columns which is equivalent to GROUP BY clause we have in typical SQL. val columnsNameArray=schema. This topic demonstrates a number of common Spark DataFrame functions using Python. This is similar to what we have in SQL like MAX, MIN, SUM etc. To give your kid a best environment and learning it is the right way to join in play and pre school were kids can build there physically, emotionally and mentally skills developed. let’s take all entries with A > 3: >>> a_df. // IMPORT DEPENDENCIES import org. How to Update Spark DataFrame Column Values using Pyspark? The Spark dataFrame is one of the widely used features in Apache Spark. DataFrames also allow you to intermix operations seamlessly with custom Python, R, Scala, and SQL code. val resultDf = PersonDf. The class has been named PythonHelper. Create a spark dataframe from sample data; Load spark dataframe into non existing hive table; How to add new column in Spark Dataframe; How to read JSON file in Spark; How to execute Scala script in Spark without creating Jar; Spark-Scala Quiz-1; Hive Quiz – 1; Join in hive with example; Trending now. scala> spark res1: org. You have learned multiple ways to add a constant literal value to DataFrame using Spark SQL lit() function and have learned the difference between lit and typedLit functions. createDataFrame(padas_df) … but its taking to much time. join function: [code]df1. Currying functions. If you have select multiple columns, use data. For the standard deviation, see scala - Calculate the standard deviation of grouped data in a Spark DataFrame - Stack Overflow. Other relevant attribute of Dataframes is that they are not located in one simple computer, in fact they can be splitted through hundreds of machines. I will introduce 2 ways, one is normal load using Put , and another way is to use Bulk Load API. This post has NOT been accepted by the mailing list yet. What are User-Defined functions ? They are function that operate on a DataFrame's column. A dataframe in Spark is similar to a SQL table, an R dataframe, or a pandas dataframe. 0 Dataset vs DataFrame. Catalyst uses features of the Scala programming language,. The Scala foldLeft method can be used to iterate over a data structure and perform multiple operations on a Spark DataFrame. Thus, on Spark DataFrame, performing any SQL-like operations such as SELECT COLUMN-NAME , GROUPBY and COUNT to mention a few becomes relatively easy. Pyspark Joins by Example This entry was posted in Python Spark on January 27, 2018 by Will Summary: Pyspark DataFrames have a join method which takes three parameters: DataFrame on the right side of the join, Which fields are being joined on, and what type of join (inner, outer, left_outer, right_outer, leftsemi). val resultDf = PersonDf. A Dataframe’s schema is a list with its columns names and the type of data that each column stores. spark / sql / core / src / main / scala / org / apache / spark / sql / DataFrameStatFunctions. The skew join optimization is performed on the DataFrame for which you specify the skew hint. Can somebody please help me simplify my code? Here is my existing code. Though we have covered most of the examples in Scala here, the same concept can be used in PySpark to rename a DataFrame column (Python Spark). Prevent Duplicated Columns when Joining Two DataFrames. Values must be of the same type. I am trying to implement a sample as explained below, I am quite new to this spark/scala, so need some inputs as to how this can be implemented in an efficient way. What is Spark Dataframe? In Spark, Dataframes are distributed collections of data, organized into rows and columns. Inner equi-join with another DataFrame using the given column. For grouping by percentiles, I suggest defining a new column via a user-defined function (UDF), and using groupBy on that column. 6 release introduces a preview of the new Dataset API. There are multiple ways to define a. S licing and Dicing. Tehcnically, we're really creating a second DataFrame with the correct names. Spark SQL and DataFrames - Spark 1. SparkSession import org. …Now with Spark SQL we can join DataFrames. Spark generate multiple rows based on column value anonfun$1 cannot be cast to scala. count Now the execution time get back to normal. …I also have. * * Different from other join functions, the join columns will only appear once in the output, * i. {SQLContext, Row, DataFrame, Column} import. pyspark sort dataframe by multiple columns 0. * Assigns the given aliases to the results of a table generating function. Scala Spark DataFrame : dataFrame. How Mutable DataFrames Improve Join Performance in Spark SQL The ability to combine database-like mutability into Spark provides a way to stream processing and SQL querying within the comforts of. spark / sql / core / src / main / scala / org / apache / spark / sql / DataFrameStatFunctions. * Gives the column an alias. Spark-Scala recipes can read and write datasets, even when their storage backend is not HDFS. Join the world's most active Tech Community! Welcome back to the World's most active Tech Community!. Aggregating Data. Apache Spark Started in UC Berkeley ~ 2010 Most popular and de facto standard framework in big data One of the largest OSS projects written in Scala (but with user-facing APIs in Scala, Java, Python, R, SQL) Many companies introduced to Scala due to Spark. sql( "select * from t1, t2 where t1. NumberFormatException: empty String" exception. Problem You have a Spark DataFrame, and you want to do validation on some its fields. Multiple Joins. I have to divide a dataframe into multiple smaller dataframes based on values in columns like - gender and state , the end goal is to pick up random samples from each dataframe. This offers users a more flexible way to design beautiful map visualization effects including scatter plots and. Data frame A PIs usually supports elaborate methods for slicing-and-dicing the data. How can I return only the details of the student that h. Spark SQl is a Spark module for structured data processing. get specific row from spark dataframe apache-spark apache-spark-sql Is there any alternative for df[100, c("column")] in scala spark data frames. If there is no match, the missing side will contain null. multiple Perform a typed join in Scala with Spark Datasets this functions to provide me with the right column name as a String. Join the world's most active Tech Community! Welcome back to the World's most active Tech Community!. When possible try to use predefined Spark SQL functions as they are a little bit more compile-time safety and perform better when compared to user-defined functions. value_counts() This method is applicable to pandas. I can write a function something like. Let’s try a simple filter operation in our Spark dataframe, e. If this not desired, use `as` with explicitly empty metadata. In my work project using Spark, I have two dataframes that I am trying to do some simple math on, subject to some conditions. If you are referring to [code ]DataFrame[/code] in Apache Spark, you kind of have to join in order to use a value in one [code ]DataFrame[/code] with a value in another. 6 was the ability to pivot data, creating pivot tables, with a DataFrame (with Scala, Java, or Python). If you would explicitly like to perform a cross join use the * `crossJoin` method. similar to SQL's JOIN USING syntax. Just like SQL, you can join two dataFrames and perform various actions and transformations on Spark dataFrames. How to Update Spark DataFrame Column Values using Pyspark? The Spark dataFrame is one of the widely used features in Apache Spark. Pivoting is used to rotate the data from one column into multiple columns. We can do in the below way: Say you have a dataframe named DF We can use below syntax: DF. So, I was how can I convert Spark DataFrame to Spark RDD?. I am trying to implement a sample as explained below, I am quite new to this spark/scala, so need some inputs as to how this can be implemented in an efficient way. 4 release, DataFrames in Apache Spark provides improved support for statistical and mathematical functions, including random data generation, summary and descriptive statistics, sample covariance and correlation, cross tabulation, frequent items, and mathematical functions. post-6153552357650495369 2018-05-22T05:33:00. This is an expected behavior. With the recent changes in Spark 2. explode takes a single column as input and lets you split it or convert it into multiple values and then join the original row back onto the new rows. Spark DataFrames API is a distributed collection of data organized into named columns and was created to support modern big data and data science applications. Is >> there a better way to achieve this? >> >> scala> val df = sqlContext. Introduction to Spark SQL DataFrame. The Scala interface for Spark SQL supports automatically converting an RDD containing case classes to a DataFrame. Prevent Duplicated Columns when Joining Two DataFrames. The udf will be invoked on every row of the DataFrame and adds a new column “sum” which is addition of the existing 2 columns. You can define a Dataset JVM objects and then manipulate them using functional transformations ( map , flatMap , filter , and so on) similar to an RDD. Tehcnically, we're really creating a second DataFrame with the correct names. setLogLevel(newLevel). Multiple Joins. Renaming column names of a DataFrame in Spark Scala - Wikitechy. 0 tutorial series, we've already showed that Spark's dataframe can hold columns of complex types such as an Array of values. I would like to add several columns to a spark (actually pyspark) dataframe , these columns all being functions of several input columns in the df. Here we want to find the difference between two dataframes at a column level. Scala Spark DataFrame : dataFrame. similar to SQL's `JOIN USING` syntax. This is an expected behavior. $\begingroup$ a function that takes the columns of a dataframe that I give as an input and maps the new values onto old values,just in those columns ,is what I'm trying to figure out ,without using loops. As a result, the generated Data Frame is comprised completely of string data types. Efficient Spark Dataframe Transforms // under scala spark. scala import. agg (avg(colname)). If you are from SQL background then please be very cautious while using UNION operator in SPARK dataframes. Create a Spark DataFrame: Read and Parse Multiple (Small) Files We take a look at how to work with data sets without using UTF -16 encoded files in Apache Spark using the Scala language. We explored a lot of techniques and finally came upon this one which we found was the easiest. Dataframe in Apache Spark is a distributed collection of data, organized in the form of columns. In SQL, if we have to check multiple conditions for any column value then we use case statament. This is similar to a LATERAL VIEW in HiveQL. The answers in this post : Spark Dataframe distinguish columns with duplicated name are not relevant to me as it is related more to pyspark than Scala and it had explained how to rename all the columns of a dataframe whereas my requirement is to rename only one or few columns. sql( "select * from t1, t2 where t1. Today, I will show you a very simple way to join two csv files in Spark. A way to Merge Columns of DataFrames in Spark with no Common Column Key March 22, 2017 Made post at Databricks forum, thinking about how to take two DataFrames of the same number of rows and combine, merge, all columns into one DataFrame. If there is no match, the missing side will contain null. Spark generate multiple rows based on column value anonfun$1 cannot be cast to scala. This topic demonstrates a number of common Spark DataFrame functions using Scala. Assuming having some knowledge on Dataframes and basics of Python and Scala. If you will not mention any specific select at the end all the columns from dataframe 1 & dataframe 2 will come in the output. columns) in order to ensure both df have the same column order before the union. * (Scala-specific) Assigns the given aliases to the results of a table generating function. Example - Spark - Add new column to Spark Dataset. similar to SQL's `JOIN USING` syntax. This is an introduction of Apache Spark DataFrames. similar to SQL's JOIN USING syntax. Inner equi-join with another DataFrame using the given column. The same concept will be applied to Scala as well. Since this is inner join, only the matching records will come in the output. withColumn('age2', sample. Sometime, when the dataframes to combine do not have the same order of columns, it is better to df2. join(broadcast(df2), "key")). DataFrame and column name. For doing more complex computations, map is needed. Each dataframe has a "value" column, so when I join them I rename the second table's value column to "Df2 value" let's say.