Spark How to Avoid Duplicate Columns After Join rimmalapudi Spark By {Examples}

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How to avoid duplicate columns on Spark DataFrame after joining? Apache Spark is a distributed computing framework designed for processing large-scale data sets in a parallel and fault-tolerant manner. When joining two DataFrames in Spark, you would find duplicate columns when you have the same column names on both tables. It’s important to avoid duplicate columns after joining the DataFrames.

Duplicate columns can arise when the joining criteria involve columns with the same name in both DataFrames or when the columns have overlapping names but represent different information.

In this article, we shall discuss different approaches we can follow to avoid Duplicate columns after joining two DataFrames in Spark.

1. Create DataFrame to illustrate and apply join

Let us create two sample DataFrames using the Spark createDataFrame() method holding information related to products, quantity, and revenue generated after sales.

//. Import
import org.apache.spark.sql.{SparkSession, DataFrame}

// Create a SparkSession
val spark = SparkSession.builder().appName(“DuplicateColumnsExample”).getOrCreate()

// Create the first DataFrame
val data1 = Seq(
(1, “Product A”, 100),
(2, “Product B”, 200),
(3, “Product C”, 150)
)
val df1: DataFrame = spark.createDataFrame(data1).toDF(“id”, “product”, “quantity”)

// Create the second DataFrame
val data2 = Seq(
(1, “Product A”, 1000),
(2, “Product B”, 1500),
(3, “Product C”, 1200)
)
val df2: DataFrame = spark.createDataFrame(data2).toDF(“id”, “product”, “revenue”)

In the above example,

We have two DataFrames df1 having columns id, product and quantity.

df2 having columns id, product and revenue.

Now, Let’s apply a join between these two DataFrames df1 and df2 using column id.

// Join the df1 DataFrame with df2
val joinedDF1 = df1.join(df2, Seq(“id”))
display(joinedDF1)

The resultant DataFrame after applying join is as follows.

Here we can see that the column product attribute was duplicated since it is available in both Dataframes also when we try to access the product attribute from the resultant joined DataFrame, we get the error AMBIGUOUS_REFERENCE since it’s not able to conclude which product attribute it should pickup out of the available columns.

Now let us see different ways we could avoid duplicate columns after joining two Dataframes.

2. Different ways to avoid duplicate columns after join

To avoid duplicate columns after joining two DataFrames in Spark, you can follow several approaches. Let us see few among them.

2.1 Rename Specifying column names to select before joining

In this approach, we rename the “product” column in the first DataFrame (df1) to “product_name” before joining. After the join, we explicitly select the desired columns by specifying their names.

// Rename columns in df1 before joining
val df1Renamed = df1.withColumnRenamed(“product”, “product_name”)

// Join the renamed DataFrame with df2
val joinedDF1 = df1Renamed.join(df2, Seq(“id”))

// Select the desired columns explicitly
val result1 = joinedDF1.select(“id”, “product_name”, “quantity”, “product”, “revenue”)
display(result1)

In this example,

To avoid duplicate columns after join, we rename the conflicting column in df1 before joining. We use the withColumnRenamed() method to rename the “product” column in df1 to “product_name” and create a new DataFrame called df1Renamed.

Next, we perform the join operation between df1Renamed and df2 using the common “id” column by specifying Seq(“id”) as the join key. This ensures that only one “id” column is present in the resulting DataFrame.

Finally, we explicitly select the desired columns (“id”, “product_name”, “quantity”, “product”, “revenue”) from the joined DataFrame using the select() method. By specifying the column names directly, we ensure that only the required columns are included in the resulting DataFrame, avoiding any duplicate columns.

The resultant DataFrame after renaming and joining looks as:

2.2 Using alias

This approach involves assigning aliases to the columns in the DataFrames (df1 and df2) before joining. Aliases provide alternative names for the columns, allowing us to differentiate them. After the join, we refer to the columns using their aliases when selecting the desired columns.

//Imports
import org.apache.spark.sql.functions.col

// Assign aliases to columns in df1 and df2 before joining
val df1Aliased = df1.select(col(“id”).alias(“id1”), col(“product”).alias(“product_name”), col(“quantity”))
val df2Aliased = df2.select(col(“id”).alias(“id2”), col(“product”), col(“revenue”))

// Join the aliased DataFrames
val joinedDF2 = df1Aliased.join(df2Aliased, col(“id1”) === col(“id2”))

// Select the desired columns using aliases
val result2 = joinedDF2.select(“id1”, “product_name”, “quantity”, “product”, “revenue”)
result2.show()

In this example,

To avoid duplicate columns after join., we assign aliases to the columns before joining. We use the alias method from org.apache.spark.sql.functions to assign aliases to the columns. In this example, we alias the “id” column in df1 as “id1” and the “id” column in df2 as “id2”.

After assigning aliases, we perform the join operation using the aliased DataFrames. We specify the join condition as col(“id1”) === col(“id2”), which ensures that the join is performed on the correctly aliased “id” columns.

Finally, we select the desired columns using the aliases (“id1”, “product_name”, “quantity”, “product”, “revenue”) from the joined DataFrame. By selecting columns with aliases, we avoid duplicate columns in the resulting DataFrame.

The resultant dataframe after assigning aliases to the columns and joining looks as:

+—+————+——–+———+——-+
|id1|product_name|quantity| product|revenue|
+—+————+——–+———+——-+
| 1| Product A| 100|Product A| 1000|
| 2| Product B| 200|Product B| 1500|
| 3| Product C| 150|Product C| 1200|
+—+————+——–+———+——-+

2.3 Dropping duplicate columns

To avoid duplicate columns after join, we use the drop method on the joined DataFrame. The drop method takes column names as arguments and returns a new DataFrame with the specified columns removed.

// Join the DataFrames
val joinedDF3 = df1.join(df2, Seq(“id”))

// Drop the duplicate column(s)
val result3 = joinedDF3.drop(“product”)
result3.show()

In the example,

we drop the “product” column by calling drop(“product”) on the joined DataFrame. This removes the duplicate “product” column from the resulting DataFrame.

The resulting DataFrame, named “result”, will have the columns “id”, “quantity”, and “revenue” without any duplicate columns.

Using the drop method allows us to explicitly remove the duplicate column(s) from the DataFrame, ensuring that the resulting DataFrame does not contain any redundant columns.

The resultant dataframe after joining and dropping duplicate columns looks as:

+—+——–+——-+
| id|quantity|revenue|
+—+——–+——-+
| 1| 100| 1000|
| 2| 200| 1500|
| 3| 150| 1200|
+—+——–+——-+

2.4. Using coalesce to resolve column conflicts:

Using coalesce provides a flexible and concise way to handle column conflicts during DataFrame joins, ensuring that the resulting DataFrame does not contain duplicate columns after join.

//Imports
import org.apache.spark.sql.functions.coalesce

// Join the DataFrames
val joinedDF4 = df1.join(df2, Seq(“id”))

// Resolve column conflicts using coalesce
val result4 = joinedDF4.select(
col(“id”),
coalesce(df1(“product”), df2(“product”)).alias(“product”),
col(“quantity”),
col(“revenue”)
)
result4.show()

In this example,

we have two DataFrames, df1 and df2, representing sales data. We join them using the “id” column as the join key. However, both DataFrames have a column named “product” that may result in a duplicate column after the join.

To resolve this conflict, we use the coalesce function from Spark SQL. The coalesce function takes multiple columns as arguments and returns the first non-null value among them. By applying coalesce to the “product” column, we can select a single value from either df1 or df2, effectively avoiding duplicate columns.

In the select statement, we use coalesce(df1(“product”), df2(“product”)) to select the resolved “product” column as “product”. We also select the remaining columns “id”, “quantity”, and “revenue” from the joined DataFrame.

The resulting DataFrame, named “result”, will have the resolved “product” column without any duplicates, along with the other selected columns:

+—+———+——–+——-+
| id| product|quantity|revenue|
+—+———+——–+——-+
| 1|Product A| 100| 1000|
| 2|Product B| 200| 1500|
| 3|Product C| 150| 1200|
+—+———+——–+——-+

3. Conclusion

In conclusion, when working with Spark and joining two DataFrames, it’s essential to handle potential duplicate columns that may arise. Duplicate columns can occur when both DataFrames have columns with the same name. To avoid duplicate columns after join, we explored several approaches:

Specifying column names to select: This approach involves explicitly selecting the desired columns from the joined DataFrame by specifying their names. It allows us to avoid including duplicate columns in the resulting DataFrame.

Using alias: Assigning aliases to columns before joining helps differentiate columns with the same name. Aliases provide alternative names for the columns, ensuring unique column names in the joined DataFrame.

Dropping duplicate columns: After joining the DataFrames, we can use the drop method to remove the duplicate column(s) explicitly. This approach ensures that only the required columns remain in the resulting DataFrame.

Using coalesce to resolve column conflicts: coalesce function helps resolve conflicts by selecting the first non-null value among multiple columns. It allows us to choose a single value from conflicting columns, effectively avoiding duplicate columns in the joined DataFrame.

By applying these approaches appropriately, we can avoid duplicate columns after joining two DataFrames in Spark. Each approach offers its advantages, providing flexibility and control over the resulting DataFrame structure. Choosing the most suitable approach depends on the specific requirements and the nature of the data being processed.

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 How to avoid duplicate columns on Spark DataFrame after joining? Apache Spark is a distributed computing framework designed for processing large-scale data sets in a parallel and fault-tolerant manner. When joining two DataFrames in Spark, you would find duplicate columns when you have the same column names on both tables. It’s important to avoid duplicate  Read More Apache Spark, Spark avoid duplicate columns after join, spark drop duplicate column, spark select distinct columns