In Apache Spark, both createOrReplaceTempView() and registerTempTable() methods can be used to register a DataFrame as a temporary table and query it using Spark SQL. In this article, we shall discuss a few points on Spark createOrReplaceTempview() vs registerTempTable(), and the Differences among these using their properties and with an example

1. Spark createOrReplaceTempView()

In Apache Spark, the method createOrReplaceTempView() is used to create a temporary view of a DataFrame.

When you create a temporary view, you can query it using Spark SQL. The view is temporary, which means it exists only for the duration of the Spark session and is not persistent.

Here is an example of how to use the createOrReplaceTempView method:


// Create DataFrame df
val df = Seq((1, "John"), (2, "Jane"), (3, "Bob")).toDF("id", "name")

// Create Temporary View People on top of DataFrame df
df.createOrReplaceTempView("people")

In this example, we create a DataFrame with three rows and two columns (id and name), and then we create a temporary view of the DataFrame called “people” using the createOrReplaceTempView method.

Now that we have created the view, we can query it using Spark SQL, for example:


// Spark SQL to select data from created temporary view "people"
spark.sql("SELECT * FROM people WHERE id > 1").show()

The output of the Spark SQL looks as :


// Output:
+---+----+
| id|name|
+---+----+
|  2|Jane|
|  3| Bob|
+---+----+

Note that createOrReplaceTempView() is a method of the DataFrame class in Spark, so you need to have a DataFrame object to be able to use it.

To get more details about this method, Please find the detailed article Spark createOrReplaceTempView() Usage with Examples

2. Spark registerTempTable()

Spark registerTempTable() is a deprecated method in Apache Spark that was used to register a DataFrame as a temporary table. The method was available in Spark versions prior to 2.0, and has been replaced by the createOrReplaceTempView method.

Here’s an example of how to use registerTempTable:


// Create DataFrame df
val df = Seq((1, "John"), (2, "Jane"), (3, "Bob")).toDF("id", "name")

// Register Temporary View People on top of DataFrame df
df.registerTempTable("people")

In this example, we create a DataFrame with three rows and two columns (id and name), and then we register it as a temporary table called “people” using the registerTempTable method.

Once you have registered the DataFrame as a temporary table, you can query it using Spark SQL, for example:


// Spark SQL to select data from created temporary view "people"
spark.sql("SELECT * FROM people WHERE id > 1").show()

This will result in the same result as Spark createOrReplaceTempView


// Output:
+---+----+
| id|name|
+---+----+
|  2|Jane|
|  3| Bob|
+---+----+

Note that Spark registerTempTable is a method of the DataFrame class in Spark, so you need to have a DataFrame object to be able to use it.

To get more details about Spark registerTempTable, Please find the detailed article here: Spark registerTempView

As the functionality of both Spark createOrReplaceTempView and registerTempTable same, then we may get this question of why does spark replaced registerTempTable method with createOrReplaceTempView. The reason behind this was:

3. Why Spark replaced createOrReplaceTempView vs registerTempTable?

Apache Spark introduced the createOrReplaceTempView method as a replacement for the registerTempTable method to provide a more flexible and efficient way of creating temporary views of DataFrames. Here are a few reasons why createOrReplaceTempView was introduced:

  1. Improved performance: createOrReplaceTempView is more efficient than registerTempTable because it creates an in-memory optimized representation of the data, which can be directly used by Spark SQL. This avoids the overhead of creating a separate metadata object for the temporary table, which was created with registerTempTable.
  2. More flexible: createOrReplaceTempView allows you to create or replace a temporary view with a single method call, which is more flexible than the two-step process required with registerTempTable. Additionally, createOrReplaceTempView allows you to create temporary views on top of a subset of columns in the DataFrame, which can improve performance when working with large datasets.
  3. Consistency with SQL: createOrReplaceTempView is consistent with SQL syntax, making it easier for users familiar with SQL to work with Spark SQL. In contrast, registerTempTable requires users to work with a separate API, which can be confusing for users who are not familiar with Spark.
  4. Compatibility: registerTempTable was deprecated in Spark 2.0 and removed in Spark 3.0, whereas createOrReplaceTempView is a stable API that is supported in all versions of Spark since its introduction.

Spark createOrReplaceTempView provides a more efficient, flexible, and consistent way of creating temporary views of DataFrames in Spark SQL, which is why it was introduced as a replacement for registerTempTable.

4. Difference between Spark createOrReplaceTempView vs registerTempTable with properties

Both Spark createOrReplaceTempView and registerTempTable are methods in Apache Spark that allow you to register a DataFrame as a temporary table and query it using Spark SQL. However, there are some differences between them, including the ability to specify additional properties for the temporary table.

The main difference between createOrReplaceTempView and registerTempTable is that the former creates a temporary view that is available only in the current SparkSession, while the latter registers the DataFrame as a temporary table that is available across the entire SparkContext.

Also, createOrReplaceTempView returns a Unit and does not allow you to specify any additional properties, whereas registerTempTable returns a TableIdentifier and allows you to specify additional properties for the temporary table using a Map[String, String].

Here’s an example of using registerTempTable with additional properties:


// Imports
import org.apache.spark.sql.catalyst.TableIdentifier

// Create DataFrame using toDF
val df = Seq((1, "John"), (2, "Jane"), (3, "Bob")).toDF("id", "name")

// Set properties of the temporary table 
val props = Map("spark.sql.sources.provider" -> "csv")

// Initialize TableIdentifier to register the table created in my_database
val tableId = TableIdentifier("people", Some("my_database"))

// Register temporary table with properties
df.registerTempTable(tableId.quotedString, props)

// Query the temporary table using Spark SQL
spark.sql("SELECT * FROM my_database.people WHERE id > 1").show()

In this example, we create a DataFrame with three rows and two columns (id and name), and then we register it as a temporary table called “people” in the “my_database” database with additional properties specified using the props map. We also use the TableIdentifier class to specify the database and table name for the temporary table.

Once the temporary table is registered, we can query it using Spark SQL, for example, selecting all rows with an id greater than 1.


// Output:
+---+----+
| id|name|
+---+----+
|  2|Jane|
|  3| Bob|
+---+----+

Note that createOrReplaceTempView does not allow you to specify additional properties for the temporary view, and is only used to create a temporary view in the current SparkSession.

5. Conclusion

In conclusion, Spark createOrReplaceTempView vs registerTempTable are two methods in Apache Spark that can be used to register a DataFrame as a temporary table and query it using Spark SQL. However, createOrReplaceTempView is the recommended method for creating temporary views of DataFrames in Spark SQL, while registerTempTable is a deprecated method that should not be used in new code.

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rimmalapudi

Data Engineer. I write about BigData Architecture, tools and techniques that are used to build Bigdata pipelines and other generic blogs.