Spark registerTempTable() is a method in Apache Spark’s DataFrame API that allows you to register a DataFrame as a temporary table in the Spark SQL catalog so that you can run SQL queries. In this article, we shall discuss on Definition, Scope, and application of Spark registerTempTable()

Related: Spark DataFrame createOrReplaceTempView() method

1. Spark registerTempTable() Usage

Spark registerTempTable() method is used to make a DataFrame available for querying using Spark SQL.

The syntax for registerTempTable() is as follows:


// Register the DataFrame as a temporary table named my_table
df.registerTempTable("my_table")

Here, my_table is the name of the temporary table created. The name must be a string and should not contain any spaces.

Once a DataFrame is registered as a temporary table using registerTempTable(), it is available for the lifetime of the SparkSession or SQLContext in which it is registered. The temporary table is automatically removed from the Spark SQL catalog when the session is terminated.

NOTE: In Spark 3.0 and later versions, registerTempTable() has been deprecated and replaced by createOrReplaceTempView() method. The new method has the same functionality as registerTempTable() but provides more consistency with the other DataFrame methods.

2. Scope of Spark registerTempTable()

The scope of the table defines the region in which the user/application can access the table. In Spark/PySpark, the registerTempTable() method has the following scope:

  1. Session Scope: When you register a DataFrame as a temporary table using registerTempTable() method, it is only available for the lifetime of the SparkSession in which it is registered. Once the session is terminated, the temporary table is automatically removed from the Spark SQL catalog.
  2. SQL Scope: The registered temporary table can be queried using Spark SQL from any part of the Spark application that has access to the SparkSession. The temporary table is visible to all the Spark SQL queries executed within the same SparkSession.
  3. Concurrency: The registered temporary table can be queried concurrently by multiple threads or tasks in the Spark application. The degree of concurrency depends on the configuration of the Spark cluster and the number of partitions in the DataFrame.

If we want to make the table live irrespective of the Spark session availability then we should use Spark createOrReplaceView() to create a table with permanent scope. we shall discuss this Spark createOrReplaceView() method in a separate article.

3. Spark registerTempTable() properties

Each method available has unique properties based on its functionality. The Spark registerTempTable() method in Spark DataFrame API has the following properties:

  1. Table Name: The name of the temporary table to be created, which is a string and should not contain any spaces. It is a mandatory property to create a temporary table.
  2. Scope of the Table: The temporary table created using registerTempTable() method is only available for the lifetime of the SparkSession or SQLContext in which it is registered. Once the session or context is terminated, the table is automatically removed from the Spark SQL catalog.
  3. Data Persistence: Registering a DataFrame as a temporary table does not persist the data to disk. It only creates a logical table view that can be queried using Spark SQL. If you want to persist the data to disk, you need to use the write() method on the DataFrame and save it in a file format supported by Spark
  4. Immutability: The data in the registered temporary table is immutable. You cannot modify the data in the table directly using SQL statements. If you want to modify the data, you need to create a new DataFrame using the select() method with appropriate transformations.
  5. Parallelism: The registered temporary table can be queried in parallel by multiple threads or nodes in the Spark cluster. The degree of parallelism depends on the configuration of the Spark cluster and the number of partitions in the DataFrame.
  6. Schema Definition: The schema of the temporary table is inferred automatically from the DataFrame schema. However, you can also explicitly specify the schema using the createDataFrame() method in the SparkSession or SQLContext.

4. Example of Spark registerTempTable()

As we know the feature of this method is to store Spark Dataframe into a Temporary Table, Let us create a sample Orders DataFrame having “order_id“, “order_date“, “order_amount“, “order_status” Columns and try to save it into a temporary Table


// Imports
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._

// Create a SparkSession
val spark = SparkSession.builder().appName("OrdersDataFrame").master("local").getOrCreate()

// Create a sample orders DataFrame
val ordersDF: DataFrame = Seq(
  (1, "2019-01-01", 100.0, "COMPLETE"),
  (2, "2019-01-02", 50.0, "COMPLETE"),
  (3, "2019-01-03", 75.0, "PENDING"),
  (4, "2019-01-04", 125.0, "COMPLETE"),
  (5, "2019-01-05", 200.0, "PENDING")
).toDF("order_id", "order_date", "order_amount", "order_status")

// Register the DataFrame as a temporary table
ordersDF.registerTempTable("orders")

// Query the temporary table using Spark SQL
val result = spark.sql("SELECT * FROM orders WHERE order_status = 'COMPLETE'")
result.show()

In this example, we first create a SparkSession and then create a sample orders DataFrame ordersDF using Seq.toDF() method. We then register the DataFrame as a temporary table using registerTempTable() method and name it orders. Finally, we perform a SQL query on the temporary table to get all orders with a COMPLETE status using spark.sql() method and print the results using show() method.

The result of the select query on the orders table looks like this:

 Spark registerTempTable()

5. Conclusion

In conclusion, Spark registerTempTable() method is to register a DataFrame as a temporary table in the Spark SQL catalog. This allows you to perform SQL operations on the DataFrame using Spark SQL. The temporary table created using registerTempTable() has session scope and is automatically removed from the Spark SQL catalog when the SparkSession is terminated.

However, in Spark 3.0 and later versions, registerTempTable() has been deprecated and replaced by createOrReplaceTempView() method, which provides the same functionality as registerTempTable() but with a more consistent DataFrame API. Overall, the createOrReplaceTempView() method is a better option for registering a DataFrame as a temporary table in Spark Scala, and it should be used instead of registerTempTable() in newer versions of Spark.

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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.