Spark SQL: Temporary and Global Temporary Views
In Apache Spark, views are essentially saved queries that you can reference by name, much like tables in a traditional database. They are incredibly useful for simplifying complex queries, promoting code reusability, and improving the readability of your Spark SQL code. Spark offers two primary types of views: Temporary Views and Global Temporary Views.
Temporary Views
Temporary views are session-scoped. This means they are only visible and accessible within the current Spark session. Once the Spark session ends, the temporary view is automatically dropped. They are ideal for intermediate results or for breaking down complex analytical tasks within a single session.
Temporary views are session-specific and disappear when the session ends.
Think of a temporary view as a scratchpad for your current Spark session. It's a handy way to name a query result for easy reuse within that session, but it won't persist beyond it.
When you create a temporary view using createOrReplaceTempView(), it registers a view with the SparkSession's catalog. This view is associated with the specific SparkSession instance. If you have multiple SparkSessions running concurrently, a temporary view created in one session will not be visible in another. This isolation is a key characteristic and a benefit for managing query logic within distinct analytical contexts.
Session-scoped. It exists only within the current Spark session.
Global Temporary Views
Global temporary views, on the other hand, are tied to the Spark application's lifetime. They are accessible across all SparkSessions within the same Spark application. When you create a global temporary view using
createOrReplaceGlobalTempView()
global_temp
global_temp.
Global temporary views persist for the entire Spark application and are accessed via the 'global_temp' database.
Global temporary views are like shared bookmarks for your entire Spark application. They remain available as long as your application is running, and you need to explicitly reference them using global_temp..
The creation of a global temporary view involves registering it with the SparkContext's catalog. This makes it a more persistent construct within the application's lifecycle. If your application involves multiple SparkSessions (e.g., for different users or different analytical tasks running concurrently within the same application), a global temporary view ensures that a common intermediate result or a frequently used query definition is available to all of them. However, remember that they are still temporary in the sense that they are dropped when the Spark application terminates.
| Feature | Temporary View | Global Temporary View |
|---|---|---|
| Scope | Current SparkSession | Entire Spark Application |
| Persistence | Ends with SparkSession | Ends with Spark Application |
| Access Prefix | Directly by name | global_temp. prefix required |
| Creation Method | createOrReplaceTempView() | createOrReplaceGlobalTempView() |
When deciding between temporary and global temporary views, consider the lifespan and accessibility needed for your query results. For isolated, session-specific operations, use temporary views. For shared, application-wide definitions, opt for global temporary views.
Practical Examples
Let's illustrate with simple examples. Assume you have a DataFrame named
sales_df
Creating a temporary view:
sales_df.400">createOrReplaceTempView(400">"daily_sales")spark.400">sql(400">"SELECT * FROM daily_sales WHERE sale_date = '2023-10-27'").400">show()
Creating a global temporary view:
sales_df.400">createOrReplaceGlobalTempView(400">"all_sales_summary")spark.400">sql(400">"SELECT 400">COUNT(*) FROM global_temp.all_sales_summary").400">show()
You access it using global_temp.my_view.
Learning Resources
The official Apache Spark documentation provides a comprehensive overview of views, including temporary and global temporary views, with detailed explanations and examples.
Direct link to the Java API documentation for the `createOrReplaceTempView` method, explaining its usage and parameters.
Direct link to the Java API documentation for the `createOrReplaceGlobalTempView` method, detailing its functionality.
A blog post from Databricks that clearly explains the differences and use cases for temporary and global temporary views in Spark.
A tutorial covering Spark SQL views, offering practical examples of creating and using both temporary and global temporary views.
A video tutorial demonstrating how to create and use temporary views in Spark SQL with practical code examples.
A lecture from a Coursera course on Spark SQL that covers the concepts of views and tables, including their creation and management.
A LinkedIn article providing a specific example and explanation of how to implement and use global temporary views in Spark.
This blog post dives into the nuances between temporary and global temporary views, helping users choose the right one for their needs.
Wikipedia's page on Apache Spark provides a broad overview of the technology, including its SQL capabilities and the context in which views are used.