Bug: Spark Materialized Views: Improve Query Performance and Data Management
Status: Open | Priority: Major | Resolution: 
Created: 03/May/24 18:00
Affects Version/s: 4.0.0

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There is a wish to introduce Materialized Views to Apache Spark. Materialized Views are pre-computed and optimized SQL queries that store the results for faster retrieval. Their inclusion would enhance Spark's capabilities 

Currently, users might resort to manual techniques like caching or temporary tables to achieve similar results. However, these approaches lack the efficiency, consistency, and management features offered by native Materialized Views.

I am outlining the benefits, potential challenges, and implementation considerations for integrating Materialized Views into Spark. 
h2. Benefits 
 # *Improved Query Performance (especially for Streaming Data):* Materialized Views can significantly improve query performance, particularly for use cases involving Spark Structured Streaming. When dealing with continuous data streams, materialized views can pre-compute and store frequently accessed aggregations or transformations. Subsequent queries on the materialized view can retrieve the results much faster compared to continuously processing the entire streaming data. This is crucial for real-time analytics where low latency is essential.{*}{{*}}
 # *Enhancing Data Management:* They offer a way to pre-aggregate or transform data, making complex queries more efficient.
 # *Reduced Data Movement:* Materialized Views can be materialized on specific clusters or storage locations closer to where the data will be consumed. This minimizes data movement across the network, further improving query performance and reducing overall processing time.
 # *Simplified Workflows:* Developers and analysts can leverage pre-defined Materialized Views that represent specific business logic or data subsets. This simplifies data access, reduces development time for queries that rely on these views, and fosters code reuse.

h2. Potential challenges
 # *View Consistency Management:* Maintaining consistency between the underlying data and the materialized view is crucial. Ways  need to be defined to handle updates to the base data and ensure the view reflects the latest state. This might involve refresh triggers or background invalidation mechanisms.
 # *Overhead of Maintaining Views:* Materialized Views require additional storage space and processing overhead for updates and maintenance. The benefits of performance improvement need to be weighed against the cost of maintaining these views.
 # *Backward Compatibility Concerns:* Introducing Materialized Views might require changes to existing Spark code that relies on specific caching mechanisms or temporary tables. Careful consideration needs to be given to ensure minimal disruption to existing workflows.

h2. Implementation Considerations
 * *API Design:* Well-designed APIs are essential for creating, managing, and querying Materialized Views. These APIs should integrate seamlessly with the existing Spark SQL functionalities and allow for efficient management of view lifecycles.
 * *Storage and Partitioning:* Strategies for storing materialized views, including leveraging Apache Parquet or other efficient columnar formats, need to be determined. Partitioning views based on frequently used query filters can further optimize performance.
 * *Refresh Strategies:* Defining mechanisms for refreshing materialized views when the underlying data changes is crucial. This could involve manual refresh, scheduled refreshes, or change data capture (CDC) triggers to update views automatically.               {panel}