A cache sink is a specific action within a data flow process where data is written into the Spark cache rather than being stored in a traditional data store.
Understanding Cache Sink
In the context of data processing, particularly within mapping data flows, a cache sink serves as a destination for data. However, unlike typical sinks that write to databases, files, or other external storage, a cache sink stores the data directly in the Spark cache.
According to the provided reference, "A cache sink is when a data flow writes data into the Spark cache instead of a data store."
How Cache Sinks Work
When a data flow stage is configured as a cache sink, the processed data at that point is materialized and held in memory (or on disk, depending on Spark's caching strategy) within the Spark execution environment.
Key aspects include:
- Target Location: Data goes into the Spark cache, not an external database or file system.
- Referencing Data: Once data is in the cache via a cache sink, it can be accessed by subsequent steps within the same data flow.
- Cache Lookup: This referencing is typically done using a mechanism called a cache lookup. The reference states, "In mapping data flows, you can reference this data within the same flow many times using a cache lookup."
Benefits and Use Cases
The primary benefit of using a cache sink is efficient data reusability within a single data flow execution without incurring the overhead of reading from an external source multiple times or performing explicit joins just to reference data.
- Efficient Referencing: It allows you to quickly look up values from the cached data.
- Simplified Expressions: As noted in the reference, "This is useful when you want to reference data as part of an expression but don't want to explicitly join the columns to it." This means you can use values from the cached data directly in transformations like derived columns or filters without joining the entire dataset.
- Performance: Accessing data from the Spark cache is generally much faster than reading from persistent storage.
Summary
In essence, a cache sink is a powerful feature in data flow tools that leverages Spark's caching capabilities. It allows intermediate or lookup data to be stored temporarily in memory for rapid access and reuse within the same data flow, simplifying logic and potentially improving performance by avoiding redundant data reads and complex joins for lookup purposes.
