Spark cache articles

4-day workshop Β· In-person or online

What would it take for you to trust your Databricks pipelines in production?

A 3-day bug hunt on a 3-person team costs up to €7,200 in lost engineering time. This workshop teaches you to prevent that β€” unit tests, data tests, and integration tests for PySpark and Databricks Lakeflow, including Spark Declarative Pipelines.

Unit, data & integration tests
Medallion architecture & Lakeflow SDP
Max 10 participants Β· production-ready templates
See the full curriculum β†’ €7,000 flat fee Β· cohort of up to 10
Bartosz Konieczny
Bartosz
Konieczny

Cache in Spark

Cache is an appreciable tool when we have a greedy computation generating a lot of data. Spark also uses this feature to better handle the case of RDD which generation is heavy (for example necessities database connection or data retrieval from external web services).

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isEmpty() trap in Spark

In general Spark's actions reflects logic implemented in a lot of equivalent methods in programming languages. As an example we can consider isEmpty() that in Spark checks the existence of only 1 element and similarly in Java's List. But it can often lead to troubles, especially when more than 1 action is invoked.

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