Limitations and attention points

Spark is a fairly complex execution engine; tuning and troubleshooting Spark jobs require some experience.

Spark’s additional possibilities come with a few limitations:

  • Sampling with filter is not supported for input datasets; prefer a filtering recipe instead.

  • HDFS datasets perform much better on Spark than other datasets, for both reading and writing.

  • Sampling an HDFS dataset (except with a fixed ratio) can be slower than loading it unsampled.


Spark’s overhead is non-negligible and its support has some limitations (see above and Usage of Spark in DSS). If your data fits in the memory of a single machine, other execution engines might be faster and easier to tune. It is recommended that you only use Spark for data that does not fit in the memory of a single machine.