Compute and Runtimes

📄️ Runtime Attachment in Notebook Workspaces

Most notebook work is fine on the default kernel — you don't have to attach anything to start. Attach a runtime when you outgrow the default: for example, when you need a bigger driver, more executors, GPU access, or a runtime that has been pre-configured with specific Spark settings and dependencies. This article will walks through the runtime toolbar at the top of every notebook, the two attachment flows, and what happens when you click each button.

📄️ Spark Session Information

Type spark in a notebook cell and run it. Both Python and Scala kernels render a small HTML block summarizing the current Spark session — it is the \repr\html\_ of the SparkSession object. Useful for confirming what your kernel is connected to, especially after attaching or detaching a runtime.

📄️ Notebook Process (Jobs)

A Notebook Process runs a notebook non-interactively as part of a Syntasa workflow. It is one process type among many — alongside Spark code processes, app processes, and so on — and uses the same job orchestration model. Manual runs, scheduled runs, aborting, run history: all of it works the same way as for any other Syntasa process. This article covers what is specific to notebooks; for the rest, see the Syntasa workflow documentation.