There is an obvious trade-off here between saving copies and memory usage. If you find yourself with a heavy Jupyter fil

Query (SELECT 1 AS n) now have a name — R. We refer to that name in SELECT n 1 FROM R. Here R is a single row, single column table containing number 1. The result of the whole expression is number 2.

Important to note that base query doesn’t involve R, but recursive query references R. From the first look it seems like infinite loop, to compute R we need compute R. But here is a catch. R actually don’t reference itself, it just references previous result and when previous result is empty table, recursion stops. Actually it could help to thing of it as an iteration rather then recursion!

Base query returns number 1 , recursive query takes it under the countUp name and produces number 2, which is the input for the next recursive call. When recursive query returns empty table (n >= 3), the results from the calls are stacked together.

Recursion in SQL? But why? Oh, there are many uses for that. It’s common to store hierarchical data in SQL and recursive queries are a convenient way to extract information from such graphs. Organizational structure, application menu structure, a set of tasks with sub-tasks in the project, links between web pages, breakdown of an equipment module into parts and sub-parts are examples of the hierarchical data. The post will not go into great details of those many use cases rather look at two toy examples to understand the concept - the simplest possible case of recursion on numbers and querying data from the family tree.

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Here’s what is happening: base query executed first, taking whatever it needs to compute the result R0. Second recursive query is executed taking R0 as input, that is R references R0 in the recursive query when first executed. Recursive query produces the result R1 and that is what R will reference to at the next invocation. And so on until recursive query returns empty result. At that point all intermediate results are combined together.

Recursion is achieved by WITH statement, in SQL jargon called Common Table Expression (CTE). It allows to name the result and reference it within other queries sometime later.

Having been working with Jupyter almost daily for the past three years, I have experienced serious undesired situations that took me back days, or even weeks in my project completion time. These are simple things that I have learned to do to prevent serious data loss, time loss, and project phase confusion.

Now this tree traversal query could be the basis to augment the query with some other information of interest. For example, having a birth year in the table we can calculate how old the parent was when the child was born. Next query do exactly that, together with showing lineages. To do that it traverses the tree from top to bottom. parentAge is zero in the first row because we don’t know when Alice was born from the data we have.

If you have identified a rough number of significant phases of your project where you will change the dataset significantly, make sure to save it as another name by the of the operation, such as:

“They have the advantage of coming to California without being tainted and polluted by what’s in the water supply and air of Silicon Valley,” says Moritz, a partner at Sequoia Capital and a Stripe board member. “They’re more humble and well-rounded.”

You can then choose to work on the copy of the original data frame or the original data frame (there’s no difference) but the important thing is that you will be able to go back to one of those original files if you make an irreparable mistake and only notice way down the line when it’s too late or too confusing to “undo cell operation”.

Base query finds Frank’s parent — Mary, recursive query takes this result under the Ancestor name and finds parents of Mary, which are Dave and Eve and this continues until we can’t find any parents anymore.

Watch out, counting up like that can only go that far. There is a limit for recursion. It defaults to 100, but could be extended with MAXRECURSION option (MS SQL Server specific). Practically, it could be a bad idea to crank recursion limit up. Graphs might have cycles and limited recursion depth can be a good defense mechanism to stop poorly behaving query.

Now this tree traversal query could be the basis to augment the query with some other information of interest. For example, having a birth year in the table we can calculate how old the parent was when the child was born. Next query do exactly that, together with showing lineages. To do that it traverses the tree from top to bottom. parentAge is zero in the first row because we don’t know when Alice was born from the data we have.