This is yet another programming example in my Scala-on-Spark blog series. Again, while it starts with the same minuscule weather data used in previous examples of the blog series, it can be viewed as an independent programming exercise.

In this example, we’re going to create a table that shows the streaks of consecutive months with non-zero precipitation.

Result should be similar to the following:

We’ll explore using Spark’s window functions in this example. As a side note, some of the previous examples in the blog series could be resolved using window functions as well. By means of aggregating over partitioned sliding windows of data, Spark’s window functions readily perform certain kinds of complex aggregations which would otherwise require repetitive nested groupings. They are similar to how PostgreSQL’s window functions work.

Now, let’s load up the same old minuscule weather data.

First, create a DataFrame of precipitation by weather station and month and filter it to consist of only months with positive precipitation.

Next, using window function, we capture sequences of row numbers ordered by month over partitions by weather station. For each row, we then use an UDF to calculate the base date by dating back from the corresponding month of the row in accordance with the row number. As shown in the following table, these base dates help trace chunks of contiguous months back to their common base dates.

Finally, we apply another row-number window function, but this time, over partitions by weather station as well as base date. This partitioning allows contiguous common base dates to generate new row numbers as the wanted streaks.

Using the same logic flow, we can also generate similar streak reports for temperature high/low (e.g. streak of temperature high above 75F). I’ll leave that as exercise for the readers.

This is another programming example in my Scala-on-Spark blog series. While it uses the same minuscule weather data created in the first example of the blog series, it can be viewed as an independent programming exercise.

In this example, we want a table of total precipitation over custom past periods by weather stations. The specific periods in this example are the previous month, previous 3 months, and all previous months. We have data from July through December, and let’s say it’s now January hence the previous month is December.

The result should be like this:

User-defined functions (UDF) will be used in this example. Spark’s UDF supplements its API by allowing the vast library of Scala (or any of the other supported languages) functions to be used. That said, a method from Spark’s API should be picked over an UDF of same functionality as the former would likely perform more optimally.

First, let’s load up the said weather data.

We first create a DataFrame of precipitation by weather station and month, each with the number of months that lag the current month.

Next, we combine the list of months-lagged with monthly precipitation by means of a UDF to create a map column. To do that, we use Scala’s zip method within the UDF to create a list of tuples from the two input lists and convert the resulting list into a map.

Note that the map content might look different depending on when it is generated, as the months-lagged is relative to the current month when the application is run.

Using another UDF to sum precipitation counting backward from the previous months based on the number of months lagged, we create the result DataFrame.

Again, note that the months-lagged is relative to the current month when the application is executed, hence the months-lagged parameters for the aggMapValues UDF should be adjusted accordingly.

We can use similar approach to come up with a table for temperature high/low over the custom periods. Below are the steps for creating the result table for temperature high.

I’ll leave creating the temperature low result table as a programming exercise for the readers. Note that rather than calculating temperature high and low separately, one could aggregate both of them together in some of the steps with little code change. For those who are up for a slightly more challenging exercise, both temperature high and low data can in fact be transformed together in every step of the way.