Menetapkan biaya pengisian minimum

Perusahaan pemilik stasiun pengisian ini ingin menetapkan biaya pengisian minimum untuk setiap kota tempat mereka beroperasi. Untuk itu, mereka ingin menjadikan biaya pengisian termurah kesepuluh di setiap kota sebagai jumlah minimum yang harus dikeluarkan setiap pengguna saat mengisi daya kendaraannya dalam satu sesi. Sebagai ahli Snowflake di perusahaan, tugas Anda adalah menemukan nilai tersebut untuk setiap kota agar nantinya setiap catatan dapat dibandingkan dengannya.

Latihan ini adalah bagian dari kursus

Fungsi Window di Snowflake

Petunjuk latihan

Gunakan fungsi window untuk menemukan nilai terkecil ke sepuluh dari charging_cost.
Partisi data berdasarkan charging_station_location untuk memastikan nilai minimum ditemukan untuk setiap kota.
Beri alias hasilnya sebagai minimum_cost.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

SELECT
	charging_station_location,
	user_id,
	charging_cost,
    
    -- Find the tenth-smallest charging cost
    ___(___, ___) OVER(
      	-- Segment the data by charging_station_location
		___
    	ORDER BY charging_cost
    -- Alias the result as minimum_cost
    ) ___ ___
    
FROM ELECTRIC_VEHICLES.charging;

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Fungsi Window di Snowflake

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

Open the window to a world of possibilities with Snowflake window functions! You'll get the ball rolling by differentiating window functions from traditional functions. Then, you'll learn how to provide a row number and ranking for each record in a query. Once you've nailed down the basics, you'll put the "window" in window functions, using PARTITION BY. You'll explore how to find and use the first and last value of a certain window before wrapping up with a sneak peek into aggregation functions.

Exercise 1: Assigning row numbers Exercise 2: Building a window function Exercise 3: Adding row numbers to charging sessions Exercise 4: Riding the heat wave Exercise 5: Ranking window functions Exercise 6: Was that a fast charge?Exercise 7: Comparing energy consumed Exercise 8: Partitioning data in a window function Exercise 9: Follow the money!Exercise 10: Digging into charging costs

Time to crank it up! In this chapter, you’ll take ranking functions to the next level. You’ll start with a variant of RANK, called DENSE_RANK, which handles ties in a bit of a different way. You’ll also explore a more robust version of the functions you saw in the previous lesson using NTH_VALUE. Next, you’ll create “buckets” of data using NTILE, which is more useful than you may think. You’ll also pick up a nifty little tool called CUME_DIST to find the number of records less than or equal to a certain record in a window. You’ll wrap up the chapter with one of the most powerful applications of window functions you’ve seen so far; LAG and LEAD.

Exercise 1: Fungsi Peringkat Lanjutan Exercise 2: Memasarkan kendaraan yang tepat Exercise 3: Mengoptimalkan penjadwalan pengisian daya Exercise 4: Menetapkan biaya pengisian minimum

Latihan Saat Ini

Exercise 5: NTILE dan CUME_DIST Exercise 6: Mensurvei pemilik kendaraan Exercise 7: Mengevaluasi durasi pengisian daya Exercise 8: Seberapa lama pemilik kendaraan jeda antar pengisian daya?Exercise 9: LAG dan LEAD Exercise 10: Melihat ke masa lalu!Exercise 11: Menjaga efisiensi pengisi daya Exercise 12: Beban pengisi daya cepat

You’ll start this final chapter with aggregation functions like AVG, COUNT, and SUM. You’ll compare the output of these functions to individual records in a window, as well as to perform additional calculations. After this, you’ll master the most exciting application of window functions; running and moving calculations! You’ll start by calculating running averages and totals for different metrics for electric vehicle charging. Finally, you’ll wrap up the course by generating moving totals and averages with a sliding window!

Exercise 1: Aggregate window functions Exercise 2: Engaging vehicle owners with metrics Exercise 3: Visualizing charging session duration and cost Exercise 4: Who's consuming the least?Exercise 5: Window frames Exercise 6: Are charging durations getting longer?Exercise 7: Identifying inefficient chargers Exercise 8: Moving Averages and Totals Exercise 9: Moving vs. running Exercise 10: Impact of temperature on charging rate Exercise 11: Putting it all together!Exercise 12: Congratulations!