Menambahkan nomor baris ke sesi pengisian daya

Sepanjang kursus, Anda akan membangun window function menggunakan tabel charging dalam skema Snowflake ELECTRIC_VEHICLES. Setiap rekaman dalam tabel ini berisi informasi tentang sesi "charging" untuk kendaraan listrik.

Anda akan mulai dengan menggunakan window function untuk menambahkan nomor baris ke setiap rekaman.

Latihan ini adalah bagian dari kursus

Fungsi Window di Snowflake

Petunjuk latihan

Tambahkan pada pernyataan SELECT untuk mengembalikan field charging_station_location dan user_id.
Dengan menggunakan window function, berikan nomor baris pada setiap rekaman dan urutkan berdasarkan charging_cost.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

SELECT
	-- Return the charging_station_location and user_id
    ___,
    ___,
    charging_start_time,
    charging_cost,
    
    -- Assign a row number to each record, ordering by charging_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: Memberikan nomor baris Exercise 2: Membangun window function Exercise 3: Menambahkan nomor baris ke sesi pengisian daya

Latihan Saat Ini

Exercise 4: Menyusuri gelombang panas Exercise 5: Fungsi jendela untuk pemeringkatan Exercise 6: Apakah itu pengisian cepat?Exercise 7: Membandingkan energi yang dikonsumsi Exercise 8: Mempartisi data dalam fungsi window Exercise 9: Ikuti aliran uang!Exercise 10: Menyelami biaya pengisian daya

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: More Ranking Functions Exercise 2: Marketing the right vehicles Exercise 3: Optimizing charging scheduling Exercise 4: Setting a minimum charging cost Exercise 5: NTILE and CUME_DIST Exercise 6: Surveying vehicle owners Exercise 7: Evaluating charging duration Exercise 8: How long are vehicle owners going between charges?Exercise 9: LAG and LEAD Exercise 10: Looking into the past!Exercise 11: Maintaining charger efficiency Exercise 12: Lightning charger load

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!