Mengoptimalkan penjadwalan pengisian daya

Beruntung bagi Anda, Anda kini menjadi pengguna Snowflake andalan untuk menjawab pertanyaan harian tentang setiap stasiun pengisian. Tim operasional Anda ingin memberi peringkat pada setiap proses pengisian berdasarkan durasinya, untuk setiap stasiun pengisian. Ini akan membantu mereka mengoptimalkan blok penjadwalan untuk aplikasi baru mereka. Saatnya mulai!

Latihan ini adalah bagian dari kursus

Fungsi Window di Snowflake

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SELECT
    charger_type,
    charging_duration * 60 AS charging_minutes,
	
    -- Assign a ranking to reach record based on charging
    -- duration, from shortest to longest
    ___() ___(
      	-- Segment the result set by charger type
        ___ ___ ___
        ORDER BY ___
    ) AS ___

FROM ELECTRIC_VEHICLES.charging;

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Latihan ini adalah bagian dari kursus

Fungsi Window di Snowflake

SkillTag.level.intermediateSkillTag.label

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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

Latihan Saat Ini

Exercise 4: Menetapkan biaya pengisian minimum 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!