SQL grupp õpetuse järgi: arv, summa, keskmine ja klauslite selgitamine

GROUP BYKlausel on võimas, kuid mõnikord keeruline avaldus mõelda.

Isegi kaheksa aastat hiljem pean iga kord, kui GROUP BYkasutan, peatuma ja mõtlema, mida see tegelikult teeb.

Selles artiklis uurime, kuidas koostada GROUP BYklauslit, mida see teie päringule teeb, ja kuidas saate seda kasutada koondamise ja andmete kogumise kohta.

Siit räägime:

  • Oma andmebaasi seadistamine
  • Näidisandmete seadistamine (müügi loomine)
  • Kuidas GROUP BYtöötab?
  • kirjutamine GROUP BYklauslid
  • Liitmiste ( COUNT, SUM, AVG)
  • Töö mitme rühmaga
  • Funktsioonide kasutamine GROUP BY
  • Rühmade filtreerimine rakendusega HAVING
  • Koguneb kaudse grupeerimisega

Oma andmebaasi seadistamine

Enne päringute kirjutamist peame oma andmebaasi seadistama.

Nende näidete jaoks kasutame PostgreSQL-i, kuid siin näidatud päringud ja mõisted tõlgitakse hõlpsasti mis tahes muusse kaasaegsesse andmebaasisüsteemi (nagu MySQL, SQL Server ja nii edasi).

PostgreSQL-i andmebaasiga töötamiseks võime kasutada psql - interaktiivset PostgreSQL-i käsureaprogrammi. Kui teil on mõni teine ​​andmebaasiklient, kellega teile meeldib töötada, on see ka hea.

Alustuseks loome oma andmebaasi. Kui PostgreSQL on juba installitud, saame käitada createdb oma terminalis uue andmebaasi loomiseks käsku . Ma helistasin omale fcc:

$ createdb fcc 

Järgmisena alustame interaktiivset konsooli käsu abil psqlja ühendame andmebaasi, mille me just kasutasime \c :

$ psql psql (11.5) Type "help" for help. john=# \c fcc You are now connected to database "fcc" as user "john". fcc=# 
Märkus. Olen psqllugemise hõlbustamiseks puhastanud nende näidete väljundi, nii et ärge muretsege, kui siin näidatud väljund pole täpselt see, mida olete oma terminalis näinud.

Soovitan teil järgida neid näiteid ja täita need päringud ise. Õpite ja mäletate palju rohkem, kui töötate nende näidete abil läbi, mitte lihtsalt lugege neid.

Andmete seadistamine (müügi loomine)

Näidete jaoks kasutame tabelit, kuhu on salvestatud erinevate toodete müügiarved erinevates kauplustes.

Nimetame seda tabelit salesja see kujutab endast lihtsalt kaupluse müüki: asukoha nimi, toote nimi, hind ja müügi aeg.

Kui me seda lauda ehitasime reaalses rakenduses, seadistasime teiste tabelite (näiteks locationsvõi products) välised võtmed . GROUP BYMõistete illustreerimiseks kasutame lihtsalt lihtsaid TEXTveerge.

Koostame tabeli ja sisestame mõned müügiandmed:

CREATE TABLE sales( location TEXT, product TEXT, price DECIMAL, sold_at TIMESTAMP ); INSERT INTO sales(location, product, price, sold_at) VALUES ('HQ', 'Coffee', 2, NOW()), ('HQ', 'Coffee', 2, NOW() - INTERVAL '1 hour'), ('Downtown', 'Bagel', 3, NOW() - INTERVAL '2 hour'), ('Downtown', 'Coffee', 2, NOW() - INTERVAL '1 day'), ('HQ', 'Bagel', 2, NOW() - INTERVAL '2 day'), ('1st Street', 'Bagel', 3, NOW() - INTERVAL '2 day' - INTERVAL '1 hour'), ('1st Street', 'Coffee', 2, NOW() - INTERVAL '3 day'), ('HQ', 'Bagel', 3, NOW() - INTERVAL '3 day' - INTERVAL '1 hour'); 

Meil on kolm asukohta: peakorter , kesklinn ja 1. tänav.

Meil on kaks toodet: Coffee ja Bagel ning lisame need sold_atväärtused erinevatele päevadele ja kellaaegadele müüdavate kaupade tähistamiseks erineva väärtusega.

Täna on mõni müük, mõni eile ja mõni üleeile.

Kuidas GROUP BYtöötab?

GROUP BYKlausli toimimise illustreerimiseks räägime kõigepealt näite kaudu.

Kujutage ette, et meil oleks tuba täis inimesi, kes on sündinud erinevates riikides.

Kui sooviksime leida ruumis viibivate inimeste keskmise kõrguse riigi järgi, paluksime kõigepealt neil inimestel eralduda rühmadesse vastavalt nende sünnimaale.

Kui nad olid oma rühmadesse jaotatud, saime arvutada selle rühma keskmise kõrguse.

Nii see GROUP BYklausel töötab. Kõigepealt määratleme, kuidas tahame ridu rühmitada - siis saame rühmade jaoks teha arvutusi või liitmisi.

Mitu rühma

Saame andmed rühmitada nii paljudesse rühmadesse või alarühmadesse kui soovime.

Näiteks pärast paludes inimestel eraldi rühmadesse vastavalt nende sünnist riikides, võiksime öelda kõigi nende riikide rühmade eraldamiseks veelgi rühmadesse vastavalt nende silmade värv.

Seda tehes on meil inimrühmad, lähtudes nende sünnimaa ja silmavärvi kombinatsioonist .

Nüüd võiksime leida keskmise kõrguse nendest väiksematest rühmadest ja meil oleks täpsem tulemus: keskmine kõrgus riigi ja silma värvi kohta .

GROUP BY§ kasutatakse sageli olukordi, kus saab kasutada fraasi kohta midagi või iga midagi :

  • Keskmine kõrgus kohta sünnimaa
  • Kokku inimeste arv iga silma ja juuksevärv kombinatsioon
  • Kogumüügist kohta toote

kirjutamine GROUP BYklauslid

GROUP BYKlausel on väga lihtne kirjutada-me lihtsalt kasutada märksõnu GROUP BYja seejärel määrake valdkonnas (s) me tahame rühma:

SELECT ... FROM sales GROUP BY location;

See lihtne päring rühmitab meie salesandmed locationveeru järgi.

Oleme grupeerimise teinud - aga mida me oma sisse paneme SELECT?

Ilmselge asi, mida valida, on meie - locationme rühmitame selle järgi, nii et me tahame vähemalt näha loodud rühmade nime:

SELECT location FROM sales GROUP BY location; 

Tulemuseks on meie kolm asukohta:

 location ------------ 1st Street HQ Downtown (3 rows) 

If we look at our raw table data (SELECT * FROM sales;), we'll see that we have four rows with a location of HQ, two rows with a location of Downtown, and two rows with a location of 1st Street:

 product | location | price | sold_at ---------+------------+-------+---------------------------- Coffee | HQ | 2 | 2020-09-01 09:42:33.085995 Coffee | HQ | 2 | 2020-09-01 08:42:33.085995 Bagel | Downtown | 3 | 2020-09-01 07:42:33.085995 Coffee | Downtown | 2 | 2020-08-31 09:42:33.085995 Bagel | HQ | 2 | 2020-08-30 09:42:33.085995 Bagel | 1st Street | 3 | 2020-08-30 08:42:33.085995 Coffee | 1st Street | 2 | 2020-08-29 09:42:33.085995 Bagel | HQ | 3 | 2020-08-29 08:42:33.085995 (8 rows) 

By grouping on the location column, our database takes these inputs rows and identifies the unique locations among them—these unique locations serve as our "groups."

But what about the other columns in our table?

If we try to select a column like product that we didn't group by...

SELECT location, product FROM sales GROUP BY location; 

...we run into this error:

ERROR: column "sales.product" must appear in the GROUP BY clause or be used in an aggregate function 

The problem here is we've taken eight rows and squished or distilled them down to three.

We can't just return the rest of the columns like normal—we had eight rows, and now we have three.

What do we do with the remaining five rows of data? Which of the eight rows' data should be displayed on these three distinct location rows?

There's not a clear and definitive answer here.

To use the rest of our table data, we also have to distill the data from these remaining columns down into our three location groups.

This means that we have to aggregate or perform a calculation to produce some kind of summary information about our remaining data.

Aggregations (COUNT, SUM, AVG)

Once we've decided how to group our data, we can then perform aggregations on the remaining columns.

These are things like counting the number of rows per group, summing a particular value across the group, or averaging information within the group.

To start, let's find the number of sales per location.

Since each record in our sales table is one sale, the number of sales per location would be the number of rows within each location group.

To do this we'll use the aggregate function COUNT() to count the number of rows within each group:

SELECT location, COUNT(*) AS number_of_sales FROM sales GROUP BY location; 

We use COUNT(*) which counts all of the input rows for a group.

(COUNT() also works with expressions, but it has slightly different behavior.)

Here's how the database executes this query:

  • FROM sales — First, retrieve all of the records from the sales table
  • GROUP BY location — Next, determine the unique location groups
  • SELECT ... — Finally, select the location name and the count of the number of rows in that group

We also give this count of rows an alias using AS number_of_sales to make the output more readable. It looks like this:

 location | number_of_sales ------------+----------------- 1st Street | 2 HQ | 4 Downtown | 2 (3 rows) 

The 1st Street location has two sales, HQ has four, and Downtown has two.

Here we can see how we've taken the remaining column data from our eight independent rows and distilled them into useful summary information for each location: the number of sales.

SUM

In a similar way, instead of counting the number of rows in a group, we could sum information within the group—like the total amount of money earned from those locations.

To do this we'll use the SUM() function:

SELECT location, SUM(price) AS total_revenue FROM sales GROUP BY location; 

Instead of counting the number of rows in each group we sum the dollar amount of each sale, and this shows us the total revenue per location:

 location | total_revenue ------------+--------------- 1st Street | 5 HQ | 9 Downtown | 5 (3 rows) 

Average (AVG)

Finding the average sale price per location just means swapping out the SUM() function for the AVG() function:

SELECT location, AVG(price) AS average_revenue_per_sale FROM sales GROUP BY location; 

Working with multiple groups

So far we've been working with just one group: location.

What if we wanted to sub-divide that group even further?

Similar to the "birth countries and eye color" scenario we started with, what if we wanted to find the number of sales per product per location?

To do this all we need to do is add the second grouping condition to our GROUP BY statement:

SELECT ... FROM sales GROUP BY location, product;

By adding a second column in our GROUP BY we further sub-divide our location groups into location groups per product.

Kuna me rühmitame nüüd ka productveeru järgi, saame selle nüüd oma kauba tagastada SELECT!

(Ma viskan ORDER BYnendele päringutele mõned klauslid, et väljundit oleks hõlpsam lugeda.)

SELECT location, product FROM sales GROUP BY location, product ORDER BY location, product; 

Vaadates meie uue grupeerimise tulemust, näeme meie ainulaadseid asukoha / toote kombinatsioone:

 location | product ------------+--------- 1st Street | Bagel 1st Street | Coffee Downtown | Bagel Downtown | Coffee HQ | Bagel HQ | Coffee (6 rows) 

Nüüd, kui meil on oma grupid olemas, mida me tahame teha ülejäänud veergude andmetega?

Noh, leiame müügi arvu toote ja asukoha kohta samade koondfunktsioonide abil nagu varem:

SELECT location, product, COUNT(*) AS number_of_sales FROM sales GROUP BY location, product ORDER BY location, product; 
 location | product | number_of_sales ------------+---------+----------------- 1st Street | Bagel | 1 1st Street | Coffee | 1 Downtown | Bagel | 1 Downtown | Coffee | 1 HQ | Bagel | 2 HQ | Coffee | 2 (6 rows) 
Kuna harjutus Reader ™: leida kogutulust (summa) iga toote kohta asukohast.

Funktsioonide kasutamine GROUP BY

Järgmisena proovime leida kogu päeva müügi arvu .

If we follow a similar pattern as we did with our locations and group by our sold_at column...

SELECT sold_at, COUNT(*) AS sales_per_day FROM sales GROUP BY sold_at ORDER BY sold_at; 

...we might expect to have each group be each unique day—but instead we see this:

 sold_at | sales_per_day ----------------------------+--------------- 2020-08-29 08:42:33.085995 | 1 2020-08-29 09:42:33.085995 | 1 2020-08-30 08:42:33.085995 | 1 2020-08-30 09:42:33.085995 | 1 2020-08-31 09:42:33.085995 | 1 2020-09-01 07:42:33.085995 | 1 2020-09-01 08:42:33.085995 | 1 2020-09-01 09:42:33.085995 | 1 (8 rows) 

It looks like our data isn't grouped at all—we get each row back individually.

But, our data is actually grouped! The problem is each row's sold_at is a unique value—so every row gets its own group!

The GROUP BY is working correctly, but this is not the output we want.

The culprit is the unique hour/minute/second information of the timestamp.

Each of these timestamps differ by hours, minutes, or seconds—so they are each placed in their own group.

We need to convert each of these date and time values into just a date:

  • 2020-09-01 08:42:33.085995 =>2020-09-01
  • 2020-09-01 09:42:33.085995 =>2020-09-01

Converted to a date, all of the timestamps on the same day will return the same date value—and will therefore be placed into the same group.

To do this, we'll cast the sold_at timestamp value to a date:

SELECT sold_at::DATE AS date, COUNT(*) AS sales_per_day FROM sales GROUP BY sold_at::DATE ORDER BY sold_at::DATE; 

In our GROUP BY clause we use ::DATE to truncate the timestamp portion down to the "day." This effectively chops off the hours/minutes/seconds of the timestamp and just returns the day.

In our SELECT, we also return this same expression and give it an alias to pretty up the output.

For the same reason we couldn't return product without grouping by it or performing some kind of aggregation on it, the database won't let us return just sold_at—everything in the SELECT must either be in the GROUP BY or some kind of aggregate on the resulting groups.

The result is the sales per day that we originally wanted to see:

 date | sales_per_day ------------+--------------- 2020-08-29 | 2 2020-08-30 | 2 2020-08-31 | 1 2020-09-01 | 3 (4 rows) 

Filtering groups with HAVING

Next let's look at how to filter our grouped rows.

To do this, let's try to find days where we had more than one sale.

Without grouping, we would normally filter our rows by using a WHERE clause. For example:

SELECT * FROM sales WHERE product = 'Coffee'; 

With our groups, we may want to do something like this to filter our groups based on the count of rows...

SELECT sold_at::DATE AS date, COUNT(*) AS sales_per_day FROM sales WHERE COUNT(*) > 1 -- filter the groups? GROUP BY sold_at::DATE; 

Unfortunately, this doesn't work and we receive this error:

ERROR:  aggregate functions are not allowed in WHERE

Aggregate functions are not allowed in the WHERE clause because the WHERE clause is evaluated before the GROUP BY clause—there aren't any groups yet to perform calculations on.

But, there is a type of clause that allows us to filter, perform aggregations, and it is evaluated after the GROUP BY clause: the HAVING clause.

The HAVING clause is like a WHERE clause for your groups.

To find days where we had more than one sale, we can add a HAVING clause that checks the count of rows in the group:

SELECT sold_at::DATE AS date, COUNT(*) AS sales_per_day FROM sales GROUP BY sold_at::DATE HAVING COUNT(*) > 1; 

This HAVING clause filters out any rows where the count of rows in that group is not greater than one, and we see that in our result set:

 date | sales_per_day ------------+--------------- 2020-09-01 | 3 2020-08-29 | 2 2020-08-30 | 2 (3 rows) 

Just for the sake of completeness, here's the order of execution for all parts of a SQL statement:

  • FROM — Retrieve all of the rows from the FROM table
  • JOIN — Perform any joins
  • WHERE — Filter rows
  • GROUP BY - Form groups
  • HAVING - Filter groups
  • SELECT - Select the data to return
  • ORDER BY - Order the output rows
  • LIMIT - Return a certain number of rows

Aggregates with implicit grouping

The last topic we'll look at is aggregations that can be performed without a GROUP BY—or maybe better said they have an implicitgrouping.

These aggregations are useful in scenarios where you want to find one particular aggregate from a table—like the total amount of revenue or the greatest or least value of a column.

For example, we could find the total revenue across all locations by just selecting the sum from the entire table:

SELECT SUM(price) FROM sales; 
 sum ----- 19 (1 row) 

So far we've done $19 of sales across all locations (hooray!).

Another useful thing we could query is the first or last of something.

For example, what is the date of our first sale?

To find this we just use the MIN() function:

SELECT MIN(sold_at)::DATE AS first_sale FROM sales; 
 first_sale ------------ 2020-08-29 (1 row) 

(To find the date of the last sale just substitute MAX()for MIN().)

Using MIN / MAX

While these simple queries can be useful as a standalone query, they're often parts of filters for larger queries.

For example, let's try to find the total sales for the last day that we had sales.

One way we could write that query would be like this:

SELECT SUM(price) FROM sales WHERE sold_at::DATE = '2020-09-01'; 

This query works, but we've obviously hardcoded the date of 2020-09-01.

09/01/2020 may be the last date we had a sale, but it's not always going to be that date. We need a dynamic solution.

This can be achieved by combining this query with the MAX() function in a subquery:

SELECT SUM(price) FROM sales WHERE sold_at::DATE = ( SELECT MAX(sold_at::DATE) FROM sales ); 

In our WHERE clause we find the largest date in our table using a subquery: SELECT MAX(sold_at::DATE) FROM sales.

Then, we use this max date as the value we filter the table on, and sum the price of each sale.

Implicit grouping

I say that these are implicit groupings because if we try to select an aggregate value with a non-aggregated column like this...

SELECT SUM(price), location FROM sales; 

...we get our familiar error:

ERROR: column "sales.location" must appear in the GROUP BY clause or be used in an aggregate function 

GROUP BY is a tool

As with many other topics in software development, GROUP BY is a tool.

There are many ways to write and re-write these queries using combinations of GROUP BY, aggregate functions, or other tools like DISTINCT, ORDER BY, and LIMIT.

Understanding and working with GROUP BY's will take a little bit of practice, but once you have it down you'll find an entirely new batch of problems are now solvable to you!

Kui teile see postitus meeldis, võite mind jälgida twitteris, kus räägin andmebaasiasjadest ja kuidas arendajakarjääris läbi lüüa.

Täname lugemast!

John