Where VS Having

Where and Having are two clauses that we will best explain using examples. This time we will create only one table.

1) CREATE TABLE WhereOrHaving ( Letter CHAR DEFAULT 'D', Number TINYINT ); 2) INSERT INTO WhereOrHaving VALUES ( 'A', 1 ), ( 'A', 2 ),                                    ( 'B', 3 ), ( 'B', 4 ),                                    ( 'C', 5 ), ( 'C', 6 );

Where

Where is simple. It is a filter that will exclude all the rows which don't satisfy the condition. SELECT * FROM WhereOrHaving WHERE Number > 3;

Having

Having is more complex. First, we will group our table by the first column.   SELECT Letter, SUM( Number ) As sumOfGroup FROM WhereOrHaving GROUP BY Letter;

Then we will remove all the rows where sumOfGroup is smaller than 3. This time we do not use WHERE clause. We are not filtering detail rows, this time we are filtering groups. For that we use HAVING clause. SELECT Letter, SUM( Number ) As sumOfGroup FROM WhereOrHaving GROUP BY Letter HAVING SUM( Number ) > 3

Let's combine these two clauses to better understand them.

1) This is our starting table. We will transform it step by step.

2) Next, we will filter only those rows where Number is bigger than 3.

3) We will now group the table from the previous step by the "Letter" column.

4) And finally, we will filter only groups that are bigger than 6.

So, first we have to use WHERE. WHERE is always used before grouping, because it works on the detail rows. After this comes grouping. When we get our groups then is the time to pick only some of them by using HAVING condition.

INSERT

INSERT statement is used to enter data into table. We will insert some more rows to table WhereOrHaving. Basic way of using INSERT is to make a list of values. These values will be placed in the table as a new row. Our table WhereOrHaving has two columns of CHAR and TINYINT types, so we should be careful to insert data of that data types.

INSERT INTO WhereOrHaving VALUES ( 'E', 36 );

It is also possible to enter several rows at once. INSERT INTO WhereOrHaving VALUES ( 'X', 36 ), ( 'Y', 46 ), ( 'Z', 56 );

IF we don't provide values for all the columns, insert will fail. INSERT INTO WhereOrHaving VALUES ( 'M' );
	We have to provide values for all the columns, even if some of values are nulls. INSERT INTO WhereOrHaving VALUES ( 'M', null );

Sometimes we want to be specific into which columns we want to enter values. Now we can provide values in any order we want.

INSERT INTO WhereOrHaving ( Number, Letter ) VALUES ( 13, 'P'); We will first provide value for Number and then for Letter.

If we use syntax where some of the columns are specified, but other are not, then all other columns will be filled with nulls.

INSERT INTO WhereOrHaving ( Letter ) VALUES ( 'G' );

If we enter value for Number, and not for Letter, then the DEFAULT value will be used for the Letter.

INSERT INTO WhereOrHaving ( Number ) VALUES ( 77 );

This image will revise all of our results.

INSERT With SELECT

We will take small sample from the table above.

SELECT * FROM WhereOrHaving WHERE Letter = 'D';

We can now add this sample into any other table. Presumption is that destination table should have the same structure as our sample. For simplicity we will not add this sample to same other table, but we will add it back to our WhereOrHaving table. Then, our table will have two ( 'D', 77 ) rows.

INSERT INTO WhereOrHaving SELECT * FROM WhereOrHaving WHERE Letter = 'D';

UPDATE

We saw that we now have two rows in WhereOrHaving table with nulls. We have "M, null" and "G, null".

UPDATE WhereOrHaving SET Number = 101 WHERE Letter = 'G';

We have updated "G, null" to "G, 101".

We can update values "M, null" to "Q, null".

UPDATE WhereOrHaving SET Letter = 'Q' WHERE Number IS null;

To find rows where column value is null, we use operator IS, and not =.

DEFAULT value for Letter columns is "D". We can set any field in Letter column to this default value.

UPDATE WhereOrHaving SET Letter = DEFAULT WHERE Number = 101;

Now we have default letter "D" beside number 101.

DELETE

We use DELETE for deletion of rows. We can return our table, WhereOrHaving, to its original state, by deleting all the added rows.     DELETE FROM WhereOrHaving WHERE NOT Letter in ( 'A', 'B', 'C' );

Built-In Variables

MonetDB has a collection of built-in variables. Those variables can provide us with valuable information about our current session and current time.

CURRENT_SCHEMA	CURRENT_DATE	CURRENT_TIMESTAMP	LOCALTIME
CURRENT_USER	CURRENT_TIME	CURRENT_TIMEZONE	LOCALTIMESTAMP

SELECT CURRENT_SCHEMA AS CurrentSchema, CURRENT_USER AS CurrentUser, CURRENT_DATE AS CurrentDate
     , CURRENT_TIME AS CurrentTime, CURRENT_TIMESTAMP AS CurrentTimestamp, CURRENT_TIMEZONE AS CurrentTimezone
     , LOCALTIME AS LocTime, LOCALTIMESTAMP AS LocTimeStamp;

SET OPERATORS

Let's say we have a purple and a green data set. The data set is the result of a query. Using the SET operators, we can treat the rows in those two data sets as mathematical sets.

With joins, we concatenate tables horizontally. If we want to fuse tables vertically then we would use a union. For union to work we need to meet three conditions: – Two data sets must have the same number of rows. – Order of columns in one data set must be the same as order of the columns in another data set. – Columns at the same position must have similar data type.   There are two kinds of unions, we have "UNION" and we have "UNION ALL". "UNION ALL" would just glue two data sets together. If the first data set has X rows, and the second one Y rows, final data set would have X + Y rows.   "UNION" will return the same thing, but without duplicates.   "INTERSECT" means that we are looking for the rows that belong both to the first and the second data set. "INTERSECT" will return only distinct rows. "EXCEPT" returns only rows from the first dataset, that do not have equivalents in the second data set. Again, only distinct rows will be returned.

Sample tables

We will create two sample tables. Notice that columns in these tables are using compatible data types ( CHAR vs VARCHAR(1), TINYINT vs SMALLINT ).

UNION ALL

Our tables are meeting conditions for a UNION ALL. SELECT * FROM aboveTable UNION ALL SELECT * FROM  belowTable;

UNION

UNION will remove duplicate rows. SELECT * FROM aboveTable UNION SELECT * FROM belowTable;

INTERSECT

Intersect will give us overlap between two tables. SELECT * FROM aboveTable INTERSECT SELECT * FROM belowTable;

EXCEPT

Let's see rows that exist in the aboveTable, but not in the belowTable.   SELECT * FROM aboveTable EXCEPT SELECT * FROM belowTable;

INTERSECT and EXCEPT with duplicates

If we can use ALL with UNION, is it possible to use it with INTERSECT and EXCEPT? It is possible. This way, all of the duplicates will remain.

SELECT * FROM aboveTable INTERSECT ALL SELECT * FROM  belowTable;	SELECT * FROM aboveTable EXCEPT ALL SELECT * FROM  belowTable;

ORDER BY and SET operators

When we use ORDER BY, it must be applied on the last query.   SELECT * FROM aboveTable UNION SELECT * FROM belowTable ORDER BY Number DESC;

Column names and SET operations

The above SELECT is determining names of columns. Names of columns in above and below SELECT don't have to be the same. SELECT Letter as L, Number as N FROM aboveTable UNION SELECT * FROM belowTable ORDER BY N DESC;

Connecting More Than 2 Tables with Set Operators

How EXCEPT works

SELECT * FROM aboveTable UNION ALL SELECT * FROM belowTable EXCEPT SELECT * FROM belowTable;

Chained operations are conducted in sequence manner. First we make an UNION, and then we apply EXCEPT.   Let's see below steps that are leading to our results.

1) In the first step, UNION ALL will unite our two tables. Next we have to do EXCEPT vs belowTable.

2) In the second step, all duplicates from both tables will be removed. Then we would do EXCEPT operation.   3) The only row that exist in the first table, and not in the second table is a row "A-1". That is our result.

How EXCEPT ALL works

SELECT * FROM aboveTable UNION ALL SELECT * FROM belowTable EXCEPT ALL SELECT * FROM belowTable;

When we use EXCEPT ALL, we would get 4 rows as a result. Let's see below steps that have leaded to this outcome.

1) In the first step, UNION ALL will unite our two tables. Next, we have to do EXCEPT ALL vs belowTable.

2) This time there is no DISTINCT. Rows from the two tables will destroy each other like matter and antimatter. What remains in the left table will be our final result.

EXCEPT ALL with DISTINCT

Statement below will return table on the right image. Now we have 6 rows in the result data set. SELECT * FROM aboveTable UNION ALL SELECT * FROM belowTable EXCEPT ALL SELECT DISTINCT * FROM belowTable;

This time only two rows from the UNION ALL tables will be excluded so we will be left with 6 remaining rows.

UNION AND INTERSECT

Here is one example with INTERSECT. Again, first two data sets will be connected with UNION ALL. Their result will be than intersected with the last data set. SELECT * FROM aboveTable UNION ALL SELECT * FROM belowTable INTERSECT ALL SELECT DISTINCT * FROM belowTable;

1) We will start two intermediate data sets. First one is the result of UNION and it will have 8 rows. The other one is created with DISTINCT from the belowTable, and it has 2 rows. 2) Intersection between these two data sets is this:

Two Conclusions

1) SET operators don't have priority among them. They are applied from the top to the bottom. This is different than in some other databases where INTERSECTION has the top priority. This would be the result if we enforce priority of an INTERSECT operator.

2) SET operators without ALL specifiers will first remove duplicates from their operands. SET operators with ALL specifiers will leave their operands untouched. This is true not only for EXCEPT (like in previous examples), but also for UNION and INTERSECT. The message here is that removal of the duplicates is not conducted on the result, but on the operands, and after that SET operators will apply their logic.

Corresponding

THIS IS NOT WORKING IN MONETDB, although documentation claims that it works.

When working with SET operators we need to be careful to order our columns correctly. Their position has to match between tables. This makes things harder. We will face errors many times.

CORRESPONDING clause can fix this problem. CORRESPONDING clause will correctly pair columns so that UNION is successful. Unfortunately, statement on the right side will work in some other databases, but not in the MonetDB, so I will just stop explaining it. You can read more about this clause on this blog: https://blog.jooq.org/a-rarely-seen-but-useful-sql-feature-corresponding/

SELECT Number, Letter FROM aboveTable UNION ALL CORRESPONDING SELECT Letter, Number FROM belowTable;

Sample tables

We will create these two tables in the MonetDB database:

We can create these two tables using these statements. The first statement will create the table. The second statement will populate that table with data. These are the same tables we have used in the previous blog post, so maybe you already have them in your database.

1) CREATE TABLE Employees ( EmployeeID INT, Employee VARCHAR(10 ) ); 2) INSERT INTO Employees ( EmployeeID, Employee ) VALUES ( 1, 'Hugo' ), ( 2, 'Gabriel' ), ( 3, 'Aurora' ), ( 4, 'Adam' );

1) CREATE TABLE Sales ( SalesID INT, DateCol DATE,  EmployeeID INT, Product CHAR(1), Qty INTEGER, Price INTEGER ); 2) INSERT INTO Sales ( SalesID, DateCol, EmployeeID, Product, Qty, Price ) VALUES      ( 1, '2024-03-05', 2, 'X', 10, 4)    , ( 2, '2024-03-05', 3, 'X', 20, 5)    , ( 3, '2024-03-05', 2, 'Y', 30, 6)    , ( 4, '2024-03-06', 3, 'Y', 40, 7)    , ( 5, '2024-03-06', 2, 'Z', 50, 8)    , ( 6, '2024-03-06', 3, 'Z', 60, 9)

Crossjoin

A cross join is when we combine every row from one table with every row from another table. If one table has X rows and the other Y rows, then the result table will have X * Y rows. SELECT * FROM Employees CROSS JOIN Sales;   In our example, the Employees table has 4 rows, the Sales table has 6 rows, so the resulting table has 24 rows. In the picture we can see that each row of the Employee table is combined with each row from the Sales table. "EmployeeID" column will appear twice if use SELECT with a star.

Statement with a specific column "EmployeeID" will become ambiguous. SELECT EmployeeID FROM Employees CROSS JOIN Sales; We'll have to write the full column name to avoid that ambiguity. SELECT Employees.EmployeeID FROM Employees CROSS JOIN Sales;

Diagram

Diagram shows two tables that we want to join. There are three basic ways how we can join tables, INNER join, LEFT join, and FULL OUTER join.

INNER JOIN means only matching rows will remain after join. In LEFT JOIN, we will not exclude any row from the left table and such rows will be matched with null values in the right table. A FULL OUTER JOIN means that no one will be left out. All orphan rows will be padded using nulls.

INNER JOIN

Let's filter the CROSSJOIN table so that only the rows where the Emploiees.EmploieeID column is equal to the Sales.EmploieeID column will remain.

SELECT * FROM Employees CROSS JOIN Sales WHERE Employees.EmployeeID = Sales.EmployeeID;   This is the definition of an INNER JOIN. An INNER JOIN is a CROSS JOIN where only columns that meet certain conditions are saved in the result.

For INNER JOIN we have this specialized syntax. It makes it clearer what we are trying to achieve.   SELECT * FROM Employees INNER JOIN Sales ON Employees.EmployeeID = Sales.EmployeeID;

The point of INNER JOIN is to only look for successful employees. Gabriel and Aurora each made three sales and we want to see those results. Hugo and Adam did not make any sales so they will be missing from the score.   We combine each row from the Employees table with only the matching rows from the Sales table.

LEFT JOIN

SELECT * FROM Employees LEFT JOIN Sales ON Employees.EmployeeID = Sales.EmployeeID;   LEFT OUTER JOIN is when we want to expose employees without sales. LEFT OUTER JOIN will show us all rows from the Employees table. If any rows from the Employees table do not have matching rows in the Sales table, then they will be accompanied by null values.   Nulls will help us easily see which rows from the Employees table do not have corresponding rows in the Sales table.

FULL OUTER JOIN

I will add another Sale to the Sales table. We'll pretend that the former employee made that sale.
INSERT INTO SALES ( SalesID, DateCol, EmployeeID, Product, Qty, Price ) VALUES ( 0, '2024-03-04', 0, 'Q', 15, 3 );

That will help us explain the FULL OUTER JOIN.

A FULL OUTER JOIN will explain all our sales and show employees without sales. Now we can see the complete picture. We can see the source of our sales and we can see who is not making sales. SELECT * FROM Employees FULL OUTER JOIN Sales ON Employees.EmployeeID = Sales.EmployeeID;   In a FULL OUTER JOIN, matching rows will be cross-joined, but non-matching rows, from the both tables, will be completed with nulls.

Matching condition and aliases

The matching conditions can be more complex than in our examples. It can consist of several columns.

SELECT Tab1 INNER JOIN Tab2 ON Tab1.Col1 = Tab2.Col1    AND (Tab1.Col2 = 3        OR Tab1.Col2 > Tab2.Col3)

We can notice that an overly complex SELECT statement can lead to hard-to-read code. This is a case where we can use aliases wisely. In practice, we usually use short aliases, consisting of only one letter, such as "a" and "b".

SELECT Tab1 a INNER JOIN Tab2 b ON a.Col1 = b.Col1 AND (a.Col2 = 3      OR a.Col2 > b.Col3)

The left table would be given the alias a and the right table would be given the alias b. This makes things much more readable.

It is also possible to create aliases for the columns. SELECT * FROM Employees a( EmpNumber, Name ) INNER JOIN Sales b ON a.EmpNumber = b.EmployeeID;

Join with more than two tables

We will add one more table to our database in order to explain JOIN with more than two tables.

1) CREATE TABLE Calendar ( DateCol DATE, MonthCol INTEGER, YearCol INTEGER ); 2) INSERT INTO Calendar ( DateCol, MonthCol, YearCol )    VALUES ( '2024-03-05', 3, 2024 ), ( '2024-03-06' , 3, 2024 );

The tables are connected in a chain manner. We can join as many tables as we want.

SELECT * FROM Employees FULL OUTER JOIN Sales    ON Employees.EmployeeID = Sales.EmployeeID FULL OUTER JOIN Calendar    ON Sales.DateCol = Calendar.DateCol;

Special syntaxes

These special syntaxes are not often used. These syntaxes are based on the fact that tables are usually joined by columns with the same name and data type.

NATURAL JOIN

A NATURAL JOIN is an INNER JOIN between two tables. The condition is based on equality between columns that have the same name and the same data type. Let's say we have an INNER JOIN between the Emploees and Sales tables.

INNER JOIN   SELECT * FROM Employees INNER JOIN Sales ON Employees.EmployeeID = Sales.EmployeeID;	An easier way to achieve this is to use NATURAL JOIN. A NATURAL JOIN will give us the same result based on the fact that the EmployeeID column exists in both tables and is of the same data type. There are no other columns with such quality.   SELECT * FROM Employees NATURAL JOIN Sales;

USING Clause

The USING clause also assumes that we have some columns in both tables that have the same name and are of the same type. With the USING clause, we can specify any type of join (INNER, LEFT, LEFT OUTER). Let's make a left join between the tables Employees and Sales.

LEFT JOIN   SELECT * FROM Employees LEFT JOIN Sales ON Employees.EmployeeID = Sales.EmployeeID;	An easier way to achieve this is to use the USING clause. In the USING clause, we only need to specify the columns that exist in both tables and have the same name and data type. The JOIN condition will be based on the equality of those columns.   SELECT * FROM Employees LEFT JOIN Sales USING ( EmployeeID );

Sample tables

We will create these two tables in the MonetDB database:

We can create these two tables using these statements. The first statement will create the table. The second statement will populate that table with data.

1) CREATE TABLE Employees ( EmployeeID INT, Employee VARCHAR(10 ) ); 2) INSERT INTO Employees ( EmployeeID, Employee ) VALUES ( 1, 'Hugo' ), ( 2, 'Gabriel' ), ( 3, 'Aurora' ), ( 4, 'Adam' );

1) CREATE TABLE Sales ( SalesID INT, DateCol DATE,  EmployeeID INT, Product CHAR(1), Qty INTEGER, Price INTEGER ); 2) INSERT INTO Sales ( SalesID, DateCol, EmployeeID, Product, Qty, Price ) VALUES      ( 1, '2024-03-05', 2, 'X', 10, 4)    , ( 2, '2024-03-05', 3, 'X', 20, 5)    , ( 3, '2024-03-05', 2, 'Y', 30, 6)    , ( 4, '2024-03-06', 3, 'Y', 40, 7)    , ( 5, '2024-03-06', 2, 'Z', 50, 8)    , ( 6, '2024-03-06', 3, 'Z', 60, 9)

Simple SELECT statement

The simplest SELECT statement will read the entire table. The star "*" is a symbol that represents all columns. This statement will read all columns and all rows from the table. SELECT * FROM Employees;

If we don't need all the columns, we have to specify them. After the SELECT keyword, we need to type the list of columns we want to read. Only those columns will be fetched. SELECT Product, Qty FROM Sales;

If we want to rename columns, we can use "AS Alias" after the column name. SELECT EmployeeID AS ID, Employee AS Name FROM Employees;

We don't need a table for our SELECT statement. We can use expressions to create values. If we don't provide aliases for these expressions, their columns will have generic names like "%2, %3, %4". SELECT 3+3, CURRENT_DATE;

We can combine columns and expressions. SELECT Employee, CURRENT_DATE as Today FROM Employees;

An expression can reference values from columns. In our example, the Multiplication column is calculated by multiplying the value from the EmploeeID column by 11. If we put double quotes around the alias "Multiplication", then this alias will preserve its case in the result. Otherwise, it will be written in lowercase letters. SELECT EmployeeID, EmployeeID * 11 AS "Multiplication" FROM Employees;

Reminder of the Identifiers

If we create a table with double quotes, like CREATE TABLE Tab1 ( "Col1" CHAR ), then the header of this table will be like this:

The column name will be case sensitive. This will force us to write the SELECT statement with double quotes as well. If we omitted the double quotes, our SELECT statement would not work. If we want our column name to have any non-standard characters or spaces, then we would always have to use double quotes.

Filtering and Ordering Rows

After the FROM clause we can use WHERE. With WHERE we can define a condition that will filter only matching rows. Within that expression we can reference any column. Expressions can be much more complex than this. SELECT * FROM Employees WHERE EmployeeID >= 3;

The server will read rows from the table in a way that achieves the best performance. We don't know in advance the order in which the server will read the records, so we should make the server sort the result using the ORDER statement. The ORDER command will have a list of columns by which the rows will be sorted. We can sort using ASC or DESC order, but ASC is the default. The table will be sorted first by EmploeeID and then by Qty, so the order of the columns in the ORDER statement is important. SELECT * FROM Sales ORDER BY EmployeeID DESC, Qty;

If we add DISTINCT to our statement, we will remove duplicate rows. SELECT Product FROM Sales; 6 rows SELECT DISTINCT Product FROM Sales; 3 rows

We can limit the number of rows we want the server to return. For that we use the keywords LIMIT and OFFSET. We will also use ORDER BY in this example to better understand what MonetDB did. MonetDB will skip one row and then read the next two rows. SELECT * FROM Employees ORDER BY EmployeeID LIMIT 2 OFFSET 1;

Using only LIMIT, we can read a limited number of lines from the beginning. Using only OFFSET, we can read everything except the lines at the beginning.

Grouping our Data

If we want to remove duplicates from our columns, we can use GROUP BY. In this simplest case, we must mention all columns after both SELECT and GROUP BY. This result could be made much easier with the DISTINCT keyword. In practice we use GROUP BY to achieve something different and this will become clearer in the example below. SELECT DateCol, EmployeeID FROM SALES GROUP BY DateCol, EmployeeID;

GROUP BY will remove rows for non-aggregated columns by removing duplicates. For columns like Quantity and Value, we can consolidate rows by aggregating the values. SELECT DateCol      , EmployeeID      , SUM( Qty ) AS Qty      , SUM( Price * Qty ) AS Value  FROM Sales GROUP BY DateCol, EmployeeID;

If one of the columns we are grouping on is an expression, then that expression will simply need to be repeated in the GROUP BY statement. SELECT DateCol      , 2 * 3 AS Expression      , SUM( Qty ) AS Qty FROM Sales GROUP BY DateCol, 2 * 3;

Using VALUES Clause

We can create a SELECT statement that will return our hard-coded values. It is mandatory to give aliases to the result columns.

SELECT * FROM ( VALUES (0,'cero'), (1,'uno'), (2,'dos'), (3,'tres') ) AS ( Number, Word );   Aliases have to be inside brackets.

We can also give such a table an alias. SELECT * FROM ( VALUES (0,'cero'), (1, 'uno' ) ) AS TableName(Number, Word); This alias will come in handy in some more complex statements.