Archive for June, 2022
MySQL Query from JSON
One of my students asked how you could get JSON data out in tabular format. I said they should look at Øystein Grøvlen’s JSON_TABLE – Best of Both Worlds blog post from 2018. Unfortunately, the student wanted another example with the Video Store model that we use in class.
For clarity, all path definitions start with a $ followed by other selectors:
- A period followed by a name, such as $.website
- [N] where N is the position in a zero-indexed array
- The .[*] wildcard evaluates all members of an object
- The [*] wildcard evaluates all members of an array
- The prefix and suffix wildcard, **, evaluates to all paths that begin with the named prefix and end with the named suffix
So, here’s a quick supplement to what’s already there. It assumes you created an example table based on my prior blog post that looks like this:
+----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | struct |
+----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 1 | {"contact": [{"last_name": "Winn", "first_name": "Randi"}, {"last_name": "Winn", "first_name": "Brian"}], "account_number": "US00001"} |
| 2 | {"contact": [{"last_name": "Vizquel", "first_name": "Oscar"}, {"last_name": "Vizquel", "first_name": "Doreen"}], "account_number": "US00002"} |
| 3 | {"contact": [{"last_name": "Sweeney", "first_name": "Meaghan"}, {"last_name": "Sweeney", "first_name": "Matthew"}, {"last_name": "Sweeney", "first_name": "Ian"}], "account_number": "US00003"} |
| 4 | {"contact": [{"last_name": "Clinton", "first_name": "Goeffrey"}], "account_number": "US00004"} |
| 5 | {"contact": [{"last_name": "Moss", "first_name": "Wendy"}], "account_number": "US00005"} |
| 6 | {"contact": [{"last_name": "Gretelz", "first_name": "Simon"}], "account_number": "US00006"} |
| 7 | {"contact": [{"last_name": "Royal", "first_name": "Elizabeth"}], "account_number": "US00007"} |
| 8 | {"contact": [{"last_name": "Smith", "first_name": "Brian"}], "account_number": "US00008"} |
| 9 | {"contact": [{"last_name": "Potter", "first_name": "Harry"}, {"last_name": "Potter", "first_name": "Ginny"}, {"last_name": "Potter", "first_name": "Lily"}], "account_number": "US00011"} |
+----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
9 rows in set (0.01 sec) |
You can query the account_number key value like this:
SELECT id , JSON_EXTRACT(struct, "$.account_number") AS account_no FROM example; |
It returns:
+----+------------+ | id | account_no | +----+------------+ | 1 | "US00001" | | 2 | "US00002" | | 3 | "US00003" | | 4 | "US00004" | | 5 | "US00005" | | 6 | "US00006" | | 7 | "US00007" | | 8 | "US00008" | | 9 | "US00011" | +----+------------+ 9 rows in set (0.00 sec) |
You use the JSON_TABLE function to get the embedded array elements of first and last name, like:
SELECT id , contact.* FROM example CROSS JOIN JSON_TABLE( struct ,"$.contact[*]" COLUMNS( lname JSON PATH "$.last_name" , fname JSON PATH "$.first_name")) AS contact; |
It returns:
+----+-----------+-------------+ | id | lname | fname | +----+-----------+-------------+ | 1 | "Winn" | "Randi" | | 1 | "Winn" | "Brian" | | 2 | "Vizquel" | "Oscar" | | 2 | "Vizquel" | "Doreen" | | 3 | "Sweeney" | "Meaghan" | | 3 | "Sweeney" | "Matthew" | | 3 | "Sweeney" | "Ian" | | 4 | "Clinton" | "Goeffrey" | | 5 | "Moss" | "Wendy" | | 6 | "Gretelz" | "Simon" | | 7 | "Royal" | "Elizabeth" | | 8 | "Smith" | "Brian" | | 9 | "Potter" | "Harry" | | 9 | "Potter" | "Ginny" | | 9 | "Potter" | "Lily" | +----+-----------+-------------+ 15 rows in set (0.00 sec) |
You can combine both approaches, as shown below.
SELECT id , JSON_EXTRACT(struct, "$.account_number") AS account_no , contact.* FROM example CROSS JOIN JSON_TABLE( struct ,"$.contact[*]" COLUMNS( lname JSON PATH "$.last_name" , fname JSON PATH "$.first_name")) AS contact; |
It returns:
+----+------------+-----------+-------------+ | id | account_no | lname | fname | +----+------------+-----------+-------------+ | 1 | "US00001" | "Winn" | "Randi" | | 1 | "US00001" | "Winn" | "Brian" | | 2 | "US00002" | "Vizquel" | "Oscar" | | 2 | "US00002" | "Vizquel" | "Doreen" | | 3 | "US00003" | "Sweeney" | "Meaghan" | | 3 | "US00003" | "Sweeney" | "Matthew" | | 3 | "US00003" | "Sweeney" | "Ian" | | 4 | "US00004" | "Clinton" | "Goeffrey" | | 5 | "US00005" | "Moss" | "Wendy" | | 6 | "US00006" | "Gretelz" | "Simon" | | 7 | "US00007" | "Royal" | "Elizabeth" | | 8 | "US00008" | "Smith" | "Brian" | | 9 | "US00011" | "Potter" | "Harry" | | 9 | "US00011" | "Potter" | "Ginny" | | 9 | "US00011" | "Potter" | "Lily" | +----+------------+-----------+-------------+ 15 rows in set (0.00 sec) |
Lastly, if you want to get rid of the enclosing double quotes you can do the following:
WITH raw AS (SELECT id , JSON_EXTRACT(struct, "$.account_number") AS account_no , contact.* FROM example CROSS JOIN JSON_TABLE( struct ,"$.contact[*]" COLUMNS( lname JSON PATH "$.last_name" , fname JSON PATH "$.first_name")) AS contact) SELECT id , REGEXP_REPLACE(account_no,'"','') AS account_no , REGEXP_REPLACE(lname,'"','') AS lname , REGEXP_REPLACE(fname,'"','') AS fname FROM raw; |
It’s also possible to use the JSON_UNQUOTE function to cleanup the double quotes. I hope this helps those extracting JSON data into tabular result sets.
MySQL Backslashes
Yesterday, I wrote a blog post that showed you how to write a query returning a JSON structure for a 1:many relationship. The relationship was between the member and contact table. It returns one account_number from the member table and a list of first_name and last_name columns from the contact table in a JSON structure.
One of my students asked why I choose to strip the backslashes with Python, and my reply was the SQL was already complex for most blog readers. The student asked but how would you do it in SQL. OK, that’s a fair question for two reasons. First, you don’t need to do in your local programs because it’ll run faster on the server. Second, if you strip the backslashes you can insert it into a standard JSON column. This blog post will show you how to do both.
You would use three REGEXP_REPLACE function calls, like:
SELECT REGEXP_REPLACE( REGEXP_REPLACE( REGEXP_REPLACE( JSON_OBJECT( 'account_number', account_number ,'contact', CONCAT('[' , GROUP_CONCAT( JSON_OBJECT('first_name',first_name ,'last_name',last_name ) SEPARATOR ',') ,']') ) ,'\\\\','') ,'"\\\[','\\\[') ,'\\\]"','\\\]') AS json_result FROM member m INNER JOIN contact c ON m.member_id = c.member_id GROUP BY m.account_number; |
It returns the following:
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {"contact": [{"last_name": "Winn", "first_name": "Randi"},{"last_name": "Winn", "first_name": "Brian"}], "account_number": "US00001"} |
| {"contact": [{"last_name": "Vizquel", "first_name": "Oscar"},{"last_name": "Vizquel", "first_name": "Doreen"}], "account_number": "US00002"} |
| {"contact": [{"last_name": "Sweeney", "first_name": "Meaghan"},{"last_name": "Sweeney", "first_name": "Matthew"},{"last_name": "Sweeney", "first_name": "Ian"}], "account_number": "US00003"} |
| {"contact": [{"last_name": "Clinton", "first_name": "Goeffrey"}], "account_number": "US00004"} |
| {"contact": [{"last_name": "Moss", "first_name": "Wendy"}], "account_number": "US00005"} |
| {"contact": [{"last_name": "Gretelz", "first_name": "Simon"}], "account_number": "US00006"} |
| {"contact": [{"last_name": "Royal", "first_name": "Elizabeth"}], "account_number": "US00007"} |
| {"contact": [{"last_name": "Smith", "first_name": "Brian"}], "account_number": "US00008"} |
| {"contact": [{"last_name": "Potter", "first_name": "Harry"},{"last_name": "Potter", "first_name": "Ginny"},{"last_name": "Potter", "first_name": "Lily"}], "account_number": "US00011"} |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
9 rows in set (0.00 sec) |
Let’s create a table with a JSON structure with the following script:
/* Drop table if it exists. */ DROP TABLE IF EXISTS example; /* Create a example table. */ CREATE TABLE example ( id int unsigned auto_increment , struct json , PRIMARY KEY (id)); |
Now, we can embed the query inside an INSERT statement:
INSERT INTO example ( struct ) (SELECT REGEXP_REPLACE( REGEXP_REPLACE( REGEXP_REPLACE( JSON_OBJECT( 'account_number', account_number ,'contact', CONCAT('[' , GROUP_CONCAT( JSON_OBJECT('first_name',first_name ,'last_name',last_name ) SEPARATOR ',') ,']') ) ,'\\\\','') ,'"\\\[','\\\[') ,'\\\]"','\\\]') AS json_result FROM member m INNER JOIN contact c ON m.member_id = c.member_id GROUP BY m.account_number); |
A query of the example table, like:
SELECT * FROM example; |
Returns:
+----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | struct |
+----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 1 | {"contact": [{"last_name": "Winn", "first_name": "Randi"}, {"last_name": "Winn", "first_name": "Brian"}], "account_number": "US00001"} |
| 2 | {"contact": [{"last_name": "Vizquel", "first_name": "Oscar"}, {"last_name": "Vizquel", "first_name": "Doreen"}], "account_number": "US00002"} |
| 3 | {"contact": [{"last_name": "Sweeney", "first_name": "Meaghan"}, {"last_name": "Sweeney", "first_name": "Matthew"}, {"last_name": "Sweeney", "first_name": "Ian"}], "account_number": "US00003"} |
| 4 | {"contact": [{"last_name": "Clinton", "first_name": "Goeffrey"}], "account_number": "US00004"} |
| 5 | {"contact": [{"last_name": "Moss", "first_name": "Wendy"}], "account_number": "US00005"} |
| 6 | {"contact": [{"last_name": "Gretelz", "first_name": "Simon"}], "account_number": "US00006"} |
| 7 | {"contact": [{"last_name": "Royal", "first_name": "Elizabeth"}], "account_number": "US00007"} |
| 8 | {"contact": [{"last_name": "Smith", "first_name": "Brian"}], "account_number": "US00008"} |
| 9 | {"contact": [{"last_name": "Potter", "first_name": "Harry"}, {"last_name": "Potter", "first_name": "Ginny"}, {"last_name": "Potter", "first_name": "Lily"}], "account_number": "US00011"} |
+----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
9 rows in set (0.00 sec) |
I hope this answers the question about whether you can use SQL remove the backslashes from the original result set and how you can insert the JSON result set into a JSON data type.
MySQL JSON Tricks
Are they really tricks or simply basic techniques combined to create a solution. Before writing these mechanics for using native MySQL to create a compound JSON object, let me point out that the easiest way to get one is to use the MySQL Node.js library, as shown recently in my “Is SQL Programming” blog post.
Moving data from a relational model output to a JSON structure isn’t as simple as a delimited list of columns in a SQL query. Let’s look at it in stages based on the MySQL Server 12.18.2 Functions that create JSON values.
Here’s how you return single row as a JSON object, which is quite straightforward:
SELECT JSON_OBJECT('first_name',c.first_name,'last_name',c.last_name) AS json_result FROM contact c WHERE first_name = 'Harry' AND last_name = 'Potter'; |
It returns:
+------------------------------------------------+
| json_result |
+------------------------------------------------+
| {"last_name": "Potter", "first_name": "Harry"} |
+------------------------------------------------+
1 row in set (0.00 sec) |
With a GROUP_CONCAT function, let’s capture a JSON array of all three Potter family members:
SELECT CONCAT('[' , GROUP_CONCAT( JSON_OBJECT('first_name',first_name ,'last_name',last_name ) SEPARATOR ',') ,']') AS json_result FROM contact c WHERE c.last_name = 'Potter'; |
It returns an array of JSON objects:
+-----------------------------------------------------------------------------------------------------------------------------------------------+
| [{"last_name": "Potter", "first_name": "Harry"},{"last_name": "Potter", "first_name": "Ginny"},{"last_name": "Potter", "first_name": "Lily"}] |
+-----------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.01 sec) |
Next, let’s put a 1:many relationship between the member and contact table into a JSON structure with a single account number and an array of contact. It requires a second call to the JSON_OBJECT function and the addition of a GROUP BY clause in the query.
SELECT JSON_OBJECT( 'account_number', account_number ,'contact', CONCAT('[' , GROUP_CONCAT( JSON_OBJECT('first_name',first_name ,'last_name',last_name ) SEPARATOR ',') ,']') ) AS json_result FROM member m INNER JOIN contact c ON m.member_id = c.member_id WHERE c.last_name = 'Potter' GROUP BY m.account_number; |
It returns the following string with an annoying set of backslashes. It also inverts the column order, which appears unavoidable but it shouldn’t matter because the order of name-value pairs in JSON is immaterial.
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {"contact": "[{\"last_name\": \"Potter\", \"first_name\": \"Harry\"},{\"last_name\": \"Potter\", \"first_name\": \"Ginny\"},{\"last_name\": \"Potter\", \"first_name\": \"Lily\"}]", "account_number": "US00011"} |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.00 sec) |
The following quick little Python code cleans up the JSON string by removing the backslashes and extraneous quotes around the array of contacts.
# Import the library. import mysql.connector from mysql.connector import errorcode try: # Open connection. cnx = mysql.connector.connect(user='student', password='student', host='127.0.0.1', database='studentdb') # Create cursor. cursor = cnx.cursor() # Set the query statement. query = ("SELECT JSON_OBJECT( " "'account_number', m.account_number " ",'contact', CONCAT('[' " " , GROUP_CONCAT( " " JSON_OBJECT('first_name', c.first_name " " ,'last_name', c.last_name ) SEPARATOR ',') " " ,']')) AS json_result " "FROM contact c INNER JOIN member m " "ON c.member_id = m.member_id " "WHERE c.last_name = %s " "GROUP BY account_number") # Execute cursor. cursor.execute(query,["Potter"]) # Display the column returned by the query stripped of backslashes and # extraneous quotes. for (row) in cursor: for column in range(len(row)): print(row[column].replace("\\","").replace("\"[","[").replace("]\"","]")) # Close cursor. cursor.close() # ------------------------------------------------------------ # Handle exception and close connection. except mysql.connector.Error as e: if e.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif e.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print("Error code:", e.errno) # error number print("SQLSTATE value:", e.sqlstate) # SQLSTATE value print("Error message:", e.msg) # error message # Close the connection when the try block completes. else: cnx.close() |
It returns:
{"contact": [{"last_name": "Potter", "first_name": "Harry"},{"last_name": "Potter", "first_name": "Ginny"},{"last_name": "Potter", "first_name": "Lily"}], "account_number": "US00011"} |
I hope this helps exhibit less well known MySQL syntax.
PL/pgSQL Transactions
There are many nuances that I show students about PL/pgSQL because first I teach them how to use PL/SQL. These are some of the differences:
- PL/SQL declares the function or procedure and then uses the IS keyword; whereas, PL/pgSQL uses the AS keyword.
- PL/SQL uses the RETURN keyword for functions declarations, like:
RETURN [data_type} IS
Whereas, PL/pgSQL uses the plural RETURNS keyword in the function declaration, like:
RETURNS [data_type] AS
- PL/SQL considers everything after the function or procedure header as the implicit declaration section; whereas, PL/pgSQL requires you block the code with something like $$ (double dollar symbols) and explicitly use the DECLARE keyword.
- PL/SQL supports local functions (inside the DECLARE block of a function or procedure); whereas, PL/pgSQL doesn’t.
- PL/SQL puts the variable modes (IN, INOUT, OUT) between the parameter name and type; whereas, PL/pgSQL puts them before the variable name.
- PL/SQL declares cursors like:
CURSOR cursor_name (parameter_list) IS
Whereas, PL/pgSQL declares them like
cursor_name CURSOR (parameter_list) FOR
- PL/SQL terminates and runs the block by using an END keyword, an optional module name, a semicolon to terminate the END; statement, and a forward slash to dispatch the program to PL/SQL statement engine:
END [module_name]; /
Whereas, PL/pgSQL terminates and runs the block by using an END keyword, a semicolon to terminate the END; statement, two dollar signs to end the PL/pgSQL block, and a semicolon to dispatch the program.
END LANGUAGE plpgsql; $$;
After all that basic syntax discussion, we try to create a sample set of tables, a function, a procedure, and a test case in PL/pgSQL. They’ve already done a virtually equivalent set of tasks in PL/SQL.
Here are the steps:
- Create the grandma and tweetie_bird tables:
/* Conditionally drop grandma table and grandma_s sequence. */ DROP TABLE IF EXISTS grandma CASCADE; /* Create the table. */ CREATE TABLE GRANDMA ( grandma_id SERIAL , grandma_house VARCHAR(30) NOT NULL , PRIMARY KEY (grandma_id) ); /* Conditionally drop a table and sequence. */ DROP TABLE IF EXISTS tweetie_bird CASCADE; /* Create the table with primary and foreign key out-of-line constraints. */ SELECT 'CREATE TABLE tweetie_bird' AS command; CREATE TABLE TWEETIE_BIRD ( tweetie_bird_id SERIAL , tweetie_bird_house VARCHAR(30) NOT NULL , grandma_id INTEGER NOT NULL , PRIMARY KEY (tweetie_bird_id) , CONSTRAINT tweetie_bird_fk FOREIGN KEY (grandma_id) REFERENCES grandma (grandma_id) );
- Create a get_grandma_id function that returns a number, which should be a valid primary key value from the grandma_id column of the grandma table.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
CREATE OR REPLACE FUNCTION get_grandma_id ( IN pv_grandma_house VARCHAR ) RETURNS INTEGER AS $$ /* Required for PL/pgSQL programs. */ DECLARE /* Local return variable. */ lv_retval INTEGER := 0; -- Default value is 0. /* Use a cursor, which will not raise an exception at runtime. */ find_grandma_id CURSOR ( cv_grandma_house VARCHAR ) FOR SELECT grandma_id FROM grandma WHERE grandma_house = cv_grandma_house; BEGIN /* Assign a value when a row exists. */ FOR i IN find_grandma_id(pv_grandma_house) LOOP lv_retval := i.grandma_id; END LOOP; /* Return 0 when no row found and the ID # when row found. */ RETURN lv_retval; END; $$ LANGUAGE plpgsql;
- Create a Warner_brother procedure that writes data across two tables as a transaction. You con’t include any of the following in your functions or procedures because all PostgreSQL PL/pgSQL functions and procedures are transaction by default:
- SET TRANSACTION
- START TRANSACTION
- SAVEPOINT
- COMMIT
A ROLLBACK should be placed in your exception handler as qualified on lines #33 thru #36. The warner_brother procedure inserts rows into the grandma and tweetie_bird tables.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
/* Create or replace procedure warner_brother. */ CREATE OR REPLACE PROCEDURE warner_brother ( pv_grandma_house VARCHAR , pv_tweetie_bird_house VARCHAR ) AS $$ /* Required for PL/pgSQL programs. */ DECLARE /* Declare a local variable for an existing grandma_id. */ lv_grandma_id INTEGER; BEGIN /* Check for existing grandma row. */ lv_grandma_id := get_grandma_id(pv_grandma_house); IF lv_grandma_id = 0 THEN /* Insert grandma. */ INSERT INTO grandma ( grandma_house ) VALUES ( pv_grandma_house ) RETURNING grandma_id INTO lv_grandma_id; END IF; /* Insert tweetie bird. */ INSERT INTO tweetie_bird ( tweetie_bird_house , grandma_id ) VALUES ( pv_tweetie_bird_house , lv_grandma_id ); EXCEPTION WHEN OTHERS THEN ROLLBACK; RAISE NOTICE '[%] [%]', SQLERRM, SQLSTATE; END; $$ LANGUAGE plpgsql;
You should take note of the RETURNING-INTO statement on line #22. The alternative to this clause isn’t pretty if you know that PostgreSQL uses a table name, column name, and the literal seq value separated by underscores (that is, snake case), like:
/* Assign current value to local variable. */ lv_grandma_id := CURRVAL('grandma_grandma_id_seq');
It would be even uglier if you had to look up the sequence name, like:
/* Assign current value to local variable. */ lv_grandma_id := CURRVAL(pg_get_serial_sequence('grandma','grandma_id'));
- You can test the combination of these two stored procedures with the following DO-block:
/* Test the warner_brother procedure. */ DO $$ BEGIN /* Insert the yellow house. */ CALL warner_brother( 'Yellow House', 'Cage'); CALL warner_brother( 'Yellow House', 'Tree House'); /* Insert the red house. */ CALL warner_brother( 'Red House', 'Cage'); CALL warner_brother( 'Red House', 'Tree House'); END; $$ LANGUAGE plpgsql;
Then, query the results:
SELECT * FROM grandma g INNER JOIN tweetie_bird tb ON. g.grandma_id = tb.grandma_id;
It should return:
grandma_id | grandma_house | tweetie_bird_id | tweetie_bird_house | grandma_id ------------+---------------+-----------------+--------------------+------------ 1 | Red House | 1 | Cage | 1 1 | Red House | 2 | Tree House | 1 2 | Yellow House | 3 | Cage | 2 2 | Yellow House | 4 | Tree House | 2 (4 rows)
As always, I hope writing a clear and simple examples helps those looking for sample code.
Is SQL Programming
Is SQL, or Structured Query Language, a programming language? That’s a great question! A question that many answer with emphasis: “No, SQL is not a programming language!” There are some who answer yes; and they usually qualify that answer with something like: “SQL is a programming language designed to communicate with relational databases.”
It strikes me that those saying “yes” are saying that SQL is only a collection of interface methods to read from and write to a database engine. Those saying SQL is not a programming language often qualify that a programming language must have conditional logic and iterative structures, which don’t exist in SQL.
There’s a third group that are fence sitters. They decline to say whether SQL is a programming language, but they also say individuals who only write SQL aren’t programmers. That’s a bit harsh from my perspective.
Before determining whether SQL is a programming language let’s define a programming language. Let’s define a programming language as a collection of lexical units, or building blocks, that build program units. Lexical units are typically organized as delimiters, identifiers, literals, and comments:
- Delimiters include single or double quotes to identify strings and operators that let you assign and compare values.
- Identifiers are reserved words, keywords, predefined identifiers (like data type names), user-defined variables, subroutines, or types.
- Literals are typically numbers and strings, where some strings qualify as dates because they implement a default format mask to convert strings to dates or date-times.
- Comments are simply delimited text that the program ignores but the programmer uses.
That means a programming language must let you define a variable, assign a value to a variable, iterate across a set of values, and make conditional statements. SQL meets these four conditions, but it does, as a set-programming language, qualify all variables as lists of tuples. Though it is possible to have variables with zero to many elements and one to many members in any given tuple. That means you can assign a literal value to to a one-element list with a single-member tuple, like you would a string or integer to a variable of that type.
As Kris Köhntopp commented, computer science defines a programming language as Turing Complete. As his comment qualifies and the Wikipedia page explains: “Turing completeness in declarative SQL is implemented through recursive common table expressions. Unsurprisingly, procedural extensions to SQL (PLSQL, etc.) are also Turing-complete.” While PostgreSQL introduces recursive query syntax through CTEs, it recently added the search and cycle feature in PostgreSQL 14. The recursive query feature has existed in the Oracle database since Oracle 8, but their documentation calls them hierarchical queries. I wrote a quick a tutorial on hierarchical queries in 2008.
For clarity, define and declare are two words that give grief to some newbies. Let’s qualify what they mean. Declare means to give a variable a name and data type. Define means to declare a variable and assign it a value. Another word for assigning a variable is initializing it. Unassigned variables are automatically assigned a default value or a null dependent on the programming language.
Let’s first declare a local variable, assign it to variable, and display the variable. The following example uses Node.js to define the input variable, assign the input variable to the display variable, and then print the display variable to console. Node.js requires that you assign an empty string to the display variable to define it as a string otherwise its type would be undefined, which is common behavior in dynamically typed languages.
/* Declare the display variable as a string. */ var display = "" /* Define the input variable. */ var input = "Hello World!" /* Assign the input variable contents to the display variable. */ display = input /* Print the display variable contents to console. */ console.log(display) |
It prints:
Hello World! |
Let’s write the same type of program in MySQL. Like the Node.js, there are implementation differences. The biggest difference in MySQL or other relational databases occurs because SQL is a declarative set-based language. That means every variable is a collection of a record structure . You can only mimic a scalar or primitive data type variable by creating a record structure with a single member.
In the case below, there are four processing steps:
- The ‘Hello World!’ literal value is assigned to an input variable.
- The SELECT-list (or comma-delimited set of values in the SELECT clause) is assigned like a tuple to the struct collection variable by treating the query of the literal value as an expression.
- The FROM clause returns the struct collection as the data set or as a derived table.
- The topmost SELECT clause evaluates the struct collection row-by-row, like a loop, and assigns the input member to a display variable.
The query is:
SELECT struct.input AS display FROM (SELECT 'Hello World!' AS input) struct; |
Since the struct collection contains only one element, it displays the original literal value one time, like
+--------------+ | display | +--------------+ | Hello World! | +--------------+ 1 row in set (0.00 sec) |
Let’s update the SQL syntax to the more readable, ANSI 1999 and forward, syntax with a Common Table Expression (CTE). CTEs are implemented by the WITH clause.
WITH struct AS (SELECT 'Hello World!' AS input) SELECT struct.input AS display FROM struct; |
The best thing about CTE values they run one-time and are subsequently available anywhere in your query, subqueries, or correlated subqueries. In short, there’s never an excuse to write a subquery twice in the same query.
Let’s look at loops and if-statements. Having established that we can assign a literal to a variable, re-assign the value from one variable to another, and then display the new variable, let’s assign a set of literal values to an array variable. As before, let’s use Node.js to structure the initial problem.
The program now assigns an array of strings to the input variable, uses a for-loop to read the values from the input array, and uses an if-statement with a regular expression evaluation. The if-statement determines which of the array value meets the condition by using a negating logical expression. That’s because the search() function returns a 0 or greater value when the needle value is found in the string and a -1 when not found. After validating that the needle variable value is found in an input string, the input value is assigned to the display variable.
/* Declare the display variable as a string. */ var display = "" /* Declare a lookup variable. */ var needle = "Goodbye" /* Define the input variable as an array of strings. */ var input = ["Hello World!" ,"Goodbye, Cruel World!" ,"Good morning, too early ..."] /* Read through an array and assign the value that meets * the condition to the display variable. */ for (i = 0; i < input.length; i++) if (!(input[i].search(needle) < 0)) display = input[i] /* Print the display variable contents to console. */ console.log(display) |
Then, it prints the display value:
Goodbye, Cruel World! |
To replicate the coding approach in a query, there must be two CTEs. The needle CTE assigns a literal value of ‘goodbye’ to a one-element collection of a single-member tuple variable. The struct CTE creates a collection of strings by using the UNION ALL operator to append three unique tuples instead of one tuple as found in the early example.
The needle CTE returns a one-element collection of a single-member tuple variable. The struct CTE returns a three-element collection of a single-member tuple, which mimics an array of strings. The needle and struct CTEs return distinct variables with different data types. A cross join operation between the two CTEs puts their results together into the same context. It returns a Cartesian product that:
- Adds a single-row tuples to each row of the query’s result set or derived table.
- Adds a multiple-tuples to each row of the query’s result set or derived table by creating copies of each row (following the Cartesian set theory which multiplies rows and adds columns).
In this case, the Cartesian join adds a one-element needle CTE value to each element, or row, returned by the multiple-element struct CTE and produces the following derived table:
+-----------------------------+---------+ | display | lookup | +-----------------------------+---------+ | Hello World! | goodbye | | Goodbye, cruel world! | goodbye | | Good morning, too early ... | goodbye | +-----------------------------+---------+ 3 rows in set (0.00 sec) |
The following query reads through the CTE collection like a loop and filters out any invalid input values. It uses the MySQL regular expression like function in the WHERE clause, which acts as a conditional or if-statement.
WITH needle AS (SELECT 'goodbye' AS lookup) , struct AS (SELECT 'Hello World!' AS input UNION ALL SELECT 'Goodbye, cruel world!' AS input UNION ALL SELECT 'Good morning, too early ...' AS input) SELECT struct.input AS display FROM struct CROSS JOIN needle WHERE REGEXP_LIKE(struct.input, CONCAT('^.*',needle.lookup,'.*$'),'i'); |
It returns the one display value that meets the criteria:
+-----------------------+ | display | +-----------------------+ | Goodbye, cruel world! | +-----------------------+ 1 row in set (0.00 sec) |
The comparisons of the imperative programming approach in Node.js and declarative programming approach should have established that SQL has all the elements of a programming language. That is, SQL has variable declaration and assignment and both iterative and conditional statements. SQL also has different styles for implementing variable declaration and the examples covered subqueries and CTEs with cross joins placing variables in common scope.
Comparative Approaches:
Next, let’s examine a problem that a programmer might encounter when they think SQL only queries or inserts, updates, or deletes single rows. With that perspective of SQL there’s often a limited perspective on how to write queries. Developers with this skill set level typically write only basic queries, which may include inner and outer joins and some aggregation statements.
Let’s assume the following for this programming assignment:
- A sale table as your data source, and
- A requirement to display the type, number, pre-tax sale amount, and percentage by type.
The sale table definition:
+------------+--------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+--------------+------+-----+---------+----------------+ | sale_id | int unsigned | NO | PRI | NULL | auto_increment | | item_desc | varchar(20) | YES | | NULL | | | unit_price | decimal(8,2) | YES | | NULL | | | serial_no | varchar(10) | YES | | NULL | | +------------+--------------+------+-----+---------+----------------+ |
A basic Node.js program may contain a SQL query that returns the item_desc and unit_price columns while counting the number of serial_no rows and summing the unit_price amounts (that assumes no discount sales, after all its Apple). That type of query leaves calculating the total amount of sales and percentage by type to the Node.js program.
const mysql = require('mysql') const connection = mysql.createConnection({ host: 'localhost', user: 'student', password: 'student', database: 'studentdb' }) connection.connect((err) => { if (err) throw err else { console.log('Connected to MySQL Server!\n') connection.query("SELECT s.item_desc " + ", s.unit_price " + ", COUNT(s.serial_no) AS quantity_sold " + ", SUM(s.unit_price) AS sales " + "FROM sale s " + "GROUP BY s.item_desc " + ", s.unit_price", function (err, result) { if (err) throw err else { // Prints the index value in the RowDataPacket. console.log(result) connection.end() }})} }) |
This program would return a JSON structure, like:
[ RowDataPacket { item_desc: 'MacBook Pro 16', unit_price: 2499, quantity_sold: 16, sales: 39984 }, ... RowDataPacket { item_desc: 'MacBook Air M1', unit_price: 999, quantity_sold: 22, sales: 21978 } ] |
While the remaining JavaScript code isn’t difficult to write, it’s unnecessary effort if the developer knew SQL well enough to program in it. The developer could simply re-write the query like the following and return the percentage by type value in the base JSON structure.
WITH sales AS (SELECT SUM(unit_price) AS total FROM sale) SELECT s.item_desc , s.unit_price , COUNT(s.serial_no) AS quantity_sold , SUM(s.unit_price) AS sales , CONCAT(FORMAT((s.unit_price * COUNT(s.serial_no))/sales.total * 100,2),'%') AS percentage FROM sale s CROSS JOIN sales GROUP BY s.item_desc , s.unit_price , sales.total; |
The query uses the sales CTE to calculate and define a tuple with the total sales and adds a derived column calculating the percentage by type of device. It’s probably important to note that aggregation rules require you add the sales.total CTE tuple to the group by clause.
The new query returns this JSON list:
[ RowDataPacket { item_desc: 'MacBook Pro 16', unit_price: 2499, quantity_sold: 16, sales: 39984, percentage: '17.70%' }, ... RowDataPacket { item_desc: 'MacBook Air M1', unit_price: 999, quantity_sold: 22, sales: 21978, percentage: '9.73%' } ] |
The developer would get a complete JSON list when the new query replaces the old. It also would eliminate the need to write additional JavaScript to calculate the percentage by type of device.
Conclusions:
Leveraging the programming power of SQL is frequently possible in many frontend and backend programming solutions. However, the programming power of SQL is infrequently found in programming solutions. That leaves me to ask: “Is it possible that the almost systemic failure to leverage the programming capabilities of SQL is a result of biases by instructors and mentors to their own limited skill sets?” That likely might be true if their instructors and mentors held the belief that: “No, SQL is not a programming language!”
Candidly, folks that write SQL at the programming level almost always have concurrent mastery in two or more imperative programming languages. They’re probably the ones who say, “SQL is a programming language designed to communicate with relational databases.”
Who are those pesky fence sitters? You remember those, don’t you. They’re the ones who declined to take a position on whether SQL is a programming language. Are they the developers who are still learning, and those without an entrenched, preconceived, or learned bias? Or, do they wonder if SQL is Turing complete?
Node.js MySQL Error
While I blogged about how to setup Node.js and MySQL almost two years ago, it was interesting when a student ran into a problem. The student said they’d configured the environment but were unable to use Node.js to access MySQL.
The error is caused by this import statement:
const mysql = require('mysql') |
The student got the following error, which simply says that they hadn’t installed the Node.js package for MySQL driver.
internal/modules/cjs/loader.js:638 throw err; ^ Error: Cannot find module 'mysql' at Function.Module._resolveFilename (internal/modules/cjs/loader.js:636:15) at Function.Module._load (internal/modules/cjs/loader.js:562:25) at Module.require (internal/modules/cjs/loader.js:692:17) at require (internal/modules/cjs/helpers.js:25:18) at Object.<anonymous> (/home/student/Data/cit325/oracle-s/lib/Oracle12cPLSQLCode/Introduction/query.js:4:15) at Module._compile (internal/modules/cjs/loader.js:778:30) at Object.Module._extensions..js (internal/modules/cjs/loader.js:789:10) at Module.load (internal/modules/cjs/loader.js:653:32) at tryModuleLoad (internal/modules/cjs/loader.js:593:12) at Function.Module._load (internal/modules/cjs/loader.js:585:3) |
I explained they could fix the problem with the following two Node.js Package Manager (NPM) commands:
npm init --y npm install --save mysql |
The student was able to retest the code with success. The issue was simply that the Node.js couldn’t find the NPM MySQL module.
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