Archive for the ‘sql’ Category
PostgreSQL Trigger 1
This entry covers how to write a statement logging trigger for PostgreSQL. It creates two tables: avenger and avenger_log; one avenger_t1 trigger, and a testing INSERT statement.
It was written to help newbies know how and what to return from a function written for a statement-level trigger. They often get stuck on the following when they try to return true. The term non-composite is another way to describe the tuple inserted.
psql:basics_postgres.sql: 59: ERROR: cannot return non-composite value from function returning composite type CONTEXT: PL/pgSQL function write_avenger_t1() line 15 at RETURN |
The avenger table:
/* Conditionally drop table. */ DROP TABLE IF EXISTS avenger; /* Create table. */ CREATE TABLE avenger ( avenger_id SERIAL , avenger_name VARCHAR(30) , first_name VARCHAR(20) , last_name VARCHAR(20)); |
Seed the avenger table:
/* Seed the avenger table with data. */ INSERT INTO avenger ( first_name, last_name, avenger_name ) VALUES ('Anthony', 'Stark', 'Iron Man') ,('Thor', 'Odinson', 'God of Thunder') ,('Steven', 'Rogers', 'Captain America') ,('Bruce', 'Banner', 'Hulk') ,('Clinton', 'Barton', 'Hawkeye') ,('Natasha', 'Romanoff', 'Black Widow') ,('Peter', 'Parker', 'Spiderman') ,('Steven', 'Strange', 'Dr. Strange') ,('Scott', 'Lange', 'Ant-man'); |
The avenger_log table:
/* Conditionally drop table. */ DROP TABLE IF EXISTS avenger_log; /* Create table. */ CREATE TABLE avenger_log ( avenger_log_id SERIAL , trigger_name VARCHAR(30) , trigger_timing VARCHAR(6) , trigger_event VARCHAR(6) , trigger_type VARCHAR(12)); |
The INSERT statement that tests the trigger:
DROP FUNCTION IF EXISTS avenger_t1_function; CREATE FUNCTION avenger_t1_function() RETURNS TRIGGER AS $$ BEGIN /* Insert a row into the avenger_log table. * Also, see PostrgreSQL 39.9 Trigger Procedures. */ INSERT INTO avenger_log ( trigger_name , trigger_timing , trigger_event , trigger_type ) VALUES ( UPPER(TG_NAME) , TG_WHEN , TG_OP , TG_LEVEL ); /* A statement trigger doesn't use a composite type or tuple, * it should simply return an empty composite type or void. */ RETURN NULL; END; $$ LANGUAGE plpgsql; |
The avenger_t1 statement trigger:
CREATE TRIGGER avenger_t1 BEFORE INSERT ON avenger EXECUTE FUNCTION avenger_t1_function(); |
The INSERT statement:
INSERT INTO avenger ( first_name, last_name, avenger_name ) VALUES ('Hope', 'van Dyne', 'Wasp'); |
The results logged to the avenger_log table from a query:
avenger_log_id | trigger_name | trigger_timing | trigger_event | trigger_type
----------------+--------------+----------------+---------------+--------------
1 | AVENGER_T1 | BEFORE | INSERT | STATEMENT
(1 row) |
As always, I hope this helps those looking for a solution.
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.
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.
MySQL Partitioned Tables
MySQL Partitioned Tables
Learning Outcomes
- Learn about List Partitioning.
- Learn about Range Partitioning.
- Learn about Columns Partitioning.
- Learn about Hash Partitioning.
- Learn about Key Partitioning.
- Learn about Subpartitioning.
Lesson Material
MySQL supports partitioning of tables. It supports range, list, hash, and key partitioning. Range partitioning lets you partition based on column values that fall within given ranges. List partitioning lets you partition based on columns matching one of a set of discrete values. Hash partitioning lets you partition based on the return value from a user-defined expression (the result from a stored SQL/PSM function). Key partitioning performs like hash partitioning, but it lets a user select one or more columns from the set of columns in a table; a hash manages the selection process for you. A hash is a method of organizing keys to types of data, and hashes speed access to read and change data in tables.
Each of the following subsections discusses one of the supported forms of partitioning in MySQL. Naturally, there are differences between other databases and MySQL’s implementation.
List Partitioning
A MySQL list partition works by identifying a column that contains an integer value, the franchise_number in the following example. Partitioning clauses follow the list of columns and constraints and require a partitioning key to be in the primary key or indexed.
The following list partition works with literal numeric values. MySQL uses the IN keyword for list partitions. Note that there’s no primary key designated and an index is on the auto-incrementing surrogate key column. A complete example is provided to avoid confusion on how to index the partitioning key:
CREATE TABLE franchise ( franchise_id INT UNSIGNED AUTO_INCREMENT , franchise_number INT UNSIGNED , franchise_name VARCHAR(20) , city VARCHAR(20) , state VARCHAR(20) , index idx (franchise_id)) PARTITION BY LIST(franchise_number) ( PARTITION offshore VALUES IN (49,50) , PARTITION west VALUES IN (34,45,48) , PARTITION desert VALUES IN (46,47) , PARTITION rockies VALUES IN (38,41,42,44)); |
The inclusion of a PRIMARY KEY constraint on the franchise_id column would trigger an ERROR 1503 when the partitioning key isn’t the primary key. The reason for the error message is that a primary key implicitly creates a unique index, and that index would conflict with the partitioning by list instruction. The use of a non-unique idx index on the franchise_id column is required when you want to partition on a non-primary key column.
Range Partitioning
Range partitioning works only with an integer value or an expression that resolves to an integer against the primary key column. The limitation of the integer drives the necessity of choosing an integer column for range partitioning. You can’t define a range-partitioned table with a PRIMARY KEY constraint unless the primary key becomes your partitioning key, like
the one below.
CREATE TABLE ordering ( ordering_id INT UNSIGNED AUTO_INCREMENT , item_id INT UNSIGNED , rental_amount DECIMAL(15,2) , rental_date DATE , index idx (ordering_id)) PARTITION BY RANGE(item_id) ( PARTITION jan2011 VALUES LESS THAN (10000) , PARTITION feb2011 VALUES LESS THAN (20000) , PARTITION mar2011 VALUES LESS THAN (30000)); |
Range partitioning is best suited to large tables that you want to break into smaller pieces based on the integer column. You can also use stored functions that return integers as the partitioning key instead of the numeric literals shown. Few other options are available in MySQL.
Columns Partitioning
Columns partitioning is a new variant of range and list partitioning. It is included in MySQL 5.5 and forward. Both range and list partitioning work on an integer-based column (using TINYINT, SMALLINT, MEDIUMINT, INT [alias INTEGER], and BIGINT). Columns partitioning extends those models by expanding the possible data types for the partitioning column to include CHAR, VARCHAR, BINARY, and VARBINARY string data types, and DATE, DATETIME, or TIMESTAMP data types. You still can’t use other number data types such as DECIMAL and FLOAT. The TIMESTAMP data type is also available only in range partitions with the caveat that you use a UNIX_TIMESTAMP function, according to MySQL Bug 42849.
Hash Partitioning
Hash partitions ensure an even distribution of rows across a predetermined number of partitions. It is probably the easiest way to partition a table quickly to test the result of partitioning on a large table. You should base hash partitions on a surrogate or natural primary key.
The following provides a modified example of the ordering table:
CREATE TABLE ordering ( ordering_id INT UNSIGNED PRIMARY KEY AUTO_INCREMENT , item_id INT UNSIGNED , rental_amount DECIMAL(15,2) , rental_date DATE) PARTITION BY HASH(ordering_id) PARTITIONS 8; |
This is the partitioning type that benefits from a PRIMARY KEY constraint because it automatically creates a unique index that can be used by the hash. A non-unique index such as the list partitioning example doesn’t work for a hash partition.
Key Partitioning
Key partitioning is valuable because you can partition on columns that aren’t integers. It performs along the line of hash partitioning, except the MySQL Server uses its own hashing expression.
CREATE TABLE orders_list ( order_list_id INT UNSIGNED AUTO_INCREMENT , customer_surname VARCHAR(30) , store_id INT UNSIGNED , salesperson_id INT UNSIGNED , order_date DATE , index idx (order_list_id)) PARTITION BY KEY (order_date) PARTITIONS 8; |
This is the only alternative when you want to partition by date ranges. Like the hash partition, it’s easy to deploy. The only consideration is the number of slices that you want to make of the data in the table.
Subpartitioning
The concept of subpartitioning is also known as composite partitioning. You can subpartition range or list partitions with a hash, linear hash, or linear key.
A slight change to the previously created ordering table is required to demonstrate composite partitioning: we’ll add a store_id column to the table definition. The following is an example of a range partition subpartitioned by a hash:
CREATE TABLE ordering INT UNSIGNED INT UNSIGNED DATE ( ordering_id INT UNSIGNED AUTO_INCREMENT , item_id INT UNSIGNED , store_id INT UNSIGNED , rental_amount DECIMAL(15,2) , rental_date DATE , index idx (ordering_id)) PARTITION BY RANGE(item_id) SUBPARTITION BY HASH(store_id) SUBPARTITIONS 4 ( PARTITION jan2011 VALUES LESS THAN (10000) , PARTITION feb2011 VALUES LESS THAN (20000) , PARTITION mar2011 VALUES LESS THAN (30000)); |
Composite partitioning is non-trivial and might require some experimentation to achieve optimal results. Plan on making a few tests of different scenarios before you deploy a solution.
Oracle Partitioned Tables
Oracle Partitioned Tables
Learning Outcomes
- Learn about List Partitioning.
- Learn about Range Partitioning.
- Learn about Hash Partitioning.
- Learn about Composite Partitioning.
Lesson Material
Partitioning is the process of breaking up a data source into a series of data sources. Partitioned tables are faster to access and transact against. Partitioning data becomes necessary as the amount of data grows in any table. It speeds the search to find rows and insert, update, or delete rows.
Oracle Database 21c supports four types of table partitioning: list, range, hash, and composite partitioning.
List Partitioning
A list partition works by identifying a column that contains a value, such as a STATE column in an ADDRESS table. Partitioning clauses follow the list of columns and constraints.
A list partition could use a STATE column, like the following (the complete example is avoided to conserve space, and the three dots represent the balance of partitions not shown):
CREATE TABLE franchise ( franchise_id NUMBER CONSTRAINT pk_franchise PRIMARY KEY , franchise_name VARCHAR(20) , city VARCHAR(20) , state VARCHAR(20)) PARTITION BY LIST(state) ( PARTITION offshore VALUES('Alaska', 'Hawaii') , PARTITION west VALUES('California', 'Oregon', 'Washington') , PARTITION desert VALUES ('Arizona','New Mexico') , PARTITION rockies VALUES ('Colorado', 'Idaho', 'Montana', 'Wyoming') , ... ); |
This can be used with other values such as ZIP codes with great effect, but the maintenance of list partitioning can be considered costly. Cost occurs when the list of values changes over time. Infrequent change means low cost, while frequent change means high costs. In the latter case, you should consider other partitioning strategies. Although an Oracle database supports partitioning on a variable-length string, MySQL performs list partitioning only on integer columns.
Range Partitioning
Range partitioning is very helpful on any column that contains a continuous metric, such as dates or time. It works by stating a minimum set that is less than a certain value, and then a group of sets of higher values until you reach the top most set of values. This type of partition helps you improve performance by letting you search ranges rather than complete data sets. Range partitioning is also available in MySQL.
A range example based on dates could look like this:
PARTITION BY RANGE(rental_date) ( PARTITION rental_jan2011 VALUES LESS THAN TO_DATE('31-JAN-11','DD-MON-YY') , PARTITION rental_feb2011 VALUES LESS THAN TO_DATE('28-FEB-11','DD-MON-YY') , PARTITION rental_mar2011 VALUES LESS THAN TO_DATE('31-MAR-11','DD-MON-YY') , ... ); |
The problem with this type of partitioning, however, is that the new months require constant management. Many North American businesses simply add partitions for all months in the year as an annual maintenance task during the holidays in November or December. Companies that opt for bigger range increments reap search and access benefits from range partitioning, while minimizing ongoing maintenance expenses.
Hash Partitioning
Hash partitioning is much easier to implement than list or range partitioning. Many DBAs favor it because it avoids the manual maintenance of list and range partitioning. Oracle Database 21c documentation recommends that you implement a hash for the following reasons:
- There is no concrete knowledge about how much data maps to a partitioning range.
- The sizes of partitions are unknown at the outset and difficult to balance as data is added to the database.
- A range partition might cluster data in an ineffective way.
This next statement creates eight partitions and stores them respectively in one of the eight tablespaces. The hash partition manages nodes and attempts to balance the distribution of rows across the nodes.
PARTITION BY HASH(store) PARTITIONS 8 STORE IN (tablespace1, tablespace2, tablespace3, tablespace4 ,tablespace5, tablespace6, tablespace7, tablespace8); |
As you can imagine the maintenance for this type of partitioning is low. Some DBAs choose this method to get an initial sizing before adopting a list or range partitioning plan. Maximizing the physical resources of the machine ultimately rests with the DBAs who manage the system. Developers need to stand ready to assist DBAs with analysis and syntax support.
Composite Partitioning
Composite partitioning requires a partition and subpartition. The composites are combinations of two types of partitioning—typically, list and range partitioning, or range and hash composite partitioning. Which of these you should choose depends on a few considerations. List and range composite partitioning is done for historical information and is well suited for data warehouses. This method lets you partition on unordered or unrelated column values.
A composite partition like this uses the range as the partition and the list as the subpartition, like the following:
PARTITION BY RANGE (rental_date) SUBPARTITION BY LIST (state) (PARTITION FQ1_1999 VALUES LESS THAN (TO_DATE('1-APR-2011','DD-MON-YYYY')) (SUBPARTITION offshore VALUES('Alaska', 'Hawaii') , SUBPARTITION west VALUES('California', 'Oregon', 'Washington') , SUBPARTITION desert VALUES ('Arizona','New Mexico') , SUBPARTITION rockies VALUES ('Colorado', 'Idaho', 'Montana', 'Wyoming') , ... ) ,(PARTITION FQ2_1999 VALUES LESS THAN (TO_DATE('1-APR-2011','DD-MON-YYYY')) (SUBPARTITION offshore VALUES('Alaska', 'Hawaii') , SUBPARTITION west VALUES('California', 'Oregon', 'Washington') , SUBPARTITION desert VALUES ('Arizona','New Mexico') , SUBPARTITION rockies VALUES ('Colorado', 'Idaho', 'Montana', 'Wyoming') , ... ) , ... ) |
Range and hash composite partitioning is done for historical information when you also need to stripe data. Striping is the process of creating an attribute in a table that acts as a natural subtype or separator of data. Users typically view data sets of one subtype, which means organizing the data by stripes (subtypes) can speed access based on user access patterns.
Range is typically the partition and the hash is the subpartition in this composite partitioning schema. The syntax for this type of partition is shown next:
PARTITION BY RANGE (rental_date) SUBPARTITION BY HASH(store) SUBPARTITIONS 8 STORE IN (tablespace1, tablespace2, tablespace3 ,tablespace4, tablespace5, tablespace6 ,tablespace7, tablespace8) ( PARTITION rental_jan2011 VALUES LESS THAN TO_DATE('31-JAN-11','DD-MON-YY') , PARTITION rental_feb2011 VALUES LESS THAN TO_DATE('28-FEB-11','DD-MON-YY') , PARTITION rental_mar2011 VALUES LESS THAN TO_DATE('31-MAR-11','DD-MON-YY') , ... ) |
PL/SQL Table Function
An Oracle example was requested as a comparison against the quick tutorial I wrote on how to do this in PostgreSQL’s PL/pgSQL. Unfortunately, there are many more moving parts to deliver this type of solution in Oracle’s PL/SQL.
The functions is same and simple. It returns the list of conquistadors that were originally German. It does that by filtering on the lang column in the table. For example, you use ‘de‘ for German. The additional moving parts are the required User-Defined Types (UDTs); one is a record structure and the other is a list (or Oracle parlance table).
The drops are unconditional and as such will trigger errors the first time they’re run but including PL/SQL blocks to make them conditional would have made the code much larger. It’s already larger because Oracle doesn’t support comma-delimited lists in the VALUES clause.
I’ll stage this with the same conquistador table used in the last post. Then, connect to the psql shell and run the following script file:
/* Drop the conquistador table. */ DROP TABLE conquistador; /* Create the conquistador table. */ CREATE TABLE conquistador ( conquistador_id NUMBER , conquistador VARCHAR(30) , actual_name VARCHAR(30) , nationality VARCHAR(30) , lang VARCHAR(2)); /* Drop the conquistador sequence. */ DROP SEQUENCE conquistador_seq; /* Create the conquistador_seq with a 1001 start value. */ CREATE SEQUENCE conquistador_seq START WITH 1001; /* Insert 9 rows into the table. */ INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Juan de Fuca','Ioánnis Fokás','Greek','el'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Nicolás de Federmán','Nikolaus Federmann','German','de'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Sebastián Caboto','Sebastiano Caboto','Venetian','it'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Jorge de la Espira','Georg von Speyer','German','de'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Eusebio Francisco Kino','Eusebius Franz Kühn','Italian','it'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Wenceslao Linck','Wenceslaus Linck','Bohemian','cs'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Fernando Consag','Ferdinand Konšcak','Croatian','sr'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Américo Vespucio','Amerigo Vespucci','Italian','it'); INSERT INTO conquistador ( conquistador_id, conquistador, actual_name, nationality, lang ) VALUES (conquistador_seq.NEXTVAL,'Alejo García','Aleixo Garcia','Portuguese','pt'); |
While unnecessary in PL/pgSQL, you must create User-Defined Types (UDTs) to write a table function. You must also create a local procedure to avoid allocating memory before assigning values to the list. These are the UDTs required:
/* Drop the dependency before the dependent type. */ DROP TYPE conquistador_table; DROP TYPE conquistador_struct; /* Create the UDT for a record structure accessible in SQL. */ CREATE OR REPLACE TYPE conquistador_struct IS OBJECT ( conquistador VARCHAR(30) , actual_name VARCHAR(30) , nationality VARCHAR(30)); / /* Create a list of the UDT. */ CREATE OR REPLACE TYPE conquistador_table IS TABLE OF conquistador_struct; / |
Drop any existing function or procedure of the same name before you try to build it. Oracle’s OR REPLACE fails when you try to use it for a function when there is already a procedure using the same name, and vice versa.
/* Drop the function to avoid any conflict with a procedure of the same name. */ DROP FUNCTION getConquistador; |
Now, you can build another script file to create the getConquistador function, like:
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 | /* Create the function. */ CREATE OR REPLACE FUNCTION getConquistador (pv_lang IN VARCHAR) RETURN conquistador_table IS /* Declare a return variable. */ lv_retval CONQUISTADOR_TABLE := conquistador_table(); /* Declare a dynamic cursor. */ CURSOR get_conquistador ( cv_lang VARCHAR2 ) IS SELECT c.conquistador , c.actual_name , c.nationality FROM conquistador c WHERE c.lang = cv_lang; /* Local procedure to add to the song. */ PROCEDURE ADD ( pv_input CONQUISTADOR_STRUCT ) IS BEGIN lv_retval.EXTEND; lv_retval(lv_retval.COUNT) := pv_input; END ADD; BEGIN /* Read through the cursor and assign to the UDT table. */ FOR i IN get_conquistador(pv_lang) LOOP add(conquistador_struct( i.conquistador , i.actual_name , i.nationality )); END LOOP; /* Return collection. */ RETURN lv_retval; END; / |
While there is some white space for readability, the Oracle version is basically twice as long as the PL/pgSQL version. It also requires you to add UDTs to the data dictionary to make it work. PL/pgSQL actually doesn’t let you add references to type definitions and requires you use enumerated descriptions with column definitions.
Then, you can test it with the following syntax. The TABLE function is required to convert the list to a SQL consumable result set:
COL conquistador FORMAT A21 COL actual_name FORMAT A21 COL nationality FORMAT A12 SELECT * FROM TABLE(getConquistador('de')); |
It will return the following:
CONQUISTADOR ACTUAL_NAME NATIONALITY --------------------- --------------------- ------------ Nicolás de Federmán Nikolaus Federmann German Jorge de la Espira Georg von Speyer German 2 rows selected. |
As always, I hope this helps with a technique that’s useful.
PostgreSQL Unicode
It seems unavoidable to use Windows. Each time I’m compelled to run tests on the platform I find new errors. For example, they don’t use 4-byte unicode and as a result when you want to use Unicode in PostgreSQL there’s a mismatch.
For example, change the Active Console Code Page with the chcp (change code page) to match the one PostgreSQL uses, like:
chip 1252 |
It lets you avoid this warning message:
Password for user postgres:
psql (14.1)
WARNING: Console code page (437) differs from Windows code page (1252)
8-bit characters might not work correctly. See psql reference
page "Notes for Windows users" for details.
Type "help" for help.
postgres=# |
However, it won’t avoid display issues with real Unicode values. For example, let’s use a small international table like the following:
/* Conditionally drop the conquistador table. */ DROP TABLE IF EXISTS conquistador; /* Create the conquistador table. */ CREATE TABLE conquistador ( conquistador_id SERIAL , conquistador VARCHAR(30) , actual_name VARCHAR(30) , nationality VARCHAR(30) , lang VARCHAR(2)); /* Insert some conquistadors into the table. */ INSERT INTO conquistador ( conquistador , actual_name , nationality , lang ) VALUES ('Juan de Fuca','Ioánnis Fokás','Greek','el') ,('Nicolás de Federmán','Nikolaus Federmann','German','de') ,('Sebastián Caboto','Sebastiano Caboto','Venetian','it') ,('Jorge de la Espira','Georg von Speyer','German','de') ,('Eusebio Francisco Kino','Eusebius Franz Kühn','Italian','it') ,('Wenceslao Linck','Wenceslaus Linck','Bohemian','cs') ,('Fernando Consag','Ferdinand Konšcak','Croatian','sr') ,('Américo Vespucio','Amerigo Vespucci','Italian','it') ,('Alejo García','Aleixo Garcia','Portuguese','pt'); /* Query the values from the conquistador table. */ SELECT * FROM conquistador; |
When you call the script to load it, like:
\i testScript.sql |
It’ll display the following, which you can check against the strings in the VALUES clause above. There are encoding issues on lines 1, 2, 3, 5, 7, and 8 below.
conquistador_id | conquistador | actual_name | nationality | lang
-----------------+------------------------+----------------------+-------------+------
1 | Juan de Fuca | Ioánnis Fokás | Greek | el
2 | Nicolás de Federmán | Nikolaus Federmann | German | de
3 | Sebastián Caboto | Sebastiano Caboto | Venetian | it
4 | Jorge de la Espira | Georg von Speyer | German | de
5 | Eusebio Francisco Kino | Eusebius Franz Kühn | Italian | it
6 | Wenceslao Linck | Wenceslaus Linck | Bohemian | cs
7 | Fernando Consag | Ferdinand Konšcak | Croatian | sr
8 | Américo Vespucio | Amerigo Vespucci | Italian | it
9 | Alejo GarcÃa | Aleixo Garcia | Portuguese | pt
(9 rows) |
If you’re like me, it was annoying. The problem is that the native 2-byte Unicode of Microsoft sends values into PostgreSQL that are invalid. Those codes are read back with unintended values from other character encoding sets.
While you can’t set Windows generic encoding to 65001 without causing the system problems, you can set Active Console Code Page value in the scope of a Command-Line session before running the script.
The chcp command lets you set it to 4-byte Unicode, like:
chcp 65001 |
Now, rerun the script and PostgreSQL will display the correct character encoding set with some spacing irregularities. However, that’s not what’s important when you call table from another programming language through the ODBC-layer. The data will be returned in a 4-byte Unicode encoding stream.
conquistador_id | conquistador | actual_name | nationality | lang
-----------------+------------------------+----------------------+-------------+------
1 | Juan de Fuca | Ioánnis Fokás | Greek | el
2 | Nicolás de Federmán | Nikolaus Federmann | German | de
3 | Sebastián Caboto | Sebastiano Caboto | Venetian | it
4 | Jorge de la Espira | Georg von Speyer | German | de
5 | Eusebio Francisco Kino | Eusebius Franz Kühn | Italian | it
6 | Wenceslao Linck | Wenceslaus Linck | Bohemian | cs
7 | Fernando Consag | Ferdinand Konšcak | Croatian | sr
8 | Américo Vespucio | Amerigo Vespucci | Italian | it
9 | Alejo García | Aleixo Garcia | Portuguese | pt
(9 rows) |
A similar error to what I encountered testing MySQL Workbench’s ability to export SQL Server databases 10 years ago. I thought giving a solution to get coerce correct 4-byte Unicode data insertion may help those who also may be surprised by the behavior.
PL/SQL List Function
Students wanted to see how to write PL/SQL functions that accept, process, and return lists of values. I thought it would be cool to also demonstrate coupling of loop behaviors and wrote the example using the 12-Days of Christmas lyrics.
The twelve_days function accepts two different collections. One is an Attribute Data Type (ADT) and the other a User-Defined Type (UDT). An ADT is based on a scalar data type, and a UDT is based on an object type. Object types are basically data structures, and they support both positional and named notation for variable assignments.
The twelve_days function returns a list of string, which is an ADT of the VARCHAR2 data type. Creating the ADT types is easy and a single step, like:
/* Create a days object type. */ CREATE OR REPLACE TYPE days IS TABLE OF VARCHAR2(8); / /* Create a string object type. */ CREATE OR REPLACE TYPE song IS TABLE OF VARCHAR2(36); / |
Creating the UDT is more complex and requires two steps. You need to create the UDT object type, or structure, and then the list based on the UDT object type, like:
/* Create a lyric object type. */ CREATE OR REPLACE TYPE lyric IS OBJECT ( DAY VARCHAR2(8) , gift VARCHAR2(24)); / /* Create a lyrics object type. */ CREATE OR REPLACE TYPE lyrics IS TABLE OF LYRIC; / |
Now, you can create the twelve_days function that uses these ADT and UDT types, like:
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 39 | CREATE OR REPLACE FUNCTION twelve_days ( pv_days DAYS , pv_gifts LYRICS ) RETURN song IS /* Initialize the collection of lyrics. */ lv_retval SONG := song(); /* Local procedure to add to the song. */ PROCEDURE ADD ( pv_input VARCHAR2 ) IS BEGIN lv_retval.EXTEND; lv_retval(lv_retval.COUNT) := pv_input; END ADD; BEGIN /* Read forward through the days. */ FOR i IN 1..pv_days.COUNT LOOP ADD('On the ' || pv_days(i) || ' day of Christmas'); ADD('my true love sent to me:'); /* Read backward through the lyrics based on the ascending value of the day. */ FOR j IN REVERSE 1..i LOOP IF i = 1 THEN ADD('-'||'A'||' '||pv_gifts(j).gift); ELSE ADD('-'||pv_gifts(j).DAY||' '||pv_gifts(j).gift); END IF; END LOOP; /* A line break by verse. */ ADD(CHR(13)); END LOOP; /* Return the song's lyrics. */ RETURN lv_retval; END; / |
You may notice the local add procedure on lines 10 thru 15. It lets you perform the two tasks required for populating an element in a SQL object type list in one line in the main body of the twelve_days function.
The add procedure first uses the EXTEND function to allocate space before assigning the input value to the newly allocated element in the list. Next, you can call the function inside the following SQL query:
SELECT column_value AS "12-Days of Christmas" FROM TABLE(twelve_days(days('first','second','third','fourth' ,'fifth','sixth','seventh','eighth' ,'nineth','tenth','eleventh','twelfth') ,lyrics(lyric(DAY => 'and a', gift => 'Partridge in a pear tree') ,lyric(DAY => 'Two', gift => 'Turtle doves') ,lyric(DAY => 'Three', gift => 'French hens') ,lyric(DAY => 'Four', gift => 'Calling birds') ,lyric(DAY => 'Five', gift => 'Golden rings' ) ,lyric(DAY => 'Six', gift => 'Geese a laying') ,lyric(DAY => 'Seven', gift => 'Swans a swimming') ,lyric(DAY => 'Eight', gift => 'Maids a milking') ,lyric(DAY => 'Nine', gift => 'Ladies dancing') ,lyric(DAY => 'Ten', gift => 'Lords a leaping') ,lyric(DAY => 'Eleven',gift => 'Pipers piping') ,lyric(DAY => 'Twelve',gift => 'Drummers drumming')))); |
It will print:
12-Days of Christmas ------------------------------------ On the first day of Christmas my true love sent to me: -A Partridge in a pear tree On the second day of Christmas my true love sent to me: -Two Turtle doves -and a Partridge in a pear tree On the third day of Christmas my true love sent to me: -Three French hens -Two Turtle doves -and a Partridge in a pear tree ... redacted for space ... On the twelfth day of Christmas my true love sent to me: -Twelve Drummers drumming -Eleven Pipers piping -Ten Lords a leaping -Nine Ladies dancing -Eight Maids a milking -Seven Swans a swimming -Six Geese a laying -Five Golden rings -Four Calling birds -Three French hens -Two Turtle doves -and a Partridge in a pear tree |
As always, I hope the example helps those looking for a solution to this type of problem.
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