2017 P H P W O R L D

MySQL 8 .0: PreviewWhat is coming?

Gabriela D. Ferrara@gabidavila

http://gabriela.io

What We Will Cover• Character Set

• Data Dictionary & Atomic DDL

• InnoDB Performance

• CTE - Common Table Expressions

• Window Functions

• JSON Improvements

• Roles

Quick Disclaimer: 8.0.3 RC1

I Am Gabi!• over 30,000 Lego bricks

• Data Engineer / Software Engineer focused on Data

• Blogger wannabe at http://gabriela.io

• Extroverted introvert

• yes, that is a thing!

What Happened to 6.0 and 7.0?

Character Set

New Default Charset• Default:

5.7: latin1 8.0: utf8mb4

• Allows

• ➕ Mathematical Equations 𝑒=𝑚·𝑐²

• 😁🙄$

• & more SMP (Supplementary Multilingual Plane) Characters

New Default Collation• utf8mb4_0900_ai_ci

• UTF-8 9.0 support

• Accent Insensitive

• Case Insensitive

• No more 🍣 = 🍺 bug

• Caused by utf8mb4_general_ci or utf8mb4_unicode_ci

More information on how collations behave here.

Data Dictionary & Atomic DDL

Data Dictionary• No more meta-data on MyIsam tables into the mysql schema

• No more meta-data on files: .FRM, .PAR, .TRG, .TRN

• System tables still exists, but in InnoDB tables

• information_schema:

• implemented as views over Data Dictionary

• uses the optimizer to read the meta-data from the Data Dictionary

• up to 100x faster

Atomic DDL• Either all DDL works or no change is made

• DDL Executed as an internal InnoDB Transactions

• No more crashes when executing DDL (DROP TABLES, TRUNCATE TABLE)

InnoDB Performance

Transaction Scheduling• RDBMS generally uses FIFO System

Transaction A

Transaction B

Transaction C

Start

End

CATS First Database in the World To Implement

Contention-Aware Transaction Scheduling

CATS - TPS

0

5500

11000

16500

22000

32 64 128 256 512

FIFO CATS

Data

ext

ract

ed fr

om M

ySQ

LSer

verT

eam

web

site.

Transactions per Second x # Clients Higher is better

CATS - Latency

0

45

90

135

180

32 64 128 256 512

FIFO CATS

Mean Latency x # Clients Lower is better

Data

ext

ract

ed fr

om M

ySQ

LSer

verT

eam

web

site.

Indexes

Invisible Indexes• Not used by the optimizer

• Visible by default, to create an invisible index:

• Toggle visibility

ALTER TABLE orders ALTER INDEX ix_total INVISIBLE; ALTER TABLE orders ALTER INDEX ix_total VISIBLE;

ALTER TABLE orders ADD INDEX ix_total (total) INVISIBLE;

Invisible IndexesSELECT id, total FROM orders WHERE total BETWEEN 100 AND 1000 ORDER BY total DESC LIMIT 10

Visible

Query cost: 1885.58

Invisible

Query cost: 2158.90

Descending Indexes• Up to 5.7:

An index_col_name specification can end with ASC or DESC. These keywords are permitted for future extensions for specifying ascending or descending index value storage. Currently, they are parsed but ignored; index values are always stored in ascending order.

MySQL 8.0 HAS IT!

Descending Indexes• No longer ignored (and forcibly created as ASC) ALTER TABLE orders ADD INDEX ix_orders_created_at (created_at DESC);

Descending Indexes• No longer ignored (and forcibly created as ASC)

• Can be scanned backwards:

ALTER TABLE orders ADD INDEX ix_orders_created_at (created_at DESC);

SELECT * FROM orders WHERE created_at BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 23:59:59' ORDER BY created_at DESC LIMIT 100;

-- OR WITH THE SAME COST

SELECT * FROM orders WHERE created_at BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 23:59:59' ORDER BY created_at LIMIT 100;

Descending Indexes• No longer ignored (and forcibly created as ASC)

• Can be scanned backwards:

ALTER TABLE orders ADD INDEX ix_orders_created_at (created_at DESC);

SELECT * FROM orders WHERE created_at BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 23:59:59' ORDER BY created_at DESC LIMIT 100;

-- OR WITH THE SAME COST

SELECT * FROM orders WHERE created_at BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 23:59:59' ORDER BY created_at LIMIT 100;

mysql> EXPLAIN SELECT * FROM orders WHERE created_at BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 23:59:59' ORDER BY created_at DESC LIMIT 100 \G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: orders partitions: NULL type: range possible_keys: ix_orders_created_at key: ix_orders_created_at key_len: 4 ref: NULL rows: 10201 filtered: 100.00 Extra: Using index condition 1 row in set, 1 warning (0.00 sec)

mysql> EXPLAIN SELECT * FROM orders WHERE created_at BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 23:59:59' ORDER BY created_at LIMIT 100 \G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: orders partitions: NULL type: range possible_keys: ix_orders_created_at key: ix_orders_created_at key_len: 4 ref: NULL rows: 10201 filtered: 100.00 Extra: Using index condition; Backward index scan 1 row in set, 1 warning (0.00 sec)

More information about EXPLAIN

Exercise

Proposed Problem• Given the below table daily_show_guests, show per year the most frequent occupations of

the guests

CREATE TABLE `daily_show_guests` ( `id` bigint(20) NOT NULL AUTO_INCREMENT, `year` int(11) DEFAULT NULL, `occupation` varchar(255) DEFAULT NULL, `guest` varchar(255) DEFAULT NULL, PRIMARY KEY (`id`), KEY `ix_year_occupation` (`year`,`occupation`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4

Step 1: Count Appearances

[42000][1055] Expression #1 of SELECT list is not in GROUP BY clause and contains nonaggregated column 'phpworld.daily_show_guests.id' which is not functionally dependent on columns in GROUP BY clause;

this is incompatible with sql_mode=only_full_group_by

SELECT *, COUNT(*) AS appearances FROM daily_show_guests GROUP BY year, occupation;

More information about only_full_group_by

Step 1: Count AppearancesSELECT year, occupation, COUNT(*) AS appearances FROM daily_show_guests GROUP BY year, occupation;

+------+-------------------------------+-------------+ | year | occupation | appearances | +------+-------------------------------+-------------+ | 2001 | Diplomat | 1 | | 1999 | singer | 4 | | 2004 | diplomat | 2 | | 2004 | social activist | 1 | | 2014 | former us senator | 1 | | 2011 | American football quarterback | 1 | | 2003 | Film actor | 1 | | 2001 | Rock duo | 1 | | 2004 | Baseball player | 1 | | 2013 | Executive | 1 | +------+-------------------------------+-------------+

10 rows in set (0.01 sec)

Click on the image or here to see animation

Step 2: Get the max. appearancesSELECT year, MAX(dsg.appearances) AS total FROM (SELECT year, occupation, COUNT(*) AS appearances FROM daily_show_guests GROUP BY year, occupation ) AS dsg GROUP BY year

+------+-------+ | year | total | +------+-------+ | 1999 | 53 | | 2000 | 61 | | 2001 | 62 | | 2002 | 64 | | 2003 | 47 | | 2004 | 31 | | 2005 | 25 | | 2006 | 33 | | 2007 | 15 | | 2008 | 28 | | 2009 | 22 | | 2010 | 30 | | 2011 | 29 | | 2012 | 20 | | 2013 | 37 | | 2014 | 31 | | 2015 | 19 | +------+-------+

17 rows in set (0.60 sec)

Step 3: …SELECT b.year, b.occupation, c.total FROM daily_show_guests b INNER JOIN (SELECT year, MAX(dsg.appearances) AS total FROM (SELECT year, occupation, COUNT(*) AS appearances FROM daily_show_guests GROUP BY year, occupation ) AS dsg GROUP BY year) AS c ON c.year = b.year GROUP BY year, occupation HAVING c.total = COUNT(*);

+------+------------+-------+ | year | occupation | total | +------+------------+-------+ | 1999 | actor | 53 | | 2000 | actor | 61 | | 2001 | actor | 62 | | 2002 | actor | 64 | | 2003 | actor | 47 | | 2004 | actor | 31 | | 2005 | actor | 25 | | 2006 | actor | 33 | | 2007 | actor | 15 | | 2008 | journalist | 28 | | 2009 | journalist | 22 | | 2010 | actor | 30 | | 2011 | actor | 29 | | 2012 | actor | 20 | | 2013 | actor | 37 | | 2014 | actor | 31 | | 2015 | actor | 19 | +------+------------+-------+ 17 rows in set (0.47 sec)

Step 3: WTH?

CTE- Common Table Expressions

CTE• Similar to CREATE [TEMPORARY] TABLE

• Doesn’t need CREATE privilege

• Can reference other CTEs

• Can be recursive

• Easier to read

Solving the Same ProblemWITH appearances_occupation_count AS ( SELECT year, occupation, COUNT(*) AS total FROM daily_show_guests GROUP BY year, occupation ), SELECT * FROM appearances_occupation_count;

+------+-----------------------------+-------+ | year | occupation | total | +------+-----------------------------+-------+ | 2011 | actress | 10 | | 2013 | television host | 3 | | 2012 | former hhs secretary | 1 | | 2010 | musician | 2 | | 2012 | Musician | 1 | | 2006 | former governor of missouri | 1 | | 2014 | Writer | 4 | | 2005 | radio host | 1 | | 2003 | Film actress | 1 | | 2006 | political expert | 1 | +------+-----------------------------+-------+

10 rows in set (0.01 sec)

Similar to Step 1

Solving the Same ProblemWITH appearances_occupation_count AS ( SELECT year, occupation, COUNT(*) AS total FROM daily_show_guests GROUP BY year, occupation ), years_count AS ( SELECT year, max(total) AS maximum FROM appearances_occupation_count GROUP BY year ) SELECT * FROM years_count;

+------+---------+ | year | maximum | +------+---------+ | 1999 | 53 | | 2000 | 61 | | 2001 | 62 | | 2002 | 64 | | 2003 | 47 | +------+---------+

5 rows in set (0.01 sec)

Similar to Step 2

• References other CTE

Solving the Same ProblemWITH appearances_occupation_count AS ( SELECT year, occupation, COUNT(*) AS total FROM daily_show_guests GROUP BY year, occupation ), years_count AS ( SELECT year, max(total) AS maximum FROM appearances_occupation_count GROUP BY year ) SELECT aoc.* FROM appearances_occupation_count aoc INNER JOIN years_count yc ON aoc.year = yc.year AND yc.maximum = aoc.total;

+------+------------+-------+ | year | occupation | total | +------+------------+-------+ | 1999 | actor | 53 | | 2000 | actor | 61 | | 2001 | actor | 62 | | 2002 | actor | 64 | | 2003 | actor | 47 | | 2004 | actor | 31 | | 2005 | actor | 25 | | 2006 | actor | 33 | | 2007 | actor | 15 | | 2008 | journalist | 28 | | 2009 | journalist | 22 | | 2010 | actor | 30 | | 2011 | actor | 29 | | 2012 | actor | 20 | | 2013 | actor | 37 | | 2014 | actor | 31 | | 2015 | actor | 19 | +------+------------+-------+

17 rows in set (0.01 sec)

Similar to Step 3

Solving the Same ProblemWITH appearances_occupation_count AS ( SELECT year, occupation, COUNT(*) AS total FROM daily_show_guests GROUP BY year, occupation ), years_count AS ( SELECT year, max(total) AS maximum FROM appearances_occupation_count GROUP BY year ) SELECT aoc.* FROM appearances_occupation_count aoc INNER JOIN years_count yc ON aoc.year = yc.year AND yc.maximum = aoc.total;

+------+------------+-------+ | year | occupation | total | +------+------------+-------+ | 1999 | actor | 53 | | 2000 | actor | 61 | | 2001 | actor | 62 | | 2002 | actor | 64 | | 2003 | actor | 47 | | 2004 | actor | 31 | | 2005 | actor | 25 | | 2006 | actor | 33 | | 2007 | actor | 15 | | 2008 | journalist | 28 | | 2009 | journalist | 22 | | 2010 | actor | 30 | | 2011 | actor | 29 | | 2012 | actor | 20 | | 2013 | actor | 37 | | 2014 | actor | 31 | | 2015 | actor | 19 | +------+------------+-------+

17 rows in set (0.01 sec)

Similar to Step 3

Subquery• Full query cost: 9708.81

• Subquery lookup: 9436.01

• Execution Time: ≅25ms

• 3 GROUP BY

• 2 Full Index scans

CTE• Full query cost: 9741.47

• Subquery lookup: 9436.01

• Execution Time: ≅25ms

• 2 GROUP BY

• 1 Full Index scans

• 1 Full table scan*

Recursive CTE• Base query comes first

• Second query comes after an UNION statement

• The stop condition should be on the recursive query part

FibonacciWITH RECURSIVE fibonacci(recursion_level, fibonacci_number, next_number) AS ( # Base Case SELECT 0 AS recursion_level, 0 AS fibonacci_number, 1 AS next_number UNION ALL # Recursion query SELECT recursion_level + 1 AS recursion_level, next_number AS fibonacci_number, fibonacci_number + next_number AS next_number FROM fibonacci # Stopping condition WHERE recursion_level < 10 )

SELECT * FROM fibonacci;

+-----------------+------------------+-------------+ | recursion_level | fibonacci_number | next_number | +-----------------+------------------+-------------+ | 0 | 0 | 1 | | 1 | 1 | 1 | | 2 | 1 | 2 | | 3 | 2 | 3 | | 4 | 3 | 5 | | 5 | 5 | 8 | | 6 | 8 | 13 | | 7 | 13 | 21 | | 8 | 21 | 34 | | 9 | 34 | 55 | | 10 | 55 | 89 | +-----------------+------------------+-------------+

11 rows in set (0.00 sec)

FibonacciWITH RECURSIVE fibonacci(fibonacci_number, next_number) AS ( # Base Condition SELECT 0 AS fibonacci_number, 1 AS next_number UNION ALL # Recursion Query SELECT next_number AS fibonacci_number, fibonacci_number + next_number AS next_number FROM fibonacci )

SELECT * FROM fibonacci LIMIT 10;

[22001][1690] Data truncation: BIGINT value is out of range in '(`fibonacci`.`fibonacci_number` + `fibonacci`.`next_number`)' Details

Window Functions

What They Do?• Allows to analyze a row aggregations

• Can behave like a GROUP BY without changing the result

• Allows you to "peek" OVER a PARTITION of a window

Window Functions• Examples:

• Enumerate rows - ROW_NUMBER()

• Show Aggregated sums - SUM()

• Rank results - RANK()

• Look at neighboring rows - LEAD(), LAG()

Orders by User+---------+------------+----------+---------------------+ | user_id | first_name | order_id | order_date | +---------+------------+----------+---------------------+ | 1 | Faye | 33141 | 2017-10-16 03:48:56 | | 1 | Faye | 52283 | 2017-10-17 22:32:24 | | 1 | Faye | 11559 | 2017-10-19 04:53:24 | | 2 | Eino | 27405 | 2017-10-17 07:08:33 | | 2 | Eino | 52328 | 2017-10-21 02:38:11 | | 2 | Eino | 50655 | 2017-10-21 03:34:55 | | 3 | Oda | 15128 | 2017-10-20 22:44:28 | | 3 | Oda | 20256 | 2017-10-22 18:05:21 | | 3 | Oda | 19437 | 2017-10-23 02:19:12 | | 4 | Agustin | 18950 | 2017-10-16 14:42:37 | | 4 | Agustin | 51785 | 2017-10-17 07:10:57 | | 4 | Agustin | 30782 | 2017-10-18 14:26:18 | | 5 | Elinor | 22830 | 2017-10-15 18:08:08 | | 5 | Elinor | 11009 | 2017-10-19 10:23:02 | | 5 | Elinor | 18088 | 2017-10-21 02:16:18 | +---------+------------+----------+---------------------+ 15 rows in set (0.08 sec)

SELECT u.id AS user_id, u.first_name AS first_name, o.id AS order_id,o.ordered_at AS order_date FROM users u INNER JOIN orders o ON o.user_id = u.id ORDER BY u.id, order_date;

+---------+------------+----------+---------------------+---------------------+ | user_id | first_name | order_id | date_previous_order | order_date | +---------+------------+----------+---------------------+---------------------+ | 1 | Faye | 33141 | NULL | 2017-10-16 03:48:56 | | 1 | Faye | 52283 | 2017-10-16 03:48:56 | 2017-10-17 22:32:24 | | 1 | Faye | 11559 | 2017-10-17 22:32:24 | 2017-10-19 04:53:24 | | 2 | Eino | 27405 | NULL | 2017-10-17 07:08:33 | | 2 | Eino | 52328 | 2017-10-17 07:08:33 | 2017-10-21 02:38:11 | | 2 | Eino | 50655 | 2017-10-21 02:38:11 | 2017-10-21 03:34:55 | | 3 | Oda | 15128 | NULL | 2017-10-20 22:44:28 | | 3 | Oda | 20256 | 2017-10-20 22:44:28 | 2017-10-22 18:05:21 | | 3 | Oda | 19437 | 2017-10-22 18:05:21 | 2017-10-23 02:19:12 | | 4 | Agustin | 18950 | NULL | 2017-10-16 14:42:37 | | 4 | Agustin | 51785 | 2017-10-16 14:42:37 | 2017-10-17 07:10:57 | | 4 | Agustin | 30782 | 2017-10-17 07:10:57 | 2017-10-18 14:26:18 | | 5 | Elinor | 22830 | NULL | 2017-10-15 18:08:08 | | 5 | Elinor | 11009 | 2017-10-15 18:08:08 | 2017-10-19 10:23:02 | | 5 | Elinor | 18088 | 2017-10-19 10:23:02 | 2017-10-21 02:16:18 | +---------+------------+----------+---------------------+---------------------+ 15 rows in set (0.17 sec)

SELECT u.id AS user_id, u.first_name AS first_name, o.id AS order_id, LAG(o.ordered_at) OVER(PARTITION BY u.id ORDER BY o.ordered_at) AS date_previous_order, o.ordered_at AS order_date FROM users u INNER JOIN orders o ON o.user_id = u.id ORDER BY u.id, order_date;

When Was the Previous Order?

+---------+------------+----------+---------------------+---------------------+----------------------+ | user_id | first_name | order_id | date_previous_order | order_date | date_following_order | +---------+------------+----------+---------------------+---------------------+----------------------+ | 1 | Faye | 33141 | NULL | 2017-10-16 03:48:56 | 2017-10-17 22:32:24 | | 1 | Faye | 52283 | 2017-10-16 03:48:56 | 2017-10-17 22:32:24 | 2017-10-19 04:53:24 | | 1 | Faye | 11559 | 2017-10-17 22:32:24 | 2017-10-19 04:53:24 | NULL | | 2 | Eino | 27405 | NULL | 2017-10-17 07:08:33 | 2017-10-21 02:38:11 | | 2 | Eino | 52328 | 2017-10-17 07:08:33 | 2017-10-21 02:38:11 | 2017-10-21 03:34:55 | | 2 | Eino | 50655 | 2017-10-21 02:38:11 | 2017-10-21 03:34:55 | NULL | | 3 | Oda | 15128 | NULL | 2017-10-20 22:44:28 | 2017-10-22 18:05:21 | | 3 | Oda | 20256 | 2017-10-20 22:44:28 | 2017-10-22 18:05:21 | 2017-10-23 02:19:12 | | 3 | Oda | 19437 | 2017-10-22 18:05:21 | 2017-10-23 02:19:12 | NULL | | 4 | Agustin | 18950 | NULL | 2017-10-16 14:42:37 | 2017-10-17 07:10:57 | | 4 | Agustin | 51785 | 2017-10-16 14:42:37 | 2017-10-17 07:10:57 | 2017-10-18 14:26:18 | | 4 | Agustin | 30782 | 2017-10-17 07:10:57 | 2017-10-18 14:26:18 | NULL | | 5 | Elinor | 22830 | NULL | 2017-10-15 18:08:08 | 2017-10-19 10:23:02 | | 5 | Elinor | 11009 | 2017-10-15 18:08:08 | 2017-10-19 10:23:02 | 2017-10-21 02:16:18 | | 5 | Elinor | 18088 | 2017-10-19 10:23:02 | 2017-10-21 02:16:18 | NULL | +---------+------------+----------+---------------------+---------------------+----------------------+ 15 rows in set (0.33 sec)

SELECT u.id AS user_id, u.first_name AS first_name, o.id AS order_id, LAG(o.ordered_at) OVER(PARTITION BY u.id ORDER BY o.ordered_at) AS date_previous_order, o.ordered_at AS order_date, LEAD(o.ordered_at) OVER(PARTITION BY u.id ORDER BY o.ordered_at) AS date_following_order FROM users u INNER JOIN orders o ON o.user_id = u.id ORDER BY u.id, order_date;

When Was the Next Order?

Is It Possible To Improve?SELECT u.id AS user_id, u.first_name AS first_name, o.id AS order_id, LAG(o.ordered_at) OVER(PARTITION BY u.id ORDER BY o.ordered_at) AS date_previous_order, o.ordered_at AS order_date, LEAD(o.ordered_at) OVER(PARTITION BY u.id ORDER BY o.ordered_at) AS date_following_order FROM users u INNER JOIN orders o ON o.user_id = u.id ORDER BY u.id, order_date;

This Looks Similar…SELECT u.id AS user_id, u.first_name AS first_name, o.id AS order_id, LAG(o.ordered_at) OVER(PARTITION BY u.id ORDER BY o.ordered_at) AS date_previous_order, o.ordered_at AS order_date, LEAD(o.ordered_at) OVER(PARTITION BY u.id ORDER BY o.ordered_at) AS date_following_order FROM users u INNER JOIN orders o ON o.user_id = u.id ORDER BY u.id, order_date;

Named Windows!SELECT u.id AS user_id, u.first_name AS first_name, o.id AS order_id, LAG(o.ordered_at) OVER(dates) AS date_previous_order, o.ordered_at AS order_date, LEAD(o.ordered_at) OVER(dates) AS date_following_order FROM users u INNER JOIN orders o ON o.user_id = u.id WINDOW dates AS ( PARTITION BY u.id ORDER BY o.ordered_at) ORDER BY u.id, order_date;

AccumulatorsSELECT id AS item_id, order_id, product_id, quantity, price AS item_price, quantity * price as item_row_total, SUM(quantity * price) OVER(order_ids ROWS UNBOUNDED PRECEDING) AS accumulated, SUM(quantity * price) OVER(order_ids) AS order_total FROM orders_items WHERE order_id = 21158 WINDOW order_ids AS (PARTITION BY order_id);

+---------+----------+------------+----------+------------+----------------+-------------+-------------+ | item_id | order_id | product_id | quantity | item_price | item_row_total | accumulated | order_total | +---------+----------+------------+----------+------------+----------------+-------------+-------------+ | 10886 | 21158 | 532 | 4 | 227.85 | 911.40 | 911.40 | 2289.17 | | 19674 | 21158 | 613 | 3 | 139.53 | 418.59 | 1329.99 | 2289.17 | | 21847 | 21158 | 1986 | 2 | 275.56 | 551.12 | 1881.11 | 2289.17 | | 23528 | 21158 | 1110 | 1 | 166.32 | 166.32 | 2047.43 | 2289.17 | | 24901 | 21158 | 531 | 1 | 241.74 | 241.74 | 2289.17 | 2289.17 | +---------+----------+------------+----------+------------+----------------+-------------+-------------+

5 rows in set (0.01 sec)

JSON

5.7: First Duplicate Wins• In 5.7 the first definition is storedSELECT JSON_OBJECT('clients', 32, 'options', '[active, inactive]', 'clients', 64, 'clients', 128) AS result;

+---------------------------------------------------+ | result | +---------------------------------------------------+ | {"clients": 32 , "options": "[active, inactive]"} | +---------------------------------------------------+ 1 row in set (0.00 sec)

8.0: Last Duplicate Wins• In 8.0 the last definition is storedSELECT JSON_OBJECT('clients', 32, 'options', '[active, inactive]', 'clients', 64, 'clients', 128) AS result;

+---------------------------------------------------+ | result | +---------------------------------------------------+ | {"clients": 128, "options": "[active, inactive]"} | +---------------------------------------------------+ 1 row in set (0.00 sec)

Partial in-place Update• Possible if:

• uses JSON_SET, JSON_REPLACE or JSON_REMOVE functions

• no new elements are added to the object or array

• new values take up the same space as the previous element

• target and origin are the same column

UPDATE users SET extra_information = JSON_SET(twitter_api_response, '$.followers', 100);

UPDATE users SET twitter_api_response = JSON_SET(twitter_api_response, '$.followers', 100); in-place

new attribution

Merge• JSON_MERGE is deprecated

• replaced by JSON_MERGE_PRESERVE

• Added JSON_MERGE_PATCH

• implements the RFC 7396 from IETF

Preserve & PatchSELECT PRESERVE PATCH

('[1, 2]', '[true, false]') [1, 2, true, false] [true, false]

('{"name": "x"}', '{"id": 47}') {"id": 47, "name": "x"} {"id": 47, "name": "x"}

('1', 'true') [1, true] true

('[1, 2]', '{"id": 47}') [1, 2, {"id": 47}] {"id": 47}

('{"a": 1, "b": 2}', '{"a": 3, "c": 4}') {"a": [1, 3], "b": 2, "c": 4} {"a": 3, "b": 2, "c": 4}

('{"a": 1, "b":2}','{"a": 3, "c":4}', '{"a": 5, "d":6}') {"a": [1, 3, 5], "b": 2, "c": 4, "d": 6} {"a": 5, "b": 2, "c": 4, "d": 6}

Examples taken from the MySQL 8.0 documentation.

Roles

Roles• Can be created or droppedCREATE ROLE 'admin'; CREATE ROLE 'dba', 'developer', 'readonly';

Roles - Grants• Can receive privileges

-- Grants access to all databases and tables to the role "dba" GRANT ALL ON *.* TO 'dba';

-- Grants access to the database "store" to the role "readonly" GRANT SELECT ON store.* TO 'readonly';

-- Grants access to developers GRANT ALTER, DELETE, INDEX, INSERT, SELECT, UPDATE, CREATE VIEW, SHOW VIEW, TRIGGER, CREATE TEMPORARY TABLES ON store.* TO 'developer';

Roles - Assignment• Proposed users:

USER ROLE

lisa_simpson dba

millhouse_houten developer

homer_simpson readonly

bart_simpson readonly

Create Users & Assign Roles• Execute:

For more detailed information about Roles in MySQL 8.0 see this post.

CREATE USER 'lisa_simpson'@'%' IDENTIFIED BY '...' DEFAULT ROLE dba;

CREATE USER 'millhouse_houten'@'localhost' IDENTIFIED BY '...' DEFAULT ROLE developer;

CREATE USER 'homer_simpson'@'localhost' IDENTIFIED BY '...' DEFAULT ROLE readonly;

CREATE USER 'bart_simpson'@'localhost' IDENTIFIED BY '...' DEFAULT ROLE readonly;

Roles - Notes• It's possible to set up mandatory_roles in the MySQL config file:

• Or set mandatory_roles at runtime:

• It’s possible to revoke a role for a user:

• Default role for root is NONE (SELECT CURRENT_ROLE())

[mysqld] mandatory_roles='developer,readonly@localhost,dba@%'

SET PERSIST mandatory_roles = 'developer,readonly@localhost,dba@%';

REVOKE readonly FROM 'bart_simpson'@'localhost';

References• http://www.mysqlserverteam.com

• http://dev.mysql.com/doc/refman/8.0/en

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Thank You• @gabidavila on Twitter

• http://gabriela.io

• Please leave your feedback on joind.in: https://joind.in/talk/0e88f

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2017 P H P W O R L D