JSON is fast becoming the standard format for data interchange and for unstructured data, and MariaDB 10.2 adds a range on JSON supporting functions, even though a JSON datatype isn't implemented yet. There are some reasons why there isn't a JSON datatype, but one is that there are actually not that many advantages to that as JSON is a text-based format. This blog post aims to describe JSON and the use cases for it, as well as to describe the MariaDB 10.2 JSON functions and uses for these, as well as showing some other additions to MariaDB 10.2 that are useful for JSON processing.
So to begin with then, why do we need JSON? Or to put it differently, why do we not store all data in JSON? Well, the reason as I see it is that some data we work with really is best treated as schemaless whereas some other data really should be handled in a more strict way in a schema. Which means that in my mind mixing relational data with unstructured data is what we really want. And using JSON for unstructured data is rather neat, and JSON is even standardized (see json.org).
There are reasons why this hasn't always been so. When the sad old git that is writing this stuff started working in this industry, which I think was during the Harding administration, computers were rare, expensive, handled only by experts (so how I got to work with them is a mystery) and built from lego-bricks, meccano and pieces of solid gold (to keep up the price). Also, they were as powerful as a slide-ruler, except it was feed with punched-cards (and probably powered by steam). Anyway, no one in their right mind would have considered string pictures of cute felines as something to be on a computer, or actually stuff to be stored in the database. The little that would fit was the really important stuff - like price, amount in stock, customer name, billing address and such - and nothing else. And not only that, stuff that was stored had some kind of value, somehow, which meant it had to follow certain rules (and following rules is something I am good at? I wonder how I ended up in this business. Again). Like, a price had to be a number of some kind, with a value 0 or higher and some other restrictions. As you see, these were hard and relentless times.
And then time moved on and people started buying things on the internet (whatever the internet is. I think it is some kind of glorified, stylish version of punched cards) and stuff such as Facebook and Google came around. The issue with computer storage was now not how to fit all that nicely structured data in it, but rather once we have filled that hard drive on your desktop with all the product, customers and transactions from Amazon (I think Amazon has something to do with Internet, but I am not sure) and a full 17.85% of that drive is now occupied by that old-style structured data, what more do we put in there? Maybe we could put some more information on the items for sale in that database, and some general information on who is buying it? That should fill up that disk nicely, right? Well, yes, but that new data, although related to the structured data I already have, is largely unstructured. Say, for example, you write a review of a product on Amazon late in the morning after a good deal of heavy "partying" (which is not an Internet thing, I think), the contents of that would hardly be considered "structured". If you didn't like the product (which you probably didn't), then the appropriate terms for large parts of that review would probably be "profanity" or "foul language").
The way to deal with the above is a mix of structured and unstructured data, with some kind of relation between the two. Like a column of unstructured data in each relational database table (or should I say "relation", just to show my age? Or maybe I should pretend to be really young, modern and cool, possibly sporting a hipster beard and all that, by calling it a "collection").
With that out of the way, let's consider an example using structured as well as non-structured JSON data. Assume we have a store selling different types of clothing, pants, jackets, shoes and we are to create a table to hold the inventory. This table would have some columns that are always there and which have the same meaning for all rows in the table, like name, amount in stock and price. These are items that are well suited for a relational format. On top of this we want to add attributes that have different meaning for each type of or even each instance of items. Here we have things like colour, width, length and size. These we consider non-relational as the interpretation of these attributes are different depending of the type of garment (like size M or shoe sizes or a "zebra striped" colour) and some garments might have some unique attribute, like designer or recommended by staff or something. Our table might then look something like this:
MariaDB> CREATE TABLE products(id INTEGER NOT NULL PRIMARY KEY AUTO_INCREMENT,
name VARCHAR(255) NOT NULL,
price DECIMAL(9,2) NOT NULL,
stock INTEGER NOT NULL,
attr VARCHAR(1024));
In this table we have a few columns that look like columns in any relational database table, and then we have a column, called attr, that can hold any relevant attribute for the garment in question and we will store that as JSON a JSON string. You probably notice that we aren't using a JSON datatype here as that is not present in MariaDB, despite that there are JSON functions, but those JSON functions act on a text-string with JSON content. These functions are introduced in MariaDB 10.2 (which is in Beta as I write this), but there are a few bugs that means you should use MariaDB 10.2.4 or higher, which means as for now we assume that MariaDB 10.2.4 or higher is being used.
But there is one issue with the above that I don't particularly care for and that is, as the attr column is plain text, any kind of data can be put in the attr column, even non-valid JSON. The good thing is that there is a fix for this in MariaDB 10.2, which is CHECK constraints that actually work, and this is a little discussed feature of MariaDB 10.2. The way this works is that this kind of constraint kicks in whenever a row is INSERTed or UPDATEed, any CHECK constraint runs and validates the data and if the validation fails the operation also fails. Before I show an example I just want to mention one JSON function we are to use here, which is JSON_VALID which takes a string and checks if it is valid JSON. Note that although CHECK constraints are particularly valid here, check constraints can be used for any kind of data validation.
Armed with this, let's rewrite the statement that creates the table like this:
MariaDB> CREATE TABLE products(id INTEGER NOT NULL PRIMARY KEY AUTO_INCREMENT,
name VARCHAR(255) NOT NULL,
price DECIMAL(9,2) NOT NULL,
stock INTEGER NOT NULL,
attr VARCHAR(1024),
CHECK (JSON_VALID(attr)));
Let's give this a try now:
MariaDB> INSERT INTO products VALUES(NULL, 'Jeans', 10.5, 165, NULL);
ERROR 4025 (23000): CONSTRAINT `CONSTRAINT_1` failed for `inventory`.`products`
Ok, that didn't work out. What happens here is that a NULL string isn't a valid JSON value, so we need to rewrite our table definition:
MariaDB> CREATE TABLE products(id INTEGER NOT NULL PRIMARY KEY AUTO_INCREMENT,
name VARCHAR(255) NOT NULL,
price DECIMAL(9,2) NOT NULL,
stock INTEGER NOT NULL,
attr VARCHAR(1024),
CHECK (attr IS NULL OR JSON_VALID(attr)));
Following this we can try it again:
MariaDB> INSERT INTO products VALUES(NULL, 'Jeans', 10.5, 165, NULL);
Query OK, 1 row affected (0.01 sec)
MariaDB> INSERT INTO products VALUES(NULL, 'Shirt', 10.5, 78, '{"size": 42, "colour": "white"}');
Query OK, 1 row affected (0.01 sec)
MariaDB> INSERT INTO products VALUES(NULL, 'Blouse', 17, 15, '{"colour": "white}');
ERROR 4025 (23000): CONSTRAINT `CONSTRAINT_1` failed for `inventory`.`products`
That last statement failed because of malformed JSON (a double quote was forgotten about), so let's correct that:
MariaDB> INSERT INTO products VALUES(NULL, 'Blouse', 17, 15, '{"colour": "white"}');
Query OK, 1 row affected (0.01 sec)
One thing that has yet to be discussed is indexes on JSON values. As the attr column in our example is a plain text, we can of course index it as usual, but that is probably not what you want to do, rather what would be neat would be to create an index on individual attributes in that JSON string. MariaDB doesn't yet support functional indexes, i.e. functions not on values but on computed values. What MariaDB does have though is Virtual Columns, and these can be indexed and as of MariaDB 10.2 these virtual columns don't have to be persistent, (read more on Virtual Columns here: Putting Virtual Columns to good use).
The easiest way to explain this is with an example. Let's say we want an index on the colour attribute, if such a thing exists. For this we need two things: A virtual column that contains the colour attribute as extracted from the attr column, and then an index on that. In this case we will be using the JSON_VALUE function that takes a JSON value and a path, the latter describing the JSON operation to be performed, somewhat like a query language for JSON.
We end up with something like this:
MariaDB> ALTER TABLE products ADD attr_colour VARCHAR(32) AS (JSON_VALUE(attr, '$.colour'));
MariaDB> CREATE INDEX products_attr_colour_ix ON products(attr_colour);
With that in place, let's see how that works:
MariaDB> SELECT * FROM products WHERE attr_colour = 'white';
+----+--------+-------+-------+---------------------------------+-------------+
| id | name | price | stock | attr | attr_colour |
+----+--------+-------+-------+---------------------------------+-------------+
| 2 | Shirt | 10.50 | 78 | {"size": 42, "colour": "white"} | white |
| 3 | Blouse | 17.00 | 15 | {"colour": "white"} | white |
+----+--------+-------+-------+---------------------------------+-------------+
2 rows in set (0.00 sec)
And let's see if that index is working as it should:
MariaDB> EXPLAIN SELECT * FROM products WHERE attr_colour = 'white';
+------+-------------+----------+------+-------------------------+-------------------------+---------+-------+------+------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Etra |
+------+-------------+----------+------+-------------------------+-------------------------+---------+-------+------+------------+
| 1 | SIMPLE | products | ref | products_attr_colour_ix | products_attr_colour_ix | 99 | const | 2 | Uing where |
+------+-------------+----------+------+-------------------------+-------------------------+---------+-------+------+------------+
1 row in set (0.00 sec)
And just to show that the column attr_colour is a computed column that depends on the attr column, lets try updating the colour for the blouse and make that red instead of white and then search that. To replace a value in a JSON object MariaDB 10.2 provides the JSON_REPLACE functions (for all JSON functions in MariaDB 10.2 see MariaDB Knowledge Base).
MariaDB> UPDATE products SET attr = JSON_REPLACE(attr, '$.colour', 'red') WHERE name = 'Blouse';
Query OK, 1 row affected (0.01 sec)
Rows matched: 1 Changed: 1 Warnings: 0
MariaDB> SELECT attr_colour FROM products WHERE name = 'blouse';
+-------------+
| attr_colour |
+-------------+
| red |
+-------------+
1 row in set (0.00 sec)
There is more to say about JSON in MariaDB 10.2 but I hope you now have a feel on what's for offer.
Happy SQL'ing
/Karlsson
