I'm currently working on a Ruby on Rails project in which I have objects with association to instructions, meaning, each object, can have zero or more instruction objects that hold some basic data, like title, data (string), and position (for ordering them in the UI). I tried looking up an answer in google but found no relevant answer. the instructions are specific to each object and shouldn't be used for lookup or search of any kind, and therefore I figured I should store them as JSON within the object's own table instead of making a join table. The reason I think of doing so is that join table would explode when there would be many objects and because of that querying for each object's instructions would get longer over time. Is that a reasonable concern for storing this data as a JSON instead of has_many association?
Think of using JSON in an RDBMS as a form of denormalization. There are legitimate reasons to use denormalization, but you must keep in mind that it always optimizes for one type of query at the expense of other types of queries.
For example, in this case you could query your object and it would include the JSON document containing all instructions. But if you wanted to search for a specific instruction, it would be quite complex to search for the row that has a JSON documenting containing a specific instruction. Have you thought about how you would query that?
Using normalized database design, i.e. the join table you mention, allows for more flexibility in queries. You can query the object table, or you can query the instruction table. Either way, then simply join to the other table to the the corresponding rows.
The way to make this more optimized is to use indexes on the columns you want to search. See my presentation How to Design Indexes, Really or the video.
Using JSON creates a lot of complexity that you probably haven't considered. See my presentation How to Use JSON in MySQL Wrong.
I want to request a high amount of records (100000 to 1000000) per select request with a join of three tables. Is the performance much better with nativeSQL instead of using spring-data-jpa for mapping it to #Entity objects?
Thx!
JPA and every ORM turn your query results into domain objects.
That of course takes resources. Spring Data JPA adds potential conversions to that and it preprocesses your query in order to support fancy ways of setting parameters.
If you are selecting large amounts of data the preprocessing of the statement probably doesn't matter that much.
But the conversion to domain objects will.
You used the word "migrating" which sounds like you are going to select data and then immediately write it somewhere else. If that is the case, use plain SQL and work directly on the ResultSet tell the driver to make it read only and forward only. See Understanding Forward Only ResultSet
In MySQL 5.7 a new data type for storing JSON data in MySQL tables has been
added. It will obviously be a great change in MySQL. They listed some benefits
Document Validation - Only valid JSON documents can be stored in a
JSON column, so you get automatic validation of your data.
Efficient Access - More importantly, when you store a JSON document in a JSON column, it is not stored as a plain text value. Instead, it is stored
in an optimized binary format that allows for quicker access to object
members and array elements.
Performance - Improve your query
performance by creating indexes on values within the JSON columns.
This can be achieved with “functional indexes” on virtual columns.
Convenience - The additional inline syntax for JSON columns makes it
very natural to integrate Document queries within your SQL. For
example (features.feature is a JSON column): SELECT feature->"$.properties.STREET" AS property_street FROM features WHERE id = 121254;
WOW ! they include some great features. Now it is easier to manipulate data. Now it is possible to store more complex data in column.
So MySQL is now flavored with NoSQL.
Now I can imagine a query for JSON data something like
SELECT * FROM t1
WHERE JSON_EXTRACT(data,"$.series") IN
(
SELECT JSON_EXTRACT(data,"$.inverted")
FROM t1 | {"series": 3, "inverted": 8}
WHERE JSON_EXTRACT(data,"$.inverted")<4 );
So can I store huge small relations in few json colum? Is it good? Does it break normalization. If this is possible then I guess it will act like NoSQL in a MySQL column. I really want to know more about this feature. Pros and cons of MySQL JSON data type.
SELECT * FROM t1
WHERE JSON_EXTRACT(data,"$.series") IN ...
Using a column inside an expression or function like this spoils any chance of the query using an index to help optimize the query. The query shown above is forced to do a table-scan.
The claim about "efficient access" is misleading. It means that after the query examines a row with a JSON document, it can extract a field without having to parse the text of the JSON syntax. But it still takes a table-scan to search for rows. In other words, the query must examine every row.
By analogy, if I'm searching a telephone book for people with first name "Bill", I still have to read every page in the phone book, even if the first names have been highlighted to make it slightly quicker to spot them.
MySQL 5.7 allows you to define a virtual column in the table, and then create an index on the virtual column.
ALTER TABLE t1
ADD COLUMN series AS (JSON_EXTRACT(data, '$.series')),
ADD INDEX (series);
Then if you query the virtual column, it can use the index and avoid the table-scan.
SELECT * FROM t1
WHERE series IN ...
This is nice, but it kind of misses the point of using JSON. The attractive part of using JSON is that it allows you to add new attributes without having to do ALTER TABLE. But it turns out you have to define an extra (virtual) column anyway, if you want to search JSON fields with the help of an index.
But you don't have to define virtual columns and indexes for every field in the JSON document—only those you want to search or sort on. There could be other attributes in the JSON that you only need to extract in the select-list like the following:
SELECT JSON_EXTRACT(data, '$.series') AS series FROM t1
WHERE <other conditions>
I would generally say that this is the best way to use JSON in MySQL. Only in the select-list.
When you reference columns in other clauses (JOIN, WHERE, GROUP BY, HAVING, ORDER BY), it's more efficient to use conventional columns, not fields within JSON documents.
I presented a talk called How to Use JSON in MySQL Wrong at the Percona Live conference in April 2018. I'll update and repeat the talk at Oracle Code One in the fall.
There are other issues with JSON. For example, in my tests it required 2-3 times as much storage space for JSON documents compared to conventional columns storing the same data.
MySQL is promoting their new JSON capabilities aggressively, largely to dissuade people against migrating to MongoDB. But document-oriented data storage like MongoDB is fundamentally a non-relational way of organizing data. It's different from relational. I'm not saying one is better than the other, it's just a different technique, suited to different types of queries.
You should choose to use JSON when JSON makes your queries more efficient.
Don't choose a technology just because it's new, or for the sake of fashion.
Edit: The virtual column implementation in MySQL is supposed to use the index if your WHERE clause uses exactly the same expression as the definition of the virtual column. That is, the following should use the index on the virtual column, since the virtual column is defined AS (JSON_EXTRACT(data,"$.series"))
SELECT * FROM t1
WHERE JSON_EXTRACT(data,"$.series") IN ...
Except I have found by testing this feature that it does NOT work for some reason if the expression is a JSON-extraction function. It works for other types of expressions, just not JSON functions. UPDATE: this reportedly works, finally, in MySQL 5.7.33.
The following from MySQL 5.7 brings sexy back with JSON sounds good to me:
Using the JSON Data Type in MySQL comes with two advantages over
storing JSON strings in a text field:
Data validation. JSON documents will be automatically validated and
invalid documents will produce an error. Improved internal storage
format. The JSON data is converted to a format that allows quick read
access to the data in a structured format. The server is able to
lookup subobjects or nested values by key or index, allowing added
flexibility and performance.
...
Specialised flavours of NoSQL stores
(Document DBs, Key-value stores and Graph DBs) are probably better
options for their specific use cases, but the addition of this
datatype might allow you to reduce complexity of your technology
stack. The price is coupling to MySQL (or compatible) databases. But
that is a non-issue for many users.
Note the language about document validation as it is an important factor. I guess a battery of tests need to be performed for comparisons of the two approaches. Those two being:
Mysql with JSON datatypes
Mysql without
The net has but shallow slideshares as of now on the topic of mysql / json / performance from what I am seeing.
Perhaps your post can be a hub for it. Or perhaps performance is an after thought, not sure, and you are just excited to not create a bunch of tables.
From my experience, JSON implementation at least in MySql 5.7 is not very useful due to its poor performance.
Well, it is not so bad for reading data and validation. However, JSON modification is 10-20 times slower with MySql that with Python or PHP.
Lets imagine very simple JSON:
{ "name": "value" }
Lets suppose we have to convert it to something like that:
{ "name": "value", "newName": "value" }
You can create simple script with Python or PHP that will select all rows and update them one by one. You are not forced to make one huge transaction for it, so other applications will can use the table in parallel. Of course, you can also make one huge transaction if you want, so you'll get guarantee that MySql will perform "all or nothing", but other applications will most probably not be able to use database during transaction execution.
I have 40 millions rows table, and Python script updates it in 3-4 hours.
Now we have MySql JSON, so we don't need Python or PHP anymore, we can do something like that:
UPDATE `JsonTable` SET `JsonColumn` = JSON_SET(`JsonColumn`, "newName", JSON_EXTRACT(`JsonColumn`, "name"))
It looks simple and excellent. However, its speed is 10-20 times slower than Python version, and it is single transaction, so other applications can not modify the table data in parallel.
So, if we want to just duplicate JSON key in 40 millions rows table, we need to not use table at all during 30-40 hours. It has no sence.
About reading data, from my experience direct access to JSON field via JSON_EXTRACT in WHERE is also extremelly slow (much slower that TEXT with LIKE on not indexed column). Virtual generated columns perform much faster, however, if we know our data structure beforehand, we don't need JSON, we can use traditional columns instead. When we use JSON where it is really useful, i. e. when data structure is unknown or changes often (for example, custom plugin settings), virtual column creation on regular basis for any possible new columns doesn't look like good idea.
Python and PHP make JSON validation like a charm, so it is questionable do we need JSON validation on MySql side at all. Why not also validate XML, Microsoft Office documents or check spelling? ;)
I got into this problem recently, and I sum up the following experiences:
1, There isn't a way to solve all questions.
2, You should use the JSON properly.
One case:
I have a table named: CustomField, and it must two columns: name, fields.
name is a localized string, it content should like:
{
"en":"this is English name",
"zh":"this is Chinese name"
...(other languages)
}
And fields should be like this:
[
{
"filed1":"value",
"filed2":"value"
...
},
{
"filed1":"value",
"filed2":"value"
...
}
...
]
As you can see, both the name and the fields can be saved as JSON, and it works!
However, if I use the name to search this table very frequently, what should I do? Use the JSON_CONTAINS,JSON_EXTRACT...? Obviously, it's not a good idea to save it as JSON anymore, we should save it to an independent table:CustomFieldName.
From the above case, I think you should keep these ideas in mind:
Why MYSQL support JSON?
Why you want to use JSON? Did your business logic just need this? Or there is something else?
Never be lazy
Thanks
Strong disagree with some of things that are said in other answers (which, to be fair, was a few years ago).
We have very carefully started to adopt JSON fields with a healthy skepticism. Over time we've been adding this more.
This generally describes the situation we are in:
Like 99% of applications out there, we are not doing things at a massive scale. We work with many different applications and databases, the majority of these are capable of running on modest hardware.
We have processes and know-how in place to make changes if performance does become a problem.
We have a general idea of which tables are going to be large and think carefully about how we optimize queries for them.
We also know in which cases this is not really needed.
We're pretty good at data validation and static typing at the application layer.
Lastly,
When we use JSON for storing complex data, that data is never referenced directly by other tables. We also tend to never need to use them in where clauses in hot paths.
So with all this in mind, using a little JSON field instead of 1 or more tables vastly reduces the complexity of queries and data model. Removing this complexity makes it easier to write certain queries, makes our code simpler and just generally saves time.
Complexity and performance is something that needs to be carefully balanced. JSON fields should not be blindly applied, but for the cases where this works it's fantastic.
'JSON fields don't perform well' is a valid reason to not use JSON fields, if you are at a place where that performance difference matters.
One specific example is that we have a table where we store settings for video transcoding. The settings table has 1 'profile' per row, and the settings themselves have a maximum nesting level of 4 (arrays and objects).
Despite this being a large database overall, there's only a few hundreds of these records in the database. Suggesting to split this into 5 tables would yield no benefit and lots of pain.
This is an extreme example, but we have plenty of others (with more rows) where the decision to use JSON fields is a few years in the past, and hasn't yet caused an issue.
Last point: it is now possible to directly index on JSON fields.
I have a MySQL 5.0 database with the following schema:
I'm using it in a web application making ajax calls to server/words, where I query the database using PDO and return json. My goal here is to get resulting json like this:
[ //array of words
{
"word": "...",
"fonetics": "...",
...
"meanigngs":[] // array of meanings (objects)
"inflections":[] // array of inflections
"examples":[]
},
...
]
One way to achive this is to query database multiple times, each for every table, and then work on json structure in php. I know it's not very good, but I'm mostly front-end guy, and I don't know much about MySQL.
I was also thinking about creating a view constituting of required tables.
Could you guide me to best solution or perhaps even write a piece of mysql that creates view/gets required data out of database?
If you need all the data, and want it structured in an object hierarchy the way you describe, then doing so at the application level is probably really your best option. Make sure to sort the tables by word_id so that you can grab the values for your sub-arrays from consecutive rows in your query results.
Alternatives would include:
Query the database once for every sub-array. This would make building the structure a lot easier, and might reduce memory consumption in the client, but it would cause a much larger number of queries. If you do this, then definitely go for prepared statements!
Create one big view by joining tables. This would massively duplicate data, in particular as you'd include the full cross product between rather unrelated tables, e.g. every combination between a meaning and an inflection and so on. Definitely not the route to take here.
Like many people, I am trying to squeeze the best performance out of my app while keeping the code simple and readable as possible. I am using Linq-to-SQL and am really trying to keep my data layer as declarative as possible.
I operate on the assumption that SQL calls are the most expensive operations. Thus, I try to minimize them in quantity, but try to avoid crazy complex queries that are hard to optimize.
Case in point: I am using DataLoadOptions with my DataContext -- its goal is to minimize the quantity of queries by preloading related entities. (Aka, eager loading vs lazy loading.)
Problem: Linq uses joins to achieve the goal. As with everything, it's a trade-off. I am getting fewer queries, but those joined queries are more complex and expensive. Going into SQL Profiler makes this clear.
So, I'd like an option in Linq to preload without joins. Is this possible? Here's what it might look like:
I have a Persons table, an Items table, and a PersonItems table to provide a many-to-many relationship. When loading a collection of Persons, I'd like to have all their PersonItems and Items eagerly loaded as well.
Linq currently does this with one large query, containing two joins. What I'd rather it do is three non-join queries: one for Persons, one for all the PersonItems relating to those Persons, and one for all Items relating to those PersonItems. Then Linq would automagically arrange them into the related entities.
Each of these would be a fast, firehose-type query. Over the long haul, it would allow for predictable, web-scale performance.
Ever seen it done?
I believe what you describe where three non-join queries are done is essentially what happens under the hood when a single join query is performed. I could be wrong but if this the case the single query will be more efficient as only one database query is involved as opposed to three. If you are having performance issues I'd make sure the columns you are joining on are indexed (you should see no table scans in SQL profiler). If this is not enough you could write a custom stored procedure to get just the data you need (assuming you don't need every column of every object, this will allow you to make use of index seeks which are faster than index scans), or alternately you could denormalise (duplicate data across your tables) so that no joining occurs at all.