I have large JSON data, greater than 2kB, in each record of my table and currently, these are being stored in JSONB field.
My tech stack is Django and Postgres.
I don't perform any updates/modifications on this json data but i do need to read it, frequently and fast. However, due to the JSON data being larger than 2kB, Postgres splits it into chunks and puts it into the TOAST table, and hence the read process has become very slow.
So what are the alternatives? Should i use another database like MongoDB to store these large JSON data fields?
Note: I don't want to pull the keys out from this JSON and turn them into columns. This data comes from an API.
It is hard to answer specifically without knowing the details of your situation, but here are some things you may try:
Use Postgres 12 (stored) generated columns to maintain the fields or smaller JSON blobs that are commonly needed. This adds storage overhead, but frees you from having to maintain this duplication yourself.
Create indexes for any JSON fields you are querying (Postgresql allows you to create indexes for JSON expressions).
Use a composite index, where the first field in the index the field you are querying on, and the second field (/json expression) is that value you wish to retrieve. In this case Postgresql should retrieve the value from the index.
Similar to 1, create a materialised view which extracts the fields you need and allows you to query them quickly. You can add indexes to the materialised view too. This may be a good solution as materialised views can be slow to update, but in your case your data doesn't update anyway.
Investigate why the toast tables are being slow. I'm not sure what performance you are seeing, but if you really do need to pull back a lot of data then you are going to need fast data access whatever database you choose to go with.
Your mileage may vary with all of the above suggestions, especially as each will depend on your particular use case. (see the questions in my comment)
However, the overall idea is to use the tools that Postgresql provides to make your data quickly accessible. Yes this may involve pulling the data out of its original JSON blob, but this doesn't need to be done manually. Postgresql provides some great tools for this.
If you just need to store and read fully this json object without using the json structure in your WHERE query, what about simply storing this data as binary in a bytea column? https://www.postgresql.org/docs/current/datatype-binary.html
I need to store json files with models in my django app and I'm wondering whether JSONField is my best choice or not. All I need is to store it for later, no searching or other querying is needed other than when I eventually need to retrieve it again. I'm using postgresql if that matters.
It's important to notice that the Django JSONField is a PostgreSQL specific model field. Internally it uses a jsonb field.
The documentation of PostgreSQL says in regard to jsonb fields:
jsonb data is stored in a decomposed binary format that makes it
slightly slower to input due to added conversion overhead, but
significantly faster to process
It also enforces that you put in valid JSON.
If read/write performance is the only thing that matters to you, I would go with a normal text field. If you want to enforce that JSON is put into the field and be open for future requirements (e.g. if you want to filter this column for a certain JSON key), then you are better off using JSONField.
Also have a look at this answer.
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 table A that contains the definition/configuration for a form (fields, display information, etc). I perform a lookup into that table to determine what the form that is being displayed looks like. We also dynamically create tables to hold data as specified in that form or record.
When working with other developers, twice it has been suggested to store the field information in JSON format in a single field in table A instead of individual fields for configuration.
My principle concern is one of performance. We are retrieving row information from Table A or we are retrieving row information from table A and parsing it in the client.
Which is better in terms of performance? In terms of code reuse?
Short answer is yes, storing configuration as a serialized JSON document will give you the flexibility of changing and propagating changes easily likely with less code. Ideally, let the client do the deserialization,
Assuming documents are fairly small (<5K) processing cost is negligible, and as long as your access pattern is key/vale based the database performance should not be different from accessing any other row by primary key. Make sure to index the key.
But more broadly I would consider the following,
A document store for this scenario (for both the configuration and data).
Consider separating schema definition from the user/system preferences.
Shard data by the key (this would be a replacement for creating separate tables)
My principle concern is one of performance. We are retrieving row
information from Table A or we are retrieving row information from
table A and parsing it in the client.
Which is better in terms of performance? In terms of code reuse?
I do not see performance as a problem here.
JSON Pros
Schema flexibility. If you change or add something, you need not to touch database tables
Configuration richness. JSON is more expressive, than a database table
Easy nested structure support
JSON Cons
Inability to change only a part of JSON object. You have to deserialize it, change, serialize again and then store.
Inability to easily change a part of many objects. Where simple UPDATE...WHERE can be issued for database table, you will have to read your database row by row and update each separately when using JSON.
Weak versioning. Changing JSON schema format is not very simple and is not obvious. When you change database structure, it's always visible and straightforward process. JSON schema change is not so obvious.
If you go with JSON, I recommend to use JSON schemas to validate current versions of data. And consider making a migration regulation. If JSON schema changes, a special migration must be prepared, which will walk database and restructure all JSON data there in a single transaction.
I am trying to understand why JSON is widely used for data transfer between client and server. I understand that it offers simple design which is easy to understand. However, on the contrary;
A JSON string includes repeated data, e.g, incase of a table, columns names (keys) are repeated in each object . Would it not be wise to send columns as first object and rest of the object should be the data (without columns/keys information) from the table.
Once we have a JSON object, the searching based on keys is expensive (in time) compared to indexes. Imagine a table with 20-30 column, doing this searching for each key for each object would cost a lot more time compare to directly using indexes.
There may be many more drawbacks and advantages, add here if you know one.
I think if you want data transfer then you want a table based format. The JSON format is not a table based format like standard databases or Excel. This can complicate analyzing data if there is a problem because someone will usually use excel for that (sorting, filtering, formulas). Also building test files will be more difficult because you can't simply use excel to export to JSON.
But, If you wanted to use JSON for data transfer you could basically build a JSON version of a CSV file. You would only use arrays.
Columns: ["First_Name", "Last_Name"]
Rows: [
["Joe", "Master"],
["Alice", "Gooberg"]
.... etc
]
Seems messy to me though.
If you wanted to use objects then you will have to embed Column names for every bit of data, which in my opinion indicates a wrong approach.