Free Json Database

[Apolonio Hicks]

Withrelational databases, JSON data needs to be parsed or stored using the NVARCHAR column (LOB storage). However, document databases like MongoDB can store JSON data in its natural format, which is readable by humans and machines.

Just like traditional databases, JSON document databases manage data partitioning, indexing, clustering, replication, and data access on their own. Apart from this, JSON databases offer many advantages.

NoSQL databases, in general, have more storage flexibility and offer better indexing methods. In a document database, each document is handled as an individual object, and there is no fixed schema, so you can store each document in the way it can be most easily retrieved and viewed. Additionally, you can evolve your data model to adapt it to your changing application requirements. The schema versioning pattern makes use of the flexible document model to allow just that.

Due to the availability of various index types, search queries are quite fast. For example, since MongoDB has no fixed schema, you can create a wildcard index on a field or set of fields to support querying that field. There are many other types of indexes, like O2-tree and T-tree, that make NoSQL databases highly performant.

JSON databases have a flexible schema and scale well vertically and horizontally, making them suitable to store huge volumes and a variety of big data. Document databases like MongoDB have a rich query language (MQL) and aggregation pipeline, eliminating the need for ETL systems for data processing and transformation. Further, these databases can easily pass data to popular data analysis programming languages like Python and R, without additional coding steps.

JSON format stores data in the form of objects. The syntax is simple and readable for anyone. A JSON database like MongoDB stores the data in a JSON-like format (binary JSON), which is the binary encoded version of JSON, and is optimized for performance and space.

This makes the MongoDB database the best natural fit for storing JSON data. You can store details of an entire object in one document, making it easier to view and query. MongoDB is the most popular JSON database as it offers many other benefits, like:

However, NoSQL document databases like MongoDB are the best, as they store JSON data in the same format and there is no additional processing required. Data can be easily viewed in the form of documents and retrieved in JSON format using queries.

I'm looking for fast and efficient data storage to build my PHP based web site. I'm aware of MySql. Can I use a JSON file in my server root directory instead of a MySQL database? If yes, what is the best way to do it?

Also, if you modify the data from user's web form, remember that it may have been modified in between. Like, load page with user details for editing, then other user deletes that user, then editer tries to save the changed details, and should probably get error instead of re-creating deleted user.

Note: This is very inefficient. If you are building a site where you expect more than say 10 simultaneous users, you have to use a more sophisticated scheme, or just use existing database... Also, you can't have too much data, because parsing JSON and generating modified JSON takes time.

As long as you have just one user at a time, it'll just get slower and slower as amount of data grows, but as user count increases, and more users means both more requests and more data, things start to get exponentially slower and you very soon hit limit where HTTP requests start to expire before file is available for handling the request...

At that point, do not try to hack it to make it faster, but instead pick some existing database framework (SQL or nosql or file-based). If you start hacking together your own, you just end up re-inventing the wheel, usually poorly :-). Well, unless it is just programming exercise, but even then it might be better to instead learn use of some existing framework.

Applications that use the JSON-oriented query language, created by MongoDB, can interact with data stored in Informix databases. The Informix database server also provides built-in JSON and BSON (binary JSON) data types.

Question - Which is the better way to store user related data (keeping in mind that number of fields is not fixed) in database using - JSON or column-per-field? Also, if the first model is implemented, how to query database as described above? Should I use both the models, by storing all the data which may be searched by a query in a separate row and the other data in JSON (is a different row)?

Since there won't be too many columns on which I need to perform search, is it wise to use both the models? Key-per-column for the data I need to search and JSON for others (in the same MySQL database)?

When this question was originally posted, MySQL had no support for JSON data types and the support in PostgreSQL was in its infancy. Since 5.7, MySQL now supports a JSON data type (in a binary storage format), and PostgreSQL JSONB has matured significantly. Both products provide performant JSON types that can store arbitrary documents, including support for indexing specific keys of the JSON object.

However, I still stand by my original statement that your default preference, when using a relational database, should still be column-per-value. Relational databases are still built on the assumption of that the data within them will be fairly well normalized. The query planner has better optimization information when looking at columns than when looking at keys in a JSON document. Foreign keys can be created between columns (but not between keys in JSON documents). Importantly: if the majority of your schema is volatile enough to justify using JSON, you might want to at least consider if a relational database is the right choice.

That said, few applications are perfectly relational or document-oriented. Most applications have some mix of both. Here are some examples where I personally have found JSON useful in a relational database:

As a side note: this isn't to say you should never store JSON in a relational database. If you're adding true metadata, or if your JSON is describing information that does not need to be queried and is only used for display, it may be overkill to create a separate column for all of the data points.

Like most things "it depends". It's not right or wrong/good or bad in and of itself to store data in columns or JSON. It depends on what you need to do with it later. What is your predicted way of accessing this data? Will you need to cross reference other data?

Because one of the temptations of using JSON is to avoid migrating schema and so if the team is not disciplined, it's very easy to stick yet another key/value pair into a JSON field. There's no migration for it, no one remembers what it's for. There is no validation on it.

My team used JSON along side traditional columns in postgres and at first it was the best thing since sliced bread. JSON was attractive and powerful, until one day we realized that flexibility came at a cost and it's suddenly a real pain point. Sometimes that point creeps up really quickly and then it becomes hard to change because we've built so many other things on top of this design decision.

Overtime, adding new features, having the data in JSON led to more complicated looking queries than what might have been added if we stuck to traditional columns. So then we started fishing certain key values back out into columns so that we could make joins and make comparisons between values. Bad idea. Now we had duplication. A new developer would come on board and be confused? Which is the value I should be saving back into? The JSON one or the column?

The JSON fields became junk drawers for little pieces of this and that. No data validation on the database level, no consistency or integrity between documents. That pushed all that responsibility into the app instead of getting hard type and constraint checking from traditional columns.

Looking back, JSON allowed us to iterate very quickly and get something out the door. It was great. However after we reached a certain team size it's flexibility also allowed us to hang ourselves with a long rope of technical debt which then slowed down subsequent feature evolution progress. Use with caution.

Basically, the first model you are using is called as document-based storage. You should have a look at popular NoSQL document-based database like MongoDB and CouchDB. Basically, in document based db's, you store data in json files and then you can query on these json files.

If you only have one (or a few pre-determined) levels of attributes for your main entity (user), you could still use an Entity Attribute Value (EAV) model in a relational database. (This also has its pros and cons.)

If you were using PostgreSQL, you could potentially get the best of both worlds. (This really depends on the actual structure of the data here... MySQL isn't necessarily the wrong choice either, and the NoSQL options can be of interest, I'm just suggesting alternatives.)

Indeed, PostgreSQL can build index on (immutable) functions (which MySQL can't as far as I know) and in recent versions, you could use PLV8 on the JSON data directly to build indexes on specific JSON elements of interest, which would improve the speed of your queries when searching for that data.

Mixing the two models isn't necessarily wrong (assuming the extra space is negligible), but it may cause problems if you don't make sure the two data sets are kept in sync: your application must never change one without also updating the other.

A good way to achieve this would be to have a trigger perform the automatic update, by running a stored procedure within the database server whenever an update or insert is made. As far as I'm aware, the MySQL stored procedure language probably lack support for any sort of JSON processing. Again PostgreSQL with PLV8 support (and possibly other RDBMS with more flexible stored procedure languages) should be more useful (updating your relational column automatically using a trigger is quite similar to updating an index in the same way).

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