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Looking inside the MySQL 5.7 document store

 | May 24, 2016 |  Posted In: MySQL

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In this blog, we’ll look at the MySQL 5.7 document store feature, and how it is implemented.

Document Store

MySQL 5.7.12 is a major new release, as it contains quite a number of new features:

  1. Document store and “MongoDB” like NoSQL interface to JSON storage
  2. Protocol X / X Plugin, which can be used for asynchronous queries (I will write about it as well)
  3. New MySQL shell

Peter already wrote the document store overview; in this post, I will look deeper into the document store implementation. In my next post, I will demonstrate how to use document store for Internet of Things (IoT) and event logging.

Older MySQL 5.7 versions already have a JSON data type, and an ability to create virtual columns that can be indexed. The new document store feature is based on the JSON datatype.

So what is the document store anyway? It is an add-on to a normal MySQL table with a JSON field. Let’s take a deep dive into it and see how it works.

First of all: one can interface with the document store’s collections using the X Plugin (default port: 33060). To do that:

  1. Enable X Plugin and install MySQL shell.
  2. Login to a shell:
  3. Run commands (JavaScript mode, can be switched to SQL or Python):

Now, how is the document store’s collection different from a normal table? To find out, I’ve connected to a normal MySQL shell:

So the document store is actually an InnoDB table with one field: doc json + Primary key, which is a generated column.

As we can also see, there are four tables in the world_x database, but db.getCollections() only shows one. So how does MySQL distinguish between a “normal” table and a “document store” table? To find out, we can enable the general query log and see which query is being executed:

As you can see, every table that has a specific structure (doc JSON or specific generation_expression) is considered to be a JSON store. Now, how does MySQL translate the .find or .add constructs to actual MySQL queries? Let’s run a sample query:

and now look at the slow query log again:

We can verify that MySQL translates all document store commands to SQL. That also means that it is 100% transparent to the existing MySQL storage level and will work with other storage engines. Let’s verify that, just for fun:

Worked fine!

Now, how about the performance? We can simply take the SQL query and run explain:

Hmm, it looks like it is not using an index. That’s because there is no index on Name. Can we add one? Sure, we can add a virtual column and then index it:

That is really cool! We have added an index, and now the original query starts using it. Note that we do not have to reference the new field, the MySQL optimizer is smart enough to translate the (JSON_EXTRACT(doc,'$.Name') = 'United States' to an index scan on the virtual column.

But please note: JSON attributes are case-sensitive. If you will use (doc,'$.name') instead of (doc,'$.Name') it will not generate an error, but will simply break the search and all queries looking for “Name” will return 0 rows.

Finally, if you looked closely at the output of db.getCollection("CountryInfo").find('Name= "United States"').limit(1) , you noticed that the database has outdated info:

Let’s change “George W. Bush” to “Barack Obama” using the .modify clause:

Conclusion

Document store is an interesting concept and a good add-on on top of the existing MySQL JSON feature. Using the new .find/.add/.modify methods instead of the original SQL statements can be convenient in some cases.

Some might ask, “why do you want to use document store and store information in JSON inside the database if it is relational anyway?” Storing data in JSON can be quite useful in some cases, for example:

  • You already have a JSON (i.e., from external feeds) and need to store it anyway. Using the JSON datatype will be more convenient and more efficient.
  • You have a flexible schema, typical for the Internet of Things for example, where some sensors might only send temperature data, some might send temperature/humidity/light (but light information is only recorded during the day), etc. Storing it in the JSON format can be more convenient so that you do not have to declare all possible fields in advance, and do not have to run “alter table” if a new sensor starts sending new types of data.

In the next two blog posts, I will show how to use document store for Internet of Things / event streaming, and how to use X Protocol for asynchronous queries in MySQL.

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Alexander Rubin

Alexander joined Percona in 2013. Alexander worked with MySQL since 2000 as DBA and Application Developer. Before joining Percona he was doing MySQL consulting as a principal consultant for over 7 years (started with MySQL AB in 2006, then Sun Microsystems and then Oracle). He helped many customers design large, scalable and highly available MySQL systems and optimize MySQL performance. Alexander also helped customers design Big Data stores with Apache Hadoop and related technologies.

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