What a great event ! I got very positive feedback for this extended preFOSDEM MySQL Day 2020.

Before my summary, I wanted to thank again all speakers and attendees. Without you this event won’t even exist. And thank you to ICAB for hosting us yet another time !

As you may know, we had 2 parallel tracks for 2 consecutive days. We had very valuable technical content from the MySQL developers and also from the external community members who talked about their favorite database.

There was a large interest in MySQL NDB Cluster and it was obvious that the nice presentations we had, encouraged people to try the worlds highest performance open source in-memory database (and they can even test it on their laptop with DBDeployer). The NDB users were also happy with the new stuff and impressive numbers that Bernd …

The schedule of the preFOSDEM Day is now available !

We had a lot of proposals to deal with. Also this is a MySQL event where we, the MySQL Team has the possibility to show to you, our Community, all what we have working on to improve MySQL but also new stuff. We also invite some of our friends from the MySQL Community to talk about their experience.

I think we did a good selection and propose you new content. We are extremely happy to have Saverio Miroddi from TicketSolve talking about MySQL 8.0, Uber talking about InnoDB Cluster, and Facebook about Binlog.

As you can see, we will have 2 rooms, where one will be dedicated mostly to SQL and Optimizer topics but also tutorials.

You can also see that this year we also want to put some spot lights to MySQL NDB Cluster. You will see what is it, how is it used and what’s new. We will also have 2 community speakers sharing their MySQL NDB knowledge: Giuseppe Maxia, …

In this part we are going to analyze a bit more complex query than before.
This query is a 6-way join.

The query is:
SELECT
        supp_nation,
        cust_nation,
        l_year,
        SUM(volume) AS revenue
FROM
        (
                SELECT
                        n1.n_name AS supp_nation,
                        n2.n_name AS cust_nation,
                        extract(year FROM l_shipdate) as l_year,
                        l_extendedprice * (1 - l_discount) AS …

In this part we will discuss how NDB batch handling works. Query execution of
complex SQL queries means that more rows can be delivered than the receiver is
capable of receiving. This means that we need to create a data flow from the
producer where the data resides and the query executor in the MySQL Server.

The MySQL Server uses a record where the storage engine have to copy the result
row into the record. This means that the storage of batches of rows is taken
care of by the storage engine.

When NDB performs a range scan it will decide on the possible parallelism before
the scan is started. The NDB API have to allocate enough memory to ensure that
we have memory prepared to receive the rows as they arrive in a flow of result
rows from the data nodes. It is possible to set batch size of hundreds and even
thousands of rows for a query.

The …

In the previous part we showed how NDB will parallelise a simple
2-way join query from TPC-H. In this part we will describe how
the pushdown of joins to a storage engine works in the MySQL Server.

First a quick introduction to how a SQL engine handles a query.
The query normally goes through 5 different phases:
1) Receive query on the client connection
2) Query parsing
3) Query optimisation
4) Query execution
5) Send result of query on client connection

The result of 1) is a text string that contains the SQL query to
execute. In this simplistic view of the SQL engine we will ignore
any such things as prepared statements and other things making the
model more complex.

The text string is parsed by 2) into a data structure that represents
the query in objects that match concepts in the SQL engine.

Query …

When we explore strategies to scale our MySQL-based database layer, we realize that there are many different options. As opposed to proprietary solutions, open-source products nourish themselves from their communities and novel architectures or enhanced versions are generated, often incorporating other open source technologies. MySQL is not an exception.

Other players build completely different engines aiming to mitigate some of MySQL well-known weaknesses, but still support its wire protocol, with the challenge of sacrificing as little compatibility as possible.

In this white paper, we briefly discuss the scale-up vs scale-out dilemma, to then review the different scale-out strategies. We are also going to go over some of the most common MySQL architectures and products with horizontal scaling in mind.

Finally, we will share some guidelines to …

I remember wanting to create MySQL Cluster sandboxes several years ago. By then, however, MySQL-Sandbox technology was not flexible enough to allow an easy inclusion, and the cluster software itself was not as easy to install as it is today. Thus, I kept postponing the implementation, until I started working with dbdeployer.

I included the skeleton of support for MySQL Cluster since the beginning (by keeping a range of ports dedicated for this technology, but I didn’t do anything until June 2018, when I made public my intentions to add support for NDB in dbdeployer with issue #20 (Add support for MySQL Cluster)). The issue had just a bare idea, but I needed help from someone, as my expertise with …

MySQL NDB Cluster uses row level locks instead of a single shared commit lock in order to prevent inconsistency in simultaneous distributed transactions. This gives NDB a great advantage over all other MySQL clustering solutions and is one reason behind cluster’s unmatched ability to scale both reads and writes. 
NDB is a transactional data store. The lowest and only isolation level available in NDB is Read Committed. There are no dirty reads in NDB and only committed rows can be read by other transactions. 
All write transactions in NDB will result in exclusive row locks of all individual rows changed during the transaction. Any other transaction is allowed to read any committed row independent of their lock status. Reads are lock-free reads.
The great advantage is that committed reads in NDB never block during writes to the same data and always the latest committed changes are read. A select doesn't block concurrent …

Last time I mentioned four great MySQL books for 2018.  I was tactfully reminded of two books I overlooked. First is Dr. Charles Bell's Introducing InnoDB Cluster which I have not read (but it is on order).

And last, but not least, is Mikael Ronstrum's MySQL Cluster 7.5 Inside and Out.  This is another book on NDB cluster and is a 'msut have' for those running NDB clusters.

Someone once told me you can tell how healthy a software project is by the number of new books each year.  For the past few years the MySQL community has been blessed with one or two books each year. Part of that was the major shift with MySQL 8 changes but part of it was that the vast majority of the changes were fairly minor and did not need detailed explanations. But this year we have been blessed with four new books.  Four very good books on new facets of MySQL.

Introducing the MySQL 8 Document Store is the latest book from Dr. Charles Bell on MySQL.  If you have read any other of Dr. Chuck's book you know they are well written with lots of examples.  This is more than a simple introduction with many intermediate and advanced concepts covered in detail.