MySQL team: make it easy to give you feedback!

There was a bold announcement during the MySQL Keynote at Oracle Open World. A new product that will mix up with the existing GA server, called MySQL InnoDB Cluster. This is an evolution of MySQL group replication, which has been in the labs for long time, and the MySQL shell, which was introduced as a side feature last April. The boldness I mentioned before is on account of wanting to add to a GA server something that was defined as release candidate despite never having been out of the labs. The product is interesting as it promises to be a quick and painless cluster deployment, with built-in high availability and scalability.

What surprised me most was a heartfelt and urgent request to test this new product and provide feedback, hinting that it would be GA soon.

Here are some thoughts on this matter:

• A product in the labs is perceived as pre-release, i.e. less than beta quality. This is what happened with previous releases on labs: GTID, multi-source replication, and data dictionary were all released in labs before eventually being integrated in the main project.
• Putting a product in labs again and declaring it release candidate feels odd.
• The problem with labs is that the previews are distributed with a limited set of packages, and without documentation. The brave souls that test these packages need to find information about the new software in blog posts or dig in the source code, without any assurance that this package would ever become officially supported.

There is some confusion about which package is of which quality. From the keynote it looked like MySQL InnoDB Cluster (MIC) was the one being RC, but when I asked for clarifications it seems that group replication is RC (from its niche in the labs) while MIC is still of unknown quality. From what I saw in the demos it seems quite alpha to me.

Back to the main topic. MySQL want feedback, but provides software in the labs, in a way that is not easy to use. Specifically:

• There is an OSX package that contains .dmg files, implying that I should install those in my main computer. Given that the perceived quality is low, I'd say "No, thanks," as I don't want to risk my laptop with alpha quality installed as root. Besides, this is cluster software, so I would need at least three nodes to make it work. There is a "sandbox mode" that allows you to simulate three nodes on a single server, but this still requires a main installation, with all the risks involved. No, thanks, again.
• There are only .rpm files for Linux, which means that I need to have either servers or VMs where to install software as root. I have the same concerns as I have for the Mac: while VMs can be thrown away and remade, it is still a big investment in time and resources to test something new.
• Missing are generic .tar.gz binaries, which would allow users to install in user space, without affecting the operating system or other MySQL servers.
• Missing are also Docker packages, which would allow users to test quickly and painlessly without any risk.
• Finally, and probably most importantly, there is no documentation. If this is RC software, there should be at least a couple of workloads that could be included in the labs packages for reference.

Summing up, I have a message for the MySQL team product managers and developers: if the software is meant to be usable, i.e. more than a proof of concept as other things in the labs, move it to the downloads section, same as it happened with the MySQL Shell and the document store early this year. Also, provide Docker images early on, so that people can test without many risks. This exercise alone would discover bugs just while you are doing it. And please add documentation for the feature you want feedback for. If the manual is not ready, don't limit the docs to a skinny blog post, but add the specifications used to create the feature (workloads) or even an alpha version of the docs. In short, if the software is worth giving feedback, it should be treated with more respect than it is shown right now. And the same respect goes for the users whom you are asking feedback from.

Multiple masters : attraction to the stars

In the last 10 years I have worked a lot with replication systems, and I have developed a keen interest in the topic of multiple masters in a single cluster. My interest has a two distinct origins:

• On one hand, I have interacted countless times with users who want to use a replication system as a drop-in replacement for a single server. In many cases, especially when users are dealing with applications that are not much flexible or modular, this means that the replication system must have several points of data entry, and such points must work independently and in symbiosis with the rest of the nodes.
• On the other hand, I am a technology lover (look it up in the dictionary: it is spelled geek), and as such I get my curiosity stirred whenever I discover a new possibility of implementing multi-master systems.

The double nature of this professional curiosity makes me sometimes forget that the ultimate goal of technology is to improve the users life. I may fall in love with a cute design or a clever implementation of an idea, but that cleverness must eventually meet with usability, or else it loses its appeal. There are areas where the distinction between usefulness and cleverness is clear cut. And there are others where we really don’t know where we stand because there are so many variables involved.

One of such cases is a star topology, where you have many master nodes, which are connected to each other through a hub. You can consider it a bi-directional master/slave. If you take a master/slave topology, and make every node able to replicate back to the master, then you have almost a star. To make it complete, you also need to add the ability of the master of broadcasting the changes received from the outside nodes, so that every node gets the changes from every other node. Compared to other popular topologies, say point-to-point all-masters, and circular replication, the star topology has the distinct advantage of requiring less connections, and of making it very easy to add a new node.

Figure #1: Star topology

However, anyone can see immediately one disadvantage of the star topology: the hub is the cornerstone of the cluster. It’s a single point of failure (SPOF). If the hub fails, there is no replication anywhere. Period. Therefore, when you are considering a multi-master topology, you have to weigh in the advantages and disadvantages of the star, and usually you consider the SPOF as the most important element to consider.

Depending on which technology you choose, though, there is also another important element to consider, i.e. that data must be replicated twice when you use a star topology. It’s mostly the same thing that happens in a circular replication. If you have nodes A, B, C, and D, and you write data in A, the data is replicated three times before it reaches D (A->B, B->C, and C->D). A star topology is similar. In a system where A, B, and D are terminal nodes, and C is the hub, data needs to travel twice before it reaches D (A->C, C->D).

Figure #2: Circular replication

This double transfer is bad for two reasons: it affects performance, and it opens to the risk of unexpected transformations of data. Let’s explore this concept a bit. When we replicate data from a master to a slave, there is little risk of mischief. The data goes from the source to a reproducer. If we use row-based-replication, there is little risk of getting the wrong data in the slave. If we make the slave replicate to a further slave, we need to apply the data, generate a further binary log in the slave host, and replicate data from that second binary log. We can deal with that, but at the price of taking into account more details, like where the data came from, when to stop replicating in a loop, whether the data was created with a given configuration set, and so on. In short, if your slave server has been configured differently from the master, chances are that the data down the line may be different. In a star topology, this translates into the possibility of data in each spoke to be replicated correctly in the hub, but to be possibly different in the other spokes.

Compare this with a point-to-point all-masters. In this topology, there are no SPOFs. You pay for this privilege by having to set a higher number of connections between nodes (every node must connect to every other node), but there is no second hand replication. Before being applied to the slave service, the data is applied only once in the originating master.

Figure #2: Point-to-point all-masters topology

Where do I want to go from all the above points? I have reached the conclusion that, much as user like star topologies, because of their simplicity, I find myself often recommending the more complex but more solid point-t-point all-masters setup. Admittedly, the risk of data corruption is minimal. The real spoiler in most scenarios is performance. When users realize that the same load will flow effortlessly in a point-to-point scenario, but cause slave lags in a star topology, then the choice is easy to make. If you use row-based replication, and in a complex topology it is often a necessary requirement, the lag grows to a point where it becomes unbearable.

As I said in the beginning, all depends on the use case: if the data load is not too big, a star topology will run just as fine as point-to-point, and if the data flow is well designed, the risk of bad data transformation becomes negligible. Yet, the full extent of star topologies weaknesses must be taken into account when designing a new system. Sometimes, investing some effort into deploying a point-to-point all-masters topology pays off in the medium to long term. Of course, you can prove that only if you deploy a star and try it out with the same load. If you deploy it on a staging environment, no harm is done. If you deploy in production, then you may regret. In the end, it all boils down to my mantra: don’t trust the theory, but test, test, test.

Replication stars

Working with replication, you come across many topologies, some of them sound and established, some of them less so, and some of them still in the realm of the hopeless wishes. I have been working with replication for almost 10 years now, and my wish list grew quite big during this time. In the last 12 months, though, while working at Continuent, some of the topologies that I wanted to work with have moved from the cloud of wishful thinking to the firm land of things that happen. My quest for star replication starts with the most common topology. One master, many slaves.

Fig 1. Master/Slave topology

Legend

It looks like a star, with the rays extending from the master to the slaves. This is the basis of most of the replication going on mostly everywhere nowadays, and it has few surprises. Setting aside the problems related to failing over and switching between nodes, which I will examine in another post, let's move to another star.

Fig 2. Fan-in slave, or multiple sources

The multiple source replication, also known as fan-in topology, has several masters that replicate to the same slave. For years, this has been forbidden territory for me. But Tungsten Replicator allows you to create multiple source topologies easily. This is kind of uni-directional, though. I am also interested in topologies where I have more than one master, and I can retrieve data from multiple points.

Fig 3. all-to-all three nodes

Fig 4. All-to-all four nodes

Tungsten Multi-Master Installation solves this problem. It allows me to create topologies where every node replicates to every other node. Looking at the three-node scheme, it appears a straightforward solution. When we add one node, though, we see that the amount of network traffic grows quite a lot. The double sided arrows mean that there is a replication service at each end of the line, and two open data channels. When we move from three nodes to four, we double the replication services and the channels needed to sustain the scheme. For several months, I was content with this. I thought: it is heavy, but it works, and it's way more than what you can do with native replication, especially if you consider that you can have a practical way of preventing conflicts using Shard Filters. But that was not enough. Something kept gnawing at me, and from time to time I experimented with Tungsten Replicator huge flexibility to create new topologies. But the star kept eluding me. Until … Until, guess what? a customer asked for it. The problem suddenly ceased to be a personal whim, and it became a business opportunity. Instead of looking at the issue in the idle way I often think about technology, I went at it with practical determination. What failed when I was experimenting in my free time was that either the pieces did not glue together the way I wanted, or I got an endless loop. Tungsten Replicator has a set of components that are conceptually simple. You deploy a pipeline between two points, open the tap, and data starts flowing in one direction. Even with multiple masters replication, the principle is the same. You deploy many pipes, and each one has one purpose only.

Fig 5. All-masters star topology

In the star topology, however, you need to open more taps, but not too many, as you need to avoid the data looping around. The recipe, as it turned out, is to create a set of bi-directional replication systems, where you enable the central node slave services to get changes only from a specific master, and the slave services on the peripheral nodes to accept changes from any master. It was as simple as that. There are, of course, benefits and drawbacks with a star topology, compared to a all-replicate-to-all design. In the star topology, we create a single point of failure. If the central node fails, replication stops, and the central node needs to be replaced. Instead, the all-to-all design has no weaknesses. Its abundance of connections makes sure that, if a node fails, the system continues working without any intervention. There is no need for fail-over.

Fig 6. extending an all-to-all topology

Fig 7. Extending a star topology

However, there is a huge benefit in the node management. If you need to add a new node, it costs two services and two connections, while the same operation in the all-to-all replication costs 8 services and 8 connections. With the implementation of this topology, a new challenge has arisen. While conflict prevention by sharding is still possible, this is not the kind of scenario where you want to apply it. We have another conflict prevention mechanism in mind, and this new topology is a good occasion make it happen. YMMV. I like the additional choice. There are cases where a all-replicate-to-all topology is still the best option, and there are cases where a star topology is more advisable.

Scalable architectures with MySQL Proxy

MySQL community, mark your calendars!. On July 8th, 2008, there is a Webinar on Designing scalable architectures with MySQL Proxy.
This is not the usual marketing sponsored webinar. Although we love to show off, this is not a "look-how-good-we-are" presentation. This is a community driven event, where a community member, using only MySQL Proxy and some creativity, solved his production problems.

This is a real story of a community member who used open source software to build a customized scalable architecture to suit his purposes. Isn't it a good story?
I won't steal his thunder and tell you in advance what was the problem about. I will introduce the general concepts about Proxy, and then our guest John Loehrer will tell hist story in full colors.
Don't miss this one!