Overview 

Percona operator for MySQL (POM) :(https://www.percona.com/doc/kubernetes-operator-for-pxc/index.html) it’s aim is a special type of controller introduced to simplify complex deployments. The Operator extends the Kubernetes API with custom resources.

The Percona Operator for MySQL solution is using Percona Xtradb Cluster behind the hood to provide a highly available, resilient and scalable MySQL service in the Kubernetes space. 

This solution comes with all the advantages/disadvantages provided by Kubernetes, plus with some advantages of its own like the capacity to scale reads on the nodes that are not Primary.

Of course there are some limitations like the way PXC handle DDLs, which may impact the service, but there is always a cost to pay to get something, expecting to have all for free is unreasonable.     

In this context we need to talk and cover what is full read consistency in this solution and why it is important to understand the role it plays.  

Stale Reads

When using Kubernetes we should talk about the service and not about the technology/product used to deliver such service. 

In our case the Percona operator is there to deliver a MySQL service. We should then see that as a whole as a single object. To be more clear what we must consider is NOT the fact we have a cluster behind the service but that we have a service that to be resilient and highly available use a cluster. 

We should not care If a node/pod goes down unless the service is discontinued.

What we have as a plus in the Percona operator for MySQL solution is a certain level of READ scalability. This achieved optimising the use of the non PRIMARY nodes, and instead having them sitting there applying only replicated data, the Percona Operator provides access to them to scale the reads.  

But… there is always a BUT  

Let us start with an image: 

(https://www.slideshare.net/lefred.descamps/galera-replication-demystified-how-does-it-work) from Fred Descamps)

By design the apply and commit finalize in Galera (PXC) may have (and has) a delay between nodes.

This means that, if using defaults, applications may have inconsistent reads if trying to access the data from different nodes than the Primary. 

POM provides access using two different solutions:

• Using HAProxy (default)
• Using ProxySQL

When using HAProxy you will have 2 entry points:

• cluster1-haproxy, which will point to the Primary ONLY, for reads and writes. This is the default entry point for the applications to the MySQL database.
• cluster1-haproxy-replicas, which will point to all the 3 nodes and is supposed to be used for READS only. This is the PLUS you can use if your application has READ/WRITE separation.

Please note that at the moment there is nothing preventing application to use the cluster1-haproxy-replicas also for write, but that is dangerous and wrong because will generate a lot of certification conflicts and BF abort given it will distribute writes all over the cluster impacting on performance as well (and not giving you any write scaling):

[marcotusa@instance-1 ~]$ for i in `seq 1 100`; do mysql -h cluster1-haproxy-replicas -e "insert into test.iamwritingto values(null,@@hostname)";done
+----------------+-------------+
| host           | count(host) |
+----------------+-------------+
| cluster1-pxc-1 |          34 |
| cluster1-pxc-2 |          33 |
| cluster1-pxc-0 |          33 |
+----------------+-------------+

When using ProxySQL the entry point is a single one, but you may define query rules to automatically split the R/W requests coming from the application.

This is the preferred method when application has no way to separate the READS from the writes.

I have done a comparison of the two methods in POM here 

Now, as mentioned above, by default PXC (any Galera base solution) comes with some relaxed settings, for performance purpose. This is normally fine in many standard cases, but if you use POM and use the PLUS of scaling reads using the second access point with HAproxy or Query Rules with Proxysql, you should NOT have stale reads, given the service must provide consistent data, as if you are acting on a single node. 

To achieve that you can change the defaults and change the parameter in PXC wsrep_sync_wait. 

When changing the parameter wsrep_sync_wait as explained in the documentation the node initiates a causality check, blocking incoming queries while it catches up with the cluster. 

Once all data on the node receiving the READ request is commit_finalized, the node performs the read.

But this has a performance impact as said before.

What is the impact?

To test the performance impact I had used a cluster deployed in GKE, with this characteristics:

• 3 Main nodes n2-standard-8 (8 vCPUs, 32 GB memory)
• 1 App node n2-standard-8 (8 vCPUs, 32 GB memory)
• PXC pods using:
  •  25GB of the 32 available 
  • 6 CPU of the 8 available
• HAProxy:
  • 600m CPU
  • 1GB RAM
• PMM agent
  • 500m CPU
  • 500 MB Ram

On The application  node I used sysbench running two instances, one in r/w mode the other only reads. Finally to test stale read I used the stale read test from my test suite (  https://github.com/Tusamarco/testsuite) .

Given I was looking for results with moderate load I just used 68/96/128 threads per sysbench instance. 

Results

Marco, did we have or not stale reads? Yes we did:

I had from 0 (with very light load) up to 37% stale reads with MODERATED load. Where moderated was the 128 threads sysbench running. 

Setting wsrep_sync_wait=3 of course I had full consistency.
But I had performance loss:

As you can see I had an average loss of 11% in case of READS

While for writes the average loss was the 16%. 

Conclusions 

At this point we need to stop and think about what is worth doing. If my application is READs heavy and READs scaling, it is probably worth enabling the full synchronicity given scaling on the additional node allows me to have a 2x or more READs. 

If instead my application is write critical, probably losing also ~16% performance is not good.

Finally if my application is stale reads tolerant, I will just go with the defaults and get all the benefits without penalties.

Also keep in mind that POM is designed to offer a MySQL service so the state of the single node is not as critical as if you are using a default PXC installation, PODs are by nature ephemeral objects while service is resilient.

References      

https://www.percona.com/doc/kubernetes-operator-for-pxc/index.html

https://github.com/Tusamarco/testsuite

https://en.wikipedia.org/wiki/Isolation_(database_systems)#Dirty_reads

https://galeracluster.com/library/documentation/mysql-wsrep-options.html#wsrep-sync-wait

https://www.slideshare.net/lefred.descamps/galera-replication-demystified-how-does-it-work

Overview

Percona Operator for MySQL (POM) comes with two different proxies, HAProxy and ProxySQL. While the initial version was based on ProxySQL, in time Percona opted to set HAProxy as the default Proxy for the operator, this without removing ProxySQL. 

While one of the main points was to guarantee users to have a 1:1 compatibility with vanilla MySQL in the way the operator allows connections. There are also other factors that are involved in the decision to have two proxies. In this article I will scratch the surface of this why.

Operator assumptions

When working with the Percona operator for MySQL, there are few things to keep in mind:

• Each deployment has to be seen as a single MySQL service as if a single MySQL instance
• The technology used to provide the service may change in time
• Pod resiliency is not guaranteed, Service resiliency is. 
• Resources to be allocated are not automatically calculated and must be identified at the moment of the deployment
• In Production you cannot set more than 5 or less than 3 nodes when using PXC

There are two very important points in the list above.

The first one is that what you get IS NOT a PXC cluster, but a MySQL service. The fact that Percona at the moment uses PXC to cover the service is purely accidental and we may decide to change it anytime.

The other point is that Service is resilient the pod is not. In short you should expect to see pods stopping to work and being re-created. What should NOT happen is that service goes down. Trying to debug each minor issue per node/pod is not what is expected when you use kubernetes. 

Given the above, review your expectations… and let us go ahead. 

The plus in the game (read scaling)

As said, what is offered with POM is a mysql service. Percona has added a proxy on top of the nodes/pods that help the service to respect the resiliency service expectations. There are two possible deployments:

• HAProxy
• ProxySQL

Both allow to optimise one aspect of POM, which is read scaling.
Infact what we were thinking was, given we must use a (virtually synchronous) cluster, why not take advantage of that and allow reads to scale on the other nodes when available? 

This approach will help all the ones using POM to have the standard MySQL service but with a plus. 

But, with it also come some possible issues like READ/WRITE splitting and stale reads. About stale reads see this article on how to deal with it (https://docs.google.com/document/d/1NyqcEKxfhgD1tDRPbiY0bENdp953DzkBfAoMi22u3J4/edit)

For R/W splitting we instead have a totally different approach in respect to what kind of proxy we implement. 

If using HAProxy, we offer a second entry point that can be used for READ operation. That entrypoint will balance the load on all the nodes available. 

Please note that at the moment there is nothing preventing application to use the cluster1-haproxy-replicas also for write, but that is dangerous and wrong because will generate a lot of certification conflicts and BF abort given it will distribute writes all over the cluster impacting on performance as well (and not giving you any write scaling). It is your responsibility to guarantee that only READS will go through that entrypoint.

If instead ProxySQL is in use it is possible to implement automatic R/W splitting. 

Global difference and comparison

At this point it is useful to have a better understanding of the functional difference between the two proxies and what is the performance difference if any. 

As we know HAProxy acts as a level 4 proxy when operating in TCP mode, it also is a forward-proxy, which means each TCP connection is established with the client with the final target and there is no interpretation of the data-flow.

ProxySQL on the other hand is a level 7 proxy and is a reverse-proxy, this means the client establishes a connection to the proxy who presents itself as the final backend. Data can be altered on the fly when it is in transit. 

To be honest, it is more complicated than that but allows me the simplification. 

On top of that there are additional functionalities that are present in one (ProxySQL) and not in the other. The point is if they are relevant for the use in this context or not. For a short list see below (source is from ProxySQL blog but data was removed)  

As you may have noticed HAProxy is lacking some of that functionalities, like R/W split, firewalling and caching, proper of the level 7 implemented in ProxySQL.  

The test environment

To test the performance impact I had used a cluster deployed in GKE, with this characteristics:

• 3 Main nodes n2-standard-8 (8 vCPUs, 32 GB memory)
• 1 App node n2-standard-8 (8 vCPUs, 32 GB memory)
• PXC pods using:
  •  25GB of the 32 available 
  • 6 CPU of the 8 available
• HAProxy:
  • 600m CPU
  • 1GB RAM
• PMM agent
  • 500m CPU
  • 500 MB Ram
• Tests using sysbench as for (https://github.com/Tusamarco/sysbench) 

What I have done is to run several tests running two Sysbench instances. One only executing reads, while the other reads and writes. 

In the case of ProxySQL I had R/W splitting thanks to the Query rules, so both sysbench instances were pointing to the same address. While testing HAProxy I was using two entry points:

• Cluster1-haproxy – for read and write
• Cluster1-haproxy-replicas – for read only

Then I also compare what happens if all requests hit one node only. For that I execute one Sysbench in R/W mode against one entry point, and NO R/W split for ProxySQL.

Finally sysbench tests were executed with the –reconnect option to force the tests to establish new connections.

As usual tests were executed multiple times, on different days of the week and moment of the day. Data reported is a consolidation of that, and images from PMM are samples coming from the execution that was closest to the average values. 

Comparing performance when scaling Reads

These tests imply that one node is mainly serving writes while the others are serving reads. To not affect performance and given I was not interested in maintaining full read consistency the parameter wsrep_sync_wait was kept as default (0). 

A first observation shows how ProxySQL seems to keep a more stable level of request served. The increasing load penalises HAProxy reducing if ⅓ the number of operations at 1024 threads.

Digging a bit more we can see that HAProxy is performing much better than ProxySQL for the WRITE operation. The number of writes remains almost steady with minimal fluctuations. ProxySQL on the other hand is performing great when load in write is low, then performance drops by 50%.

For reads we have the opposite. ProxySQL is able to scale in a very efficient way, distributing the load across the nodes and able to maintain the level of service despite the load increase. 

If we start to take a look to the latency distribution statistics (sysbench histogram information), we can see that:

In case of low load and writes both proxies stay on the left side of the graph with low value in ms. HAProxy is a bit more consistent and grouped around 55ms value, while ProxySQL is a bit more sparse and spans between 190-293ms.

About reads we have a similar behaviour, both for the large majority between 28-70ms.

We have a different picture when load increases:  

ProxySQL is having some occurrences where it performs better, but it spans in a very large range, from ~2k ms to ~29k ms. While HAProxy is substantially grouped around 10-11K ms.
As a result, in this context, HAProxy is able to better serve writes under heavy load than ProxySQL. 

Again different picture in case of reads.

Here ProxySQL is still spanning on a wide range ~76ms – 1500ms, while HAProxy is more consistent but less efficient, grouping around 1200ms the majority of the service. This is consistent with the performance loss we have seen in READ when using high load and HAProxy.  

Comparing when using only one node

But let us now discover what happens when using only one node. So using the service as it should be, without the possible Plus of read scaling. 

The first thing I want to mention is a strange behaviour that was consistently happening (no matter what proxy used) at 128 threads. I am investigating it but I do not have a good answer yet on why Operator solution with PXC, was having that significant drop in performance ONLY with 128 threads.

Aside that, the results were consistently showing HAProxy performing better in serving read/writes. Keep in mind that HAProxy just establishes the connection point-to-point and is not doing anything else. While ProxySQL is designed to eventually act on the incoming stream of data. 

This becomes even more evident when reviewing the latency distribution.
In this case no matter what load we have, HAProxy performs better:

As you can notice, HAProxy is less grouped than when we have 2 entry points, but it is still able to serve more efficiently than ProxySQL.

Conclusions

As usual my advice is to use the right tool for the job, and do not force yourself in something stupid.
As clearly stated at the beginning POM is designed to provide a mysql SERVICE, not a PXC cluster and all the configuration and utilisation should converge on that.
ProxySQL can help you IF you want to scale a bit more on READS using the possible plus. But this is not guaranteed to work as it works when using standard PXC
Not only you need to have a very good understanding of Kubernetes and ProxySQL if you want to avoid issues.
With HAProxy you can scale reads as well, but you need to be sure you have R/W separation at application level.

In any case utilising HAProxy for the service is the easier way to go.
This is one of the reasons why Percona decided to shift to HAProxy.
HAProxy is the solution that offers the proxy service more in line with the aim of the kubernetes service concept.
It is also the solution that remains closer on how a simple MySQL service should behave.

You need to set your expectations correctly to avoid being in trouble later.

References

https://www.percona.com/doc/kubernetes-operator-for-pxc/index.html