Sweet and sour can become bitter
Written by Marco Tusa   


Recently Fred published a post (http://lefred.be/content/mysql-group-replication-is-sweet-but-can-be-sour-if-you-misunderstand-it) in which he was stating, I had publish my blog (http://www.tusacentral.net/joomla/index.php/mysql-blogs/191-group-replication-sweet-a-sour.html) which contains few “misunderstanding”.


All the people that knows me, also know I have no problem to admit if I do a mistake, at the end doing mistake is a way of learning and be afraid of the ones who don’t do them.

But at the same time, I need to have proof of it. As I provide proof of what I wrote with numbers and tests (all available on github).

Let us put down the basis of the discussion with facts, not personal speculation or assertions.


1) From MySQL official documentations (https://dev.mysql.com/doc/refman/5.7/en/group-replication-use-cases.html) 

Group Replication enables you to create fault-tolerant systems with redundancy by replicating the system state throughout a set of servers. Consequently, even if some of the servers fail, as long it is not all or a majority, the system is still available, and all it could have degraded performance or scalability, it is still available. Server failures are isolated and independent. They are tracked by a group membership service which relies on a distributed failure detector that is able to signal when any servers leave the group, either voluntarily or due to an unexpected halt. There is a distributed recovery procedure to ensure that when servers join the group they are brought up to date automatically. There is no need for server fail-over, and the multi-master update everywhere nature ensures that not even updates are blocked in the event of a single server failure. Therefore MySQL Group Replication guarantees that the database service is continuously available.


2) Still from MySQL (https://dev.mysql.com/worklog/task/?id=9838Group Replication introduced flow control to avoid excessive buffering and to maintain group members reasonably close to one another. For several reasons, it's more efficient to keep buffering low but, as mentioned before, it is not a requirement that members are kept in sync for replication to work: once a slave becomes delayed it will just increase the number of pending transactions in the relay log.

The flow control mechanism enters the scene to bound the back-log members can accumulate, in terms of transactions to certify and transactions to apply. Once those bounds are exceeded, and while they remain exceeded, the flow-control mechanism limits the throughput of writer members to adjust to the capacity of the slowest members of the group. By imposing conservative bounds it tries to make sure all members are able to keep-up and can return safely to within the bounds defined by the user.



So given the above, I, as “standard” user, read that as “when using MySQL and GR, and setting (eventually) the Flow Control correctly, I will achieve to have a data platform that is continuously available given the use of GR”.

Cool, right? So what I was doing in my tests? Well two main things, one is to see if I can use GR for scaling reads as I was doing (and hundreds of customers as well) with PXC, the other is to see if in case of crash of my Master, I can safely fail-over another slave.

This doesn’t mean I am comparing the way the two product works. I cannot care less at this stage, as I am sure most of the customer will not care. What they care is what they can do SAFELY, and with what. All the mambo-jambo about the technical details (how much sync or async I can be) is not their concern.

So the point was and is… Given I am used to product X can I move to product Y and if so how and what I should be aware of?

Once more I was trying to answer to the question “If GR and InnoDB cluster has to work as alternative to other (virtually) synchronous replication mechanism, what change or shift our customers must consider if they want move from one to the other.”

The outcome of the tests is pointing to answer that, period.


Let us clarify two things more:

I am perfectly aware (Fred talking to you) that GR use a different mechanism for FC, and that the numbers set in the group_replication_flow_control_certifier_threshold/ group_replication_flow_control_applier_threshold are then use to calculate the Quota. Still they are user threshold, and Specifies the number of waiting transactions in the applier/certifier queue that trigger flow control which are connected to the final statements reported above: By imposing conservative bounds it tries to make sure all members are able to keep-up and can return safely to within the bounds defined by the user.
Bound that as for the manual can go from 0 to 2147483648. 
As such setting it to 25 (I did try also 1000 or more with even worse results) is perfectly legit and I have some issues in considering it a mistake.


I was measuring the lag with the only tools MySQL/Oracle had give us, in the article I said I had used:
select  @last_rec:=SUBSTRING_INDEX(SUBSTRING_INDEX(SUBSTRING_INDEX( Received_transaction_set,':',-2),':',1),'-',-1) last_received FROM performance_schema.replication_connection_status WHERE Channel_name = 'group_replication_applier'; select (@last_rec - @last_exec) as real_lag"

Which use the only information available at the moment regarding the incoming and the current executed transactions.
Fred says that is not valid because the certification and
This means that a transaction listed on last_received_transaction_set is certified and queued to apply on this member. On the other members it may already be certified/applied or soon be.
I agree that it may not be perfect .. and I should have said: is for sure the last apply or soon to be given the certification on the master a node can know about.
But here we are not talking of 1 or 10 entries, but in most cases lag of hundreds or thousands entries. So also if this is not perfect and I can miss in a way or another a couple or entries because still processing the certification, I still think it is a valid way to evaluate the lag given the dimensions we are talking about.

BTW if this is not … well please help us and provide the right way to calculate the REAL lag between each node in a GR cluster and the writing master, with the most accurate precision.


About the comment of the node dimension, well thanks and yes you are right here I forgot to put the details.

The four nodes are VMS on separated hosts, so the gr1/2 where hosted on Host1 while gr3/4 hosted on the another host. Nothing else running on the hosts while test was running. Applications and monitor where running on a 3 host. The exact dimension is not really relevant given all of them were the same.

I want to say that I can setup the perfect environment for GR or PXC or NDB or whatever you are testing and showing how cool that is.

But I think we should consider that real life is far to be like that, and that we must see/test is how a product is behaving in adverse conditions, or if not adverse challenging.


Given all the above, in my opinion the tests clearly shown that also if the Flow Control mechanism in GR is the coolest thing ever conceptualize, at the end it does not what it is suppose to, no matter what.

Was I setting the thresholds wrong? Well not sure about that, given the results I got.

If I have 4 nodes and 2 of them (so … no majority) are lagging behind of hundreds or even thousands of entries, while my threshold is in the order of hundreds or less, this means that the cool mechanism is not doing his job, period.

One think is write down whatever about this or that but another is doing tests and provide numbers and evidences, not just words.

As said in a comment (not published in the Fred blog) I am more than happy to perform more tests and do them in conjunctions with anyone else.

Then if I am wrong I am wrong… but until I have different evidence, just saying a car can fly because it has very nice and colorful wings, doesn’t make it fly.



Last Updated on Saturday, 12 August 2017 15:06
Group-Replication, sweet & sour
Written by Marco Tusa   

A story around replication lag and Flow-Control.

1. Overview

In the last few months we had 2 main actors in the the MySQL ecosystem, ProxySQL and Group-Replication (with the evolution to InnoDB Cluster). 

While I had extensively covered the first, my last serious work on GR, goes back to some lab version in the past years. veg sweet and sour soup
Given the decision Oracle made to declare it GA, and the Percona decision to provide some level of support to GR, I decide it was time for me to take a look at it again.
A lot of reviews were already done covering different topics. I saw articles about GR and performance, GR and basic functionalities (or lack of it like automatic node provisioning), GR and ProxySQL and so on.

But one question was coming up over and over in my mind. If GR and InnoDB cluster has to work as alternative to other (virtually) synchronous replication mechanism, what change or shift our customers must consider if they want move from one to the other.
In solutions using Galera, like Percona Xtradb Cluster (PXC), there is a main concept to which all of us must refer to. The cluster is data-centric, which at the end brings us to what matters, which is the data and its state, that must be exactly the same on each node at a given time (commit/apply). To guarantee this PXC and others use a set of data validation and FlowControl that at the end will make possible to the cluster dataset to be consistent on each node, respecting the main principle (be data-centric).
Immediate application of this principle is that an application can query ANY node in the PXC and be sure to get the same data, or to write and know that the data will be visible on all node (virtually) at the same time.
Last but not list, if a node is not consistent with the others, it will be excluded and must be rebuild, or inconsistency fix, before joining back.

Not a minor thing if you think carefully, and a very useful thing to have because allow you to transparently split write/read operations, or failover from one node to another with lees troubles, and more.

When I thought to GR (or InnoDB Cluster), I put myself in the customer shoes, and I asked to the other myself: “Aside all the other things we know (see above) what is the real impact of moving form PXC to GR/Innodb-Cluster for my application? Because when you mention me that GR is still using (basically) replication with binlogs and relaylog, also if there is a Flow-Control mechanism an alarm bell started to ring in my mind.”

My Answer was: “Let us do a POC, and see what is really going on in that terms.”



2. The POC

Given that I setup a simple set of servers using GR, with a very basic application performing writes on a single Writer node, and (eventually) reads on the other nodes.Schema definition can be found here, mainly I had use the 4 tables from my windmills test suite, nothing special, or weird of specifically design for GR. Actually I had use this test a lot for PXC in the past, so was a perfect fit.

2.1. Test definition

The application will do very simple work, and I had thought to test 4 main cases:

  • One thread performing one insert at each transaction.
  • One thread performing 50 batched inserts at each transaction.
  • 8 threads performing one insert to each transaction.
  • 8 threads performing 50 batched inserts at each transaction.

As you can see nothing crazy, a quite simple operation.
Then I decide to test it using the following 4 conditions on the servers:

  • Single slave worker FC as default
  • Single slave worker FC set to 25
  • 8 slave workers FC as default
  • 8 slave workers FC set to 25

Again nothing weird or strange from my point of view.I had used 4 nodes:

  • Gr1 Writer
  • Gr2 Reader
  • Gr3 Reader minimal latency (~10ms)
  • Gr4 Reader minimal latency (~10ms)

Finally, I had to be sure I measure the lag in a way that allow me to reference it in a consistent way on all nodes.
I think we can safely say that the incoming GTID (last_ Received_transaction_set from replication_connection_status) is for sure the last apply on the master a slave node can know about.
This because network delay can prevent the last one to really be "received".
The other point of reference is the GTID_EXECUTED which refers to the latest GTID processed on the Node itself.

The closest Query that can track the distance will be:

select @last_exec:=SUBSTRING_INDEX(SUBSTRING_INDEX(SUBSTRING_INDEX( @@global.GTID_EXECUTED,':',-2),':',1),'-',-1) last_executed;
select  @last_rec:=SUBSTRING_INDEX(SUBSTRING_INDEX(SUBSTRING_INDEX( Received_transaction_set,':',-2),':',1),'-',-1) last_received
FROM performance_schema.replication_connection_status WHERE Channel_name = 'group_replication_applier'; select (@last_rec - @last_exec) as real_lag


Or in case of a single worker

select @last_exec:=SUBSTRING_INDEX(SUBSTRING_INDEX( @@global.GTID_EXECUTED,':',-1),'-',-1) last_executed;
select  @last_rec:=SUBSTRING_INDEX(SUBSTRING_INDEX(Received_transaction_set,':',-1),'-',-1) last_received 
FROM performance_schema.replication_connection_status WHERE Channel_name = 'group_replication_applier'; select (@last_rec - @last_exec) as real_lag;


The result will be something like this:


| last_executed |
| 23607         |
| last_received |
| 23607         |
| real_lag |
|        0 |


The whole set of tests can be found here, with all the commands you may need to run the application (you can find it here), and replicate the tests.
I will focus on the results, or this blogpost will be far too long, but I invite you to see the details.

The results

Efficiency on Writer by execution time & Rows/secHere using the raw data from the tests (excel spreadsheet available here) I was interested to identify if and how the writer is affected by the use of GR and FC.


Reviewing the Graph we can see that the Writer has a linear increase of the execution time (when using default FC), with the increase of the load, nothing really concerning and all in all expected also if the load is light, we will see after that the volume of rows at the end justify the execution time.

Different scenario if we use FC, the execution time increase significantly in both cases (single  worker/multiple workers). In the worth case (8 threads, 50 inserts batch) it becomes 4 time higher the same load without FC.
What happen to the inserted rows? In the application I trace the rows inserted/sec, as such is easy to see what is going on there as well.


We can see that the Writer with FC activated is able to insert less than 1/3 of the rows it can process without FC.We can definitely say, that FC has a significant impact on the Writer performance.
To clarify let see this graph:


Without FC the Writer is able to process a high volume of rows in a limited number of time (results from test 8 workers; 8 threads; 50 insert batch).
While with FC the situation changes drastically, the Writer will take a long time processing a significant smaller amount of rows/sec. In short performance will drop significantly.

But, hey I can be ok with that if this means to have a consistent data-set cross all the nodes.
At the end also PXC and similar, pay a significant price in performance to match the data-centric Principle.

Ok let see what happen on the other nodes.

2.3. Entries Lag

Well, the scenario is not so good.



When NOT using FC, the nodes lag behind the writer significantly. Remember that by default the FC in GR is set to 25000 entries, I mean 25K of entries!!!

So what happens here is that as as soon as I put some salt (see load) on the Writer the slave nodes will start to lag.
When using the default single worker, that will be significant, while when using multiple workers, we will see that the lag will mainly happen only on the node(s) with a minimal (10ms network latency).
Sad thing is that is not really going down respect to the single thread worker, indicating the simple minimal latency of 10ms was enough to affect the replication.

Well time to activate the FC and have no lag.


Unfortunately, this is not going to be the case. As we can see the lag in case of single worker remain high also in Gr2 (154 entries).
While when using multiple workers, the Gr3/4 nodes are able to perform much better, and lag is significantly less, but still high ~1k entries.

It is important to remember that at this time the Writer is processing 1/3 or less of the rows it is normally able to do. It is important to note, that I had set 25 to the entry limit in the FC, and never the less the Gr3 (and Gr4) nodes are lagging more than 1K entries behind.

To clarify, let check the two graphs below:



Using the Writer (Master) as baseline in entry #N, without FC, the nodes (slaves) using GR, will start to lag behind the writer, in a significant way also with light load. The distance in this POC was from very minimal with 58 entries, up to 3849 entries in the case of higher load.


Using FC the Writer (Master) will diverge less, as expected, but also if it will have a significant drop in performance (1/3 or less), the Nodes will lag anyhow, worse case up to 1363 entries.Need to underline that we have not further way (I am aware of) to tune the lag and prevent it to happen.
Which means an application cannot transparently split Writes/Reads and expect consistency. The gap will be too high

A graph that tell us a story

I was using PMM to keep an eye on the nodes while doing the tests. And one of the graph was really telling me that GR has still some “limits” if we want to consider it as the replication mechanism for a cluster.


This graph shows the MySQL Queries executed on all the 4 nodes, in the 8-50 threads-batch with FC test.As you can see the Gr1 (the writer) is the first one to takeoff, followed by Gr2, the nodes Gr3 and Gr4 will require a bit more, given the binlog transmission (and 10ms delay), once the data is there, they match (inconsistently) the Gr2 node, this is an effect of the FC asking the master to slow down. But as seen previously, the nodes will never be able to match the Writer. At the end, when the load test is over, the nodes will continue to process the queue for additional ~130 sec. Considering that the whole load takes 420 sec on the Writer, this means that 1/3 of the total time on the Writer is spent AFTER on the slave to sync.


The above graph shows the same test without FC, is interesting to see how the Writer was going above the 300 Queries/sec while G2 stay around 200 and Gr3/4 far below. The writer was able to process the whole load in ~120 seconds instead 420, while Gr3/4 continue to process the load for additional ~360 seconds.This means that without FC set the Nodes will lag around 360 seconds behind the Master, while with FC set to 25, they will lag 130 seconds.

A significant gap.



3. Conclusions

Going back to the origin, and the reason why I was looking to this POC.
I, as customer of myself, think that my application(s) will not be a good fit for GR, given I have set PXC to scale out the reads, and be able to efficiently move my writer to another when in need to.
GR while based on a very interesting concept it is still based on asynchronous replication (as my colleague Kenny said). I am sure it could make sense in many other cases, but it cannot be compare to solutions that are based on virtually synchronous replication; and it still requires a lot of refinement.


On the other hand, all the applications that can afford to have a significant gap between writer and readers, will probably be fine with that.But I raise another question … was not standard replication already covering that?Reviewing the Oracle documentations (https://dev.mysql.com/doc/refman/5.7/en/group-replication-background.html) I can see why GR as part of the InnoDB cluster, can help me in improving HA, when comparing it to standard replication.But I also think it is important to understand that GR (and derived solutions like InnoDB cluster) are not comparable or a replacement of data-centric solutions as PXC. At least up to now.


Good MySQL to everyone.


4. References





Last Updated on Thursday, 20 July 2017 16:26
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