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MySQL 8 and the FRM drop. PDF Print E-mail
Written by Marco Tusa   
Tuesday, 04 December 2018 20:37

(What I should keep in mind in case of disaster)

Retrieve and maintain in SQL format the tables definition of all tables in a database, is one of the best practices that we all should adopt.

To have that under versioning is also another BP to keep in mind.
Doing that may seems redundant, but it become a life saver in several situations.
From the need to review what had historically change in that table, know who change what and why, to when you need to recover your data and have your loved MySQL instance not able to start.

But let us be honest, just few do the right thing, and even fewer keep that information up to date. Given that, what can we do when we have the need to discover/recover the table structure? From the beginning, MySQL had used some external files to describe the internal structure. For instance, if I have a schema named windmills and a table named wmillAUTOINC1, on file system I will see:

-rw-r-----. 1 mysql mysql     8838 Mar 14 2018 wmillAUTOINC1.frm
-rw-r-----. 1 mysql mysql   131072 Mar 14 2018 wmillAUTOINC1.ibd


The ibd file contains the data, while frm file contains the structure information.
Keeping aside ANY discussion about if this is safe, if it transactional and more… when we had some major crash and data corruption this approach had being helpful.
Being able to read from this file was the easiest way to get the information we need.
Simple tools like DBSake was making the task quite trivial, also allowing us to script it when in need to run long, complex tedious data recovery:

[root@master1 windmills]# /opt/tools/dbsake frmdump wmillAUTOINC1.frm
--
-- Table structure for table `wmillAUTOINC1`
-- Created with MySQL Version 5.7.20
--
 
CREATE TABLE `wmillAUTOINC1` (
  `id` bigint(11) NOT NULL AUTO_INCREMENT,
  `uuid` char(36) COLLATE utf8_bin NOT NULL,
  `millid` smallint(6) NOT NULL,
  `kwatts_s` int(11) NOT NULL,
  `date` date NOT NULL,
  `location` varchar(50) COLLATE utf8_bin NOT NULL,
  `active` tinyint(2) NOT NULL DEFAULT '1',
  `time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
  `strrecordtype` char(3) COLLATE utf8_bin NOT NULL,
  PRIMARY KEY (`id`),
  KEY `IDX_millid` (`millid`,`active`),
  KEY `IDX_active` (`id`,`active`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_bin ROW_FORMAT=DYNAMIC

Of course, if the frm file was also corrupted we could try to get the information from the ibdata dictionary.
If that is corrupted as well (trust me I saw all these) … well last resource was hoping customer has a recent table definition store somewhere, but as mentioned before, we are not so diligent, are we?
NOW in MySQL8 we do not have FRM files, the were drop.
Even more interesting is the fact that we do not have the same dictionary, most of the things that we knew had change, including the dictionary location, so what can be done?

Well Oracle had moved the FRM information and more, to what is call Serialized Dictionary Information (SDI), the SDI is wrote INSIDE the ibd file, and represent the redundant copy of the information contain in the data dictionary.

The SDI is update/modified by DDL operations on tables that reside in that tablespace.

This is it, if you have file per table normally you will have in that file ONLY the SDI for that table, but if you have multiple table on a tablespace, the SDI information will refer to ALL the tables.

To extract this information out from IBD files, Oracle provide an utility call ibd2sdi.

The application parses the SDI information and report a JSON file that can be easily manipulate to extract/build the table definition.
One exception is represented by Partitioned tables, the SDI information is contained ONLY in the first partition, and in case you drop it, is moved to the next one, will show that later.

 

Let us see now how it works.
In the next examples I will look for Tables name, attribute and datatype starting from the dictionary tables.
To take the info I will do this:

/opt/mysql_templates/mysql-8P/bin/./ibd2sdi   /opt/mysql_instances/master8/data/mysql.ibd |\
jq '.[]?|.[]?|.dd_object?| \
("------------------------------------"?,"TABLE NAME = ",.name?,"****",(.columns?|.[]?|(.name?,.column_type_utf8?)))'
The result will be something like:
"------------------------------------"
"TABLE NAME = "
"tables"
"****"
"id"
"bigint(20) unsigned"
"schema_id"
"bigint(20) unsigned"
"name"
"varchar(64)"
"type"
"enum('BASE TABLE','VIEW','SYSTEM VIEW')"
"engine"
"varchar(64)"
"mysql_version_id"
"int(10) unsigned"
"row_format"
"enum('Fixed','Dynamic','Compressed','Redundant','Compact','Paged')"
"collation_id"
"bigint(20) unsigned"
"comment"
"varchar(2048)"
<snip>
"------------------------------------"
"TABLE NAME = "
"tablespaces"
"****"
"id"
"bigint(20) unsigned"
"name"
"varchar(259)"
"options"
"mediumtext"
"se_private_data"
"mediumtext"
"comment"
"varchar(2048)"
"engine"
"varchar(64)"
"DB_TRX_ID"
""
"DB_ROLL_PTR"
""

 

As you cannot see because I cut the output for brevity, but you can if you run the above command by yourself, what I will get retrieve ALL the tables information, residing in the IBD. The other thing I hope you have noticed, is that I am NOT parsing ibdata, but mysql.ibd, why? Because the dictionary was moved out from ibdata and is now in mysql.ibd. Look what happens if I try to parse ibdata:

[root@master1 ~]# /opt/mysql_templates/mysql-8P/bin/./ibd2sdi   /opt/mysql_instances/master8/data/ibdata1 |jq '.'
[INFO] ibd2sdi: SDI is empty.

Be very careful in not messing up with your mysql.ibd file. Now what I can do to take information about my wmillAUTOINC1 table in MySQL8? That is quite simple:

/opt/mysql_templates/mysql-8P/bin/./ibd2sdi   /opt/mysql_instances/master8/data/windmills/wmillAUTOINC.ibd |jq '.'
[
  "ibd2sdi",
  {
    "type": 1,
    "id": 1068,
    "object": {
      "mysqld_version_id": 80013,
      "dd_version": 80013,
      "sdi_version": 1,
      "dd_object_type": "Table",
      "dd_object": {
        "name": "wmillAUTOINC",
        "mysql_version_id": 80011,
        "created": 20180925095853,
        "last_altered": 20180925095853,
        "hidden": 1,
        "options": "avg_row_length=0;key_block_size=0;
keys_disabled=0;pack_record=1;row_type=2;stats_auto_recalc=0;stats_sample_pages=0;", "columns": [ { "name": "id", "type": 9, "is_nullable": false, "is_zerofill": false, "is_unsigned": false, "is_auto_increment": true, "is_virtual": false, "hidden": 1, "ordinal_position": 1, "char_length": 11, "numeric_precision": 19, "numeric_scale": 0, "numeric_scale_null": false, "datetime_precision": 0, "datetime_precision_null": 1, "has_no_default": false, "default_value_null": false, "srs_id_null": true, "srs_id": 0, "default_value": "AAAAAAAAAAA=", "default_value_utf8_null": true, "default_value_utf8": "", "default_option": "", "update_option": "", "comment": "", "generation_expression": "", "generation_expression_utf8": "", "options": "interval_count=0;", "se_private_data": "table_id=1838;", "column_key": 2, "column_type_utf8": "bigint(11)", "elements": [], "collation_id": 83, "is_explicit_collation": false }, <SNIP> "indexes": [ { "name": "PRIMARY", "hidden": false, "is_generated": false, "ordinal_position": 1, "comment": "", "options": "flags=0;", "se_private_data": "id=2261;root=4;space_id=775;table_id=1838;trx_id=6585972;", "type": 1, "algorithm": 2, "is_algorithm_explicit": false, "is_visible": true, "engine": "InnoDB", <Snip> ], "foreign_keys": [], "partitions": [], "collation_id": 83 } } }, { "type": 2, "id": 780, "object": { "mysqld_version_id": 80011, "dd_version": 80011, "sdi_version": 1, "dd_object_type": "Tablespace", "dd_object": { "name": "windmills/wmillAUTOINC", "comment": "", "options": "", "se_private_data": "flags=16417;id=775;server_version=80011;space_version=1;", "engine": "InnoDB", "files": [ { "ordinal_position": 1, "filename": "./windmills/wmillAUTOINC.ibd", "se_private_data": "id=775;" } ] } } } ]

The JSON will contains:

  • A section describing the DB object at high level
  • Array of columns and related information
  • Array of indexes
  • Partition information (not here but in the next example)
  • Table space information

That is a lot more details than what we had in the FRM and is quite relevant and interesting information as well. Once extracted the SDI any JSON parser tool script can generate the information for the SQL DDL. I mention Partitions, let us cover it a second a bit more, given they can be tricky. As mentioned the SDI information is present ONLY in the first partition. All the others will have ONLY the tablespace information. Given that the first thing to do is to identify which Partition is the first… OR simple try to access all and when you are able to get the details just extract them. The process is the same:

[root@master1 ~]# /opt/mysql_templates/mysql-8P/bin/./ibd2sdi  \
 /opt/mysql_instances/master8/data/windmills/wmillAUTOINCPART#P#PT20170301.ibd |jq '.'
[
  "ibd2sdi",
  {
    "type": 1,
    "id": 1460,
    "object": {
      "mysqld_version_id": 80013,
      "dd_version": 80013,
      "sdi_version": 1,
      "dd_object_type": "Table",
      "dd_object": {
        "name": "wmillAUTOINCPART",
        "mysql_version_id": 80013,
        "created": 20181125110300,
        "last_altered": 20181125110300,
        "hidden": 1,
        "options": "avg_row_length=0;
key_block_size=0;keys_disabled=0;pack_record=1;row_type=2;stats_auto_recalc=0;stats_sample_pages=0;",
        "columns": [<snip>
          "schema_ref": "windmills",
        "se_private_id": 18446744073709552000,
        "engine": "InnoDB",
        "last_checked_for_upgrade_version_id": 80013,
        "comment": "",
        "se_private_data": "autoinc=31080;version=2;",
        "row_format": 2,
        "partition_type": 7,
        "partition_expression": "to_days(`date`)",
        "partition_expression_utf8": "to_days(`date`)",
        "default_partitioning": 1,
        "subpartition_type": 0,
        "subpartition_expression": "",
        "subpartition_expression_utf8": "",
        "default_subpartitioning": 0,
       ],
<snip>
        "foreign_keys": [],
        "partitions": [
          {
            "name": "PT20170301",
            "parent_partition_id": 18446744073709552000,
            "number": 0,
            "se_private_id": 1847,
            "description_utf8": "736754",
            "engine": "InnoDB",
            "comment": "",
            "options": "",
            "se_private_data": "autoinc=0;version=0;",
            "values": [
              {
                "max_value": false,
                "null_value": false,
                "list_num": 0,
                "column_num": 0,
                "value_utf8": "736754"
              }
            ],

The difference as you can see is the section related to Partitions and Sub Partition that will be filled with all the details you may need to recreate the partitions. We will have:

  • Partition type
  • Partition expression
  • Partition values
  • …more

Same for sub partitions. Now again see what happens if I parse the second Partition:

[root@master1 ~]# /opt/mysql_templates/mysql-8P/bin/./ibd2sdi  \
 /opt/mysql_instances/master8/data/windmills/wmillAUTOINCPART#P#PT20170401.ibd |jq '.'
[
  "ibd2sdi",
  {
    "type": 2,
    "id": 790,
    "object": {
      "mysqld_version_id": 80011,
      "dd_version": 80011,
      "sdi_version": 1,
      "dd_object_type": "Tablespace",
      "dd_object": {
        "name": "windmills/wmillAUTOINCPART#P#PT20170401",
        "comment": "",
        "options": "",
        "se_private_data": "flags=16417;id=785;server_version=80011;space_version=1;",
        "engine": "InnoDB",
        "files": [
          {
            "ordinal_position": 1,
            "filename": "./windmills/wmillAUTOINCPART#P#PT20170401.ibd",
            "se_private_data": "id=785;"
          }
        ]
      }
    }
  }
]

I will get only the information about the Tablespace but not the table. As promise let me show you now what happen if I delete the first partition, and the second one will become the first:

(root@localhost) [windmills]>alter table wmillAUTOINCPART drop partition PT20170301;
Query OK, 0 rows affected (1.84 sec)
Records: 0  Duplicates: 0  Warnings: 0

[root@master1 ~]# /opt/mysql_templates/mysql-8P/bin/./ibd2sdi \
  /opt/mysql_instances/master8/data/windmills/wmillAUTOINCPART#P#PT20170401.ibd |jq '.'|more
[
  "ibd2sdi",
  {
    "type": 1,
    "id": 1461,
    "object": {
      "mysqld_version_id": 80013,
      "dd_version": 80013,
      "sdi_version": 1,
      "dd_object_type": "Table",
      "dd_object": {
        "name": "wmillAUTOINCPART",
        "mysql_version_id": 80013,
        "created": 20181129130834,
        "last_altered": 20181129130834,
        "hidden": 1,
        "options": "avg_row_length=0;
key_block_size=0;keys_disabled=0;pack_record=1;row_type=2;stats_auto_recalc=0;stats_sample_pages=0;", "columns": [ { "name": "id", "type": 9, "is_nullable": false, "is_zerofill": false, "is_unsigned": false, "is_auto_increment": true, "is_virtual": false, "hidden": 1, "ordinal_position": 1,

As also mention before the SDI is update at each DDL, and here we go, I will have all the information on the NOW FIRST partition. Please note the created attribute between the first time I query the Other partition and the one I had from now:

/opt/mysql_instances/master8/data/windmills/wmillAUTOINCPART#P#PT20170301.ibd
       "created": 20181125110300,

/opt/mysql_instances/master8/data/windmills/wmillAUTOINCPART#P#PT20170401.ibd
       "created": 20181129130834,

  To be clear the second created is NOW (PT20170401) when I did the drop on the other partition (PT20170301).

 

Conclusions

At the end this solution is definitely more powerful of the FRM files.
It will allow us to parse the file and identify the table definition more easily, get much more details and information.

The problems will raise when and IF the IBD file will become corrupt.

As for manual For InnoDB, an SDI record requires a single index page, which is 16KB in size by default. However, SDI data is compressed to reduce the storage footprint.

Which it means that for each Table I have a page, if I associate record=table.

Which means that in case of IBD corruption I should (likely) be able to read those pages, unless bad (very bad) luck.

Still wonder how the dimension of an IBD affects the SDI retrieval, given I had not tried it yet but will let you know.

On another side I am working on a script to facilitate the SQL generation, still not ready but you can find it here

Last note but keep this in mind! It is state in the manual but in a hidden place small letters: DDL operations take longer due to writing to storage, undo logs, and redo logs instead of .frm files.

 

References

https://stedolan.github.io/jq/

https://dev.mysql.com/doc/refman/8.0/en/ibd2sdi.html

https://dev.mysql.com/doc/refman/8.0/en/serialized-dictionary-information.html

https://dev.mysql.com/doc/refman/8.0/en/data-dictionary-limitations.html

Last Updated on Tuesday, 25 December 2018 19:18
 
How Not to do MySQL High Availability: Geographic Node Distribution with Galera-Based Replication Misuse PDF Print E-mail
Written by Marco Tusa   
Thursday, 15 November 2018 00:00

lightspeedLet's talk about MySQL high availability (HA) and synchronous replication once more.
It's part of a longer series on some high availability reference architecture solutions over geographically distributed areas.
Part 1: Reference Architecture(s) for High Availability Solutions in Geographic Distributed Scenarios: Why Should I Care?
Part 2: MySQL High Availability On-Premises: A Geographically Distributed Scenario

The Problem

A question I often get from customers is: How do I achieve high availability in case if I need to spread my data in different, distant locations? Can I use Percona XtraDB Cluster? Percona XtraDB Cluster (PXC), mariadb-cluster or MySQL-Galera are very stable and well-known solutions to improve MySQL high availability using an approach based on multi-master data-centric synchronous data replication model. Which means that each data-node composing the cluster MUST see the same data, at a given moment in time. Information/transactions must be stored and visible synchronously on all the nodes at a given time. This is defined as a tightly coupled database cluster. This level of consistency comes with a price, which is that nodes must physically reside close to each other and cannot be geographically diverse. This is by design (in all synchronous replication mechanisms). This also has to be clarified over and over throughout the years. Despite that we still see installations that span across geographic locations, including AWS Regions. We still see some solutions breaking the golden rule of proximity, and trying to break the rules of physics as well. The problem/mistake is not different for solutions based on-premises or in the cloud (for whatever cloud provider). Recently I had to design a couple of customer solutions based on remote geographic locations. In both cases, the customer was misled by an incorrect understanding of how the synchronous solution works, and from a lack of understanding of the network layer. I decided I need to cover this topic again, as I have done previously in Galera geographic replication and Effective way to check network connection in a geographically distributed environment

What Happen When I Put Things on the Network?

Well, let's start with the basics. While light travels at 300 million meters per second, the propagation of the electric fields or electric signaling is slower than that. The real speed depends by the medium used to transmit it. But it can be said that the real speed normally spans from 0% to 99% of light-speed (depending on the transmission medium). This means that in optimal conditions the signal travels at approximately 299.72Km per millisecond, in good/mid condition about half that at 149.86Km per millisecond, and in bad conditions it could be 3Km per millisecond or less. To help you understand, the distance between Rome (Italy) and Mountain View (California) is about 10,062Km. At light-speed it will take 33.54ms. In good conditions (90% of light-speed) the signal will take 37.26ms to reach Mountain View, and in less optimal conditions it can easily double to 74.53 ms. Keep in mind this is the electric field propagation speed: optimal conditions with no interruption, re-routing and so on. Reality will bring all the kind of interruptions, repeaters and routing. All the physics above works as a baseline. On top of this, each human construct adds functionalities, flexibility and (unfortunately) overhead – leading to longer times and slower speeds. The final speed will be different than the simple propagation of the electric fields. It will include the transmission time of complex signaling using ICMP protocol, or even higher delays with the use of a very complex protocol like TCP/IP, which includes handshaking, package rerouting, re-sending and so on. On top of that, when sending things over the internet, we need to realize that it is very improbable we will be the only user sending data over that physical channel. As such, whatever we have “on the road” will need to face bandwidth limitation, traffic congestion and so on. I had described the difference between protocols (ICMP – TCP/IP) here, clarifying how the TCP/IP scenario is very different from using different protocols like ICMP, or the theoretical approach. What it all means is that we cannot trust the theoretical performance. We must move to a more empirical approach. But we must understand the right empirical approach or we will be misled.

An Example

I recently worked on a case where a customer had two data centers (DC) at a distance of approximately 400Km, connected with “fiber channel”. Server1 and Server2 were hosted in the same DC, while Server3 was in the secondary DC. Their ping, with default dimension, to Server3 was ~3ms. Not bad at all, right? We decided to perform some serious tests, running multiple sets of tests with netperf for many days collecting data. We also used the data to perform additional fine tuning on the TCP/IP layer AND at the network provider. The results produced a common (for me) scenario (not so common for them):

 

picture1


The red line is the first set of tests BEFORE we optimized. The yellow line is the results after we optimized. The above graph reports the number of transactions/sec (AVG) we could run against the different dimension of the dataset and changing the destination server. The full roundtrip was calculated. It is interesting to note that while the absolute numbers were better in the second (yellow) tests, this was true only for a limited dataset dimension. The larger the dataset, the higher the impact. This makes sense if you understand how the TCP/IP stack works (the article I mentioned above explains it). But what surprised them were the numbers. Keeping aside the extreme cases and focusing instead on the intermediate case, we saw that shifting from a 48k dataset dimension to 512K hugely dropped the performance. The drop for executed transactions was from 2299 to 219 on Server2 (same dc) and from 1472 to 167 Server3 (different DC). Also, note that Server3 only managed ~35% fewer transactions comparing to Server2 from the start given the latency. Latency moved from a more than decent 2.61ms to 27.39ms for Server2 and 4.27ms to 37.25ms for Server3.

 

picture2


37ms latency is not very high. If that had been the top limit, it would have worked. But it was not. In the presence of the optimized channel, with fiber and so on, when the tests were hitting heavy traffic, the congestion was such to compromise the data transmitted. It hit a latency >200ms for Server3. Note those were spikes, but if you are in the presence of a tightly coupled database cluster, those events can become failures in applying the data and can create a lot of instability. Let me recap a second the situation for Server3: We had two datacenters.

  • The connection between the two was with fiber
  • Distance Km ~400, but now we MUST consider the distance to go and come back. This because in case of real communication, we have not only the send, but also the receive packages.
  • Theoretical time at light-speed =2.66ms (2 ways)
  • Ping = 3.10ms (signal traveling at ~80% of the light speed) as if the signal had traveled ~930Km (full roundtrip 800 Km)
  • TCP/IP best at 48K = 4.27ms (~62% light speed) as if the signal had traveled ~1,281km
  • TCP/IP best at 512K =37.25ms (~2.6% light speed) as if the signal had traveled ~11,175km

Given the above, we have from ~20%-~40% to ~97% loss from the theoretical transmission rate. Keep in mind that when moving from a simple signal to a more heavy and concurrent transmission, we also have to deal with the bandwidth limitation. This adds additional cost. All in only 400Km distance. This is not all. Within the 400km we were also dealing with data congestions, and in some cases the tests failed to provide the level of accuracy we required due to transmission failures and too many packages retry. For comparison, consider Server2 which is in the same DC of Server1. Let see:

  • Ping = 0.027ms that is as if the signal had traveled ~11km light-speed
  • TCP/IP best at 48K = 2.61ms as if traveled for ~783km
  • TCP/IP best at 512K =27.39ms as if traveled for ~8,217km
  • We had performance loss, but the congestion issue and accuracy failures did not happen.

You might say, "But this is just a single case, Marco, you cannot generalize from this behavior!" You would be right IF that were true (but is not).
The fact is, I have done this level of checks many times and in many different environments. On-premises or using the cloud. Actually, in the cloud (AWS), I had even more instability.
T
he behavior stays the same. Please test it yourself (it is not difficult to use netperf).

Just do the right tests with RTT and multiple requests (note at the end of the article).
Anyhow, what I know from the tests is that when working INSIDE a DC with some significant overhead due to the TCP/IP stack (and maybe wrong cabling), I do not encounter the same congestion or bandwidth limits I have when dealing with an external DC.
This allows me to have more predictable behavior and tune the cluster accordingly.

Tuning that I cannot do to cover the transmission to Server3 because of unpredictable packages behavior and spikes. >200ms is too high and can cause delivery failures.
If we apply the given knowledge to the virtually-synchronous replication we have with Galera (Percona XtraDB Cluster), we can identify that we are hitting the problems well-explained in Jay's article Is Synchronous Replication right for your app?  There, he explains Callaghan’s Law: [In a Galera cluster] a given row can’t be modified more than once per RTT.
On top of that, when talking of geographical disperse solutions we have the TCP/IP magnifying the effect at writeset transmission/latency level.
This causes nodes NOT residing on the same physical contiguous network delay for all the certification-commit phases for an X amount of time.
When X is predictable, it may range between 80% - 3% of the light speed for the given distance.
But you can't predict the transmission-time of a set of data split into several datagrams, then sent on the internet, when using TCP/IP.
So we cannot use the X range as a trustable measure.
The effect is unpredictable delay, and this is read as a network issue from Galera.
The node can be evicted from the cluster. Which is exactly what happens, and what we experience when dealing with some “BAD” unspecified network issue.

 This means that whenever we need to use a solution based on tightly coupled database cluster (like PXC), we cannot locate our nodes at a distance that is longer than the largest RTT time of our shortest desired period of commit.
If our application must apply the data in a maximum of 200ms in one of its functions, our min RTT is 2ms and our max RTT is 250ms.
We cannot use this solution, period. To be clear, locating a node on another geolocation, and as such use the internet to transmit/receive data, is by default a NO GO given the unpredictability of that network link.

  I doubt that nowadays we have many applications that can wait an unpredictable period to commit their data.
The only case when having a node geographically distributed is acceptable is if you accept commits happening in undefined periods of time and with possible failures.

What Is the Right Thing To Do?

The right solution is easier than the wrong one, and there are already tools in place to make it work efficiently. Say you need to define your HA solution between the East and West Coast, or between Paris and Frankfurt. First of all, identify the real capacity of your network in each DC.
Then build a
tightly coupled database cluster in location A and another tightly coupled database cluster in the other location B. Then link them using ASYNCHRONOUS replication.
Finally, use a tool like Replication Manager for Percona XtraDB Cluster to automatically manage asynchronous replication failover between nodes.
On top of all of that use a tool like ProxySQL to manage the application requests.
The full architecture is described here. 

picture3

 

Conclusions

The myth of using ANY solution based on tightly coupled database cluster on distributed geographic locations is just that: a myth. It is conceptually wrong and practically dangerous.

It MIGHT work when you set it up, it MIGHT work when you test it, it MIGHT even work for some time in production.

  By definition, it will break, and it will break when it is least convenient. It will break in an unpredictable moment, but because of a predictable reason.

You did the wrong thing by following a myth. Whenever you need to distribute your data over different geographic locations, and you cannot rely on a single physical channel (fiber) to connect the two locations, use asynchronous replication, period!

References

https://github.com/y-trudeau/Mysql-tools/tree/master/PXC
http://www.tusacentral.net/joomla/index.php/mysql-blogs/164-effective-way-to-check-the-network-connection-when-in-need-of-a-geographic-distribution-replication-.html
https://www.percona.com/blog/2013/05/14/is-synchronous-replication-right-for-your-app/

Sample test

#!/bin/bash 
test_log=/tmp/results_$(date +'%Y-%m-%d_%H_%M_%S').txt
exec 9>>"$test_log"
exec 2>&9
exec 1>&9
echo "$(date +'%Y-%m-%d_%H_%M_%S')" >&9

for ip in 11 12 13; do
  echo "  ==== Processing server 10.0.0.$ip === " 

  for size in 1 48 512 1024 4096;do
    echo " --- PING ---"
    ping -M do -c 5  10.0.0.$ip -s $size
    echo "  ---- Record Size $size ---- " 
    netperf -H 10.0.0.$ip -4 -p 3307 -I 95,10 -i 3,3 -j -a 4096 -A 4096  -P 1 -v 2 -l 20 -t TCP_RR -- -b 5 -r ${size}K,48K -s 1M -S 1M
    echo "  ---- ================= ---- ";
  done
   echo "  ==== ----------------- === ";
done
Last Updated on Tuesday, 27 November 2018 17:28
 
MySQL High Availability On-Premises: A Geographically Distributed Scenario PDF Print E-mail
Written by Marco Tusa   
Thursday, 15 November 2018 00:00


mysql-high-availability-2-300x200MySQL High Availability. Shutterstock.com[/caption] In this article, we'll look at an example of an on-premises, geographically distributed MySQL high availability solution. It's part of a longer series on some high availability reference architecture solutions over geographically distributed areas. Part 1: Reference Architecture(s) for High Availability Solutions in Geographic Distributed Scenarios: Why Should I Care? Percona consulting's main aim is to identify simple solutions to complex problems. We try to focus on identifying the right tool, a more efficient solution, and what can be done to make our customers' lives easier. We believe in doing the work once, doing it well and have more time afterward for other aspects of life. In our journey, we often receive requests for help – some simple, some complicated.

Scenario

The company "ACME Inc." is moving its whole business from a monolithic application to a distributed application, split into services. Each different service deals with the requests independently from each other. Some services follow the tightly-bounded transactional model, and others work/answer asynchronously. Each service can access the data storage layer independently. In this context, ACME Inc. identified the need to distribute the application services over wide geographic regions, focusing on each region achieving scale independently. The identified regions are:

  • North America
  • Europe
  • China

ACME Inc. is also aware of the fact that different legislation acts on each region. As such, each region requires independent information handling about sales policies, sales campaigns, customers, orders, billing and localized catalogs, but will share the global catalog and some historical aggregated data. While most of the application services will work feeding and reading local distributed caches, the basic data related to the catalog, sales and billing is based on an RDBMS. Historical data is instead migrated to a “Big Data” platform, and aggregated data is elaborated and push to a DWH solution at HQ. The application components are developed using multiple programming languages, depending on the service. The RDBMS identified by ACME Inc. in collaboration with the local authorities was MySQL-oriented. There were several solutions like:

  • PostgreSQL
  • Oracle DB
  • MS SQL server

We excluded closed-source RDBMSs given that some countries imposed a specific audit plugin. This plugin was only available for the mentioned platforms. The cost of parallel development and subsequent maintenance in case of RDBMS diversification was too high. As such all the regions must use the same major RDBMS component. We excluded PostgreSQL given that compared to the adoption of MySQL, utilization cases were higher and MySQL had a well-defined code producer. Finally, the Business Continuity team of ACME Inc., had defined an ITSC (Information Technology Service Continuity) plan that defined the RPO (Recovery Point Objective), the RTO (Recovery Time Objective) and system redundancy. That’s it. To fulfill the ITSCP, each region must have the critical system redundantly replicated in the same region, but not on the proximity.

Talking About the Components

This is a not-so-uncommon scenario, and it also presents a lot of complexity if you try to address it with one solution. But let's analyze it and see how we can simplify the approach while still meeting the needs and requirements of ACME Inc. When using MySQL-based solutions, the answer to "what should we use?" is use what best fits your business needs. The "nines" availability reference table for the MySQL world (most RDBMSs) can be summarized below:

9 0. 0 0 0 % (36 days) MySQL Replication
9 9. 9 0 0 % (8 hours) Linux Heartbeat with DRBD (Obsolete DRBD)
9 9. 9 0 0 % (8 hours) RHCS with Shared Storage (Active/Passive)
9 9. 9 9 0 % (52 minutes) MHA/Orchestrator with at least three nodes
9 9. 9 9 0 % (52 minutes) DRBD and Replication (Obsolete DRBD)
9 9 .9 9 5 % (26 minutes) Multi-Master (Galera replication) 3 node minimum
9 9. 9 9 9 % (5 minutes) MySQL Cluster

An expert will tell you that it always doesn't make sense to go for the most "nines" in the list. This because each solution comes with a tradeoff: the more high availability (HA) you get, the higher the complexity of the solution and in managing the solution. For instance, the approach used in MySQL Cluster (NDB) makes this solution not suitable for generic utilization. It requires proper analysis of the application needs, data utilization and archiving before being selected. It also requires in-depth knowledge to properly manage the cluster, as it is more complex than other similar solutions. This indirectly makes a solution based on MySQL+Galera replication the one with the highest HA level a better choice, since it is close to the defaults generalized utilizations. This is why MySQL+Galera replication has become in the last six years the most used solution for platform looking for very high HA, without the need to diverge from standard MySQL/InnoDB approach. You can read more about Galera replication: http://galeracluster.com/products/ Read more about Percona XtraDB Cluster. There are several distributions implementing Galera replication:

*Note that MariaDB Cluster/Server and all related solutions coming from MariaDB have significantly diverged from the MySQL mainstream. This often means that once migrated to MariaDB; your database will not be compatible with other MySQL solutions. In short, you are locked-in to MariaDB. It is recommended that you carefully evaluate the move to MariaDB before making that move.

Choosing the Components

RDBMS

Our advice is to use Percona XtraDB Cluster (PXC), because at the moment it is one of the most flexible and reliable and compatible solutions. PXC is composed of three main components:

The cluster is normally composed of three nodes or more. Each node can be used as a Master, but the preferred and recommended way is to use one node as a Writer and the other as Readers. Application-wise, accessing the right node can be challenging since this means you need to be aware of which node is the writer, which is the reader, and be able to shift from one to the other if necessary.

Proxy

To simplify this process, it helps to have an additional component that works as a “proxy” connecting the application layer to the desired node(s). The most popular solutions are:

  • HAProxy
  • ProxySQL

There are several important differences between the two. But in summary, ProxySQL is a Level 7 proxy and is MySQL protocol aware. So, while HAProxy is just passing the connection over as a forward proxy (level 4), ProxySQL is aware of what is going through it and acts as reverse proxy. With ProxySQL is possible to decide, based on several parameters, where to send traffic (read/write split and more), what must be stopped, or if we should rewrite an incoming SQL command. A lot of information is available on the ProxySQL website https://github.com/sysown/proxysql/wiki and on the Percona Database Performance Blog .

Backup/Restore

No RDBMS platform is safe without a well-tested procedure for backup and recovery. The Percona XtraDB Cluster package distribution comes with Percona XtraBackup as the default method for node provisioning. A good backup and restore (B/R) policy start from the consideration of ACME's ITSCP, to have full and incremental backups, perfectly covering the RPO, and a good recovery procedure to keep the recovery time inside RTO whenever possible. There are several tools that allow you to plan and execute backup/restore procedure, some coming from vendors other than open source and community-oriented. In respect to being a fully open source and community-oriented, we in consulting normally suggest using: https://github.com/dotmanila/pyxbackup. Pyxbackup is a wrapper around XtraBackup that helps simplify the B/R operations, including the preparation of a full and incremental set. This helps significantly reduce the recovery time.

Disaster Recovery

Another very important aspect of the ITSC Plan is the capacity of the system to survive to major disasters. The disaster and recovery (DR) solution must be able to act as the main production environment. Therefore, it must be designed and scaled as the main production site in resources. It must be geographically separated, normally hundreds of kilometers or more. It must be completely independent of the main site. It must be as much as possible in sync with the main production site. While the first three “musts” are easy to understand, the fourth one is often the object of misunderstanding. The concept of be as much in sync with the production site as possible creates confusion in designing HA solutions with Galera replication involved. The most common misunderstanding is the misuse of the Galera replication layer. Mainly the conceptual confusion between tightly coupled database cluster and loosely coupled database cluster. Any solution based on Galera replication is a tightly coupled database cluster, because the whole idea is to be data-centric, synchronously distributed and consistent. The price is that this solution cannot be geographically distributed. Solutions like standard MySQL replication are instead loosely coupled database cluster and they are designed to be asynchronous. Given that, the nodes connected by it are completely independent in processing/apply the transaction, and the solution fits perfectly into ANY geographically distributed replication solution. The price is that data on the receiving front might not be up to date with the one from the source in that specific instant. The point is that for the DR site the ONLY valid solution is the asynchronous link (loosely coupled database cluster), because by design and requirement the two sites must be separated by a significant number of kilometers. For better understanding about why synchronous replication cannot work in a geographically distributed scenario, see "Misuse of Geographic Node distribution with Galera-based replication". In our scenario, the use of Percona XtraDB Cluster helps to create a most robust asynchronous solution. This is because each tightly coupled database cluster, no matter if source or destination, will be seen by the other tightly coupled database cluster as a single entity. What it means is that we can shift from one node to another inside the two clusters, still confident we will have the same data available and the same asynchronous stream passing from one source to the other. To ensure this procedure is fully automated, we add to our architecture the last block: replication manager for Percona XtraDB Cluster (https://github.com/y-trudeau/Mysql-tools/tree/master/PXC). RMfP is another open source tool that simplifies and automates failover inside each PXC cluster such that our asynchronous solution doesn't suffer if the node is currently acting as Master fails.

How to Link the Components

Summarizing all the different components of our solution:

  • Application stack
    • Load balancer
    • Application nodes by service
    • Distributed caching
    • Data access service
  • Database stack
    • Data proxy (ProxySQL)
    • RDBMS (Percona XtraDB Cluster)
    • Backup/Restore
      • Xtrabackup
      • Pyxbackup
      • Custom scripts
    • DR
      • Replication Manager for Percona XtraDB Cluster
  • Monitoring
    • PMM (not covered here see <link> for detailed information)

 

mysql_ha-page-2

In the solution above, we have two locations separated by several kilometers. On top of them, the load balancer(s)/DNS resolution redirects the incoming traffic to the active site. Each site hosts a full application stack, and applications connect to local ProxySQL. ProxySQL has read/write enabled to optimize the platform utilization, and is configured to shift writes from one PXC node to another in case of node failure. Asynchronous replication connects the two locations and transmits data from master to slave. Note that with this solution, it is possible to have multiple geographically distributed sites. Backups are taken at each site independently and recovery test is performed. RMfP oversees and modifies the replication channels in the case of a node failure. Finally, Percona Monitoring and Management (PMM) is in place to perform in-depth monitoring of the whole database platform.


Conclusions

We always look for the most efficient, manageable, user-friendly combination of products, because we believe in providing and supporting the community with simple but efficient solutions. What we have presented here is the most robust and stable high availability solution in the MySQL space (except for MySQL NDB that we have excluded). It is conceptualized to provide maximum service continuity, with limited bonding between the platforms/sites. It also is a well-tested solution, that has been adopted and adapted in many different scenarios where performance and real HA are a must. I have preferred to keep this digression at a high level, given the details of the implementation have already been discussed elsewhere (see reference section for more reading). Still, Percona XtraDB Cluster (as any other solution implementing Galera replication) might not fit the final use. Given that, it is important to understand where it does and doesn’t fit. This article is a good summary with examples: Is Synchronous Replication right for your app?. Check out the next article on How Not to do MySQL High Availability.

References

https://www.percona.com/blog/2016/06/07/choosing-mysql-high-availability-solutions/

https://dev.mysql.com/doc/mysql-ha-scalability/en/ha-overview.html

https://www.percona.com/blog/2014/11/17/typical-misconceptions-on-galera-for-mysql/

http://galeracluster.com/documentation-webpages/limitations.html

http://tusacentral.net/joomla/index.php/mysql-blogs/170-geographic-replication-and-quorum-calculation-in-mysqlgalera.html

http://tusacentral.net/joomla/index.php/mysql-blogs/167-geographic-replication-with-mysql-and-galera.html

http://tusacentral.net/joomla/index.php/mysql-blogs/164-effective-way-to-check-the-network-connection-when-in-need-of-a-geographic-distribution-replication-.html

http://tusacentral.net/joomla/index.php/mysql-blogs/183-proxysql-percona-cluster-galera-integration.html https://github.com/sysown/proxysql/wiki

Last Updated on Tuesday, 27 November 2018 17:22
 
MySQL 8: Load Fine Tuning With Resource Groups PDF Print E-mail
Written by Marco Tusa   
Tuesday, 28 August 2018 00:00

High-CPU

MySQL Resource Groups, introduced in MySQL 8, provide the ability to manipulate the assignment of running threads to specific resources, thereby allowing the DBA to manage application priorities. Essentially, you can assign a thread to a specific virtual CPU. In this post, I'm going to take a look at how these might work in practice. Let us start with a disclaimer. What I am going to discuss here is NOT common practice. This is advanced load optimization, and you should approach/implement it ONLY if you are 100% sure of what you are doing, and, more importantly, if you know what you are doing, and why you are doing it.

Overview

MySQL 8 introduced a feature that is explained only in a single documentation page. This feature can help a lot if used correctly, and hopefully they will not deprecate or remove it after five minutes. It is well hidden in the Optimization: Optimizing the MySQL Server chapter. I am talking about resource groups. Resource groups permit assigning threads running within MySQL to particular groups so that threads execute according to the resources available to this group. Group attributes enable control over resources to enable or restrict resource consumption by threads in the group. DBAs can modify these attributes as appropriate for different workloads. Currently, CPU affinity (ie: assigning to a specific CPU) is a manageable resource, represented by the concept of “virtual CPU” as a term that includes CPU cores, hyperthreads, hardware threads, and so forth. MySQL determines, at startup, how many virtual CPUs are available. Database administrators with appropriate privileges can associate virtual CPUs with resource groups and assign threads to these groups. In short, you can define that, this specific thread (ergo connection unless connection pooling OR ProxySQL with multiplexing), will use that specific CPU and will have the given priority. Setting this by thread can be:

  1. Dangerous
  2. Not useful

Dangerous, because if you set this to a thread when using connection pooling OR ProxySQL and multiplexing, you may end up assigning a limitation to queries that instead you wanted to run efficiently. Not useful because unless you spend the time looking at the processlist (full), and/or have a script running all the time that catches what you need, 99% of the time you will not be able to assign the group efficiently. So? Another cool useless feature??? Nope… Resource groups can be referenced inside a single statement, which means I can have ONLY that query utilizing that resource group. Something like this will do the magic:

 

1
2
SELECT /*+ RESOURCE_GROUP(NAME OF THE RG) */ id, millid, date,active,kwatts_s FROM sbtest29 WHERE id=44

 

But if I run:

 

1
SELECT id, millid, date,active,kwatts_s FROM sbtest29 WHERE id=44

 

 

 

No resource group utilization even if I am using the same connection. This is cool, isn’t it?

What is the possible usage?

In general, you can see this as a way to limit the negative impact of queries that you know will be problematic for others. Good examples are:

  • ETL processes for data archiving, reporting, data consolidation and so on
  • Applications that are not business critical and can wait, while your revenue generator application cannot
  • GUI Client applications, used by some staff of your company, that mainly create problems for you while they claim they are working.

"Marco, that could make sense … but what should I do to have it working? Rewrite my whole application to add this feature?" Good question! Thanks! We can split the task of having a good Resource Group implementation into 3 steps:

  1. You must perform an analysis of what you want to control. You need to identify the source (like tcp/ip if it is fixed, username) and design which settings you want for your resource groups. Identify if you only want to reduce the CPU priority, or if you want to isolate the queries on a specific CPU, or a combination of the two.
  2. Implement the resource groups in MySQL.
  3. Implement a way to inject the string comment into the SQL.

About the last step, I will show you how to do this in a very simple way with ProxySQL, but hey … this is really up to you. I will show you the easy way but if you prefer a more difficult route, that's good for me too.

The Setup

In my scenario, I have a very noisy secondary application written by a very, very bad developer that accesses my servers, mostly with read queries, and occasionally with write updates. Reads and writes are obsessive and create an impact on the MAIN application. My task is to limit the impact of this secondary application without having the main one affected. To do that I will create two resource groups, one for WRITE and another for READ. The first group, Write_app2, will have no cpu affiliation, but will have lowest (19) priority:

 

1
CREATE RESOURCE GROUP Write_app2 TYPE=USER THREAD_PRIORITY=19;

 

The second group, Select_app2, will have CPU affiliation AND lowest priority;

 

1
CREATE RESOURCE GROUP Select_app2 TYPE=USER VCPU=5 THREAD_PRIORITY=19;

 

 

Finally, I have identified that the application is connecting from several sources BUT it uses a common username APP2. Given that, I will use the user name to inject the instructions into the SQL using ProxySQL (I could have also used the IP, or the schema name, or destination port, or something in the submitted SQL. In short, any possible filter in the query rules). To do that I will need four query rules:

 

1
2
3
4
5
insert into mysql_query_rules (rule_id,proxy_port,username,destination_hostgroup,active,retries,match_digest,apply,active) values(80,6033,'app1',80,1,3,'^SELECT.*FOR UPDATE',1,1);
insert into mysql_query_rules (rule_id,proxy_port,username,destination_hostgroup,active,retries,match_digest,apply,active) values(81,6033,'app1',81,1,3,'^SELECT.*',1,1);
insert into mysql_query_rules (rule_id,proxy_port,username,destination_hostgroup,active,retries,match_digest,apply,active) values(82,6033,'app2',80,1,3,'^SELECT.*FOR UPDATE',1,1); 
insert into mysql_query_rules (rule_id,proxy_port,username,destination_hostgroup,active,retries,match_digest,apply,active) values(83,6033,'app2',81,1,3,'^SELECT.*',1,1); 
 

 

To identify and redirect the query for R/W split.

1
2
INSERT INTO mysql_query_rules (rule_id,active,username,match_pattern,replace_pattern,apply,comment) VALUES (32,1,'app2',"(^SELECT)\s*(.*$)","\1 /*+ RESOURCE_GROUP(Select_app2) */ \2 ",0,"Lower prio and CPU bound on Reader");
INSERT INTO mysql_query_rules (rule_id,active,username,match_pattern,replace_pattern,apply,comment) VALUES (33,1,'app2',"^(INSERT|UPDATE|DELETE)\s*(.*$)","\1 /*+ RESOURCE_GROUP(Write_app2) */ \2 ",0,"Lower prio on Writer");

 

and a user definition like:
1
2
3
insert into mysql_users (username,password,active,default_hostgroup,default_schema,transaction_persistent) values ('app2','test',1,80,'mysql',1);
insert into mysql_users (username,password,active,default_hostgroup,default_schema,transaction_persistent) values ('app1','test',1,80,'mysql',1);

 


One important step you need to do ON ALL the servers you want to include in the Resource Group utilization, is to be sure you have CAP_SYS_NICE capability set. On Linux, resource group thread priorities are ignored unless the CAP_SYS_NICE capability is set. MySQL package installers for Linux systems should set this capability. For installation using a compressed tar file binary distribution or from source, the CAP_SYS_NICE capability can be set manually using the setcap command, specifying the path name to the mysqld executable (this requires sudo access). You can check the capabilities using getcap. For example:

 

1
2
3
shell> sudo setcap cap_sys_nice+ep <Path to you mysqld executable>
shell> getcap ./bin/mysqld
./bin/mysqld = cap_sys_nice+ep

 

 

 

If manual setting of CAP_SYS_NICE is required, then you will need to do it every time you perform a new install. As reference here is a table about CPU priority:

Priority Range Windows Priority Level
-20 to -10 THREAD_PRIORITY_HIGHEST
-9 to -1 THREAD_PRIORITY_ABOVE_NORMAL
0 THREAD_PRIORITY_NORMAL
1 to 10 THREAD_PRIORITY_BELOW_NORMAL
11 to 19 THREAD_PRIORITY_LOWEST

  Summarizing here the whole set of steps on my environment: 1) Check the CAP_SYS_NICE

 

1
2
getcap /opt/mysql_templates/mysql-8P/bin/mysqld
setcap cap_sys_nice+ep /opt/mysql_templates/mysql-8P/bin/mysqld

 

2) Create the user in MySQL and resource groups

 

1
2
3
4
create user app2@'%' identified by 'test';
GRANT ALL PRIVILEGES ON `windmills2`.* TO `app2`@`%`;
CREATE RESOURCE GROUP Select_app2 TYPE=USER VCPU=5 THREAD_PRIORITY=19;
CREATE RESOURCE GROUP Write_app2 TYPE=USER THREAD_PRIORITY=19;

 

 

 

To check :

 

1
SELECT * FROM INFORMATION_SCHEMA.RESOURCE_GROUPS;

 

3) Create ProxySQL user and rules

1
2
3
4
5
6
7
8
9
10
11
INSERT INTO mysql_users (username,password,active,default_hostgroup,default_schema,transaction_persistent) VALUES ('app2','test',1,80,'mysql',1);
INSERT INTO mysql_users (username,password,active,default_hostgroup,default_schema,transaction_persistent) VALUES ('app1','test',1,80,'mysql',1);
LOAD MYSQL USERS TO RUNTIME;SAVE MYSQL USERS TO DISK;

insert into mysql_query_rules (rule_id,proxy_port,username,destination_hostgroup,active,retries,match_digest,apply,active) values(83,6033,'app2',80,1,3,'^SELECT.*FOR UPDATE',1,1);
insert into mysql_query_rules (rule_id,proxy_port,username,destination_hostgroup,active,retries,match_digest,apply,active) values(84,6033,'app2',81,1,3,'^SELECT.*',1,1);
insert into mysql_query_rules (rule_id,proxy_port,username,destination_hostgroup,active,retries,match_digest,apply,active) values(85,6033,'app2',80,0,3,'.',1,0);
INSERT INTO mysql_query_rules (rule_id,active,username,match_pattern,replace_pattern,apply,comment) VALUES (32,0,'app2',"(^SELECT)\s*(.*$)","\1 /*+ RESOURCE_GROUP(Select_app2) */ \2 ",0,"Lower prio and CPU bound on Reader");
INSERT INTO mysql_query_rules (rule_id,active,username,match_pattern,replace_pattern,apply,comment) VALUES (33,0,'app2',"^(INSERT|UPDATE|DELETE)\s*(.*$)","\1 /*+ RESOURCE_GROUP(Write_app2) */ \2 ",0,"Lower prio on Writer");
 
LOAD MYSQL QUERY RULES TO RUNTIME;SAVE MYSQL QUERY RULES TO DISK;


For several reasons I will add the resource groups query rules as INACTIVE for now. Done…

Testing

Will this work? We need to see the impact of the bad application on my production application. Then we need to see IF implementing the tuning will work or not. To do a basic check I run four tests:

  • test1 run both apps with read/write and rule disabled for RG
  • test2 run an application a time without RG
  • test3 run only App2 with RG to see the cost on the execution
  • test4 run both to see what happen with RG

Test 1

Master

master1_T1

Slave

master2_T1

The aim of this test is to have an idea, right away, of what happens when both applications are running, without limits. As we can see during the test all cores are utilized, some more consistently and some a bit less so, but nothing huge. What is interesting is to see the effect on the response time and the number of events each application is able to execute:

execution_time1

The execution graph indicates a very high time in Insert, and Delete for App1, with the results showing very bad performance only 9 inserts, 1333 deletes and 165 selects.

events_by_crud1

But what is the application actually supposed to do? Test 2 will tell us, creating de facto our baseline.

Test 2

In this test I had run each application separately, so no interference.

Master App1

master1_T2app1

Master App2

master1_T2app2

Slave App1

master2_T2app1

Slave App2

master2_T2app2

Nothing significantly different in the CPU utilization when App1 was running, while we can see a bit less utilization in the case of App2.

The impact on the performance is, however, more significant: 

execution_time2

events_by_crud2

Execution time for insert, delete drops significantly for App1 and we can see that the application SHOULD be able to insert ~1320 events and perform a significantly higher number of operations. Same for App2, but here we care more about the OLTP than the ETL application. So, what will happen IF we activate the Resource Group flags to the App2 (ETL) performance? Let's see with test 3.

Test 3

Running only App2 with active resource groups Master App2

master1_T3app2

Slave App2

master2_T3app2

On the master, what the RG settings will do is just reduce the priority, given that no other process is running and no other application is connected, the impact is not high. On the other hand, on the slave we can clearly see that now App2 can only use core 5 as indicated in our configuration. So far so good, what will be the performance loss? Let's see:

execution_time3

Comparing the two tests 2 and 3, we can see that in applying the resource groups our ETL application has a minimal but existing impact. That is expected, desired and must be noted. The impact is not high in this test, but it can expand the running time in real world.

events_by_crud3

It's time to combine all and see what is going on.

Test 4

Run our OLTP application while the ETL is running under Resource Group. Master

master1_T4

Slave

master2_T4

Looking at the CPU utilization these graphs are very similar to the ones in test1, but the result is totally different:

 

execution_time4

The execution time for App1 (OLTP) has dropped significantly while the performance has increased almost as if nothing else is running. At the same time App2 has lost performance, and this must be taken into account, but it will not stop/prevent the ETL process to run.

events_by_crud4

It is possible to do more tuning in the case that ETL is too compromised. Or maybe modify the Servers layout such as adding a Slave and dedicating it to ETL reads. The combinations and possibilities are many.

Conclusion

Just looking to the final graphs will help us to reach our conclusions: 

execution_time

events_by_crud

 

Comparing the two tests 1 and 4 we can see how using the Resource Group will help us to correctly balance the workload and optimize the performance in the case of unavoidable contention between different applications. At the same time, using Resource Group alone as a blanket setting is not optimal because it can fail its purpose. Instead of providing some improvement, it can unpredictably affect all the traffic. It is also not desirable to modify the code in order to implement it at query level, given the possible impact of doing that in cost and time. The introduction of ProxySQL with query rewrite, allows us to utilize the per query option, without the need for any code modification, and allow us to specify what we want, with very high level of granularity. Once more do not do this by yourself unless you are more than competent and know 100% what you are doing. In any case, remember that an ETL process may take longer and that you need to plan your work/schedule accordingly. Good MySQL everyone.

References

Last Updated on Tuesday, 28 August 2018 09:21
 
PXC loves firewalls (and System Admins loves iptables) PDF Print E-mail
Written by Marco Tusa   
Monday, 18 June 2018 00:00

pxc-setting-a-firewall-iptables-300x199

 

 

Let them stay together. In the last YEARS, I have seen quite often that users, when installing a product such as PXC, instead of spending five minutes to understand what to do just run iptables -F and save. In short, they remove any rules for their firewall.

With this post, I want to show you how easy it can be to do the right thing instead of putting your server at risk. I'll show you how a slightly more complex setup like PXC (compared to MySQL), can be easily achieved without risky shortcuts. iptables is the utility used to manage the chains of rules used by the Linux kernel firewall, which is your basic security tool. Linux comes with a wonderful firewall built into the kernel.

As an administrator, you can configure this firewall with interfaces like ipchains — which we are not going to cover — and iptables, which we shall talk about. iptables is stateful, which means that the firewall can make decisions based on received packets. This means that I can, for instance, DROP a packet if it's coming from bad-guy.com. I can also create a set of rules that either will allow or reject the package, or that will redirect it to another rule. This potentially can create a very complex scenario.

 

 

However, for today and for this use case let's keep it simple… Looking at my own server:

iptables -v -L
Chain INPUT (policy ACCEPT 0 packets, 0 bytes)
 pkts bytes target     prot opt in     out     source               destination
 250K   29M ACCEPT     all  --  any    any     anywhere             anywhere             state RELATED,ESTABLISHED
    6   404 ACCEPT     icmp --  any    any     anywhere             anywhere
    0     0 ACCEPT     all  --  lo     any     anywhere             anywhere
    9   428 ACCEPT     tcp  --  any    any     anywhere             anywhere             state NEW tcp dpt:ssh
    0     0 ACCEPT     tcp  --  any    any     anywhere             anywhere             state NEW tcp dpt:mysql
    0     0 ACCEPT     tcp  --  any    any     anywhere             anywhere             
  210 13986 REJECT     all  --  any    any     anywhere             anywhere             reject-with icmp-host-prohibited

Chain FORWARD (policy ACCEPT 0 packets, 0 bytes)
 pkts bytes target     prot opt in     out     source               destination
    0     0 REJECT     all  --  any    any     anywhere             anywhere             reject-with icmp-host-prohibited

Chain OUTPUT (policy ACCEPT 241K packets, 29M bytes)
 pkts bytes target     prot opt in     out     source               destination

That's not too bad, my server is currently accepting only SSH and packets on port 3306. Please note that I used the -v option to see more information like IN/OUT and that allows me to identify that actually row #3 is related to my loopback device, and as such it's good to have it open. The point is that if I try to run the PXC cluster with these settings it will fail, because the nodes will not be able to see each other. A quite simple example when try to start the second node of the cluster:

2018-05-21T17:56:14.383686Z 0 [Note] WSREP: (3cb4b3a6, 'tcp://10.0.0.21:4567') connection to peer 584762e6 with addr tcp://10.0.0.23:4567 timed out, no messages seen in PT3S

Starting a new node will fail, given that the connectivity will not be established correctly. In the Percona documentation there is a notes section in which we mention that these ports must be open to have the cluster working correctly.:

  • 3306 For MySQL client connections and State Snapshot Transfer that use the mysqldump method.
  • 4567 For Galera Cluster replication traffic, multicast replication uses both UDP transport and TCP on this port.
  • 4568 For Incremental State Transfer.
  • 4444 For all other State Snapshot Transfer.

Of course, if you don’t know how to do it that could be a problem, but it is quite simple. Just use the following commands to add the needed rules:

iptables -I INPUT 2 --protocol tcp --match tcp --dport 3306 --source 10.0.0.1/24 --jump ACCEPT
iptables -I INPUT 3 --protocol tcp --match tcp --dport 4567 --source 10.0.0.1/24 --jump ACCEPT
iptables -I INPUT 4 --protocol tcp --match tcp --dport 4568 --source 10.0.0.1/24 --jump ACCEPT
iptables -I INPUT 5 --protocol tcp --match tcp --dport 4444 --source 10.0.0.1/24 --jump ACCEPT
iptables -I INPUT 6 --protocol udp --match udp --dport 4567 --source 10.0.0.1/24 --jump ACCEPT

Once you have done this check the layout again and you should have something like this:

[root@galera1h1n5 gal571]# iptables -L
Chain INPUT (policy ACCEPT)
target prot opt source destination
ACCEPT all -- anywhere anywhere state RELATED,ESTABLISHED
ACCEPT tcp -- 10.0.0.0/24 anywhere tcp dpt:mysql
ACCEPT tcp -- 10.0.0.0/24 anywhere tcp dpt:tram
ACCEPT tcp -- 10.0.0.0/24 anywhere tcp dpt:bmc-reporting
ACCEPT tcp -- 10.0.0.0/24 anywhere tcp dpt:krb524
ACCEPT udp -- 10.0.0.0/24 anywhere udp dpt:tram
ACCEPT icmp -- anywhere anywhere
ACCEPT tcp -- anywhere anywhere tcp dpt:ssh
ACCEPT tcp -- anywhere anywhere tcp dpt:mysql
REJECT all -- anywhere anywhere reject-with icmp-port-unreachable

Chain FORWARD (policy ACCEPT)
target prot opt source destination
REJECT all -- anywhere anywhere reject-with icmp-port-unreachable

Chain OUTPUT (policy ACCEPT)
target prot opt source destination

Try to start the secondary node, and — tadaaa — the node will connect, will provision itself, and finally will start correctly. All good? Well not really, you still need to perform a final step. We need to make our server accessible also for PMM monitoring agents. You have PMM right? If you don’t take a look here and you will want it. :D Anyhow PMM will not work correctly with the rules I have, and the result will be an empty set of graphs when accessing the server statistics. Luckily, PMM has a very easy way to help you identify the issue:

[root@galera1h1n5 gal571]# pmm-admin check-network
PMM Network Status

Server Address | 192.168.1.52
Client Address | 192.168.1.205

* System Time
NTP Server (0.pool.ntp.org) | 2018-05-24 08:05:37 -0400 EDT
PMM Server | 2018-05-24 12:05:34 +0000 GMT
PMM Client | 2018-05-24 08:05:37 -0400 EDT
PMM Server Time Drift | OK
PMM Client Time Drift | OK
PMM Client to PMM Server Time Drift | OK

* Connection: Client --> Server
-------------------- -------
SERVER SERVICE STATUS
-------------------- -------
Consul API OK
Prometheus API OK
Query Analytics API OK

Connection duration | 1.051724ms
Request duration | 311.924µs
Full round trip | 1.363648ms

* Connection: Client <-- Server
-------------- ------------ -------------------- ------- ---------- ---------
SERVICE TYPE NAME REMOTE ENDPOINT STATUS HTTPS/TLS PASSWORD
-------------- ------------ -------------------- ------- ---------- ---------
linux:metrics galera1h1n5 192.168.1.205:42000 DOWN NO NO
mysql:metrics gal571 192.168.1.205:42002 DOWN NO NO

When an endpoint is down it may indicate that the corresponding service is stopped (run 'pmm-admin list' to verify).
If it's running, check out the logs /var/log/pmm-*.log

When all endpoints are down but 'pmm-admin list' shows they are up and no errors in the logs,
check the firewall settings whether this system allows incoming connections from server to address:port in question.

Also you can check the endpoint status by the URL: http://192.168.1.52/prometheus/targets

What you want more? You have all the information to debug and build your new rules. I just need to open the ports 42000 42002 on my firewall:

iptables -I INPUT 7 --protocol tcp --match tcp --dport 42000 --source 192.168.1.1/24 --jump ACCEPT
iptables -I INPUT 8 --protocol tcp --match tcp --dport 42002 --source 192.168.1.1/24 --jump ACCEPT

Please note that we are handling the connectivity for PMM using a different range of IPs/subnet. This because it is best practice to have PXC nodes communicate to a dedicated network/subnet (physical and logical). Run the test again:

* Connection: Client <-- Server
-------------- ------------ -------------------- ------- ---------- ---------
SERVICE TYPE NAME REMOTE ENDPOINT STATUS HTTPS/TLS PASSWORD
-------------- ------------ -------------------- ------- ---------- ---------
linux:metrics galera1h1n5 192.168.1.205:42000 OK YES YES
mysql:metrics gal571 192.168.1.205:42002 OK YES YES

Done … I just repeat this on all my nodes and I will have set my firewall to handle the PXC related security. Now that all my settings are working well I can save my firewall’s rules:

iptables-save > /etc/sysconfig/iptables

For Ubuntu you may need some additional steps as for (https://help.ubuntu.com/community/IptablesHowTo#Using_iptables-save.2Frestore_to_test_rules) There are some nice tools to help you even more, if you are very lazy, like UFW and the graphical one, GUFW. Developed to ease iptables firewall configuration, ufw provides a user friendly way to create an IPv4 or IPv6 host-based firewall. By default UFW is disabled in Ubuntu. Given that ultimately they use iptables, and their use is widely covered in other resources such as the official Ubuntu documentation, I won't cover these here.

Conclusion

Please don't make the mistake of flushing/ignoring your firewall, when to make this right is just a matter of 5 commands. It's easy enough to be done by everyone and it's good enough to stop the basic security attacks. Happy MySQL (and PXC) to everyone.

Last Updated on Wednesday, 18 July 2018 16:25
 
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