Feb
20
2018
--

xMatters snares $40 million Series D led by Goldman Sachs Private Capital Investing

 When a crisis happens and your system is down, it’s easy for panic and chaos to ensue. Large companies use a range of tools to monitor system health, and finding the source of the problem and ensuring the proper personnel are involved is not always easy. That’s where xMatters comes in. It acts as an uber monitoring tool allowing you to understand the source of your problem and… Read More

Feb
14
2018
--

Amazon Aurora MySQL Monitoring with Percona Monitoring and Management (PMM)

Amazon Aurora MySQL Monitoring small

In this blog post, we’ll review additional Amazon Aurora MySQL monitoring capabilities we’ve added in Percona Monitoring and Management (PMM) 1.7.0. You can see them in action in the MySQL Amazon Aurora Metrics dashboard.

Amazon Aurora MySQL Transaction CommitsAmazon Aurora MySQL Monitoring

This graph looks at the number of commits the Amazon Aurora engine performed, as well as the average commit latency. As you can see from this graph, latency does not always correlate with the number of commits performed and can be quite high in certain situations.

Amazon Aurora MySQL LoadAmazon Aurora MySQL Monitoring 2

In Percona Monitoring and Management, we often use the concept of “Load” – which roughly corresponds to the number of operations of a type in progress. This graph shows us what statements contribute the most load on the system, as well as what load corresponds to the Amazon Aurora transaction commits (which we observed in the graph before).

Amazon Aurora MySQL Memory Usage

Amazon Aurora MySQL Monitoring 3

This graph is pretty self-explanatory. It shows how much memory is used by the Amazon Aurora lock manager, as well as the amount of memory used by Amazon Aurora to store Data Dictionary.

Amazon Aurora MySQL Statement Latency

Amazon Aurora MySQL Monitoring 4

This graph shows the average latency for the most important types of statements. Latency spikes, as shown in this example, are often indicative of the instance overload.

Amazon Aurora MySQL Special Command Counters

Amazon Aurora MySQL Monitoring 5

Amazon Aurora MySQL allows a number of commands that are not available in standard MySQL. This graph shows the usage of such commands. Regular “unit_test” calls can be seen in the default Amazon Aurora install, and the rest depends on your workload.

Amazon Aurora MySQL Problems

Amazon Aurora MySQL Monitoring 6

This graph is where you want to see a flat line. It shows different kinds of internal Amazon Aurora MySQL problems, which in normal operation should generally be zero.

I hope you find these Amazon Aurora MySQL monitoring improvements useful. Let us know if there is any other Amazon Aurora information that would be helpful to display!

Feb
09
2018
--

Collect PostgreSQL Metrics with Percona Monitoring and Management (PMM)

Collecting PostgreSQL Information using Percona Monitoring and Management

In this article, we’ll describe how to collect PostgreSQL metrics with Percona Monitoring and Management (PMM).

We designed Percona Monitoring and Management (PMM) to be the best tool for MySQL and MongoDB performance investigation. At the same time, it’s built on mature opensource components: Prometheus’ time series database and Grafana. Starting from PMM 1.4.0. it’s possible to add monitoring for any service supported by Prometheus.

Demo

# install docker and docker-compose.
git clone https://github.com/ihanick/pmm-postgresql-demo.git
cd pmm-postgresql-demo
docker-compose build
docker-compose up

At this point, we are running exporter, PostgreSQL and the PMM server, but pmm-client on the PostgreSQL server isn’t configured.

docker-compose exec pg sh /root/initpmm.sh

Now we configured pmm client and added external exporter.

Let’s assume that you have executed commands above on the localhost. At this point we have several URLs:

We also need to create graphs for our new exporter. This could be done manually (import JSON), or you can import the existing dashboard Postgres_exporter published in the Grafana gallery by number in the catalog:

  1. Go to your PMM server web interface and press on the Grafana icon at the top left corner, then dashboards, the import.
  2. Copy and paste the dashboard ID from the Grafana site to “Grafana.com Dashboard” field, and press load.
  3. In the next dialog, choose Prometheus as a data source and continue.

PostgreSQL performance graphs can be seen at: http://localhost:8080/graph/dashboard/db/postgres_exporter?orgId=1

collect PostgreSQL metrics with Percona Monitoring and Management
PMM PostgreSQL postgres_exporter template

 

PMM-PostgreSQL Demo Under the Hood

To move this configuration to production, we need to understand how this demo works.

PMM Server

First of all, you need an existing PMM Server. You can find details on new server configuration at Deploying Percona Monitoring and Management.

In my demo I’m starting PMM without volumes, and all metrics dropped after using the docker-compose down command. Also, there is no need to use port 8080 for PMM, set it up with SSL support and password in production.

PostgreSQL Setup

I’m modifying the latest default PostgreSQL image to:

Of course, you can use a dedicated PostgreSQL server instead of one running inside a docker-compose sandbox. The only requirement is that the PMM server should be able to connect to this server.

User creation and permissions:

CREATE DATABASE postgres_exporter;
CREATE USER postgres_exporter PASSWORD 'password';
ALTER USER postgres_exporter SET SEARCH_PATH TO postgres_exporter,pg_catalog;
-- If deploying as non-superuser (for example in AWS RDS)
-- GRANT postgres_exporter TO :MASTER_USER;
CREATE SCHEMA postgres_exporter AUTHORIZATION postgres_exporter;
CREATE VIEW postgres_exporter.pg_stat_activity
AS
  SELECT * from pg_catalog.pg_stat_activity;
GRANT SELECT ON postgres_exporter.pg_stat_activity TO postgres_exporter;
CREATE VIEW postgres_exporter.pg_stat_replication AS
  SELECT * from pg_catalog.pg_stat_replication;
GRANT SELECT ON postgres_exporter.pg_stat_replication TO postgres_exporter;

To simplify setup, you can use a superuser account and access pg_catalog directly. To improve security, allow this user to connect only from exporter host.

PMM Client Setup on PostgreSQL Host

You can obtain database-only statistics with just the external exporter, and you can use any host with pmm-client installed. Fortunately, you can also export Linux metrics from the database host.

After installing the pmm-client package, you still need to configure the system. We should point it to the PMM server and register the external exporter (and optionally add the linux:metrics exporter).

#!/bin/sh
pmm-admin config --client-name pg1 --server pmm-server
pmm-admin add external:metrics postgresql pgexporter:9187
# optional
pmm-admin add linux:metrics
# other postgresql instances
pmm-admin add external:instances postgresql 172.18.0.3:9187

It’s important to keep the external exporter job name as “postgresql”, since all existing templates check it. There is a bit of inconsistency here: the first postgresql server is added as external:metrics, but all further servers should be added as external:instances.

The reason is the first command creates the Prometheus job and first instance, and further servers can be added without job creation.

PMM 1.7.0 external:service

Starting from PMM 1.7.0 the setup simplified if exporter located on the same host as pmm-client:

pmm-admin config --client-name pg1 --server pmm-server
pmm-admin add external:service --service-port=9187 postgresql

pmm-admin add external:metrics or pmm-admin add external:instances are not required if you are running exporter on the same host as pmm-client.

Exporter Setup

Exporter is a simple HTTP/HTTPS server returning one page. The format is:

curl -si http://172.17.0.4:9187/metrics|grep pg_static
# HELP pg_static Version string as reported by postgres
# TYPE pg_static untyped
pg_static{short_version="10.1.0",version="PostgreSQL 10.1 on x86_64-pc-linux-gnu, compiled by gcc (Debian 6.3.0-18) 6.3.0 20170516, 64-bit"} 1

As you can see, it’s a self-describing set of counters and string values. The Prometheus time series database built-in to PMM connects to the web server and stores the results on disk. There are multiple exporters available for PostgreSQL. postgres_exporter is listed as a third-party on the official Prometheus website.

You can compile exporter by yourself, or run it inside docker container. This and many other exporters are written in Go and compiled as a static binary so that you can copy the executable from the host with same CPU architecture. For production setups, you probably will run exporter from a database host directly and start the service with systemd.

In order to check network configuration issues, login to pmm-server and use the curl command from above. Do not forget to replace 172.17.0.4:9187 with the appropriate host:port (use the same IP address or DNS name as the pmm-admin add command).

You configure postgres_exporter with a single environment variable:

DATA_SOURCE_NAME=postgresql://postgres_exporter:password@pg:5432/postgres_exporter?sslmode=disable

Make sure that you provide the correct credentials, including the database name.

Run external exporter directly on database server

In order to simplify production setup, you can run exporter directly from the same server as you are using for running PostgreSQL.
This method allows you to use pmm-admin add external:service command recently added to PMM.

# Copy exporter binary from docker container to the local directory to skip build from sources
docker cp pmmpostgres_pgexporter_1:/postgres_exporter ./
# copy exporter binary to database host, use scp instead for existing database server.
docker cp postgres_exporter pmmpostgres_pg_1:/root/
# login to database server shell
docker exec -it pmmpostgres_pg_1 bash
# start exporter
DATA_SOURCE_NAME='postgresql://postgres_exporter:password@127.0.0.1:5432/postgres_exporter?sslmode=disable' ./postgres_exporter

Grafana Setup

In the demo, I’ve used Postgres_exporter dashboard. Use the same site and look for other PostgreSQL dashboards if you need more. The exporter provides many parameters, and not all of them are visualized in this dashboard.

For huge installations, you may find that filtering servers by “instance name” is not comfortable. Write your own JSON for the dashboard, or try to use one from demo repository. It’s the same as dashboard 3742, but uses the hostname for filtering and Prometheus job name in the legends.

All entries of instance=~"$instance" get replaced with instance=~"$host:.*".

The modification allows you to switch between PostgreSQL servers with host instead of “instance”, and see CPU and disk details for the current database server instead of the previously selected host.

Notice

This blog post on how to collect PostgreSQL metrics with Percona Monitoring and Management is not an official integration of PostgreSQL and PMM. I’ve tried to describe complex external exporters setup. Instead of PostgreSQL, you can use any other services and exporters with a similar setup, or even create your own exporter and instrument your application. It’s a great thing to see correlations between application activities and other system components like databases, web servers, etc.

Feb
06
2018
--

Announcing Experimental Percona Monitoring and Management (PMM) Functionality via Percona Labs

Experimental Percona Monitoring and Management

Experimental Percona Monitoring and ManagementIn this blog post, we’ll introduce how you can look at some experimental Percona Monitoring and Management (PMM) features using Percona Labs builds on GitHub.

Note: PerconaLabs and Percona-QA are open source GitHub repositories for unofficial scripts and tools created by Percona staff. While not covered by Percona support or services agreements, these handy utilities can help you save time and effort.

Percona software builds located in the PerconaLabs and Percona-QA repositories are not officially released software, and also aren’t covered by Percona support or services agreements. 

Percona Monitoring and Management (PMM) is a free and open-source platform for managing and monitoring MySQL® and MongoDB® performance. You can run PMM in your environment for maximum security and reliability. It provides thorough time-based analysis for MySQL and MongoDB servers to ensure that your data works as efficiently as possible.

This month we’re announcing access to Percona Labs builds of Percona Monitoring and Management so that you can experiment with new functionality that’s not yet in our mainline product. You can identify the unique builds at:

https://hub.docker.com/r/perconalab/pmm-server/tags/

Most of the entries here are the pre-release candidate images we use for QA, and they follow a format of all integers (for example “201802061627”). You’re fine to use these images, but they aren’t the ones that have the experimental functionality.

Today we have two builds of note (these DO have the experimental functionality):

  • 1.6.0-prom2.1
  • 1.5.3-prometheus2

We’re highlighting Prometheus 2.1 on top of our January 1.6 release (1.6.0-prom2.1), available in Docker format. Some of the reasons you might want to deploy this experimental build to take advantage of the Prometheus 2 benefits are:

  • Reduced CPU usage by Prometheus, meaning you can add more hosts to your PMM Server
  • Performance improvements, meaning dashboards load faster
  • Reduced disk I/O, disk space usage

Please keep in mind that as this is a Percona Labs build (see our note above), so in addition note the following two criteria:

  • Support is available from our Percona Monitoring and Management Forums
  • Upgrades might not work – don’t count on upgrading out of this version to a newer release (although it’s not guaranteed to block upgrades)

How to Deploy an Experimental Build from Percona Labs

The great news is that you can follow our Deployment Instructions for Docker, and the only change is where you specify a different Docker container to pull. For example, the standard way to deploy the latest stable PMM Server release with Docker is:

docker pull percona/pmm-server:latest

To use the Percona Labs build 1.6.0-prom2.1 with Prometheus 2.1, execute the following:

docker pull perconalab/pmm-server:1.6.0-prom2.1

Please share your feedback on this build on our Percona Monitoring and Management Forums.

If you’re looking to deploy Percona’s officially released PMM Server (not the Percona Labs release, but our mainline version which currently is release 1.7) into a production environment, I encourage you to consider a Percona Support contract, which includes PMM at no additional charge!

Feb
05
2018
--

Percona Monitoring Plugins 1.1.8 Release Is Now Available

Percona Monitoring Plugins 1.1.7

Percona Monitoring Plugins 1.1.8Percona announces the release of Percona Monitoring Plugins 1.1.8.

Changelog

  • Add MySQL 5.7 support
  • Changed a canary check to use timestamp.now() and return a timedelta.seconds
  • Remove an additional condition for the Dictionary memory allocated
  • Fixed a false-positive problem when the calculated delay was less than 0 and the -m was not set.
  • Fixed the problem where slaves would alert due to deadlocks on the master.
  • If using pt-heartbeat, get_slave_status was only called when the -s option is set to MASTER
  • Disabled UNK alerts by default (it is possible to enable them explicitly).
  • A fix was added for MySQL Multi-Source replication.
  • The graph Percona InnoDB Memory Allocation showed zeroes for the
    metrics Total memory (data source item nl) and Dictionary memory
    (data source item nm) when used for MySQL 5.7.18, because the syntax
    of SHOW ENGINE INNODB STATUS has changed in MySQL 5.7 (see https://dev.mysql.com/doc/refman/5.7/en/innodb-standard-monitor.html).
  • The graph Percona InnoDB I/O Pending showed NaN for the metrics
    Pending Log Writes (data source item hn) and Pending Chkp Writes
    (data source item hk) when used for MySQL 5.7.18, because the syntax
    of SHOW ENGINE INNODB STATUS has changed in MySQL 5.7 (see https://dev.mysql.com/doc/refman/5.7/en/innodb-standard-monitor.html).
  • Added server @@hostname as a possible match to avoid DNS lookups while allowing hostname-match.

A new tarball is available from downloads area or in packages from our software repositories. The plugins are fully supported for customers with a Percona Support contract and free installation services are provided as part of some contracts. You can find links to the documentation, forums and more at the project homepage.

About Percona Monitoring Plugins
Percona Monitoring Plugins are monitoring and graphing components designed to integrate seamlessly with widely deployed solutions such as Nagios, Cacti and Zabbix.

Jan
22
2018
--

Webinar Wednesday, January 24, 2018: Differences between MariaDB and MySQL

MariaDB and MySQL

MariaDB and MySQLJoin Percona’s Chief Evangelist, Colin Charles as he presents Differences Between MariaDB and MySQL on Wednesday, January 24, 2018, at 7:00 am PDT / 10:00 am EDT (UTC-7).

Tags: MariaDB, MySQL, Percona Server for MySQL, DBA, SysAdmin, DevOps
Experience Level: Novice

MariaDB and MySQL. Are they syntactically similar? Where do these two query languages differ? Why would I use one over the other?

MariaDB is on the path of gradually diverging from MySQL. One obvious example is the internal data dictionary currently under development for MySQL 8.0. This is a major change to the way metadata is stored and used within the server. MariaDB doesn’t have an equivalent feature. Implementing this feature could mark the end of datafile-level compatibility between MySQL and MariaDB.

There are also non-technical differences between MySQL and MariaDB, including:

  • Licensing: MySQL offers their code as open-source under the GPL, and provides the option of non-GPL commercial distribution in the form of MySQL Enterprise. MariaDB can only use the GPL because they derive their work from the MySQL source code under the terms of that license.
  • Support services: Oracle provides technical support, training, certification and consulting for MySQL, while MariaDB has their own support services. Some people will prefer working with smaller companies, as traditionally it affords them more leverage as a customer.
  • Community contributions: MariaDB touts the fact that they accept more community contributions than Oracle. Part of the reason for this disparity is that developers like to contribute features, bug fixes and other code without a lot of paperwork overhead (and they complain about the Oracle Contributor Agreement). However, MariaDB has its own MariaDB Contributor Agreement — which more or less serves the same purpose.

Colin will take a look at some of the differences between MariaDB and MySQL and help answer some of the common questions our Database Performance Experts get about the two databases.

Register for the webinar now.

Colin CharlesColin Charles, Chief Evangelist

Colin Charles is the Chief Evangelist at Percona. He was previously on the founding team for MariaDB Server in 2009, worked in MySQL since 2005, and been a MySQL user since 2000. Before joining MySQL, Colin worked actively on the Fedora and OpenOffice.org projects. He’s well-known within many open source communities and speaks on the conference circuit.

Jan
16
2018
--

Webinar January 18, 2018: MySQL Troubleshooting and Performance Optimization with Percona Monitoring and Management (PMM) Part 2

Percona Monitoring and Management

Percona Monitoring and ManagementJoin Percona’s Product Manager Michael Coburn as he presents MySQL Troubleshooting and Performance Optimization with Percona Monitoring and Management (PMM) Part 2 on Thursday, January 18, 2018, at 11:00 am PST / 2:00 pm EST (UTC-8).

Tags: Percona Monitoring and Management, PMM, Monitoring, MySQL, Performance, Optimization, DBA, SysAdmin, DevOps
Experience Level: Expert

Optimizing MySQL performance and troubleshooting MySQL problems are two of the most critical and challenging tasks for MySQL DBAs. The databases powering your applications need to handle heavy traffic loads while remaining responsive and stable. This is so that you can deliver an excellent user experience. Furthermore, DBA’s are also expected to find cost-efficient means of solving these issues.

In this webinar — the second part of a two-part series — Michael discusses how you can optimize and troubleshoot MySQL performance and demonstrate how Percona Monitoring and Management (PMM) enables you to solve these challenges using free and open source software. We will look at specific, common MySQL problems and review the essential components in PMM that allow you to diagnose and resolve them.

By the end of this webinar, you will have a better understanding of how you can troubleshoot MySQL problems in your database.

Register for the webinar now.

Percona Monitoring and ManagementMichael Coburn, Product Manager

Michael joined Percona as a Consultant in 2012 and progressed through various roles including Managing Consultant, Principal Architect, Technical Account Manager, and Technical Support Engineer. He is now leading the Product Manager of Percona Monitoring and Management.

Oct
25
2017
--

JSON Output of the pmm-admin list Command

In this blog post, we’ll look at Percona Monitoring and Management’s pmm-admin list command.

The pmm-admin list command shows all monitoring services you have added using the pmm-admin add command. Starting with version 1.4.0, Percona Monitoring and Management (PMM) also lists external monitoring services when you run pmm-admin list, i.e., those services that monitor the backends not supported out of the box (such as PostgreSQL databases).

In the output, the external monitoring services appear at the bottom:

The tabular output of the pmm-admin list command

JSON Output for Automatic Verification

But there is also another feature of pmm-admin list. If you run this command with the –json parameter, the command gives you a JSON document as output. This option now enables inspecting the monitoring services by computers due to the strict JSON syntax rules. JSON has become a de-facto standard for exchanging data for many tools. The JSON output provided by the pmm-admin list command can be used by configuration management tools such as ansible or chef.

The output is captured as keys and values. The general information about the computer where this pmm-client is installed is given as top-level elements:

  • Version
  • ServerAddress
  • ServerSecurity
  • ClientName
  • ClientAddress
  • ClientBindAddress
  • Platform

You can quickly determine if there are any errors in built-in monitoring services by inspecting the Err top level element in the JSON output. Similarly, the ExternalErr element reports errors on external services:

The JSON parsing friendly version produced by the pmm-admin list command

Representing Monitoring Services

Two elements contain lists as their values. The Services top-level element contains a list of documents that represent enabled monitoring services. The ExternalServices element contains a list of documents that represent enabled external monitoring services. Each attribute in the Services and ExternalServices elements provides the same information as a column in the tabular output.

Hope this brief post provides some valuable information regarding new Percona Monitoring and Management 1.4.0 functionality. Let me know about any questions in the comments.

Oct
03
2017
--

MyRocks Metrics Now in PMM 1.3.0

MyRocks

One of the most exciting features shipped in the Percona Monitoring and Management 1.3.0 (PMM) release is support for MyRocks metrics via a new Metrics Monitor dashboard titled MySQL MyRocks Metrics. The support in PMM follows the recent Percona Server for MySQL release 5.7.19 from September 6, where Percona delivered an EXPERIMENTAL version of MyRocks for non-Production usage.

The MyRocks storage engine from Facebook is based on RocksDB, a persistent key-value store for fast storage environments. MyRocks is optimized for fast storage and combines outstanding space and write efficiency with acceptable read performance. As a result, MyRocks has the following advantages compared to other storage engines (if your workload uses fast storage, such as SSD):

  • Requires less storage space
  • Provides more storage endurance
  • Ensures better IO capacity

MyRocks Database Operations

This graph will help you visualize MyRocks database operations of Next and Seek attributes:

MyRocks Cache Activity

We also have a graph to help you visualize the count of Hits and Misses on the MyRocks cache:

MyRocks Cache Data Bytes Read/Write

Finally, another important MyRocks graph will help you understand the volume of data read and written to the MyRocks cache:

Please note that the MyRocks storage engine is not suitable (yet) for production workloads, but if you are testing this technology take a moment to install PMM in order to take advantage of our new MySQL MyRocks Metrics dashboard!

In PMM, you can view the metrics provided by the information schema as well as various data reported by the RocksDB engine’s status used by your MySQL database instance.

Sep
25
2017
--

Percona Monitoring and Management 1.3.0 Is Now Available

Percona Monitoring and Management 1.3.0

Percona Monitoring and Management 1.3.0Percona announces the release of Percona Monitoring and Management 1.3.0 on September 26, 2017.

Percona Monitoring and Management 1.3.0 introduces basic support for the MyRocks storage engine. There is a special dashboard in Metrics Monitor that presents the essential metrics of MyRocks as separate graphs. Also, Metrics Monitor graphs now feature on-demand descriptions that remain visible as long as hover over them.

For example, this graph helps you visualize MyRocks database operations of Next and Seek attributes:

There are many improvements to QAN (Query Analytics) both in the user interface design and in its capabilities. In this release, QAN starts supporting all types of MongoDB queries. For example, if you need to limit the list of available queries to only those that you are interested in, use the Query Filter field next to the database selection button:

Orchestrator is not enabled by default because leaving it in a non-configured state was confusing to users. It is still possible to enable it along with the docker run command.

For install and upgrade instructions, see Deploying Percona Monitoring and Management.

New Features

  • PMM-1290: Basic support for the metrics of the MyRocks storage engine in MySQL via the mysqld-exporter.
  • PMM-1312: Metrics Monitor now features a MyRocks dashboard.
  • PMM-1330: Basic telemetry data are collected from PMM Servers.
  • PMM-1417: A new dashboard in Metrics Monitor designed to enable exploring any data in Prometheus
  • PMM-1437pmm-admin allows passing parameters to exporters
  • PMM-685: The EXPLAIN command is now supported in QAN.

Improvements

  • PMM-1262: The system checks for updates much faster
  • PMM-1015QAN should shows all collections from a mongod instance. Make sure that profiling is enabled in MongoDB.
  • PMM-1057QAN supports all MongoDB query types.
  • PMM-1270: In Metrics Monitor, the dashboard filter displays only MariaDB hosts.
  • PMM-1287: In pmm-admin mongodb:queries is not experimental anymore and the dev-enable option is no longer needed.
  • PMM-1446: In Metrics Monitor, the MySQL Active Threads graph displays data more accurately.
  • PMM-1455: In Metrics Monitor, features descriptions of graphs
  • PMM-1476: QAN2 is used by default in pmmdemo.percona.com
  • PMM-1479: It is now possible to go to QAN directly from Metrics Monitor.
  • PMM-515Orchestrator is disabled by default. It is possible to enable it when running your docker container.

Bug fixes

  • PMM-1298: In QAN, the query abstract could be empty for MySQL hosts for low-ranking queries. This bug is fixed to contain Low Ranking Queries as the value of the query abstract.
  • PMM-1314: The selected time range in QAN could be applied incorrectly. This bug is now fixed.
  • PMM-1398: Prometheus memory was not updated after PMM upgrade. This bug is now fixed.
  • PMM-1427: The CPU Usage/Load graph in the MySQL Overview dashboard was displayed with slightly incorrect dimensions. This bug is now solved.
  • PMM-1439: If the EXPLAIN command was not supported for the selected query, there could appear a JavaScript error.
  • PMM-1472: It could happen that monitoring of queries for MongoDB with replication could not be enabled.
  • PMM-943: InnoDB AHI Usage Graph had incorrect naming and hit ratio computation.

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