Percona XtraDB Cluster 5.6.41-28.28 Is Now Available

Percona XtraDB Cluster 5.7

Percona XtraDB Cluster 5.6Percona announces the release of Percona XtraDB Cluster 5.6.41-28.28 (PXC) on September 18, 2018. Binaries are available from the downloads section or our software repositories.

Percona XtraDB Cluster 5.6.41-28.28 is now the current release, based on the following:

Fixed Bugs

  • PXC-1017: Memcached API is now disabled if node is acting as a cluster node, because InnoDB Memcached access is not replicated by Galera.
  • PXC-2164: SST script compatibility with SELinux was improved by forcing it to look for port associated with the said process only.
  • PXC-2155: Temporary folders created during SST execution are now deleted on cleanup.
  • PXC-2199: TOI replication protocol was fixed to prevent unexpected GTID generation caused by the  DROP TRIGGER IF EXISTS statement logged by MySQL as a successful one due to its IF EXISTS clause.

Help us improve our software quality by reporting any bugs you encounter using our bug tracking system. As always, thanks for your continued support of Percona!

The post Percona XtraDB Cluster 5.6.41-28.28 Is Now Available appeared first on Percona Database Performance Blog.


Monitoring Processes with Percona Monitoring and Management

Memory utilization during compaction process

A few months ago I wrote a blog post on How to Capture Per Process Metrics in PMM. Since that time, Nick Cabatoff has made a lot of improvements to Process Exporter and I’ve improved the Grafana Dashboard to match.

I will not go through installation instructions, they are well covered in original blog post.  This post covers features available in release 0.4.0 Here are a few new features you might find of interest:

Used Memory

Memory usage in Linux is complicated.  You can look at resident memory, which shows how much space is used in RAM. However, if you have a substantial part of process swapped out because of memory pressure, you would not see it. You can also look at virtual memory–but it will include a lot of address space which was allocated and never mapped either to RAM or to swap space.   Especially for processes written in Go, the difference can be extreme. Let’s look at the process exporter itself: it uses 20MB of resident memory but over 2GB of virtual memory.

top processes by resident memory

top processes by virtual memory

Meet the Used Memory dashboard, which shows the sum of resident memory used by the process and swap space used:

used memory dashboard

There is dashboard to see process by swap space used as well, so you can see if some processes that you expect to be resident are swapped out.

Processes by Disk IO

processes by disk io

Processes by Disk IO is another graph which I often find very helpful. It is the most useful for catching the unusual suspects, when the process causing the IO is not totally obvious.

Context Switches

Context switches, as shown by VMSTAT, are often seen as an indication of contention. With contention stats per process you can see which of the process are having those context switches.

top processes by voluntary context switches

Note: while large number of context switches can be a cause of high contention, some applications and workloads are just designed in such a way. You are better off looking at the change in the number of context switches, rather than at the raw number.

CPU and Disk IO Saturation

As Brendan Gregg tells us, utilization and saturation are not the same. While CPU usage and Disk IO usage graphs show us resource utilization by different processes, they do not show saturation.

top running processes graph

For example, if you have four CPU cores then you can’t get more than four CPU cores used by any process, whether there are four or four hundred concurrent threads trying to run.

While being rather volatile as gauge metrics, top running processes and top processes waiting on IO are good metrics to understand which processes are prone to saturation.

These graphs roughly provide a breakdown of “r” and “b”  VMSTAT columns per process

Kernel Waits

Finally, you can see which kernel function (WCHAN) the process is sleeping on, which can be very helpful to access processes which are not using a lot of CPU, but are not making much progress either.

I find this graph most useful if you pick the single process in the dashboard picker:

kernel waits for sysbench

In this graph we can see sysbench has most threads sleeping in


  which corresponds to reading the response from MySQL from UNIX socket – exactly what you would expect!


If you ever need to understand what different processes are doing in your system, then Nick’s Process Exporter is a fantastic tool to have. It just takes few minutes to get it added into your PMM installation.

If you enjoyed this post…

You might also like my pre-recorded webinar MySQL troubleshooting and performance optimization with PMM.

The post Monitoring Processes with Percona Monitoring and Management appeared first on Percona Database Performance Blog.


Using the keyring_vault Plugin with Percona Server for MySQL 5.7

keyring_vault store database encryption keys

keyring_vault store database encryption keysThis is the first of a two-part series on using the keyring_vault plugin with Percona Server for MySQL 5.7. The second part will walk you through on how to use Percona Xtrabackup to backup from this instance and restore to another server and set it up as a slave with keyring_vault plugin.

What is the keyring_vault plugin?

The keyring_vault is a plugin that allows the database to interface with a Hashicorp Vault server to store and secure encryption keys. The Vault server then acts as a centralized encryption key management solution which is critical for security and for compliance with various security standards.

Configuring Vault

Create SSL certificates to be used by Vault. You can use the sample ssl.conf template below to generate the necessary files.

[root@vault1 ~]# cat /etc/sslkeys/ssl.conf
distinguished_name = req_distinguished_name
x509_extensions = v3_req
prompt = no
C = US
L =  R
O = Percona
CN = *
subjectKeyIdentifier = hash
authorityKeyIdentifier = keyid,issuer
basicConstraints = CA:TRUE
subjectAltName = @alt_names
IP =

Then run the two commands below to generated the cert and key files and the certificate chain:

$ openssl req -config ssl.conf -x509 -days 365 -batch -nodes -newkey rsa:2048 -keyout vault.key -out vault.crt
$ cat vault.key vault.crt > vault.pem

Once the SSL certificates are created start Vault with the sample configuration below. Take note that you should follow the suggested best practices when deploying Vault in production, this example is to get us by with a simple working setup.

[root@vault1 ~]# cat /etc/vault.hcl
listener "tcp" {
address = ""
storage "file" {
path = "/var/lib/vault"

Assuming Vault started up fine and you are able to unseal Vault, the next step is to create the policy file. For more details on initializing and unsealing Vault please read the manual here.

[root@vault1 ~]# cat /etc/vault/policy/dc1.hcl
path "secret/*" {
capabilities = ["list"]
path "secret/dc1/*" {
capabilities = ["create", "read", "delete", "update", "list"]

Create a Vault policy named dc1-secrets using the dc1.hcl file like this:

[root@vault1 ~]# vault policy write dc1-secrets /etc/vault/policy/dc1.hcl -ca-cert=/etc/sslkeys/vault.pem
Success! Uploaded policy: dc1-secrets

Next, create a token associated with the newly created policy:

[root@vault1 ~]# vault token create -policy=dc1-secrets -ca-cert=/etc/sslkeys/vault.pem > dc1-token
[root@vault1 ~]# cat dc1-token
Key                  Value
---                  -----
token                be515093-b1a8-c799-b237-8e04ea90ad7a
token_accessor       4c1ba5c5-3fed-e9bb-d230-5bf1392e2d7e
token_duration       8760h
token_renewable      true
token_policies       ["dc1-secrets" "default"]
identity_policies    []
policies             ["dc1-secrets" "default"]

Setting up MySQL

The following instructions should work starting from Percona Server for MySQL 5.7.20-18 and through later versions.

Configure my.cnf with the following variables:


Create the keyring_vault.conf file in the path above with the following contents:

[root@mysql1 ~]# cat /var/lib/mysql-keyring/keyring_vault.conf
vault_url =
secret_mount_point = secret/dc1/master
token = be515093-b1a8-c799-b237-8e04ea90ad7a
vault_ca = /etc/vault_ca/vault.pem

Here we are using the vault.pem file generated by combining the vault.crt and vault.key files. Observe that our secret_mount_point is secret/dc1/master. We want to make sure that this mount point is unique across all servers, this is in fact advised in the manual here.

Ensure that the CA certificate is owned by mysql user:

[root@mysql1 ~]# ls -la /etc/vault_ca/
total 24
drwxr-xr-x  2 mysql mysql   41 Jul 14 11:39 .
drwxr-xr-x 63 root  root  4096 Jul 14 13:17 ..
-rw-------  1 mysql mysql 1139 Jul 14 11:39 vault.pem

Initialize the MySQL data directory on the Master:

[root@mysql1 ~]# mysqld --initialize-insecure --datadir=/var/lib/mysql --user=mysql

For production systems we do not recommend using --initialize-insecure option, this is just to skip additional steps in this tutorial.

Finally, start mysqld instance and then test the setup by creating an encrypted table.

[root@mysql1 ~]# systemctl status mysqld
? mysqld.service - MySQL Server
Loaded: loaded (/usr/lib/systemd/system/mysqld.service; disabled; vendor preset: disabled)
Active: active (running) since Sat 2018-07-14 23:53:16 UTC; 2s ago
Docs: man:mysqld(8)
Process: 1401 ExecStart=/usr/sbin/mysqld --daemonize --pid-file=/var/run/mysqld/ $MYSQLD_OPTS (code=exited, status=0/SUCCESS)
Process: 1383 ExecStartPre=/usr/bin/mysqld_pre_systemd (code=exited, status=0/SUCCESS)
Main PID: 1403 (mysqld)
CGroup: /system.slice/mysqld.service
??1403 /usr/sbin/mysqld --daemonize --pid-file=/var/run/mysqld/
Jul 14 23:53:16 mysql1 systemd[1]: Starting MySQL Server...
Jul 14 23:53:16 mysql1 systemd[1]: Started MySQL Server.

At this point you should have Percona Server for MySQL instance with tablespace encryption using Vault.

Researching database security?

You might also enjoy this pre-recorded webinar securing your database servers from external attacks presented by my colleague Colin Charles.

The post Using the keyring_vault Plugin with Percona Server for MySQL 5.7 appeared first on Percona Database Performance Blog.


This Week in Data With Colin Charles 52: London MySQL Meetup

Colin Charles

Colin CharlesJoin Percona Chief Evangelist Colin Charles as he covers happenings, gives pointers and provides musings on the open source database community.

This week wraps up the London MySQL meetup, where there were four presentations, for the intimate yet diverse crowd. We saw representation from Oracle MySQL, MariaDB Corporation, Pythian, and Percona. Long-time organizer Ivan Zoratti has also handed off the baton to Maria Luisa Raviol, and going forward she will ensure meetups are at least once per quarter. It was a real pleasure to see MySQL Community Manager Dave Stokes at the event, too.

A new book to read: AWS System Administration: Best Practices for Sysadmins in the Amazon Cloud. There is coverage of RDS, from the standpoint of an example application stack as well as backups.

A most interesting tweet from the Chief Marketing Officer of MongoDB, Meagen Eisenberg, of an ad on a billboard: friend’s don’t let friends use relational databases.


Link List

Industry Updates

  • Catalyst IT Australia acquires Open Query – Arjen Lentz is a long-time MySQL community member, and he ran his company for the last 11 years pre-acquisition. Congratulations!
  • Elastic files for IPO – the financials are solid, *”Our revenue was $159.9 million and $88.2 million in fiscal 2018 and 2017, respectively, representing year-over-year growth of 81% for fiscal 2018.” The filing is worth reading.
  • New Cloud Unicorn: PagerDuty Scores $1.3 Billion Valuation In $90 Million Round – total raised now $173 million, valuing the company at $1.3 billion. Some of their competitors have been purchased recently, VictorOps by Splunk for $120 million and OpsGenie by Atlassian for $295 million. There are not many independents left in this space beyond PagerDuty and xMatters(who recently picked up Series D financing, total raised $96.5 million).
  • PingCap raises $50m in Series C funding. They are behind TiDB and TiKV. Raised a total of $72 million, and this is a significant increase from the $15m Series B raise in June 2017!
  • Tague Griffith has departed Redis Labs where he was Head of Developer Advocacy and is now at Google.
  • Manyi Lu who has been in the MySQL world for a very long time, leading much of the changes in the MySQL optimizer, who was most recently Director of Software Development at Oracle has departed to be a Senior Director at Alicloud.

Upcoming Appearances


I look forward to feedback/tips via e-mail at or on Twitter @bytebot.

The post This Week in Data With Colin Charles 52: London MySQL Meetup appeared first on Percona Database Performance Blog.


Encryption of the InnoDB System Tablespace and Parallel Doublewrite Buffer

encryption of InnoDB tablespace parallel doublewrite buffer

encryption of InnoDB tablespace parallel doublewrite bufferIn my last post I compared data at-rest encryption features available for MySQL and MariaDB. As noted at the time, some of the features available for Percona Server for MySQL were in development, and the latest version (5.7.23) sees two of them released as ALPHA quality.

Encrypting the InnoDB system tablespace

The first of the new features is InnoDB system tablespace encryption via innodb_sys_tablespace_encrypt, which would provide encryption of the following data:

  • the change buffer, which caches changes to secondary index pages as a result of DML operations for pages that are not in the InnoDB buffer pool
  • The undo logs if they have not been configured to be stored in separate undo tablespaces
  • data from any tables that exist in the main tablespace, which occurs when innodb_file_per_table is disabled

There are some related changes on the horizon that would allow this to be applied to an existing instance. However, for now this is only available for new instances as it can only be applied during bootstrap. This means that it would require a logical restore of your data to use it with an existing cluster–I should restate that this is an ALPHA feature and not production-ready.

There are some extra points to note about this new variable:

  • an instance with an encrypted tablespace cannot be downgraded to use a version prior to 5.7.23, due to the inability to read the tablespace
  • as noted, it is not currently possible to convert the tablespace between encrypted and unencrypted states, or vice versa
  • the key for the system tablespace can be manually rotated using ALTER INSTANCE ROTATE INNODB MASTER KEY as per any other tablespace

Encrypting the parallel doublewrite buffer

To complement the encryption of the system tablespace, it is also possible to encrypt the parallel doublewrite buffer using innodb_parallel_dblwr_encrypt, a feature unique to Percona Server for MySQL.  This means that any data for an encrypted tablespace is also only written in an encrypted form in the parallel doublewrite buffer; unencrypted tablespace data remains in plaintext. Unlike innodb_sys_tablespace_encrypt, you are able to set innodb_parallel_dblwr_encrypt dynamically on an existing instance.

There are more encryption features planned–or already in development–for Percona Server for MySQL so watch this space!

The post Encryption of the InnoDB System Tablespace and Parallel Doublewrite Buffer appeared first on Percona Database Performance Blog.


Percona Toolkit 3.0.12 Is Now Available

percona toolkit

percona toolkitPercona announces the release of Percona Toolkit 3.0.12 on September 13, 2018.

Percona Toolkit is a collection of advanced open source command-line tools, developed and used by the Percona technical staff, that are engineered to perform a variety of MySQL®, MongoDB® and system tasks that are too difficult or complex to perform manually. With over 1,000,000 downloads, Percona Toolkit supports Percona Server for MySQL, MySQL®, MariaDB®, Percona Server for MongoDB and MongoDB.

Percona Toolkit, like all Percona software, is free and open source. You can download packages from the website or install from official repositories.

This release includes the following changes:

Fixed bugs:


  • PT-1611: pt-archiver failed to output UTF-8 characters.
  • PT-1603: pt-table-sync incorrectly calculated chunk boundaries in case of unsorted ENUM fields in indexes.
  • PT-1574: pt-online-schema-change failed on tables with a nullable unique key and a row with NULL values.
  • PT-1572: ENUM fields usage in keys was improved, resulting in higher speed for expressions with sorted ENUM items.
  • PT-1422: pt-mysql-summary could hang when NULL values appear in the processlist Time column.

Documentation change:

  • PT-1321: The required MySQL privileges were detailed in pt-online-schema-change documentation

Help us improve our software quality by reporting any bugs you encounter using our bug tracking system.

The post Percona Toolkit 3.0.12 Is Now Available appeared first on Percona Database Performance Blog.


Analyzing Amazon Aurora Slow Logs with pt-query-digest

Amazon Aurora MySQL slow query logs with pt-query-digest slow

Amazon Aurora MySQL slow query logs with pt-query-digest slowIn this blog post we shall discuss how you can analyze slow query logs from Amazon Aurora for MySQL, (referred to as Amazon Aurora in the remaining blog). The tools and techniques explained here apply to the other MySQL compatible services available under Amazon Aurora. However, we’ll focus specially on analyzing slow logs from Amazon Aurora version 2 (MySQL 5.7 compatible) using pt-query-digest. We believe there is a bug in Aurora where it logs really big numbers for query execution and lock times for otherwise really fast queries.

So, the main steps we need are:

  1. Enable slow query logging on your Amazon Aurora DB parameter group, apply the change when appropriate.
  2. Download the slow log(s) that match the time that you are interested to investigate, and optionally concatenate them.
  3. Run pt-query-digest on the downloaded logs and check the results.

Enable slow query logging

For our testing we decided to capture all the SELECT queries that were hitting our Amazon Aurora instance, mainly because we had a sysbench OLTP read only workload and that wouldn’t really have a lot of slow queries. An easy way to do so is to enable the capture of slow query logs and set long_query_time to 0 — you will need to enable slow query logging. To achieve that, we created a new DB parameter group and applied it to our test Aurora instance with the following three parameters set as below:


Once you have the above configuration applied to Amazon RDS, you will be able to see slow query logs being created in the Amazon RDS console.

Download the log file

You can download the log file of your choice using either the Amazon RDS console OR you can use the following AWS CLI command to achieve the same:

$ aws rds download-db-log-file-portion --db-instance-identifier perconasupport  --starting-token 0 --output text --log-file-name slowquery/mysql-slowquery.log.2018-09-03.09 > mysql-slowquery.log.2018-09-03.09

Depending on the size of the chosen log file, the above command will take some time to complete the download.

Run pt-query-digest on the log file

Once the file has been downloaded you can analyse that using the following pt-query-digest command.

$ pt-query-digest --group-by fingerprint --order-by Query_time:sum mysql-slowquery.log.2018-09-03.09

On our Aurora test slow log file, the initial results didn’t look right so we had to apply a workaround. Here is the header of the initial results from pt-query-digest:

# 456.2s user time, 2.5s system time, 43.80M rss, 141.48M vsz
# Current date: Tue Sep 4 15:54:21 2018
# Hostname: aahmed-GL503VD
# Files: mysql-slowquery.log.2018-09-03.09
# Overall: 5.13M total, 60 unique, 1.43k QPS, 507.43Gx concurrency _______
# Time range: 2018-09-03T08:00:04 to 2018-09-03T09:00:03
# Attribute total min max avg 95% stddev median
# ============ ======= ======= ======= ======= ======= ======= =======
# Exec time 1826227663297288s 1us 18446744073710s 355917782s 761us 80127878922s 93us
# Lock time 1401952549601936s 0 18446744073710s 273229812s 44us 70205933577s 23us
# Rows sent 94.71M 0 100 19.35 97.36 37.62 0.99
# Rows examine 216.26M 0 300 44.19 299.03 84.74 0.99
# Query size 196.24M 5 1.24k 40.08 72.65 18.90 36.69
# Profile
# Rank Query ID Response time Calls R/Call
# ==== ====================== =========================== ======= ========
# 1 0xE81D0B3DB4FB31BC5... 1346612317380813.0000 73.7% 3194111 421592210.5966 18... SELECT sbtest?
# 2 0x9934EF6887CC7A638... 147573952589685.0625 8.1% 319381 462062403.8051 18... SELECT sbtest?
# 3 0x8D589AFA4DFAEEED8... 110680464442264.1094 6.1% 319411 346514254.1812 18... BEGIN
# 4 0xFF7C69F51BBD3A736... 92233720368565.1875 5.1% 319388 288782673.0139 18... SELECT sbtest?
# 5 0xFFFCA4D67EA0A7888... 73786976294861.9844 4.0% 321238 229695665.8143 18... COMMIT
# MISC 0xMISC 55340232221335.8281 3.0% 657509 84166501.4796 0.0 <43 ITEMS>

What’s wrong with the above results is that the total query Exec time and Lock time are very large numbers. Digging deeper into the logs revealed a problem with the slow logs themselves that had very large numbers for Query time & Lock time for some queries. For instance in our case, of 5.13 million queries in the log file, only 111 had the anomaly. Even so, it was enough to skew the results.

# Time: 2018-09-03T08:41:47.363522Z
SELECT c FROM sbtest1 WHERE id=24278;
# Time: 2018-09-03T08:41:49.363224Z
# User@Host: perconasupport[perconasupport] @ [] Id: 20869
# Query_time: 18446744073709.550781 Lock_time: 18446744073709.550781 Rows_sent: 1 Rows_examined: 1
SET timestamp=1535964109;
SELECT c FROM sbtest2 WHERE id=989322;
# Time: 2018-09-03T08:41:49.363296Z
# Time: 2018-09-03T08:41:53.362947Z
# User@Host: perconasupport[perconasupport] @ [] Id: 20873
# Query_time: 18446744073709.550781 Lock_time: 18446744073709.550781 Rows_sent: 1 Rows_examined: 1
SET timestamp=1535964113;
SELECT c FROM sbtest1 WHERE id=246889;
# Time: 2018-09-03T08:41:53.363003Z

Incorrect logging

The above two queries are, in fact, really fast, but for some reason the execution time & lock times are wrongly logged in the slow query log. Since the number of such query log records is statistically negligible compared to the total number of queries, we decided to ask pt-query-digest to ignore them using the command line parameter –attribute-value-limit . The default value of this parameter is 0. We decided to increase that to 2^32, and make it ignore the large numbers from the slow query log. So, the pt-query-digest command became:

$ pt-query-digest --group-by fingerprint --order-by Query_time:sum --attribute-value-limit=4294967296 mysql-slowquery.log.2018-09-03.09

This caused the 111 queries with the bad log times to be ignored and the results looked good. In our case, the ignored queries were bad variants of queries for which good versions existed. You can tell this because the number of unique queries remained the same as before after the bad variants were ignored. However, this may not always hold true and one should expect to lose some fidelity, especially if you are analyzing a smaller slow log.

# 441s user time, 450ms system time, 38.19M rss, 111.76M vsz
# Current date: Tue Sep 4 16:23:33 2018
# Hostname: aahmed-GL503VD
# Files: mysql-slowquery.log.2018-09-03.09
# Overall: 5.13M total, 60 unique, 1.43k QPS, 0.30x concurrency __________
# Time range: 2018-09-03T08:00:04 to 2018-09-03T09:00:03
# Attribute total min max avg 95% stddev median
# ============ ======= ======= ======= ======= ======= ======= =======
# Exec time 1096s 1us 198ms 213us 761us 431us 93us
# Lock time 180s 0 103ms 34us 44us 161us 23us
# Rows sent 94.71M 0 100 19.35 97.36 37.62 0.99
# Rows examine 216.26M 0 300 44.19 299.03 84.74 0.99
# Query size 196.24M 5 1.24k 40.08 72.65 18.90 36.69
# Profile
# Rank Query ID Response time Calls R/Call V/M Ite
# ==== =========================== ============== ======= ====== ===== ===
# 1 0xE81D0B3DB4FB31BC558CAE... 400.1469 36.5% 3194111 0.0001 0.00 SELECT sbtest?
# 2 0xF0C5AE75A52E847D737F39... 161.4065 14.7% 319453 0.0005 0.00 SELECT sbtest?
# 3 0xFFFCA4D67EA0A788813031... 155.8740 14.2% 321238 0.0005 0.00 COMMIT
# 4 0x8D589AFA4DFAEEED85FFF5... 107.9827 9.9% 319411 0.0003 0.00 BEGIN
# 5 0x9934EF6887CC7A6384D1DE... 94.1002 8.6% 319381 0.0003 0.00 SELECT sbtest?
# 6 0xFF7C69F51BBD3A736EEB1B... 79.9279 7.3% 319388 0.0003 0.00 SELECT sbtest?
# 7 0xA729E7889F57828D3821AE... 75.3969 6.9% 319398 0.0002 0.00 SELECT sbtest?
# MISC 0xMISC 21.1212 1.9% 18658 0.0011 0.0 <41 ITEMS>
# Query 1: 1.27k QPS, 0.16x concurrency, ID 0xE81D0B3DB4FB31BC558CAEF5F387E929 at byte 358647353
# Scores: V/M = 0.00
# Time range: 2018-09-03T08:00:04 to 2018-09-03T08:42:00
# Attribute pct total min max avg 95% stddev median
# ============ === ======= ======= ======= ======= ======= ======= =======
# Count 62 3194111
# Exec time 36 400s 10us 198ms 125us 332us 300us 80us
# Lock time 74 134s 0 26ms 42us 49us 154us 27us
# Rows sent 3 3.01M 0 1 0.99 0.99 0.11 0.99
# Rows examine 1 3.01M 0 1 0.99 0.99 0.11 0.99
# Query size 57 112.37M 32 38 36.89 36.69 0.53 36.69
# String:
# Databases perconasupport
# Hosts
# Users perconasupport
# Query_time distribution
# 1us
# 10us ################################################################
# 100us ##############
# 1ms #
# 10ms #
# 100ms #
# 1s

That number looks familiar

The really big number 18446744073709.550781 seemed to ring a bell. A quick web search revealed that it could be a regression of an old bug in MySQL’s code. The following bugs were found to have the same value being reported for query exec time & query lock time.

Once slow logs were enabled, we used this sysbench command  to generate the workload for the Amazon Aurora instance. You might like to try it yourselves. Please note that this used sysbench version 1.0.14.
$ sysbench --db-driver=mysql --mysql-user=perconasupport --mysql-password=XXXXXXX  --mysql-db=perconasupport --range_size=100 --table_size=1000000 --tables=2 --threads=6 --events=0 --time=600 --rand-type=uniform /usr/share/sysbench/oltp_read_only.lua run

If you are an Amazon Aurora user, have you found any problems analyzing slow query logs? You are welcome to use the comments section, below, to let me know.

Percona Toolkit

pt-query-digest is part of Percona Toolkit, a collection of advanced open source command-line tools, developed and used by the Percona technical staff. Percona Toolkit is open source and free to download and use.

The post Analyzing Amazon Aurora Slow Logs with pt-query-digest appeared first on Percona Database Performance Blog.


Percona Server for MySQL 5.7.23-23 Is Now Available

Percona Server for MySQL 5.6

Percona Server for MySQL 5.7Percona announces the release of Percona Server for MySQL 5.7.23-23 on September 12, 2018 (downloads are available here and from the Percona Software Repositories). This release merges changes of MySQL 5.7.23, including all the bug fixes in it. Percona Server for MySQL 5.7.23-23 is now the current GA release in the 5.7 series. All of Percona’s software is open-source and free.

New Features
  • The max_binlog_files variable is deprecated, and the binlog_space_limit variable should be used instead of it. The behavior of binlog_space_limit is consistent with the variable relay-log-space-limit used for relay logs; both variables have the same semantics. For more information, see #275.
  • Starting with 5.7.23-23, it is possible to encrypt all data in the InnoDB system tablespace and in the parallel double write buffer. This feature is considered ALPHA quality. A new variable innodb_sys_tablespace_encrypt is introduced to encrypt the system tablespace. The encryption of the parallel double write buffer file is controlled by the variable innodb_parallel_dblwr_encrypt. Both variables are OFF by default. For more information, see #3822.
  • Changing rocksdb_update_cf_options returns any warnings and errors to the client instead of printing them to the server error log. For more information, see #4258.
  • rocksdb_number_stat_computers and rocksdb_rate_limit_delay_millis variables have been removed. For more information, see #4780.
  • A number of new variables were introduced for MyRocks: rocksdb_rows_filtered to show the number of rows filtered out for TTL in MyRocks tables, rocksdb_bulk_load_allow_sk to allow adding secondary keys using the bulk loading feature, rocksdb_error_on_suboptimal_collation toggling warning or error in case of an index creation on a char field where the table has a sub-optimal collation, rocksdb_stats_recalc_rate specifying the number of indexes to recalculate per second, rocksdb_commit_time_batch_for_recovery toggler of writing the commit time write batch into the database, and rocksdb_write_policy specifying when two-phase commit data are actually written into the database.
Bugs Fixed
  • The statement SELECT...ORDER BY produced inconsistent results with the euckr charset or euckr_bin collation. Bug fixed #4513 (upstream #91091).
  • InnoDB statistics could incorrectly report zeros in the slow query log. Bug fixed #3828.
  • With the FIPS mode enabled and performance_schema=off, the instance crashed when running the CREATE VIEW command. Bug fixed #3840.
  • The soft limit of the core file size was set incorrectly starting with PS 5.7.21-20. Bug fixed #4479.
  • The option innodb-optimize-keys could fail when a dumped table has two columns such that the name of one of them contains the other as as a prefix and is defined with the AUTO_INCREMENT attribute. Bug fixed #4524.
  • When innodb_temp_tablespace_encrypt was set to ON the CREATE TABLE command could ignore the value of the ENCRYPTION option. Bug fixed #4565.
  • If FLUSH STATUS was run from a different session, a statement could be counted twice in GLOBAL STATUS. Bug fixed #4570 (upstream #91541).
  • In some cases, it was not possible to set the flush_caches variable on systems that use systemd. Bug fixed #3796.
  • A message in the MyRocks log file did not clearly inform whether fast CRC32 was supported. Bug fixed #3988.
  • mysqld could not be started on Ubuntu if the database recovery had taken longer than ten minutes. Bug fixed #4546 (upstream #91423).
  • The ALTER TABLE command was slow when the number of dirty pages was high. Bug fixed #3702.
  • Setting the global variable version_suffix to NULL could lead to a server crash. Bug fixed #4785.
Other Bugs Fixed
  • #4620 “Enable encryption of temporary tablespace from foreground thread”
  • #4727 “intrinsic temp table behaviour shouldn’t depend on innodb_encrypt_tables”
  • #4046 “Ship assert failure: ‘res == 0’ (bulk loader)”
  • #3851 “Percona Ver 5.6.39-83.1 Failing assertion: sym_node->table != NULL”
  • #4533 “audit_log MTR tests should refer to include files without parent directories”
  • #4619 “main.flush_read_lock fails with timeout in”
  • #4561 “Read after free at Binlog_crypt_data::load_latest_binlog_key()”
  • #4587 “ROCKSDB_INCLUDE_RFR macro in wrong file”

Find the release notes for Percona Server for MySQL 5.7.23-23 in our online documentation. Report bugs in the Jira bug tracker.

The post Percona Server for MySQL 5.7.23-23 Is Now Available appeared first on Percona Database Performance Blog.


Announcement: Experimental Build of Percona XtraBackup 8.0

Percona XtraBackup 8.0

Percona XtraBackup 8.0Experimental Build of Percona XtraBackup 8.0 released

An experimental alpha version of Percona XtraBackup 8.0.1 is now available in the Percona experimental software repositories.

A few things to note about this release:

  • We removed the deprecated innobackupex in this release
  • Due to the new MySQL redo log and data dictionary formats the Percona XtraBackup 8.0.x versions will only be compatible with MySQL 8.0.x and the upcoming Percona Server for MySQL 8.0.x
  • For experimental migrations from earlier database server versions, you will need to backup and restore and using XtraBackup 2.4 and then use mysql_upgrade from MySQL 8.0.x

PXB 8.0.1 alpha is available for the following platforms:

  • RHEL/Centos 6.x
  • RHEL/Centos 7.x
  • Ubuntu 14.04 Trusty*
  • Ubuntu 16.04 Xenial
  • Ubuntu 18.04 Bionic
  • Debian 8 Jessie*
  • Debian 9 Stretch

Information on how to configure the Percona repositories for apt and yum systems and access the Percona experimental software is here.

* We might drop these platforms before GA release.

The post Announcement: Experimental Build of Percona XtraBackup 8.0 appeared first on Percona Database Performance Blog.


How To Deploy PMM on Linode With StackScripts

rebuiild from a StackScript

In my previous blog post, I showed how to deploy Percona Monitoring and Management (PMM) on Linode manually. It is pretty simple, but with a little coding it can be done even more easily using StackScripts

Here’s how:

1. Click on the “Add a Linode” and pick a Linode type you want to deploy.

2. Click on the deployed Linode and then click on the “Rebuild” Link

Rebuild the Linode

3. Click on Deploy Using StackScripts

Deploy using StackScripts

4. On the resulting page search for “PMM” and pick PMMServer from PerconaLab.

Choose PMMServer from PerconaLab

5. Provide the host name for new Linode, pick the root password and click on “Rebuild”

6. Boot the server.

boot the server

7.  You’re done. Wait for about 5 minutes for the installation to complete, then you can see PMM interface by going to this Linode IP

view PMM on Linode IP

If you think that a manual deployment with StackScripts is not much less hassle than doing it manually, you’re right. The real benefit comes with using Linode API for deployment.

There are multiple way to access this API, though for basic scripting I prefer the linode-cli tool for using the Linode API from the command line.

With linode-cli  you can deploy your PMM Server on Linode using this one liner:

linode-cli linodes create --label pmm-test  --root_pass MyRootPassword123 --stackscript_id 338458  --stackscript_data '{"hostname": "pmm-test"}'


As you can see, with Linode StackScripts you can get going with Percona Monitoring and Management on Linode in no time, especially if you chose to use the Linode API.

You might also like:

Here’s an overview from the Percona Monitoring and Management manual on deploying PMM. If you are new to PMM and would like to know more, you will find lots of resources on this site including my webinar MySQL Troubleshooting and Performance Optimization with PMM.

The post How To Deploy PMM on Linode With StackScripts appeared first on Percona Database Performance Blog.

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