Nov
01
2018
--

Percona Monitoring and Management (PMM) 1.16.0 Is Now Available

Percona Monitoring and Management

PMM (Percona Monitoring and Management) is a free and open-source platform for managing and monitoring MySQL, MongoDB, and PostgreSQL performance. You can run PMM in your own 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.

Percona Monitoring and Management

While much of the team is working on longer-term projects, we were able to provide the following feature:

  • MySQL and PostgreSQL support for all cloud DBaaS providers – Use PMM Server to gather Metrics and Queries from remote instances!
  • Query Analytics + Metric Series – See Database activity alongside queries
  • Collect local metrics using node_exporter + textfile collector

We addressed 11 new features and improvements, and fixed 21 bugs.

MySQL and PostgreSQL support for all cloud DBaaS providers

You’re now able to connect PMM Server to your MySQL and PostgreSQL instances, whether they run in a cloud DBaaS environment, or you simply want Database metrics without the OS metrics.  This can help you get up and running with PMM using minimal configuration and zero client installation, however be aware there are limitations – there won’t be any host-level dashboards populated for these nodes since we don’t attempt to connect to the provider’s API nor are we granted access to the instance in order to deploy an exporter.

How to use

Using the PMM Add Instance screen, you can now add instances from any cloud provider (AWS RDS and Aurora, Google Cloud SQL for MySQL, Azure Database for MySQL) and benefit from the same dashboards that you are already accustomed to. You’ll be able to collect Metrics and Queries from MySQL, and Metrics from PostgreSQL.  You can add remote instances by selecting the PMM Add Instance item in a PMM group of the system menu:

https://github.com/percona/pmm/blob/679471210d476a5e98d26a632318f1680cfd98a2/doc/source/.res/graphics/png/metrics-monitor.menu.pmm1.png?raw=true

where you will then have the opportunity to add a Remote MySQL or Remote PostgreSQL instance:

You’ll add the instance by supplying just the Hostname, database Username and Password (and optional Port and Name):

metrics-monitor.add-remote-mysql-instance.png

Also new as part of this release is the ability to display nodes you’ve added, on screen RDS and Remote Instances:

metrics-monitor.add-rds-or-remote-instance1.png

Server activity metrics in the PMM Query Analytics dashboard

The Query Analytics dashboard now shows a summary of the selected host and database activity metrics in addition to the top ten queries listed in a summary table.  This brings a view of System Activity (CPU, Disk, and Network) and Database Server Activity (Connections, Queries per Second, and Threads Running) to help you better pinpoint query pileups and other bottlenecks:

https://raw.githubusercontent.com/percona/pmm/86e4215a58e788a8ec7cb1ebe679e1593c484078/doc/source/.res/graphics/png/query-analytics.png

Extending metrics with node_exporter textfile collector

While PMM provides an excellent solution for system monitoring, sometimes you may have the need for a metric that’s not present in the list of node_exporter metrics out of the box. There is a simple method to extend the list of available metrics without modifying the node_exporter code. It is based on the textfile collector.  We’ve enabled this collector as on by default, and is deployed as part of linux:metrics in PMM Client.

The default directory for reading text files with the metrics is /usr/local/percona/pmm-client/textfile-collector, and the exporter reads files from it with the .prom extension. By default it contains an example file example.prom which has commented contents and can be used as a template.

You are responsible for running a cronjob or other regular process to generate the metric series data and write it to this directory.

Example – collecting docker container information

This example will show you how to collect the number of running and stopped docker containers on a host. It uses a crontab task, set with the following lines in the cron configuration file (e.g. in /etc/crontab):

*/1* * * *     root   echo -n "" > /tmp/docker_all.prom; docker ps -a -q | wc -l | xargs echo node_docker_containers_total >> /usr/local/percona/pmm-client/docker_all.prom;
*/1* * * *     root   echo -n "" > /tmp/docker_running.prom; docker ps | wc -l | xargs echo node_docker_containers_running_total >> /usr/local/percona/pmm-client/docker_running.prom;

The result of the commands is placed into the docker_all.prom and docker_running.prom files and read by exporter and will create two new metric series named node_docker_containers_total and node_docker_containers_running_total, which we’ll then plot on a graph:

pmm 1.16

New Features and Improvements

  • PMM-3195 Remove the light bulb
  • PMM-3194 Change link for “Where do I get the security credentials for my Amazon RDS DB instance?”
  • PMM-3189 Include Remote MySQL & PostgreSQL instance logs into PMM Server logs.zip system
  • PMM-3166 Convert status integers to strings on ProxySQL Overview Dashboard – Thanks,  Iwo Panowicz for  https://github.com/percona/grafana-dashboards/pull/239
  • PMM-3133 Include Metric Series on Query Analytics Dashboard
  • PMM-3078 Generate warning “how to troubleshoot postgresql:metrics” after failed pmm-admin add postgresql execution
  • PMM-3061 Provide Ability to Monitor Remote MySQL and PostgreSQL Instances
  • PMM-2888 Enable Textfile Collector by Default in node_exporter
  • PMM-2880 Use consistent favicon (Percona logo) across all distribution methods
  • PMM-2306 Configure EBS disk resize utility to run from crontab in PMM Server
  • PMM-1358 Improve Tooltips on Disk Space Dashboard – thanks, Corrado Pandiani for texts

Fixed Bugs

  • PMM-3202 Cannot add remote PostgreSQL to monitoring without specified dbname
  • PMM-3186 Strange “Quick ranges” tag appears when you hover over documentation links on PMM Add Instance screen
  • PMM-3182 Some sections for MongoDB are collapsed by default
  • PMM-3171 Remote RDS instance cannot be deleted
  • PMM-3159 Problem with enabling RDS instance
  • PMM-3127 “Expand all” button affects JSON in all queries instead of the selected one
  • PMM-3126 Last check displays locale format of the date
  • PMM-3097 Update home dashboard to support PostgreSQL nodes in Environment Overview
  • PMM-3091 postgres_exporter typo
  • PMM-3090 TLS handshake error in PostgreSQL metric
  • PMM-3088 It’s possible to downgrade PMM from Home dashboard
  • PMM-3072 Copy to clipboard is not visible for JSON in case of long queries
  • PMM-3038 Error adding MySQL queries when options for mysqld_exporters are used
  • PMM-3028 Mark points are hidden if an annotation isn’t added in advance
  • PMM-3027 Number of vCPUs for RDS is displayed incorrectly – report and proposal from Janos Ruszo
  • PMM-2762 Page refresh makes Search condition lost and shows all queries
  • PMM-2483 LVM in the PMM Server AMI is poorly configured/documented – reported by Olivier Mignault  and lot of people involved.  Special thanks to  Chris Schneider for checking with fix options
  • PMM-2003 Delete all info related to external exporters on pmm-admin list output

How to get PMM Server

PMM is available for installation using three methods:

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

Nov
01
2018
--

How To Best Use Percona Server Column Compression With Dictionary

Database Compression

column compressionVery often, database performance is affected by the inability to cache all the required data in memory. Disk IO, even when using the fastest devices, takes much more time than a memory access. With MySQL/InnoDB, the main memory cache is the InnoDB buffer pool. There are many strategies we can try to fit as much data as possible in the buffer pool, and one of them is data compression.

With regular MySQL, to compress InnoDB data you can either use “Barracuda page compression” or “transparent page compression with punch holes”. The use of the ZFS filesystem is another possibility, but it is external to MySQL and doesn’t help with caching. All these solutions are transparent, but often they also have performance and management implications. If you are using Percona Server for MySQL, you have yet another option, “column compression with dictionary“. This feature is certainly not receiving the attention it merits. I think it is really cool—let me show you why.

We all know what compression means, who has not zipped a file before attaching it to an email? Compression removes redundancy from a file. What about the dictionary? A compression dictionary is a way to seed the compressor with expected patterns, in order to improve the compression ratio. Because you can specify a dictionary, the scope of usefulness of column compression with the Percona Server for MySQL feature is greatly increased. In the following sections, we’ll review the impacts of a good dictionary, and devise a way to create a good one without any guessing.

A simple use case

A compression algorithm needs a minimal amount of data in order to achieve a reasonable compression ratio. Typically, if the object is below a few hundred bytes, there is rarely enough data to have repetitive patterns and when the compression header is added, the compressed data can end up larger than the original.

mysql> select length('Hi!'), length(compress('Hi!'));
+---------------+-------------------------+
| length('Hi!') | length(compress('Hi!')) |
+---------------+-------------------------+
|             3 |                      15 |
+---------------+-------------------------+
1 row in set (0.02 sec)

Compressing a string of three bytes results in a binary object of 15 bytes. That’s counter productive.

In order to illustrate the potential of the dictionary, I used this dataset:

http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz

It is a set of 100k Jeopardy questions written in JSON. To load the data in MySQL, I created the following table:

mysql> show create table TestColCompression\G
*************************** 1. row ***************************
Table: TestColCompression
Create Table: CREATE TABLE `TestColCompression` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`question` text NOT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=79977 DEFAULT CHARSET=latin1
1 row in set (0.00 sec)

Then, I did some formatting to create insert statements:

zcat JEOPARDY_QUESTIONS1.json.gz | perl -p -e 's/\[\{/\{/g' | perl -p -e 's/\}, \{/\}\n\{/g' | perl -p -e "s/'/''/g" | \
  (while read line; do echo "insert into testColComp (questionJson) values ('$line');"; done )

And I executed the inserts. About 20% of the rows had some formatting issues but nevertheless, I ended up with close to 80k rows:

mysql> show table status\G
*************************** 1. row ***************************
Name: TestColCompression
Engine: InnoDB
Version: 10
Row_format: Dynamic
Rows: 78110
Avg_row_length: 316
Data_length: 24690688
Max_data_length: 0
Index_length: 0
Data_free: 4194304
Auto_increment: 79977
Create_time: 2018-10-26 15:16:41
Update_time: 2018-10-26 15:40:34
Check_time: NULL
Collation: latin1_swedish_ci
Checksum: NULL
Create_options:
Comment:
1 row in set (0.00 sec)

The average row length is 316 bytes for a total data size of 23.55MB. The question JSON objects are large enough to matter, but barely large enough for compression. Here are the first five rows:

mysql> select question from TestColCompression limit 5\G
*************************** 1. row ***************************
question: {"category": "HISTORY", "air_date": "2004-12-31", "question": "'For the last 8 years of his life, Galileo was under house arrest for espousing this man's theory'", "value": "$200", "answer": "Copernicus", "round": "Jeopardy!", "show_number": "4680"}
*************************** 2. row ***************************
question: {"category": "ESPN's TOP 10 ALL-TIME ATHLETES", "air_date": "2004-12-31", "question": "'No. 2: 1912 Olympian; football star at Carlisle Indian School; 6 MLB seasons with the Reds, Giants & Braves'", "value": "$200", "answer": "Jim Thorpe", "round": "Jeopardy!", "show_number": "4680"}
*************************** 3. row ***************************
question: {"category": "EVERYBODY TALKS ABOUT IT...", "air_date": "2004-12-31", "question": "'The city of Yuma in this state has a record average of 4,055 hours of sunshine each year'", "value": "$200", "answer": "Arizona", "round": "Jeopardy!", "show_number": "4680"}
*************************** 4. row ***************************
question: {"category": "OLD FOLKS IN THEIR 30s", "air_date": "2009-05-08", "question": "'The district of conservative rep. Patrick McHenry in this state includes Mooresville, a home of NASCAR'", "value": "$800", "answer": "North Carolina", "round": "Jeopardy!", "show_number": "5690"}
*************************** 5. row ***************************
question: {"category": "MOVIES & TV", "air_date": "2009-05-08", "question": "'Tim Robbins played a public TV newsman in "Anchorman: The Legend of" him'", "value": "$800", "answer": "Ron Burgundy", "round": "Jeopardy!", "show_number": "5690"}

Let’s begin by a straight column compression without specifying a dictionary:

mysql> alter table TestColCompression modify question text COLUMN_FORMAT COMPRESSED;
Query OK, 79976 rows affected (4.25 sec)
Records: 79976 Duplicates: 0 Warnings: 0
mysql> analyze table TestColCompression;
+----------------------------+---------+----------+----------+
| Table | Op | Msg_type | Msg_text |
+----------------------------+---------+----------+----------+
| colcomp.TestColCompression | analyze | status | OK |
+----------------------------+---------+----------+----------+
mysql> show table status\G
*************************** 1. row ***************************
Name: TestColCompression
Engine: InnoDB
Version: 10
Row_format: Dynamic
Rows: 78995
Avg_row_length: 259
Data_length: 20496384
Max_data_length: 0
Index_length: 0
Data_free: 4194304
Auto_increment: 79977
Create_time: 2018-10-26 15:47:56
Update_time: 2018-10-26 15:47:56
Check_time: NULL
Collation: latin1_swedish_ci
Checksum: NULL
Create_options:
Comment:
1 row in set (0.00 sec)

As expected the data didn’t compress much. The compression ration is 0.82 or if expressed as a percentage, 18%. Since the JSON headers are always the same, and are present in all questions, we should minimally use them for the dictionary. Trying a minimal dictionary made of the headers gives:

mysql> SET @dictionary_data = 'category' 'air_date' 'question' 'value' 'answer' 'round' 'show_number' ;
Query OK, 0 rows affected (0.01 sec)
mysql> CREATE COMPRESSION_DICTIONARY simple_dictionary (@dictionary_data);
Query OK, 0 rows affected (0.00 sec)
mysql> alter table TestColCompression modify question text COLUMN_FORMAT COMPRESSED WITH COMPRESSION_DICTIONARY simple_dictionary;
Query OK, 79976 rows affected (4.72 sec)
Records: 79976 Duplicates: 0 Warnings: 0
mysql> analyze table TestColCompression;
+----------------------------+---------+----------+----------+
| Table | Op | Msg_type | Msg_text |
+----------------------------+---------+----------+----------+
| colcomp.TestColCompression | analyze | status | OK |
+----------------------------+---------+----------+----------+
1 row in set (0.00 sec)
mysql> show table status\G
*************************** 1. row ***************************
Name: TestColCompression
Engine: InnoDB
Version: 10
Row_format: Dynamic
Rows: 78786
Avg_row_length: 246
Data_length: 19447808
Max_data_length: 0
Index_length: 0
Data_free: 4194304
Auto_increment: 79977
Create_time: 2018-10-26 17:58:17
Update_time: 2018-10-26 17:58:17
Check_time: NULL
Collation: latin1_swedish_ci
Checksum: NULL
Create_options:
Comment:
1 row in set (0.00 sec)

There is a little progress, we now have a compression ratio of 0.79. Obviously, we could do more but without a tool, we’ll have to guess. A compressor like zlib builds a dictionary as part of its compression effort, could we use that? Yes, but only if we can generate it correctly and access the result. That’s not readily available with the common compressors I know. Fortunately, someone else had the same issue and wrote a compressor able to save its dictionary. Please let me introduce femtozip.

Femtozip to the rescue

The tool, by itself, has no magic algorithm. It is based on zlib, from what I can understand from the code. Anyway, we won’t compress anything with it, we’ll use it to generate a good dictionary. In order to create a dictionary, the tool looks at a set of files and try to see patterns between them. The use of a single big file defeat the purpose. So, I generated one file per question with:

mkdir questions
cd questions
l=1; mysql -u blog -pblog colcomp -e 'select question from TestColCompression' | (while read line; do echo $line > ${l}; let l=l+1; done)

Then, I used the following command to generate a 1024 bytes dictionary using all the files starting by “1”:

../femtozip/cpp/fzip/src/fzip --model ../questions_1s.mod --build --dictonly --maxdict 1024 1*
Building dictionary...

In about 10s the job was done. I tried with all the 80k files and… I had to kill the process after thirty minutes. Anyway, there are 11111 files starting with “1”, a very decent sample. Our generated dictionary looks like:

cat ../questions_1s.mod
", "air_date", "round": "Double Jeopardy!", "show_number": " of this for 00", "answer": "the 0", "question": "'e", "round": "Jeopardy!", "show_number": "r", "round": "{"cate gory": "S", "air_date": "1998-s", "round": "Double Jeopardy!", "show_number": " of the ", "air_date": "2008-{"category": "THE {"category": "As", "round": "Jeopardy!", "show_number": "4", "question": "'Jeopardy!", "show_number": "2'", "value": "$1000", "answer": "7", "question": "'The ", "question": "'A'", "value": "$600", "answer": "9", "questi on": "'In ", "question": "'This 3", "question": "'2", "question": "'e'", "value": "$", "round": "Double Jeopardy!", "show_number": "4", "round": "Jeopardy!", "show_number": "4"'", "value": "$S", "air_date": "199", "round": "Double Jeopardy!", "show_number": "5s'", "value": "$", "round": "Double Jeopardy!", "show_number": "3", "round": "Jeopardy !", "show_number": "3", "round": "Jeopardy!", "show_number": "5'", "value": "$200", "answer": "'", "value": "$800", "answer": "'", "value": "$400", "answer": "

With some formatting, I was able to create a dictionary with the above data:

mysql> SET @dictionary_data = '", "air_date", "round": "Double Jeopardy!", "show_number": " of this for 00", "answer": "the 0", "question": "''e", "round": "Jeopardy!", "show_number": "r", "round": "{"category": "S", "air_date": "1998-s", "round": "Double Jeopardy!", "show_number": " of the ", "air_date": "2008-{"category": "THE {"category": "As", "round": "Jeopardy!", "show_number": "4", "question": "''Jeopardy!", "show_number": "2''", "value": "$1000", "answer": "7", "question": "''The ", "question": "''A''", "value": "$600", "answer": "9", "question": "''In ", "question": "''This 3", "question": "''2", "question": "''e''", "value": "$", "round": "Double Jeopardy!", "show_number": "4", "round": "Jeopardy!", "show_number": "4"''", "value": "$S", "air_date": "199", "round": "Double Jeopardy!", "show_number": "5s''", "value": "$", "round": "Double Jeopardy!", "show_number": "3", "round": "Jeopardy!", "show_number": "3", "round": "Jeopardy!", "show_number": "5''", "value": "$200", "answer": "''", "value": "$800", "answer": "''", "value": "$400", "answer": "' ;
Query OK, 0 rows affected (0.00 sec)
mysql> CREATE COMPRESSION_DICTIONARY femtozip_dictionary (@dictionary_data);
Query OK, 0 rows affected (0.00 sec)
<\pre>
And then, I altered the table to use the new dictionary:

mysql> alter table TestColCompression modify question text COLUMN_FORMAT COMPRESSED WITH COMPRESSION_DICTIONARY femtozip_dictionary;
Query OK, 79976 rows affected (4.05 sec)
Records: 79976 Duplicates: 0 Warnings: 0
mysql> analyze table TestColCompression;
+----------------------------+---------+----------+----------+
| Table | Op | Msg_type | Msg_text |
+----------------------------+---------+----------+----------+
| colcomp.TestColCompression | analyze | status | OK |
+----------------------------+---------+----------+----------+
1 row in set (0.00 sec)
mysql> show table status\G
*************************** 1. row ***************************
Name: TestColCompression
Engine: InnoDB
Version: 10
Row_format: Dynamic
Rows: 79861
Avg_row_length: 190
Data_length: 15220736
Max_data_length: 0
Index_length: 0
Data_free: 4194304
Auto_increment: 79977
Create_time: 2018-10-26 17:56:09
Update_time: 2018-10-26 17:56:09
Check_time: NULL
Collation: latin1_swedish_ci
Checksum: NULL
Create_options:
Comment:
1 row in set (0.00 sec)

That’s interesting, we are now achieving a ratio of 0.61, a significant improvement. I pushed my luck and tried with a 2048 bytes dictionary. It further reduced the ratio to 0.57 but that was about the best I got. Larger dictionaries didn’t lower the ratio below 0.57. Zlib supports up to 32KB for the dictionary.

So, to recap:

  • column compression without dictionary, ratio of 0.82
  • column compression with simple dictionary, ratio of 0.79
  • column compression with a 1k dictionary from femtozip, ratio of 0.61
  • column compression with a 2k dictionary from femtozip, ratio of 0.57

The above example stores a JSON document in a text column. MySQL 5.7 includes a JSON datatype which behaves a bit differently regarding the dictionary. Delimiting characters like ‘{}’ are removed in the on disk representation of a JSON column. If you have TBs of data in similar tables, you should really consider column compression and a systematic way of determining the dictionary with femtozip. In addition to improve the compression, it is likely to be the less performance impacting solution. Would it be interesting to generate a dictionary from existing data with a command like this one?

CREATE COMPRESSION_DICTIONARY_FROM_DATA A_good_dictionary (2048, select questions from TestColCompression limit 10000);

where the dictionary creation process would implicitly includes steps similar to the ones I did with femtozip.

Oct
30
2018
--

Percona XtraBackup 8.0-3-rc1 Is Available

Percona XtraBackup 8.0

Percona XtraBackup 8.0Percona is glad to announce the release candidate of Percona XtraBackup 8.0-3-rc1 on October 31 2018. You can download it from our download site and apt and yum repositories.

This is a Release Candidate quality release and it is not intended for
production. If you want a high quality, Generally Available release, use the current stable version (the most recent stable version at the time of writing is 2.4.12 in the 2.4 series).

This release supports backing up and restoring MySQL 8.0 and Percona Server for MySQL 8.0

Things to Note

  • innobackupex was previously deprecated and has been removed
  • 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

Installation

As this is a release candidate, installation is performed by enabling the testing repository and installing the software via your package manager. For Debian based distributions see apt installation instructions, for RPM based distributions see yum installation instructions. Note that in both cases after installing the current percona-release package, you’ll need to enable the testing repository in order to install Percona XtraBackup 8.0.3-rc1.

Improvements

  • PXB-1655:  The --lock-ddl option is supported when backing up MySQL 8

Bugs Fixed

  • PXB-1678:  Incremental backup prepare run with the --apply-log-only option could roll back uncommitted transactions.
  • PXB-1672:  The MTS slave without GTID could be backed up when the --safe-slave-backup option was applied.
Oct
30
2018
--

Release Candidate for Percona Server 8.0.12-2rc1 Is Available

Percona Server for MySQL

Following the alpha release announced earlier, Percona announces the release candidate of Percona Server for MySQL 8.0.12-2rc1 on October 31, 2018. Download the latest version from the Percona website or from the Percona Software Repositories.

This release is based on MySQL 8.0.12 and includes all the bug fixes in it. It is a Release Candidate quality release and it is not intended for production. If you want a high quality, Generally Available release, use the current Stable version (the most recent stable release at the time of writing in the 5.7 series is 5.7.23-23).

Percona provides completely open-source and free software.

Installation

As this is a release candidate, installation is performed by enabling the testing repository and installing the software via your package manager.  For Debian based distributions see apt installation instructions, for RPM based distributions see yum installation instructions.  Note that in both cases after installing the current percona-release package, you’ll need to enable the testing repository in order to install Percona Server for MySQL 8.0.12-2rc1.  For manual installations you can download from the testing repository directly through our website.

New Features

  • #4550: Native Partitioning support for MyRocks storage engine
  • #3911: Native Partitioning support for TokuDB storage engine
  • #4946: Add an option to prevent implicit creation of column family in MyRocks
  • #4839: Better default configuration for MyRocks and TokuDB
  • InnoDB changed page tracking has been rewritten to account for redo logging changes in MySQL 8.0.11.  This fixes fast incremental backups for PS 8.0
  • #4434: TokuDB ROW_FORMAT clause has been removed, compression may be set by using the session variable tokudb_row_format instead.

Improvements

  • Several packaging changes to bring Percona packages more in line with upstream, including split repositories. As you’ll note from our instructions above we now ship a tool with our release packages to help manage this.

Bugs Fixed

  • #4785: Setting version_suffix to NULL could lead to handle_fatal_signal (sig=11) in Sys_var_version::global_value_ptr
  • #4788: Setting log_slow_verbosity and enabling the slow_query_log could lead to a server crash
  • #4947: Any index comment generated a new column family in MyRocks
  • #1107: Binlog could be corrupted when tmpdir got full
  • #1549: Server side prepared statements lead to a potential off-by-second timestamp on slaves
  • #4937: rocksdb_update_cf_options was useless when specified in my.cnf or on command line.
  • #4705: The server could crash on snapshot size check in RocksDB
  • #4791: SQL injection on slave due to non-quoting in binlogged ROLLBACK TO SAVEPOINT
  • #4953: rocksdb.truncate_table3 was unstable

Other bugs fixed:

  • #4811: 5.7 Merge and fixup for old DB-937 introduces possible regression
  • #4885: Using ALTER … ROW_FORMAT=TOKUDB_QUICKLZ leads to InnoDB: Assertion failure: ha_innodb.cc:12198:m_form->s->row_type == m_create_info->row_type
  • Numerous testsuite failures/crashes

Upcoming Features

Oct
18
2018
--

ProxySQL 1.4.11 and Updated proxysql-admin Tool Now in the Percona Repository

ProxySQL 1.4.11

ProxySQL 1.4.11ProxySQL 1.4.11, released by ProxySQL, is now available for download in the Percona Repository along with an updated version of Percona’s proxysql-admin tool.

ProxySQL is a high-performance proxy, currently for MySQL and its forks (like Percona Server for MySQL and MariaDB). It acts as an intermediary for client requests seeking resources from the database. René Cannaò created ProxySQL for DBAs as a means of solving complex replication topology issues.

The ProxySQL 1.4.11 source and binary packages available at https://percona.com/downloads/proxysql include ProxySQL Admin – a tool, developed by Percona to configure Percona XtraDB Cluster nodes into ProxySQL. Docker images for release 1.4.11 are available as well: https://hub.docker.com/r/percona/proxysql/. You can download the original ProxySQL from https://github.com/sysown/proxysql/releases. The documentation is hosted on GitHub in the wiki format.

Improvements

  • mysql_query_rules_fast_routing is enabled in ProxySQL Cluster. For more information, see #1674 at GitHub.
  • In this release, rmpdb checksum error is ignored when building ProxySQL in Docker.
  • By default, the permissions for proxysql.cnf are set to 600 (only the owner of the file can read it or make changes to it).

Bugs Fixed

  • Fixed the bug that could cause crashing of ProxySQL if IPv6 listening was enabled. For more information, see #1646 at GitHub.

ProxySQL is available under Open Source license GPLv3.

Oct
10
2018
--

Percona Monitoring and Management (PMM) 1.15.0 Is Now Available

Percona Monitoring and Management

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 own 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.

Percona Monitoring and Management

This release offers two new features for both the MySQL Community and Percona Customers:

  • MySQL Custom Queries – Turn a SELECT into a dashboard!
  • Server and Client logs – Collect troubleshooting logs for Percona Support

We addressed 17 new features and improvements, and fixed 17 bugs.

MySQL Custom Queries

In 1.15 we are introducing the ability to take a SQL SELECT statement and turn the result set into metric series in PMM.  The queries are executed at the LOW RESOLUTION level, which by default is every 60 seconds.  A key advantage is that you can extend PMM to profile metrics unique to your environment (see users table example), or to introduce support for a table that isn’t part of PMM yet. This feature is on by default and only requires that you edit the configuration file and use vaild YAML syntax.  The configuration file is in /usr/local/percona/pmm-client/queries-mysqld.yml.

Example – Application users table

We’re going to take a fictional MySQL users table that also tracks the number of upvotes and downvotes, and we’ll convert this into two metric series, with a set of seven labels, where each label can also store a value.

Browsing metrics series using Advanced Data Exploration Dashboard

Lets look at the output so we understand the goal – take data from a MySQL table and store in PMM, then display as a metric series.  Using the Advanced Data Exploration Dashboard you can review your metric series. Exploring the metric series  app1_users_metrics_downvotes we see the following:

PMM Advanced Data Exploration Dashboard

MySQL table

Lets assume you have the following users table that includes true/false, string, and integer types.

SELECT * FROM `users`
+----+------+--------------+-----------+------------+-----------+---------------------+--------+---------+-----------+
| id | app  | user_type    | last_name | first_name | logged_in | active_subscription | banned | upvotes | downvotes |
+----+------+--------------+-----------+------------+-----------+---------------------+--------+---------+-----------+
|  1 | app2 | unprivileged | Marley    | Bob        |         1 |                   1 |      0 |     100 |        25 |
|  2 | app3 | moderator    | Young     | Neil       |         1 |                   1 |      1 |     150 |        10 |
|  3 | app4 | unprivileged | OConnor   | Sinead     |         1 |                   1 |      0 |      25 |        50 |
|  4 | app1 | unprivileged | Yorke     | Thom       |         0 |                   1 |      0 |     100 |       100 |
|  5 | app5 | admin        | Buckley   | Jeff       |         1 |                   1 |      0 |     175 |         0 |
+----+------+--------------+-----------+------------+-----------+---------------------+--------+---------+-----------+

Explaining the YAML syntax

We’ll go through a simple example and mention what’s required for each line.  The metric series is constructed based on the first line and appends the column name to form metric series.  Therefore the number of metric series per table will be the count of columns that are of type GAUGE or COUNTER.  This metric series will be called app1_users_metrics_downvotes:

app1_users_metrics:                                 ## leading section of your metric series.
  query: "SELECT * FROM app1.users"                 ## Your query. Don't forget the schema name.
  metrics:                                          ## Required line to start the list of metric items
    - downvotes:                                    ## Name of the column returned by the query. Will be appended to the metric series.
        usage: "COUNTER"                            ## Column value type.  COUNTER will make this a metric series.
        description: "Number of upvotes"            ## Helpful description of the column.

Full queries-mysqld.yml example

Each column in the SELECT is named in this example, but that isn’t required, you can use a SELECT * as well.  Notice the format of schema.table for the query is included.

---
app1_users_metrics:
  query: "SELECT app,first_name,last_name,logged_in,active_subscription,banned,upvotes,downvotes FROM app1.users"
  metrics:
    - app:
        usage: "LABEL"
        description: "Name of the Application"
    - user_type:
        usage: "LABEL"
        description: "User's privilege level within the Application"
    - first_name:
        usage: "LABEL"
        description: "User's First Name"
    - last_name:
        usage: "LABEL"
        description: "User's Last Name"
    - logged_in:
        usage: "LABEL"
        description: "User's logged in or out status"
    - active_subscription:
        usage: "LABEL"
        description: "Whether User has an active subscription or not"
    - banned:
        usage: "LABEL"
        description: "Whether user is banned or not"
    - upvotes:
        usage: "COUNTER"
        description: "Count of upvotes the User has earned.  Upvotes once granted cannot be revoked, so the number can only increase."
    - downvotes:
        usage: "GAUGE"
        description: "Count of downvotes the User has earned.  Downvotes can be revoked so the number can increase as well as decrease."
...

We hope you enjoy this feature, and we welcome your feedback via the Percona forums!

Server and Client logs

We’ve enhanced the volume of data collected from both the Server and Client perspectives.  Each service provides a set of files designed to be shared with Percona Support while you work on an issue.

Server

From the Server, we’ve improved the logs.zip service to include:

  • Prometheus targets
  • Consul nodes, QAN API instances
  • Amazon RDS and Aurora instances
  • Version
  • Server configuration
  • Percona Toolkit commands

You retrieve the link from your PMM server using this format:   https://pmmdemo.percona.com/managed/logs.zip

Client

On the Client side we’ve added a new action called summary which fetches logs, network, and Percona Toolkit output in order to share with Percona Support. To initiate a Client side collection, execute:

pmm-admin summary

The output will be a file you can use to attach to your Support ticket.  The single file will look something like this:

summary__2018_10_10_16_20_00.tar.gz

New Features and Improvements

  • PMM-2913 – Provide ability to execute Custom Queries against MySQL – Credit to wrouesnel for the framework of this feature in wrouesnel/postgres_exporter!
  • PMM-2904 – Improve PMM Server Diagnostics for Support
  • PMM-2860 – Improve pmm-client Diagnostics for Support
  • PMM-1754Provide functionality to easily select query and copy it to clipboard in QAN
  • PMM-1855Add swap to AMI
  • PMM-3013Rename PXC Overview graph Sequence numbers of transactions to IST Progress
  • PMM-2726 – Abort data collection in Exporters based on Prometheus Timeout – MySQLd Exporter
  • PMM-3003 – PostgreSQL Overview Dashboard Tooltip fixes
  • PMM-2936Some improvements for Query Analytics Settings screen
  • PMM-3029PostgreSQL Dashboard Improvements

Fixed Bugs

  • PMM-2976Upgrading to PMM 1.14.x fails if dashboards from Grafana 4.x are present on an installation
  • PMM-2969rds_exporter becomes throttled by CloudWatch API
  • PMM-1443The credentials for a secured server are exposed without explicit request
  • PMM-3006Monitoring over 1000 instances is displayed imperfectly on the label
  • PMM-3011PMM’s default MongoDB DSN is localhost, which is not resolved to IPv4 on modern systems
  • PMM-2211Bad display when using old range in QAN
  • PMM-1664Infinite loading with wrong queryID
  • PMM-2715Since pmm-client-1.9.0, pmm-admin detects CentOS/RHEL 6 installations using linux-upstart as service manager and ignores SysV scripts
  • PMM-2839Tablestats safety precaution does not work for RDS/Aurora instances
  • PMM-2845pmm-admin purge causes client to panic
  • PMM-2968pmm-admin list shows empty data source column for mysql:metrics
  • PMM-3043 Total Time percentage is incorrectly shown as a decimal fraction
  • PMM-3082Prometheus Scrape Interval Variance chart doesn’t display data

How to get PMM Server

PMM is available for installation using three methods:

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

Oct
08
2018
--

Announcement: Second Alpha Build of Percona XtraBackup 8.0 Is Available

Percona XtraBackup 8.0

Percona XtraBackup 8.0The second alpha build of Percona XtraBackup 8.0.2 is now available in the Percona experimental software repositories.

Note that, 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 Percona Server for MySQL 8.0.x. This release supports backing up Percona Server 8.0 Alpha.

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 8.0.2 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.

Improvements

  • PXB-1658: Import keyring vault plugin from Percona Server 8
  • PXB-1609: Make version_check optional at build time
  • PXB-1626: Support encrypted redo logs
  • PXB-1627: Support obtaining binary log coordinates from performance_schema.log_status

Fixed Bugs

  • PXB-1634: The CREATE TABLE statement could fail with the DUPLICATE KEY error
  • PXB-1643: Memory issues reported by ASAN in PXB 8
  • PXB-1651: Buffer pool dump could create a (null) file during prepare stage of Mysql8.0.12 data
  • PXB-1671: A backup could fail when the MySQL user was not specified
  • PXB-1660: InnoDB: Log block N at lsn M has valid header, but checksum field contains Q, should be P

Other bugs fixed: PXB-1623PXB-1648PXB-1669PXB-1639, and PXB-1661.

Oct
03
2018
--

Percona Live Europe 2018 Session Programme Published

PLE 2018 Full Agenda Announced

PLE 2018 Full Agenda AnnouncedOffering over 110 conference sessions across Tuesday, 6 and Wednesday, 7 November, and a full tutorial day on Monday 5 November, we hope you’ll find that this fantastic line up of talks for Percona Live Europe 2018 to be one of our best yet! Innovation in technology continues to arrive at an accelerated rate, and you’ll find plenty to help you connect with the latest developments in open source database technologies at this acclaimed annual event.

Representatives from companies at the leading edge of our industry use the platform offered by Percona Live to showcase their latest developments and share plans for the future. If your career is dependent upon the use of open source database technologies you should not miss this conference!

Conference Session Schedule

Conference sessions will take place on Tuesday and Wednesday, November 6-7 and will feature more than 110 in-depth talks by industry experts. Conference session examples include:

  • Deep Dive on MySQL Databases on Amazon RDS – Chayan Biswas, AWS
  • MySQL 8.0 Performance: Scalability & Benchmarks – Dimitri Kravtchuk, Oracle
  • MySQL 8 New Features: Temptable engine – Pep Pla, Pythian
  • Artificial Intelligence Database Performance Tuning – Roel Van de Paar, Percona
  • pg_chameleon – MySQL to PostgreSQL replica made easy – Federico Campoli, Loxodata
  • Highway to Hell or Stairway to Cloud? – Alexander Kukushkin, Zalando
  • Zero to Serverless in 60 Seconds – Sandor Maurice, AWS
  • A Year in Google Cloud – Carmen Mason, Alan Mason, Vital Source Technologies
  • Advanced MySQL Data at Rest Encryption in Percona Server for MySQL – Iwo Panowicz, Percona, and Bart?omiej Ole?, Severalnines
  • Monitoring Kubernetes with Prometheus – Henri Dubois-Ferriere, Sysdig
  • How We Use and Improve Percona XtraBackup at Alibaba Cloud – Bo Wang, Alibaba Cloud
  • Shard 101 – Adamo Tonete, Percona
  • Top 10 Mistakes When Migrating From Oracle to PostgreSQL – Jim Mlodgenski, AWS
  • Explaining the Postgres Query Optimizer – Bruce Momjian, EnterpriseDB
  • MariaDB 10.3 Optimizer and Beyond – Vicentiu Ciorbaru, MariaDB FoundationHA and Clustering Solution: ProxySQL as an Intelligent Router for Galera and Group Replication – René Cannaò, ProxySQL
  • MongoDB WiredTiger WriteConflicts – Paul Agombin, ObjectRocket
  • PostgreSQL Enterprise Features – Michael Banck, credativ GmbH
  • What’s New in MySQL 8.0 Security – Georgi Kodinov, Oracle
  • The MariaDB Foundation and Security – Finding and Fixing Vulnerabilities the Open Source Way – Otto Kekäläinen, MariaDB Foundation
  • ClickHouse 2018: How to Stop Waiting for Your Queries to Complete and Start Having Fun – Alexander Zaitsev, Altinity
  • Open Source Databases and Non-Volatile Memory – Frank Ober, Intel Memory Group
  • MyRocks Production Case Studies at Facebook – Yoshinori Matsunobu, Facebook
  • Need for Speed: Boosting Apache Cassandra’s Performance Using Netty – Dinesh Joshi, Apache Cassandra
  • Demystifying MySQL Replication Crash Safety – Jean-François Gagné, Messagebird

See the full list of sessions

Tutorial schedule

Tutorials will take place throughout the day on Monday, November 5, 2018. Tutorial session examples include:

  • Query Optimization with MySQL 8.0 and MariaDB 10.3: The Basics – Jaime Crespo, Wikimedia Foundation
  • ElasticSearch 101 – Antonios Giannopoulos, ObjectRocket
  • MySQL InnoDB Cluster in a Nutshell: The Saga Continues with 8.0 – Frédéric Descamps, Oracle
  • High Availability PostgreSQL and Kubernetes with Google Cloud – Alexis Guajardo, Google
  • Best Practices for High Availability – Alex Rubin and Alex Poritskiy, Percona

See the full list of tutorials.

Sponsors

We are grateful for the support of our sponsors:

  • Platinum – AWS
  • Silver – Altinity, PingCap
  • Start Up – Severalnines
  • Branding – Intel, Idera
  • Expo – Postgres EU

If you would like to join them Sponsorship opportunities for Percona Live Open Source Database Conference Europe 2018 are available and offer the opportunity to interact with the DBAs, sysadmins, developers, CTOs, CEOs, business managers, technology evangelists, solution vendors and entrepreneurs who typically attend the event. Contact live@percona.com for sponsorship details.

Ready to register? What are you waiting for? Costs will only get higher!
Register now!

 

 

Sep
28
2018
--

This Week in Data with Colin Charles #54: Percona Server for MySQL is Alpha

Colin Charles

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

I consider this to be the biggest news for the week: Alpha Build of Percona Server for MySQL 8.0. Experiment with it in a Docker container. It is missing column compression with dictionary support, native partitioning for TokuDB and MyRocks (excited to see that this is coming!), and encryption key rotation and scrubbing. All in, this should be a fun release to try, test, and also to file bugs for!

Database paradigms are changing, and it is interesting to see Cloudflare introducing Workers KV a key-value store, that is eventually consistent and highly distributed (at their global network of 152+ data centers). You can have up to 1 billion keys per namespace, keys up to 2kB in size, values up to 64kB, and eventual global consistency within 10 seconds. Read more about the cost and other technicals too.

For some quick glossing, from a MySQL Federal Account Manager, comes Why MySQL is Harder to Sell Than Oracle (from someone who has done both). Valid concerns, and always interesting to hear the barriers MySQL faces even after 23 years in existence! For analytics, maybe this is where the likes of MariaDB ColumnStore or ClickHouse might come into play.

Lastly, for all of you asking me about when Percona Live Europe Frankfurt 2018 speaker acceptances and agendas are to be released, I am told by a good source that it will be announced early next week. So register already!

Releases

Link List

Upcoming Appearances

Feedback

I look forward to feedback/tips via Twitter @bytebot.

The post This Week in Data with Colin Charles #54: Percona Server for MySQL is Alpha appeared first on Percona Database Performance Blog.

Sep
27
2018
--

Announcement: Alpha Build of Percona Server 8.0

Percona Server for MySQL

Percona server for MySQLAlpha Build of Percona Server 8.0 released

An alpha version of Percona Server 8.0 is now available in the Percona experimental software repositories. This is a 64-bit release only. 

You may experiment with this alpha release by running it in a Docker container:

$ docker run -d -e MYSQL_ROOT_PASSWORD=password -p 3306:3306 perconalab/percona-server:8.0.12.alpha

When the container starts, connect to it as follows:

$ docker exec -ti $(docker ps | grep -F percona-server:8.0.12.alpha | awk '{print $1}') mysql -uroot -ppassword

Note that this release is not ready for use in any production environment.

Percona Server 8.0 alpha is available for the following platforms:

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

Note: The list of supported platforms may be different in the GA release.

Fixed Bugs:

  • PS-4814: TokuDB ‘fast’ replace into is incompatible with 8.0 row replication
  • PS-4834: The encrypted system tablespace has empty uuid

Other fixed bugs: PS-4788PS-4631PS-4736, PS-4818PS-4755

Unfinished Features

The following features are work in progress and are not yet in a working state:

  • Column compression with Data Dictionaries
  • Native Partitioning for TokuDB and for MyRocks
  • Encryption
    • Key Rotation
    • Scrubbing

Known Issues

  • PS-4788: Setting log_slow_verbosity and enabling the slow_query_log could lead to a server crash
  • PS-4803: ALTER TABLE … ADD INDEX … LOCK crash | handle_fatal_signal (sig=11) in dd_table_has_instant_cols
  • PS-4896: handle_fatal_signal (sig=11) in THD::thread_id likely due to enabling innodb_print_lock_wait_timeout_info
  • PS-4820: PS crashes with keyring_vault encryption
  • PS-4796: 8.0 DD and atomic DDL breaks DROP DATABASE for engines that store files in database directory
  • PS-4898: Crash during PAM authentication plugin installation.
  • PS-1782: Optimizer chooses wrong plan when joining 2 tables
  • PS-4850: Toku hot backup plugin dumps tons of info to stdout with no way to disable it
  • PS-4797: rpl.rpl_master_errors failing, likely due to binlog encryption
  • PS-4800: Recovery of prepared XA transactions seems broken in 8.0
  • PS-4853: Installing audit_log plugin causes server to crash
  • PS-4855: Replace http with https in http://bugs.percona.com in server crash messages
  • PS-4857: Improve error message handling for compressed columns
  • PS-4895: Improve error message when encrypted system tablespace was started without keyring plugin
  • PS-3944: Single variable to control logging in QRT
  • PS-4705: crash on snapshot size check in RocksDB
  • PS-4885: Using ALTER … ROW_FORMAT=TOKUDB_QUICKLZ leads to InnoDB: Assertion failure: ha_innodb.cc:12198:m_form->s->row_type == m_create_info->row_type

The post Announcement: Alpha Build of Percona Server 8.0 appeared first on Percona Database Performance Blog.

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