This session is about using GNU debugger (gdb) as a tool to study MySQL internals (namely, InnoDB locks and metadata locks) and as a last resort in cases when server hangs or has to be restarted for other reason. It never hurts to try a trick or two before giving up and restarting.
Sometimes MySQL DBAs have to work with stalled/hanged/unresponsive MySQL instance, where their usual SQL-based tricks do not work any more. Sometimes they can not even connect to check what's going on inside server.
In other cases they know what to do and everything still works, but they have to implement changes to read-only server variables. Server restart is often not an option in production, as it means some downtime and may cause negative performance impact.
In these cases one could do something given read and write access to server memory/internals. Here comes gdb, that, alone with careful reading of the source code helps to often resolve the problems described above. During this session I'll show what can be done with gdb when server already is in
troubles, and how to use gdb to "see" and understand MySQL internals (like InnoDB locks or metadata locks) better.
MySQL indexing is one of the areas where you can achieve the most performance gains. In this presentation we have a short look how we can improve MySQL performance with indexing...
This session is about using GNU debugger (gdb) as a tool to study MySQL internals (namely, InnoDB locks and metadata locks) and as a last resort in cases when server hangs or has to be restarted for other reason. It never hurts to try a trick or two before giving up and restarting.
Sometimes MySQL DBAs have to work with stalled/hanged/unresponsive MySQL instance, where their usual SQL-based tricks do not work any more. Sometimes they can not even connect to check what's going on inside server.
In other cases they know what to do and everything still works, but they have to implement changes to read-only server variables. Server restart is often not an option in production, as it means some downtime and may cause negative performance impact.
In these cases one could do something given read and write access to server memory/internals. Here comes gdb, that, alone with careful reading of the source code helps to often resolve the problems described above. During this session I'll show what can be done with gdb when server already is in
troubles, and how to use gdb to "see" and understand MySQL internals (like InnoDB locks or metadata locks) better.
MySQL indexing is one of the areas where you can achieve the most performance gains. In this presentation we have a short look how we can improve MySQL performance with indexing...
Learn about the MariaDB 10 features that exist for developers: microseconds, virtual columns, PCRE regular expressions, DELETE ... RETURNING, geospatial extensions (GIS), dynamic columns. Use cases, and a hint of storage engines
This slide deck describes the Flexviews materialized view toolkit for MySQL:
http://flexvie.ws
Learn how to use incrementally refreshable materialized views, and how they can improve your performance.
Flame Graphs for MySQL DBAs - FOSDEM 2022 MySQL DevroomValeriy Kravchuk
Flame graph is way to visualize profiling data that allows the most frequent code paths to be identified quickly and accurately. They can be generated using Brendan Gregg's open source programs on github.com/brendangregg/FlameGraph, which create interactive SVG files to be checked in browser. The source of profiling data does not really matter - it can be perf profiler, bpftrace, Performance Schema, EXPLAIN output or any other source that allows to convert the data into the expected format of comma-separated "path" plus metric per line.
Different types of Flame Graphs (CPU, Off-CPU, Memory, Differential etc) are presented. Various tools and approaches to collect profile information of different aspects of MySQL server internal working are presented Several real-life use cases where Flame Graphs helped to understand and solve the problem are discussed.
PHP classes in mumbai, Introduction to PHP/MYSQL..
best PHP/MYSQL classes in mumbai with job assistance.
our features are:
expert guidance by IT industry professionals
lowest fees of 5000
practical exposure to handle projects
well equiped lab
after course resume writing guidance
For more Visit: http://vibranttechnologies.co.in/php-classes-in-mumbai.html or http://phptraining.vibranttechnologies.co.in
Introduction to MySQL Query Tuning for Dev[Op]sSveta Smirnova
To get data, we query the database. MySQL does its best to return requested bytes as fast as possible. However, it needs human help to identify what is important and should be accessed in the first place.
Queries, written smartly, can significantly outperform automatically generated ones. Indexes and Optimizer statistics, not limited to the Histograms only, help to increase the speed of the query a lot.
In this session, I will demonstrate by examples of how MySQL query performance can be improved. I will focus on techniques, accessible by Developers and DevOps rather on those which are usually used by Database Administrators. In the end, I will present troubleshooting tools which will help you to identify why your queries do not perform. Then you could use the knowledge from the beginning of the session to improve them.
Percona Live 2016 (https://www.percona.com/live/data-performance-conference-2016/sessions/why-use-explain-formatjson). Although EXPLAIN FORMAT=JSON was first presented a long time ago, there still aren't many resources that explain how and why to use it. The most advertised feature is visual EXPLAIN in MySQL Workbench, but this format can do more than create nice pictures. It prints additional information that can't be found in good old tabular EXPLAIN, and can help to solve many tricky performance issues. In this session, I will not only describe which additional information we can get with the new syntax, but also provide examples showing how to use it to diagnose production issues.
Learn about the MariaDB 10 features that exist for developers: microseconds, virtual columns, PCRE regular expressions, DELETE ... RETURNING, geospatial extensions (GIS), dynamic columns. Use cases, and a hint of storage engines
This slide deck describes the Flexviews materialized view toolkit for MySQL:
http://flexvie.ws
Learn how to use incrementally refreshable materialized views, and how they can improve your performance.
Flame Graphs for MySQL DBAs - FOSDEM 2022 MySQL DevroomValeriy Kravchuk
Flame graph is way to visualize profiling data that allows the most frequent code paths to be identified quickly and accurately. They can be generated using Brendan Gregg's open source programs on github.com/brendangregg/FlameGraph, which create interactive SVG files to be checked in browser. The source of profiling data does not really matter - it can be perf profiler, bpftrace, Performance Schema, EXPLAIN output or any other source that allows to convert the data into the expected format of comma-separated "path" plus metric per line.
Different types of Flame Graphs (CPU, Off-CPU, Memory, Differential etc) are presented. Various tools and approaches to collect profile information of different aspects of MySQL server internal working are presented Several real-life use cases where Flame Graphs helped to understand and solve the problem are discussed.
PHP classes in mumbai, Introduction to PHP/MYSQL..
best PHP/MYSQL classes in mumbai with job assistance.
our features are:
expert guidance by IT industry professionals
lowest fees of 5000
practical exposure to handle projects
well equiped lab
after course resume writing guidance
For more Visit: http://vibranttechnologies.co.in/php-classes-in-mumbai.html or http://phptraining.vibranttechnologies.co.in
Introduction to MySQL Query Tuning for Dev[Op]sSveta Smirnova
To get data, we query the database. MySQL does its best to return requested bytes as fast as possible. However, it needs human help to identify what is important and should be accessed in the first place.
Queries, written smartly, can significantly outperform automatically generated ones. Indexes and Optimizer statistics, not limited to the Histograms only, help to increase the speed of the query a lot.
In this session, I will demonstrate by examples of how MySQL query performance can be improved. I will focus on techniques, accessible by Developers and DevOps rather on those which are usually used by Database Administrators. In the end, I will present troubleshooting tools which will help you to identify why your queries do not perform. Then you could use the knowledge from the beginning of the session to improve them.
Percona Live 2016 (https://www.percona.com/live/data-performance-conference-2016/sessions/why-use-explain-formatjson). Although EXPLAIN FORMAT=JSON was first presented a long time ago, there still aren't many resources that explain how and why to use it. The most advertised feature is visual EXPLAIN in MySQL Workbench, but this format can do more than create nice pictures. It prints additional information that can't be found in good old tabular EXPLAIN, and can help to solve many tricky performance issues. In this session, I will not only describe which additional information we can get with the new syntax, but also provide examples showing how to use it to diagnose production issues.
OSMC 2008 | Monitoring MySQL by Geert VanderkelenNETWAYS
Monitoring MySQL has a long history within Nagios. Several plugins are available already. In addition to that, there are probably lots of plugins that have been developed by the community. We take a look at some of these and discuss what kind of additional useful information could be pulled out of a MySQL Server for monitoring it even better. A simple example on how to write such plugins will be shown, also using NDB API for monitoring MySQL Cluster. Now that MySQL Enterprise Monitor (MEM) is available, we'll go through the possibilities for combining the two platforms. We will also discuss the NDOUtils for storing configuration and event data using MySQL.
This talk starts with a brief overview of MySQL itself: some history, where it's heading too, and why it is so successful.
Postgres & Redis Sitting in a Tree- Rimas Silkaitis, HerokuRedis Labs
Postgres and Redis Sitting in a Tree | In today’s world of polyglot persistence, it’s likely that companies will be using multiple data stores for storing and working with data based on the use case. Typically a company will
start with a relational database like Postgres and then add Redis for more high velocity use-cases. What if you could tie the two systems together to enable so much more?
With over 3400 available built-in function, PHP offers a tremendously rich environment. Yet, some of these functions are still unknown to most programmers. During this session, Damien Seguy will highlight a number of functions that are rarely used in PHP, but are nonetheless useful and available within standard distributions.
Replication Troubleshooting in Classic VS GTIDMydbops
This presentation talk will assist you in troubleshooting MySQL replication for the most common issues we might face with a simple comparison of how can we get them solved in the different replication methods (Classic VS GTID).
Service discovery and configuration provisioningSource Ministry
Slides from our talk "Service discovery and configuration provisioning" presented by Mariusz Gil at PHP Benelux 2016
Apache Zookeeper or Consul are almost completely unknown in the PHP world, although its use solves a lot of typical problems. In a nutshell, they are a central services of provisioning configuration information, distributed synchronization and coordination of servers/processes. It simplifies the processes of application configuration management, so it is possible to change its settings and operation in real time (eg. feature flagging). During the presentation the typical cases of use of Zookeeper/Consul in PHP applications will be presented, both strictly web and workers running from the CLI.
Similar to Using Perl Stored Procedures for MariaDB (20)
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
Using Perl Stored Procedures for MariaDB
1. Using Perl Stored
Procedures
with MariaDB
Antony T Curtis <atcurtis@gmail.com>
Special thanks to LinkedIn for permitting personal projects
2. Why Perl?
• CPAN
• "Multiplicity" / Thread-friendly.
• Lots of Perl code already written.
• MySQL UDFs have no access control.
• MySQL UDFs cannot execute dynamic SQL.
• Faster than “native” Stored Procedures.
3. Perl Stored Procedures
• Implemented as Perl modules.
• Use DBD::mysql to access database.
• Runs in-process with MariaDB.
• Easier to debug than SQL stored routines.
4. Hello World
MariaDB [Demo]> CREATE PROCEDURE PerlHello()
-> DYNAMIC RESULT SETS 1
-> NO SQL
-> LANGUAGE Perl
-> EXTERNAL NAME "HelloWorld::test";
Query OK, 0 rows affected (0.02 sec)
MariaDB [Demo]> call PerlHello();
+-----------------------+
| message |
+-----------------------+
| Hello World from Perl |
+-----------------------+
1 row in set (0.03 sec)
Query OK, 0 rows affected (0.03 sec)
package HelloWorld;
# put this file in <prefix>/lib/mysql/perl
use 5.008008;
use strict;
use warnings;
use Symbol qw(delete_package);
require Exporter;
our @ISA = qw(Exporter);
our @EXPORT_OK = qw( );
our @EXPORT = qw( test );
our $VERSION = '0.01';
sub test()
{
return {
'message' => 'Hello World from Perl',
};
}
1;
5. Hello World
MariaDB [Demo]> CREATE PROCEDURE PerlHello()
-> DYNAMIC RESULT SETS 1
-> NO SQL
-> LANGUAGE Perl
-> EXTERNAL NAME "HelloWorld::test";
Query OK, 0 rows affected (0.02 sec)
MariaDB [Demo]> call PerlHello();
+-----------------------+
| message |
+-----------------------+
| Hello World from Perl |
+-----------------------+
1 row in set (0.03 sec)
Query OK, 0 rows affected (0.03 sec)
package HelloWorld;
# put this file in <prefix>/lib/mysql/perl
use 5.008008;
use strict;
use warnings;
use Symbol qw(delete_package);
require Exporter;
our @ISA = qw(Exporter);
our @EXPORT_OK = qw( );
our @EXPORT = qw( test );
our $VERSION = '0.01';
sub test()
{
return {
'message' => 'Hello World from Perl',
};
}
1;
6. Error handling or die
MariaDB [Demo]> CREATE PROCEDURE PerlError()
-> DYNAMIC RESULT SETS 1
-> NO SQL
-> LANGUAGE Perl
-> EXTERNAL NAME "GoodbyeWorld::test";
Query OK, 0 rows affected (0.00 sec)
MariaDB [Demo]> call PerlError();
ERROR 1220 (HY000): Cannot open file: No
such file or directory at /usr/local/mysql/
lib/plugin/perl/GoodbyeWorld.pm line 16.
package GoodbyeWorld;
# put this file in <prefix>/lib/mysql/perl
use 5.008008;
use strict;
use warnings;
use Symbol qw(delete_package);
require Exporter;
our @ISA = qw(Exporter);
our @EXPORT_OK = qw( );
our @EXPORT = qw( test );
our $VERSION = '0.01';
sub test(
{
open FILE, "</something/nonexist"
or die "Cannot open file: $!";
return {
'message' => 'Goodbye World from Perl',
};
}
1;
7. Reading Data
MariaDB [employees]> CREATE PROCEDURE employee(
-> empno INT)
-> DYNAMIC RESULT SETS 1
-> READS SQL DATA
-> LANGUAGE Perl
-> EXTERNAL NAME "ReadData::test";
Query OK, 0 rows affected (0.02 sec)
MariaDB [employees]> call employee(10034)G
*************************** 1. row
***************************
birth_date: 1962-12-29
emp_no: 10034
first_name: Bader
gender: M
hire_date: 1988-09-21
last_name: Swan
1 row in set (0.00 sec)
Query OK, 0 rows affected (0.00 sec)
package ReadData;
# put this file in <prefix>/lib/mysql/perl
use 5.008008;
use strict;
use warnings;
use Symbol qw(delete_package);
require Exporter;
our @ISA = qw(Exporter);
our @EXPORT_OK = qw( );
our @EXPORT = qw( test );
our $VERSION = '0.01';
our $DSN = ‘dbi:mysql:employees’;
use DBI;
use DBD::mysql;
sub test($)
{
my($emp_no) = @_;
my $dbh = DBI->connect($DSN, undef, undef) ||
die "Failed in call to DBI->connect, $!";
my $s = $dbh->prepare(
“SELECT * FROM employees WHERE emp_no=?”);
$s->execute(int $emp_no);
return $s->fetchrow_hashref;
}
1;
8. Reading Data
MariaDB [employees]> CREATE PROCEDURE employee(
-> empno INT)
-> DYNAMIC RESULT SETS 1
-> READS SQL DATA
-> LANGUAGE Perl
-> EXTERNAL NAME "ReadData::test";
Query OK, 0 rows affected (0.02 sec)
MariaDB [employees]> call employee(10034)G
*************************** 1. row
***************************
birth_date: 1962-12-29
emp_no: 10034
first_name: Bader
gender: M
hire_date: 1988-09-21
last_name: Swan
1 row in set (0.00 sec)
Query OK, 0 rows affected (0.00 sec)
package ReadData;
# put this file in <prefix>/lib/mysql/perl
use 5.008008;
use strict;
use warnings;
use Symbol qw(delete_package);
require Exporter;
our @ISA = qw(Exporter);
our @EXPORT_OK = qw( );
our @EXPORT = qw( test );
our $VERSION = '0.01';
our $DSN = ‘dbi:mysql:employees’;
use DBI;
use DBD::mysql;
sub test($)
{
my($emp_no) = @_;
my $dbh = DBI->connect($DSN, undef, undef) ||
die "Failed in call to DBI->connect, $!";
my $s = $dbh->prepare(
“SELECT * FROM employees WHERE emp_no=?”);
$s->execute(int $emp_no);
return $s->fetchrow_hashref;
}
1;
9. Modifying Data
MariaDB [employees]> CREATE PROCEDURE
-> makesummary(
-> empno INT)
-> MODIFIES SQL DATA
-> LANGUAGE Perl
-> EXTERNAL NAME "ModifyData::test";
Query OK, 0 rows affected (0.00 sec)
MariaDB [employees]> call makesummary;
sub test()
{
my $dbh = DBI->connect($DSN, undef, undef) ||
die "Failed in call to DBI->connect, $!";
$dbh->begin_work or die $!;
my $ins = $dbh->prepare("INSERT INTO summary VALUES (?,?)");
my $emps = $dbh->selectall_arrayref(
"select emp_no, max(salary) salary from employees ".
"left join salaries using (emp_no) group by emp_no",
{ Slice => {} });
foreach my $emp (@$emps)
{
$ins->execute($emp->{emp_no}, $emp->{salary}) or die $!;
}
$dbh->commit or die $!;
return undef;
}
10. Modifying Data
MariaDB [employees]> CREATE PROCEDURE
-> makesummary(
-> empno INT)
-> MODIFIES SQL DATA
-> LANGUAGE Perl
-> EXTERNAL NAME "ModifyData::test";
Query OK, 0 rows affected (0.00 sec)
MariaDB [employees]> call makesummary;
Query OK, 0 rows affected (18.02 sec)
sub test()
{
my $dbh = DBI->connect($DSN, undef, undef) ||
die "Failed in call to DBI->connect, $!";
$dbh->begin_work or die $!;
my $ins = $dbh->prepare("INSERT INTO summary VALUES (?,?)");
my $emps = $dbh->selectall_arrayref(
"select emp_no, max(salary) salary from employees ".
"left join salaries using (emp_no) group by emp_no",
{ Slice => {} });
foreach my $emp (@$emps)
{
$ins->execute($emp->{emp_no}, $emp->{salary}) or die $!;
}
$dbh->commit or die $!;
return undef;
}
11. Modifying Data
MariaDB [employees]> CREATE PROCEDURE
-> makesummary(
-> empno INT)
-> MODIFIES SQL DATA
-> LANGUAGE Perl
-> EXTERNAL NAME "ModifyData::test";
Query OK, 0 rows affected (0.00 sec)
MariaDB [employees]> call makesummary;
Query OK, 0 rows affected (18.02 sec)
MariaDB [employees]> select count(*)
-> from summary;
+----------+
| count(*) |
+----------+
| 300024 |
+----------+
1 row in set (0.15 sec)
sub test()
{
my $dbh = DBI->connect($DSN, undef, undef) ||
die "Failed in call to DBI->connect, $!";
$dbh->begin_work or die $!;
my $ins = $dbh->prepare("INSERT INTO summary VALUES (?,?)");
my $emps = $dbh->selectall_arrayref(
"select emp_no, max(salary) salary from employees ".
"left join salaries using (emp_no) group by emp_no",
{ Slice => {} });
foreach my $emp (@$emps)
{
$ins->execute($emp->{emp_no}, $emp->{salary}) or die $!;
}
$dbh->commit or die $!;
return undef;
}
12. Modifying Data
MariaDB [employees]> CREATE PROCEDURE
-> makesummary(
-> empno INT)
-> MODIFIES SQL DATA
-> LANGUAGE Perl
-> EXTERNAL NAME "ModifyData::test";
Query OK, 0 rows affected (0.00 sec)
MariaDB [employees]> call makesummary;
Query OK, 0 rows affected (18.02 sec)
MariaDB [employees]> select count(*)
-> from summary;
+----------+
| count(*) |
+----------+
| 300024 |
+----------+
1 row in set (0.15 sec)
sub test()
{
my $dbh = DBI->connect($DSN, undef, undef) ||
die "Failed in call to DBI->connect, $!";
$dbh->begin_work or die $!;
my $ins = $dbh->prepare("INSERT INTO summary VALUES (?,?)");
my $emps = $dbh->selectall_arrayref(
"select emp_no, max(salary) salary from employees ".
"left join salaries using (emp_no) group by emp_no",
{ Slice => {} });
foreach my $emp (@$emps)
{
$ins->execute($emp->{emp_no}, $emp->{salary}) or die $!;
}
$dbh->commit or die $!;
return undef;
}
select statement - 2 seconds
300k inserts in 16 seconds
== 18k inserts per second
13. More than just procs
• CPAN is a large library...
• Can extend MariaDB’s functionality.
• How about using HTTP::Daemon?
14. Server Monitoring
sub daemon()
{
my $d = HTTP::Daemon->new(
LocalAddr => '127.0.0.1',
LocalPort => 8080,
) ||
die "Failed in call to HTTP::Daemon->new, $!";
my $dbh = DBI->connect("dbi:mysql:mysql", undef, undef) ||
die "Failed in call to DBI->connect, $!";
while (my $c = $d->accept)
{
while (my $r = $c->get_request)
{
if (exists $pages{$r->url->path})
{
$pages{$r->url->path}->($dbh, $c, $r);
}
else
{
$c->send_error(RC_NOT_FOUND);
}
}
$c->close;
undef $c;
}
}
What if we can use
HTTP::Daemon?
15. Server Monitoring
'/statusz' => sub {
my ($dbh,$c,$r) = @_;
return $c->send_error(RC_FORBIDDEN)
if $r->method ne 'GET';
my $sth = $dbh->prepare_cached(q{
SELECT VARIABLE_NAME name, VARIABLE_VALUE value
FROM INFORMATION_SCHEMA.GLOBAL_STATUS
}, { Slice => {} });
$sth->execute() || die $sth->errstr;
my $response = HTTP::Response->new(200, undef,
HTTP::Headers->new(
Content_Type => "application/json",
));
my $result = {};
while (my $row = $sth->fetchrow_arrayref)
{
$result->{$row->[0]} = $row->[1];
}
$response->add_content(to_json($result,
{ ascii => 1, pretty => 1 }));
return $c->send_response($response);
},
Fetching current
server status could be
as simple as fetching
http://127.0.0.1/statusz
16. Status
• Code hosted at LaunchPad.
• Contributing to MariaDB 10.0 soon.
• Stuff needs to be tidied up.
• Future steps include better Table Functions.