Today I am going write a blog on how to Monitor Memory and Disk custom metrics and creating alarm in Ubuntu.

To do this, we can use Amazon CloudWatch, which provides a flexible, scalable and reliable solution for monitoring our server.

Amazon Cloud Watch will allow us to collect the custom metrics from our applications that we will monitor to troubleshoot any issues, spot trends, and configure operational performance. CloudWatch functions display alarms, graphs, custom metrics data and including statistics.

Installing the Scripts

Before we start installing the scripts for monitoring, we should install all the dependent packages need to perform on Ubuntu.

First login to your AWS server, and from our terminal, install below packages

sudo apt-get update
sudo apt-get install unzip
sudo apt-get install libwww-perl libdatetime-perl

Now Install the Monitoring Scripts

Following are the steps to download and then unzip we need to configure the Cloud Watch Monitoring scripts on our server:
1. In the terminal, we need to change our directory and where we want to add our monitoring scripts.
2. Now run the below command and download the source:

curl https://aws-cloudwatch.s3.amazonaws.com/downloads/CloudWatchMonitoringScripts-1.2.2.zip -O

3. Now uncompress the currently downloaded sources using the following commands

unzip CloudWatchMonitoringScripts-1.2.2.zip && \

rm CloudWatchMonitoringScripts-1.2.2.zip && \

cd aws-scripts-mon

The directory will contain Perl scripts, because of the execution of these scripts only report memory run and disk space utilization metrics will run in our Ubuntu server.
Currently, our folder will contain the following files:
mon-get-instance-stats.pl - This Perl file is used to displaying the current utilization statistics reports for our AWS instance on which these file scripts will be executed.
mon-put-instance-data.pl - This Perl script file will be used for collecting the system metrics on our ubuntu server and which will send them to the Amazon Cloud Watch.
awscreds.template - This Perl script file will contain an example for AWS credentials keys and secret access key named with access key ID.
CloudWatchClient.pm - This Perl script file module will be used to simplify by calling Amazon Cloud Watch from using other scripts.
LICENSE.txt – This file contains the license details for Apache 2.0.
NOTICE.txt – This file contains will gives us information about Copyright notice.
4. For performing the Cloud Watch operations, we need to confirm that whether our scripts have corresponding permissions for the actions:

If we are associated with an IAM role with our EC2 Ubuntu instance, we need to verify that which will grant the permissions to perform the below-listed operations:

cloudwatch:GetMetricStatistics

cloudwatch:PutMetricData

ec2:DescribeTags

cloudwatch:ListMetrics

Now we need to copy the ‘awscreds.template’ file into ‘awscreds.conf’ by using the command below and which will update the file with details of the AWS credentials.

cp awscreds.template awscreds.conf

AWSAccessKeyId = my_access_key_id

AWSSecretKey = my_secret_access_key

Now we completed the configuration.

mon-put-instance-data.pl

This Perl script file will collect memory, disk space utilization data and swap the current system details and then it makes handling a remote call to Amazon Cloud Watch to reports details to the collected cloud watch data as a custom metrics.

We can perform a simple test run, by running the below without sending data to Amazon CloudWatch

./mon-put-instance-data.pl --mem-util --verify --verbose

Now we are going to set a cron for scheduling our metrics and we will send them to Amazon CloudWatch
1. Now we need to edit the crontab by using below command:

 crontab -e

2. Now we will update the file using the following query which will disk space utilization and report memory for particular paths to Amazon CloudWatch in every five minutes:

*/5 * * * * ~/STORAGE/cloudwatch/aws-scripts-mon/mon-put-instance-data.pl --mem-util --mem-avail --mem-used --disk-space-util --disk-space-avail --disk-space-used --disk-path=/ --disk-path=/STORAGE --from-cron

If there is an error, the scripts will write an error message in our system log.

Use of Options

--mem-used
The above command will collect the information about used memory and which will send the details of the reports in MBs into the MemoryUsed metrics. This will give us information about the metric counts memory allocated by applications and the OS as used.
--mem-util
The above command will collect the information about memory utilization in percentages and which will send the details of the Memory Utilization metrics and it will count the usage of the memory applications and the OS.
--disk-space-util
The above command will collect the information to collect the current utilized disk space and which will send the reports in percentages to the DiskSpaceUtilization for the metric and for the selected disks.
--mem-avail
The above command will collect the information about the available memory and which will send the reports in MBs to the MemoryAvailable metrics details. This is the metric counts memory allocated by the applications and the OS as used.
--disk-path=PATH
The above command will collect the information and will point out the which disk path to report disk space.
--disk-space-avail
The above command will collect the information about the available disk space and which will send the reports in GBs to the DiskSpaceAvailable metric for the selected disks.
--disk-space-used
The above command will collect the information about the disk space used and which will send the reports in GBs to the DiskSpaceUsed metric for the selected disks.

The PATH can specify to point or any of the files can be located on which are mounted point for the filesystem which needs to be reported.

If we want to points to the multiple disks, then specify both of the disks like below:

--disk-path=/ --disk-path=/home

Setting an Alarm for Custom Metrics

Before we are going to running our Perl Scripts, then we need to create an alarm that will be listed in our default metrics except for the custom metrics. You can see some default metrics are listed in below image:



Once we completed setting the cron, then the custom metrics will be located in Linux System Metrics.

Now we are going to creating the alarm for our custom metrics
1. We need to open the cloudwatch console panel at https://console.aws.amazon.com/cloudwatch/home
2. Now navigate to the navigation panel, we need to click on Alarm and we can Create Alarm.
3. This will open a popup which with the list of the CloudWatch metrics by category.
4. Now click on the Linux System Metrics . This will be listed out with custom metrics you can see in the below pictures






5. Now we need to select metric details and we need to click on the NEXT button. Now we need to navigate to Define Alarm step.



6. Now we need to define an Alarm with required fields

Now we need to enter the Alarm name for identifying them. Then we need to give a description of our alarm.

Next, we need to give the condition with the maximum limit of bytes count or percentage when it notifies the alarm. If the condition satisfies, then the alarm will start trigger.

We need to provide a piece of additional information about for our alarm.

We need to define what are the actions to be taken when our alarm changes it state.

We need to select or create a new topic with emails needed for sending notification about alarm state.
7. Finally, we need to choose the Create Alarm.

So its completed. Now the alarm is created for our selected custom metrics.

Finished!

Now the alarm will be listed out under the selected state in our AWS panel. Now we need to select an alarm from the list seen and we can see the details and history of our alarm.

What is a Daemon?

The term daemon was coined by the programmers of Project MAC at MIT. It is inspired on Maxwell's demon in charge of sorting molecules in the background. The UNIX systems adopted this terminology for daemon programs.

It also refers to a character from Greek mythology that performs the tasks for which the gods do not want to take. As stated in the "Reference System Administrator UNIX", in ancient Greece, the concept of "personal daemon" was, in part, comparable to the modern concept of "guardian angel." BSD family of operating systems use the image as a demon's logo.

Daemons are usually started at machine boot time. In the technical sense, a demon is considered a process that does not have a controlling terminal, and accordingly there is no user interface. Most often, the ancestor process of the deamon is init - process root on UNIX, although many daemons run from special rcd scripts started from a terminal console.

Richard Stevenson describes the following steps for writing daemons:
    . Resetting the file mode creation mask to 0 function umask(), to mask some bits of access rights from the starting process.
    . Cause fork() and finish the parent process. This is done so that if the process was launched as a group, the shell believes that the group finished at the same time, the child inherits the process group ID of the parent and gets its own process ID. This ensures that it will not become process group leader.
    . Create a new session by calling setsid(). The process becomes a leader of the new session, the leader of a new group of processes and loses the control of the terminal.
    . Make the root directory of the current working directory as the current directory will be mounted.
    . Close all file descriptors.
    . Make redirect descriptors 0,1 and 2 (STDIN, STDOUT and STDERR) to /dev/null or files /var/log/project_name.out because some standard library functions use these descriptors.
    . Record the pid (process ID number) in the pid-file: /var/run/projectname.pid.
    . Correctly process the signals and SigTerm SigHup: end with the destruction of all child processes and pid - files and / or re-configuration.

How to Create Daemons in PHP

To create demons in PHP you need to use the extensions pcntl and posix. To implement the fast communication withing daemon scripts it is recommended to use the extension libevent for asynchronous I/O.

Lets take a closer look at the code to start a daemon:

umask(0);
$pid = pcntl_fork(); 
if ($pid < 0) {
 print('fork failed');
 exit 1; 
}

After a fork, the execution of the program works as if there are two branches of the code, one for the parent process and the second for the child process. What distinguishes these two processes is the result value returned the fork() function call. The parent process ID receives the newly created process number and the child process receives a 0.

if ($pid > 0) { echo "daemon process started
";
 exit;
} 
 
$sid = posix_setsid();
if ($sid < 0) {
 exit 2;
}

chdir('/');
file_put_contents($pidFilename, getmypid() ); 
run_process();

The implementation of step 5 "to close all file descriptors" can be done in two ways. Well, closing all file descriptors is difficult to implement in PHP. You just need to open any file descriptors before fork(). Second, you can override the standard output to an error log file using init_set() or use buffering using ob_start() to a variable and store it in log file:

ob_start();
var_dump($some_object);
$content = ob_get_clean();
fwrite($fd_log, $content); 

Typically, ob_start() is the start of the daemon life cycle and ob_get_clean() and fwrite() calls are the end. However, you can directly override STDIN, STDOUT and STDERR:

ini_set('error_log', $logDir.'/error.log');
fclose(STDIN); 
fclose(STDOUT);
fclose(STDERR);
$STDIN = fopen('/dev/null', 'r'); 
$STDOUT = fopen($logDir.'/application.log', 'ab');
$STDERR = fopen($logDir.'/application.error.log', 'ab');

Now, our process is disconnected from the terminal and the standard output is redirected to a log file.

Handling Signals

Signal processing is carried out with the handlers that you can use either via the library pcntl (pcntl_signal_dispatch()), or by using libevent. In the first case, you must define a signal handler:

function sig_handler($signo)
{
 global $fd_log;
 switch ($signo) {
 case SIGTERM:
 fclose($fd_log); unlink($pidfile); exit;
 break;
 case SIGHUP:
 init_data(); break;
 default:
 }
}


pcntl_signal(SIGTERM, "sig_handler");
pcntl_signal(SIGHUP, "sig_handler");

Note that signals are only processed when the process is in an active mode. Signals received when the process is waiting for input or in sleep mode will not be processed. Use the wait function pcntl_signal_dispatch(). We can ignore the signal using flag SIG_IGN: pcntl_signal(SIGHUP, SIG_IGN); Or, if necessary, restore the signal handler using the flag SIG_DFL, which was previously installed by default: pcntl_signal(SIGHUP, SIG_DFL);

Asynchronous I/O with Libevent

In the case you use blocking input / output signal processing is not applied. It is recommended to use the library libevent which provides non-blocking as input / output, processing signals, and timers. Libevent library provides a simple mechanism to start the callback functions for events on file descriptor: Write, Read, Timeout, Signal.

Initially, you have to declare one or more events with an handler (callback function) and attach them to the basic context of the events:

$base = event_base_new();
$event = event_new();
$errno = 0;
$errstr = '';
$socket = stream_socket_server("tcp://$IP:$port", $errno, $errstr);
stream_set_blocking($socket, 0);
event_set($event, $socket, EV_READ | EV_PERSIST, 'onAccept', $base);

Function handlers 'onRead', 'onWrite', 'onError' must implement the processing logic. Data is written into the buffer, which is obtained in the non-blocking mode:

function onRead($buffer, $id)
{
 while($read = event_buffer_read($buffer, 256)) {
 var_dump($read);
 }
}

The main event loop runs with the function event_base_loop($base);. With a few lines of code, you can exit the handler only by calling: event_base_loobreak(); or after the specified time (timeout) event_loop_exit();.

Error handling deals with failure Events:

function onError($buffer, $error, $id)
{
 global $id, $buffers, $ctx_connections;
 event_buffer_disable($buffers[$id], EV_READ | EV_WRITE);
 event_buffer_free($buffers[$id]);
 fclose($ctx_connections[$id]);
 unset($buffers[$id], $ctx_connections[$id]);
}

It should be noted the following subtlety: Working with timers is only possible through the file descriptor. The example of official the documentation does not work. Here is an example of processing that runs at regular intervals.

$event2 = event_new();
$tmpfile = tmpfile();
event_set($event2, $tmpfile, 0, 'onTimer', $interval);
$res = event_base_set($event2, $base);
event_add($event2, 1000000 * $interval);

With this code we can have a working timer finishes only once. If we need a "permanent" Timer, using the function onTimer we need create a new event each time, and reassign it to process through a "period of time":

function onTimer($tmpfile, $flag, $interval)
{
 $global $base, $event2;

 if ($event2) {
 event_delete($event2);
 event_free($event2);
 }

 call_user_function(‘process_data’,$args);

 $event2 = event_new();
 event_set($event2, $tmpfile, 0, 'onTimer', $interval);
 $res = event_base_set($event2, $base);
 event_add($event2, 1000000 * $interval);
}

At the end of the daemon we must release all previously allocated resources:

event_delete($event);
event_free($event);
event_base_free($base);

event_base_set($event, $base);
event_add($event);

Also it should be noted that for the signal processing handler is set the flag EV_SIGNAL: event_set($event, SIGHUP, EV_SIGNAL, 'onSignal', $base);

If needed constant signal processing, it is necessary to set a flag EV_PERSIST. Here follows a handler for the event onAccept, which occurs when a new connection is a accepted on a file descriptor:

function onAccept($socket, $flag, $base) {
 global $id, $buffers, $ctx_connections;
 $id++;
 $connection = stream_socket_accept($socket);
 stream_set_blocking($connection, 0);
 $buffer = event_buffer_new($connection, 'onRead', NULL, 'onError', $id);
 event_buffer_base_set($buffer, $base);
 event_buffer_timeout_set($buffer, 30, 30);
 event_buffer_watermark_set($buffer, EV_READ, 0, 0xffffff); event_buffer_priority_set($buffer, 10); event_buffer_enable($buffer, EV_READ | EV_PERSIST); $ctx_connections[$id] = $connection;
 $buffers[$id] = $buffer;
}

Monitoring a Daemon

It is good practice to develop the application so that it was possible to monitor the daemon process. Key indicators for monitoring are the number of items processed / requests in the time interval, the speed of processing with queries, the average time to process a single request or downtime.

With the help of these metrics can be understood workload of our demon, and if it does not cope with the load it gets, you can run another process in parallel, or for running multiple child processes.

To determine these variables need to check these features at regular intervals, such as once per second. For example downtime is calculated as the difference between the measurement interval and total time daemon.

Typically downtime is determined as a percentage of a measurement interval. For example, if in one second were executed 10 cycles with a total processing time of 50ms, the time will be 950ms or 95%.

Query performance wile be 10rps (request per second). Average processing time of one request: the ratio of the total time spent on processing requests to the number of requests processed, will be 5ms.

These characteristics, as well as additional features such as memory stack size queue, number of transactions, the average time to access the database, and so on.

An external monitor can be obtain data through a TCP connection or unix socket, usually in the format of Nagios or zabbix, depending on the monitoring system. To do this, the demon should use an additional system port.

As mentioned above, if one worker process can not handle the load, usually we run in parallel multiple processes. Starting a parallel process should be done by the parent master process that uses fork() to launch a series of child processes.

Why not run processes using exec() or system()? Because, as a rule, you must have direct control over the master and child processes. In this case, we can handle it via interaction signals. If you use the exec command or system, then launch the initial interpreter, and it has already started processes that are not direct descendants of the parent process.

Also, there is a misconception that you can make a demon process through command nohup. Yes, it is possible to issue a command: nohup php mydaemon.php -master >> /var/log/daemon.log 2 >> /var/log/daemon.error.log &

But, in this case, would be difficult to perform log rotation, as nohup "captures" file descriptors for STDOUT / STDERR and release them only at the end of the command, which may overload of the process or the entire server. Overload demon process may affect the integrity of data processing and possibly cause partial loss of some data.

Starting a Daemon

Starting the daemon must happen either automatically at boot time, or with the help of a "boot script."

All startup scripts are usually in the directory /etc/rc.d. The startup script in the directory service is made /etc/init.d/ . Run command start service myapp or start group /etc/init.d/myapp depending on the type of OS.

Here is a sample script text:

#! /bin/sh
#
$appdir = /usr/share/myapp/app.php
$parms = --master –proc=8 --daemon
export $appdir
export $parms
if [ ! -x appdir ]; then
 exit 1
fi

if [ -x /etc/rc.d/init.d/functions ]; then
 . /etc/rc.d/init.d/functions
fi

RETVAL=0

start () {
 echo "Starting app"
 daemon /usr/bin/php $appdir $parms 
 RETVAL=$?
 [ $RETVAL -eq 0 ] && touch /var/lock/subsys/mydaemon
 echo
 return $RETVAL
}

stop () {
 echo -n "Stopping $prog: "
 killproc /usr/bin/fetchmail
 RETVAL=$?
 [ $RETVAL -eq 0 ] && rm -f /var/lock/subsys/mydaemon
 echo
 return $RETVAL
}

case in
 start)
 start
 ;;
 stop)
 stop
 ;;
 restart)
 stop
 start
 ;;
 status)
 status /usr/bin/mydaemon
 ;;
 *)
 echo "Usage: 

php
if (is_file('app.phar')) {
unlink('app.phar');
}
$phar = new Phar('app.phar', 0, 'app.phar');
$phar->compressFiles(Phar::GZ);
$phar->setSignatureAlgorithm (Phar::SHA1);
$files = array();
$files['bootstrap.php'] = './bootstrap.php';
$rd = new RecursiveIteratorIterator(new RecursiveDirectoryIterator('.'));
foreach($rd as $file){
if ($file->getFilename() != '..' && $file->getFilename() != '.' && $file->getFilename() != __FILE__) {
if ( $file->getPath() != './log'&& $file->getPath() != './script'&& $file->getPath() != '.')
$files[substr($file->getPath().DIRECTORY_SEPARATOR.$file->getFilename(),2)] =
$file->getPath().DIRECTORY_SEPARATOR.$file->getFilename();
}
}
if (isset($opt['version'])) {
$version = $opt['version'];
$file = "buildFromIterator(new ArrayIterator($files));
$phar->setStub($phar->createDefaultStub('bootstrap.php'));
$phar = null;
}

 {start|stop|restart|status}"
 ;;
 
RETVAL=$?
exit $RETVAL

Distributing Your PHP Daemon

To distribute a daemon it is better to pack it in a single phar archive module. The assembled module should include all the necessary PHP and .ini files.

Below is a sample build script:

#php app.phar
myDaemon version 0.1 Debug
usage:
--daemon – run as daemon
--debug – run in debug mode
--settings – print settings
--nofork – not run child processes
--check – check dependency modules
--master – run as master
--proc=[8] – run child processes

Additionally, it may be advisable to make a PEAR package as a standard unix-console utility that when run with no arguments prints its own usage instruction:
[NMD%%CODE%%]

Conclusion

Creating daemons in PHP it is not hard but to make them run correctly it is important to follow the steps described in this article.

Post a comment here if you have questions or comments on how to create daemon services in PHP.

People that visit your website face an invisible threat each time they log on. Small businesses are especially vulnerable to digital data breaches, and that can change the way your customers feel about you. But, although you cannot stop hackers from trying, there are things you can do as a business owner to make your website a safer experience for everyone. Keep reading for tips.

Mature digitally.

You may be ahead of the times when it comes to products and services, but, chances are, your website hasn't fully kept up. It's time to learn all you can about the internet and digital security. If you are already somewhat tech savvy, a PHP Security Course from Nomad PHP can help you better understand everything from cryptography to website error messages.

Adapting to today's digital environment means transforming your website to quickly and easily identify threats via machine learning and network monitoring. And, as Upwork explains, digital maturity not only keeps your website safe, but adopting this mindset can also increase your efficiency and accuracy by reducing human errors.

Understand the threats.

It is not enough to simply keep up with your website, you also have to understand the types of threats that are out there. You're likely familiar with ransomware and phishing, but, it's also a good idea to know how a website can get hacked. Your site's content management system and vulnerabilities within your operating system are all weak points that hackers can easily identify.

Insist on security measures.

When customers log into your website, they input their credentials. Each time they do so, you can best protect their information by keeping your systems up to date. You'll also want to ensure that your site is hosted on a secure service and that you have an SSL certificate installed.

If you are not already, have your IT department or managed IT services perform regular website security checks. PhoenixNAP, an IT services provider, notes that those websites working via WordPress should also be safely outfitted with the most recent security plug-ins.

Eliminate spam.

If your website allows for comments that are not manually approved, anyone on the internet can post. This leaves it open for hackers and other unscrupulous individuals to comment with spam and malicious links that your customers may inadvertently click on. While many of these simply exist as a way for the commenter to drive traffic to another website, others are designed to draw your readers' attention, gain their trust, and access their personal information.

Prioritize passwords.

Your customers' passwords are the keys by which they open the door to your website. Unfortunately, many people do not treat them with as much care as they do the keys they use in the non-digital world.

It's true, passwords can be a pain, but you are not doing yourself or your customers any favors by allowing simple one-word passcodes to access your site. Instead, design your site to require a strong password. How-To Geek asserts that this will have a minimum of 12 characters and include a combination of upper and lower case letters, symbols, and numbers.

While you will likely rely on your IT experts to secure your website, the truth is that it is ultimately up to you to ensure this is done. So even if you are not a digital mastermind, knowing all you can about web security can help you be a better business owner. Your customers will be safer, and a secure website is just one way to strengthen your business's online presence and keep up with today's -- and tomorrow's -- technology.

Technology is constantly changing the way we interact, research, and react. One such way artificial intelligence is impacting our daily lives, and we may not even realize it is in weather forecasting.

The forecast we usually have been receiving in our phones and in older times primarily in newspapers, was based on data collected via satellites, radar system and weather balloons. In recent times there has been the addition of IoT based sensors as well. However, with the advent of Artificial Intelligence (AI) finding its way in numerous areas, AI has taken a role in improving the accuracy of weather as well.

The Dataset expansion

A significantly enormous set of data is available - from the weather satellites in space, to the private and government owned weather stations which are gaining real-time data. IBM for instance has more the 0.25 million weather stations that help IBM collect real-time data. Additionally, as we are in the age of Internet of Things (IOT), each small device to big device- cellphones, solar panels and vehicles everything has become or is yet to become yet another data source. Companies like GE have installed IOT street lights, which help in monitoring air quality and humidity. These are some of the few sources which help us in collecting the vast amount of data necessary for building on the AI technology, in future these sources and the amount of available data would grow exponentially.

Google and Weather forecast

Using the AI technology Google is able to develop a weather forecast tool, it has been trained to predict rainfalls accurately as much as six hours before. The underlying technology on which this prediction is build upon is U-Net convolutional neural network which is originally used in biomedical research. It works by taking satellite images as input and uses AI technology to transform these images into high resolution images. The only off-set is this is not real-time prediction and the delay due to complex calculations results in using six-hour old data and hence can only predict six-hours before.

IBM and its efforts in weather prediction

The quest for IBM to venture into weather forecasting began with IBM acquiring The Weather Company. IBM plans on using the large amount of weather data available coupled with IBM Watson and the cloud platform to enhance weather forecasting. In 2019 IBM developed Global High-Resolution Atmospheric Forecasting System (GRAF) in order to forecast weather conditions 12 hours prior to a greater degree of accuracy. The radius encompassed by the GRAF is also more narrowed down up to 3 kilometers as opposed to generally being 10-15 kilometers. Another of its marvel is that it gives accurate predictions down to each hour and not just daily.

Artificial Intelligence and Panasonic

Panasonic is the company behind TAMDAR, the weather sensor installed on commercial airplanes. With this advantage of extensive amount of data from in-flight sensors as well as publicly available data Panasonic developed Global 4D Weather. Proving to their claim of being the most advanced global forecasting platform globally they were able to timely predict Hurricane Irma in its early days.

Uses of Weather Forecasting

Sales

Everyday life decisions are affected by weather, it makes us choose in the way we travel, things we eat and things we buy to wear. The rise in temperature may increase sales of chilled drinks, if the company is fully aware of the forecast it would be able to manage productions as per demand. AI can help brands in maximizing sales based on weather forecasts and in minimizing waste.

Natural Disasters

The Panasonic Global 4D weather predicting Hurricane Irma is just another example where timely prediction can save millions of lives in face of situations like floods and Hurricanes. Companies like IBM combine weather forecasting data with utilities distribution network, which enables them to narrow down areas with likely outages. This enables utilities to place their workforce timely so the repair process catering to damage repairs post disasters is shortened. This in turn brings huge benefits to the overall economy.

Agriculture

The weather and agriculture have the most obvious correlation, each process in farming from sowing to reaping all depends on the weather. As farmers cultivate on huge farming lands, accurate information about each part of the land can help farmers in improving their crops and yield by manifolds. Weather conditions can lead to almost 90 percent of crop losses, 25 percent of these losses can be avoided using accurate AI prediction models to forecast weather and in turn improve the yield.

Transportation

Sea travel has always been eventful, timely prediction of storms by using machine learning techniques and hyper-local data allows companies to plan shipments accordingly and avoid severe weather conditions that usually result in delays. Tools like IBM’s Operations Dashboard for Ground Transportation equips in enhancing productivity based on weather predictions.

Another of the implementation of AI in transportation industry corelating to weather is fuel consumption. For instance, using weather prediction models to reduce airplane fuel consumption during its ascent.

To conclude Artificial Intelligence has a key role to play in weather forecasting, weather direct or indirectly impacts each sector in the economy. As the amount of information available to improve predictions increases exponentially it gives a chance to AI to improve accuracy even further. As we continue narrowing down weather conditions precise to time and location the benefits of such advancements across all industries are innumerable.

In today's dynamic and fast-paced world of web development, ensuring the reliability, performance, and scalability of applications is paramount. Monitoring and observability tools play a crucial role in achieving these goals by providing insights into application metrics, performance trends, and system health. Prometheus, a popular open-source monitoring and alerting toolkit, offers robust capabilities for monitoring infrastructure and application metrics. In this article, we'll explore how to leverage Prometheus with PHP to monitor, analyze, and troubleshoot PHP-based applications effectively.

Understanding Prometheus:

Prometheus is an open-source monitoring and alerting system originally developed by SoundCloud. It is designed for reliability, scalability, and extensibility, making it suitable for monitoring complex, distributed systems. Key features of Prometheus include:

Time Series Data: Prometheus collects time-series data, allowing developers to track metrics such as CPU usage, memory consumption, request latency, and HTTP response codes over time.

PromQL: Prometheus Query Language (PromQL) enables users to query and aggregate metrics, create custom dashboards, and set up alerting rules based on specific conditions.

Scalability and Reliability: Prometheus is designed to be highly scalable and reliable, supporting a distributed architecture with multiple replicas and federated setups for global monitoring.

Integrating Prometheus with PHP:

To integrate Prometheus with PHP applications, developers can utilize client libraries and instrumentation libraries that facilitate metric collection and exposition. The following steps outline the process of integrating Prometheus with PHP:

Choose a Prometheus Client Library: Select a Prometheus client library for PHP that suits your needs. Popular options include prometheus/client_php and php-prometheus/client.

Instrument Your PHP Code: Instrument your PHP application code to collect relevant metrics. This involves adding instrumentation code to track metrics such as request duration, memory usage, database queries, and custom business metrics.

Exposing Metrics: Expose the collected metrics in a format compatible with Prometheus. This typically involves exposing an HTTP endpoint (e.g., /metrics) where Prometheus can scrape the metrics using the Prometheus exposition format.

Configure Prometheus Server: Configure the Prometheus server to scrape metrics from the PHP application's endpoint. Update the Prometheus configuration file (prometheus.yml) to include the target endpoint and define any additional scraping parameters.

Example Integration:

Let's illustrate how to integrate Prometheus with a PHP application using the prometheus/client_php library:

require 'vendor/autoload.php';

use Prometheus\CollectorRegistry;
use Prometheus\Storage\APC;
use Prometheus\RenderTextFormat;

$registry = new CollectorRegistry(new APC());

$requestDuration = $registry->registerCounter('php_requests_total', 'Total number of PHP requests');

$requestDuration->inc();

$renderer = new RenderTextFormat();
echo $renderer->render($registry->getMetricFamilySamples());

In this example, we register a custom metric (php_requests_total) to track the total number of PHP requests. We then increment this metric for each request and expose the metrics endpoint using the Prometheus exposition format.

Benefits of Using Prometheus with PHP:

Real-time Monitoring: Prometheus provides real-time monitoring capabilities, allowing developers to monitor application metrics and diagnose issues promptly.

Scalability: Prometheus scales horizontally, making it suitable for monitoring large-scale deployments and distributed systems.

Alerting: Prometheus supports alerting based on predefined rules, enabling proactive monitoring and alerting for potential issues or anomalies.

Integration: Prometheus integrates seamlessly with various programming languages, platforms, and frameworks, including PHP, enabling comprehensive monitoring across the entire technology stack.

Conclusion:

Prometheus offers powerful capabilities for monitoring and observability, making it a valuable tool for developers and DevOps teams tasked with ensuring the reliability and performance of PHP applications. By integrating Prometheus with PHP using client libraries and instrumentation, developers can gain valuable insights into application metrics, troubleshoot issues effectively, and proactively respond to performance anomalies. Embracing Prometheus as part of your monitoring strategy empowers organizations to build resilient, scalable, and high-performing PHP applications in today's dynamic digital landscape.