Do you know what your Drupal is doing_ Observe it!

Do you know what your
Drupal is doing? Observe it!
lussoluca

Luca Lusso
Drupal / PHP / Go developer @ SparkFabrik
Drupal contributor (webprofiler, monolog, …) and speaker
Drupal.org: https://www.drupal.org/u/lussoluca
Twitter: https://www.twitter.com/lussoluca
LinkedIn: www.linkedin.com/in/lussoluca

WE ARE A TECH COMPANY OF ENGINEERS,
DEVELOPERS AND DESIGNERS WHO WILL
THINK, DESIGN AND BUILD YOUR CUSTOM APPLICATIONS,
MODERNIZE YOUR LEGACY AND TAKE YOU TO THE
CLOUD NATIVE ERA.
SPARKFABRIK

We help italian businesses to bridge
the gap with China thanks to our
Official Partnership with
Alibaba Cloud.
SparkFabrik is Cloud Native
Computing Foundation
Silver Member.
SparkFabrik is Google Cloud
Platform Technology Partner.
SparkFabrik is AWS
Official Partner.
PROUD OF OUR PARTNERSHIPS

Almost everyone is working with distributed systems.
There are microservices, containers, cloud, serverless,
and a lot of combinations of these technologies. All of
these increase the number of failures that systems may encounter
because there are too many parts interacting.
And because of the distributed system’s diversity, it’s
complex to understand present problems and predict
future ones

Observability is a measure of how well
internal states of a system can be
inferred from knowledge of its external
outputs

We want to observe production environments, and
generic metrics like CPU and memory usage
are not sufficient anymore

3 pillars of
observability
● Structured logs
● Metrics
● (Distributed) Traces

Tools
OpenTelemetry
OpenTelemetry is a collection of tools, APIs,
and SDKs. It can be used to instrument,
generate, collect, and export telemetry data
(metrics, logs, and traces) to help analyze
software’s performance and behavior.
OpenTelemetry is an incubating project
from the Cloud Native Computing
Foundation, created after the merger of
OpenCensus (from Google) and
OpenTracing (from Uber).
The data collected with OpenTelemetry is
vendor-agnostic and can be exported in
many formats.
https://opentelemetry.io

Cloud vendor
● AWS Distro for OpenTelemetry: aws-otel.github.io/
● Google Cloud OpenTelemetry: google-cloud-opentelemetry.readthedocs.io
● Azure Monitor OpenTelemetry: docs.microsoft.com/en-us/azure/azure-monitor/app/opentelemetry-
overview

Tools
OpenTelemetry
Language support
PHP:
Go:
Javascript:
Tracing Metrics Logging
pre-alpha pre-alpha not yet
implemented
stable alpha not yet
implemented
stable alpha not yet
implemented

Tools
OpenTelemetry is an interesting project but it’s not yet ready to
cover the entire observability stack. We need other tools to
collect metrics and logs, and a way to store and visualize
collected data.
Logs -> Monolog
Metrics -> Prometheus (using OpenTelemetry API)
Traces -> OpenTelemetry
Storage and visualization -> Grafana

Tools
Monolog
Monolog is a standard PHP library and it can
be included in a Drupal website using a
contrib module (www.drupal.org/project/monolog)
Monolog sends logs to files, sockets,
inboxes, databases and various web
services.
Monolog implements the PSR-3 interface
that can be type-hint against in code to
keep a maximum of interoperability.
github.com/Seldaek/monolog

Tools
Prometheus
Prometheus is an open-source systems
monitoring and alerting toolkit originally built
at SoundCloud. It is now a standalone open
source project and maintained independently
of any company.
Prometheus collects and stores metrics as
time series data.
Prometheus was the second project to join
the Cloud Native Computing Foundation after
Kubernetes.
prometheus.io

Tools
Grafana
Grafana allows to query, visualize, alert on
and understand metrics, traces and logs no
matter where they are stored.
grafana.com
Application 1
Instrumentation
library
Application 2
Instrumentation
library
Application 3
Instrumentation
library
Orchestrator
Grafana Agent
Loki
Tempo
Prometheus
Grafana
Grafana Cloud

1. Logs are about storing specific events

In Drupal 8 (an in general in PHP world) we have a
battle tested library to do logging
Monolog

Monolog is a PSR-3 compliant library
from Jordi Boggiano.
There is a module for all Drupal versions (yes, also D10)

Download the Monolog module using
Composer, to have both the Drupal module and the PHP library
composer require drupal/monolog:^2.0

Monolog module doesn't have a UI, it's configured using yml files,
for example
sites/default/monolog.services.yml

services:
monolog.handler.rotating_file:
class: MonologHandlerRotatingFileHandler
arguments: ['private://logs/debug.log', 10, '%monolog.level.info%']
First of all define a new service in the service container

parameters:
monolog.channel_handlers:
default:
handlers:
- name: 'rotating_file'
formatter: 'json'
monolog.processors: [
'message_placeholder', 'current_user',
'request_uri', 'ip', 'referer',
'filter_backtrace', 'introspection'
]
Then define handlers, formatter
and processors using service
container parameters. Here we're
configuring the default channel to
catch all log messages and to save
them using the
monolog.handler.rotating_file
service, in json format and after
being processed by a set of
processors

settings.php
$settings['container_yamls'][] =
DRUPAL_ROOT . '/sites/default/monolog.services.yml';
Add monolog.services.yml to
the list of container’s yamls in settings.php file

Drupal::logger(devdays)->notice('Data from remote microservice.');

{
"message": "Data from remote microservice.",
"context": {},
"level": 250,
"level_name": "NOTICE",
"channel": "devdays",
"datetime": "2022-04-05T10:05:53.811568+02:00",
"extra": {
"referer": "",
"ip": "172.20.0.9",
"request_uri": "https://drupal10.ddev.site/microservice",
"uid": "1",
"user": "admin",
"file": "/.../devdays/src/Controller/MicroserviceController.php",
"line": 26,
"class": "DrupaldevdaysControllerMicroserviceController",
"function": "view"
}
}

Structured logs makes it simple to query them for any
sort of useful information
We can write custom Monolog processors to add
application’s custom data to our logs

In a Cloud Native environment, the application runs on multiple servers (or pods). We
need a way to export all those logs generated by every instance of the application.
In this case our logs are files stored in the local filesystem of every instance.
We have to discover, scrape and send them to a log collector.
Promtail is an agent which ships the contents of local logs to a private Grafana Loki
instance or Grafana Cloud. It is usually deployed to every machine that has applications
needed to be monitored.

logs:
configs:
- name: default
positions:
filename: /mnt/ddev_config/grafana/positions.yaml
scrape_configs:
- job_name: drupal
pipeline_stages:
- json:
expressions:
level: level_name
- labels:
level:
static_configs:
- targets: [localhost]
labels:
job: drupal
__path__: /private/logs/*log
clients:
- url: http://***.grafana.net/loki/api/v1/push
basic_auth:
username: ***
password: ***
Scraping and sending logs to Grafana Loki

Scraping and sending logs to Grafana
Monolog -> logs (filesystem) -> Promtail -> Loki -> Grafana

2. Metrics are a measurement at a point in time for the system

Examples of the sort of metrics you might have would be:
● the number of times you receive an HTTP request
● how much time was spent handling requests
● how many requests are currently in progress
● the number of errors occurred

To instrument our application and record real-time
metrics we will use the Prometheus exporter
exposed by OpenTelemetry

There’s a module for that!
Observability suite
https://www.drupal.org/project/o11y

Prometheus scrapes data at the /metrics endpoint at a configured rate
PHP uses a shared-nothing architecture by default
o11y needs a way to store data between one scrape and the next
default implementation uses Redis as a storage backend

o11y_metrics module automatically instrument a Drupal website to collect data about:
● number of requests (per route)
● time of the request (per route)
● used PHP memory

The module exposes an URL with metrics in
Prometheus format (/metrics)
# HELP php_info Information about the PHP environment.
# TYPE php_info gauge
php_info{version="8.1.3"} 1
# HELP requests The number of requests
# TYPE requests counter
requests{path="entity.user.canonical"} 1
# HELP memory The peak of memory allocated by PHP
# TYPE memory histogram
memory_bucket{path="entity.user.canonical",le="0.005"} 0
memory_bucket{path="entity.user.canonical",le="1"} 1
memory_bucket{path="entity.user.canonical",le="+Inf"} 1
memory_count{path="entity.user.canonical"} 1
memory_sum{path="entity.user.canonical"} 0.31189873814648
# HELP time The time of a request
# TYPE time histogram
time_bucket{path="entity.user.canonical",le="0.005"} 0
time_bucket{path="entity.user.canonical",le="1"} 1
time_bucket{path="entity.user.canonical",le="+Inf"} 1
time_count{path="entity.user.canonical"} 1
time_sum{path="entity.user.canonical"} 0.01953125

metrics:
global:
scrape_interval: 1m
remote_write:
- url: https://***.grafana.net/api/prom/push
basic_auth:
username: ***
password: ***
configs:
- name: default
scrape_configs:
- job_name: drupal
static_configs:
- targets: [ddev-drupal10-web]
Scraping and sending metrics to Grafana Prometheus

Manually instrument code
function example_entity_insert(EntityInterface $entity) {
/** @var Drupalo11y_metricsMeterInterface $meter */
$meter = Drupal::service('o11y_metrics.meter');
$meter->increment(
'entity_insert',
'Insert a new entity',
[
'type' => $entity->getEntityTypeId(),
'bundle' => $entity->bundle()
]
);
}

Node exporter
Prometheus exporter for hardware and OS metrics
exposed by *NIX kernels, written in Go with pluggable
metric collectors, for example:
cpu
meminfo
filesystem
diskstats
netdev

o11y_metrics -> local Redis -> Prometheus -> remote_write -> Grafana

o11y module is a POC of what we can do with the OpenTelemetry API for metrics, if you
want a more robust solution you can try the Prometheus.io Exporter module (also from
SparkFabrik):
https://www.drupal.org/project/prometheusio_exporter

2. Metrics are a measurement at a point in time for the system
3. Distributed traces deals with information that is request-
scoped

We will use the Observability suite module to instrument our
application
Internally the module uses OpenTelemetry to do the hard work

Per-process logging and metric monitoring have their
place, but neither can reconstruct the elaborate
journeys that transactions take as they propagate
across a distributed system. Distributed traces are
these journeys

We take for example a Drupal 10 website
that renders a page with some data that comes from a
remote microservice

class MicroserviceController extends ControllerBase {
private Client $httpClient;
public static function create(ContainerInterface $container) {
return new static(
$container->get('http_client')
);
}
final public function __construct(Client $httpClient) {
$this->httpClient = $httpClient;
}
public function view() {
$response = $this->httpClient->get('http://ddev-drupal10-microservice:8080/hello-instrumented');
$json = json_decode($response->getBody()->getContents());
$this->loggerFactory->get('devdays')->notice($json->message);
return [
'#type' => 'markup',
'#markup' => $json->message,
];
}
}

https://play.golang.com/p/Jznc8X6t3eU
The remote microservice is implemented in GO and its code it's available here:

traces:
configs:
- name: default
remote_write:
- endpoint: ***.grafana.net:443
basic_auth:
username: ***
password: ***
receivers:
otlp:
protocols:
http:
Collect traces and them to Grafana Tempo

o11y_traces -> Tempo -> Grafana

Observability suite automatically instrument
● Events
● Twig templates
● HTTP calls
● Services (optional)
but you can trace your own code too

public function view() {
$response =
$this->httpClient->get('http://ddev-drupal10-microservice:8080/hello-instrumented');
$json = json_decode($response->getBody()->getContents());
$this->getLogger('devdays')->notice($json->message);
$this->someComplexMethod($json);
return [
'#type' => 'markup',
'#markup' => $json->message,
];
}
private function someComplexMethod(string $json) {
/** @var OpenTelemetryAPITraceTracerInterface $tracer */
$tracer = Drupal::service('o11y_traces.tracer');
$span = $tracer->spanBuilder('someComplexMethod')->startSpan();
sleep(1);
$span->end();
}

The new span appear after the HTTP and the microservice span

private function someComplexMethod() {
/** @var OpenTelemetryAPITraceTracerInterface $tracer */
$tracer = Drupal::service('o11y_traces.tracer');
$span = $tracer->spanBuilder('someComplexMethod')
->setAttribute('someAttribute', 'someValue')
->startSpan();
sleep(1);
$span->end();
}

Attributes becomes Tags on Grafana

One last thing we need is to correlate traces with logs,
so when we found a problem with a request we can go
from the trace to the logs (and viceversa)

The O11y module provides a new processor for
Monolog that adds a trace_id argument to every log

parameters:
monolog.channel_handlers:
default:
handlers:
- name: 'rotating_file'
formatter: 'json'
monolog.processors: [
'message_placeholder', 'current_user',
'request_uri', 'ip', 'referer',
'filter_backtrace', 'introspection', 'tracer'
]

Thank you
To our wonderful sponsors, our awesome community and fantastic volunteers!
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Stay in touch
#ddd2022 on Drupal slack
@drupaldevdays
/drupaldevdays

Do you know what your Drupal is doing_ Observe it!

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