What is Observability ?

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Observability Overview

Observability is the ability to measure a system's internal states by looking at its outputs. If a system's current state can be determined solely from information from outputs, such as sensor data, then it is said to be observable.

In the realm of modern software development, Observability has become a crucial aspect of maintaining robust and high-performing systems. It encompasses various practices and tools designed to ensure that applications run smoothly and efficiently. Performance Tracing is a core component of observability, providing detailed insights into how different parts of a system interact and perform under various conditions. Coupled with Performance Tuning, which involves optimizing code and infrastructure, observability helps in achieving desired performance metrics.

The RAIL Model, which stands for Response, Animation, Idle, and Load, offers a framework for understanding user experience and guiding performance improvements. ASIC Monitoring, or Application-Specific Integrated Circuit monitoring, also plays a role in tracking performance at a hardware level. Tools like Tracing with Jaeger facilitate tracing by offering deep insights into distributed systems. For Go Application Performance Monitoring, specialized techniques ensure that these applications operate efficiently.

The Shift from Performance Testing to Performance Engineering reflects a broader focus on continuous improvement and proactive problem resolution. Performance Profiling, which analyzes various aspects of application performance, supports this shift by identifying bottlenecks and optimization opportunities. Together, these practices form the foundation of Performance Engineering, a discipline dedicated to creating systems that are not only functional but also high-performing and resilient

What is Observability?

Observability is a way to get insights into the whole infrastructure. It is essential for the operations team. It means assembling all fragments from logs and monitoring tools and organizing them in such a way that gives actionable knowledge of the whole environment, thus creating insight. It is a combination of multiple items to create a deep understanding of the actual health, real issues, and what should be done to improve the environment and troubleshoot at a root level.

A tool that enables end users, administrators and organizations to gauge and evaluate the performance of a given system. Click to explore about, Performance Monitoring Tools

It means the service can explain any questions about what is happening on the inside of the system just by observing the outside of the system without entering a new code to answer further questions. Software is enhancing exponentially extra complexity. It is a term from the control method. It is a test of how well the inner states of a system can be assumed by knowledge of its external outputs. The Observability and controllability of a system are numerical duals. It might mean various points to various characters. For unusual, it's about logs, metrics, and traces. To others, it's the first wine of monitoring in a fresh container.

Creating and operating an extra-visible system
A system that can explain itself without the need to deploy new code
Understanding connections between parts of your background or System
No complex system is always healthy.
Categorized systems are pathologically unpredictable.

What is Monitoring?

Monitoring is the routine inspection and reporting of activities taking area in a project or program. It is a method of routinely collecting information on all phases of the project. Monitoring tools examine the infrastructure log metrics to perform actions and insights. The monitor is to check on how project activities are improving.

Features of monitoring tools are:

To recognize the problems and send an alert message to the dashboard
To log real-time and historical data
Monitor the figure of users on a network

Troubleshooting/ Debugging and root cause analysis

Goal address threats to customer satisfaction
Debug novel problems in production
There needs to be data to explore
A quantitative examination can help you obtain the business case to address an issue.
Retrospectives instil confidence that issues won't happen again

A Platform for Monitoring, Logging, Tracing and Visualization of Big Data Cluster and Kubernetes with ML and Deep learning. Click to explore, Observability for Kubernetes

Importance of Observability

Feedback is essential for running the Continuous integration (CI) and continuous delivery (CD) processes. It doesn't make sense to push out changes without acknowledging whether they continually improve or worsen things. The "Monitor" Part of the DevOps circuit provides the all-important feedback that drives future repetitions.

Site Reliability Engineering Approach

Site reliability engineering (SRE) is Google's way of service management, where software engineers work production systems using a software engineering approach. It's explicit that Google is different, and they usually need to catch Software bugs and errors in various and non-conventional ways.

Site Reliability engineering reliable operating system and infrastructure scale.
Define metrics that matter most to the business, typical values for those metrics, and plan a reaction if the value isn't met.
Service level indicator, service level objectives, service level agreement
RED: The acronym stands for Rate, Errors, and Duration. These are request-scoped, not resource-scoped, as the USE method is. Duration is explicitly taken to mean distributions, not averages.
The increase means time to failure (MTTF), and the decrease means time to repair (MTTR)

Service level objective (SLO)

There is usually difficulty in using Service Level Agreement (SLA) and Service Level Objective (SLO). Certain SLOs (Service Level Objectives) usually are meant to define the precise, measurable targets that a service must meet to satisfy the expectations outlined in the SLA (Service Level Agreement). These objectives provide specific criteria for evaluating service performance, such as availability, throughput, repetition, response time, or quality. By establishing clear SLOs, organizations can effectively measure and manage service levels, ensuring that they adhere to agreed-upon standards and maintain a high level of service quality. the demanded service between the provider and the client and vary depending on the service's needs, resources, and budget.

NO SLO < Good SLO < Perfect SLO
Pick an objective and iterate
Capture a set of events and use a window and target percentage of 99.9% of events that were good in the last 30 days.
A good SLO is barely user-happy.
Determine an error budget that allowance of failure in the tradeoff is not allowing for progress and innovation.

What are the Observability Pillars?

What is Logging?

Logs: A record of an event that took place at a given time

Support by most libraries
Disciplined to put meaning logs into your code
Aggregate logs to avoid overshooting them
Java logging classes and a logging properties configuration file writing to STDOUT.
Flaunted used to scrape, process, and ship logs
Stored in a persistent data store, such as an electric search, a distributed analytics engine
Queried directly or interacted with using Kibana, a customizable visualization dashboard
Choose a tool to capture and analyze logs.
Plain logs report force is a free-form text. This is also the usual standard format of logs.
Structured logs have been much converted and advocated for in recent days. Typically, these logs are issued in JSON format.
Think logs in the Protobuf form, MySQL binlogs applied for replication and point-in-time improvement, system journal logs, the pflog format accepted by the BSD firewall pf that often serves as a frontend to tcpdump. What are Metrics?

Numeric aggregation of data describing the behavior of a component or service measured over time

Accessible to store and model
Beneficial to understand standard system performance
Supported by most libraries
JAVA matrices classes that push data to a metrics endpoint

What is Tracing?

Tracing is used to capture a request flow of a causally related event.

Each has requested a global ID- metadata inserted at each step in the flow as the ID is passed along.
Distributed tracing System like Jaeger or Zipkin is used to visualize and expect traces.
Open Telemetry:- A language-neutral approach to tracing
Forks in performance flow like OS thread
A fan-out over channels or process boundaries

Service Mesh

A service mesh is a process of managing how various elements of an application share data with each other.

The configurable infrastructure layer for the Microservice application is used to control east-west service traffic.
Monitor and control the progress of transactions through your cluster.
Sidecar pattern or node agents/ daemon set pattern.
Logs and metrics are gathered for free reduced tracing.

Integration with open-source observability tools like Garfana, Prometheus, jaeger, and Kaili- prepopulated with the dashboard

A non-functional type of testing that measures the performance of an application or software under a certain workload. Click to explore, Performance Testing Tools

Importance of Observability

Following are the reasons why it matters

Enables transparency across applications deployed in the environment.
Observability helps document the production environment and get the information needed to improve it.
It helps to understand what’s going on behind the scenes.
It allows for catching unknown issues quickly and helps understand how to handle them.
Detecting issues without it in place is hard.
Allows feedback loops essential in DevOps movement.
Enabling it inside the environment is very important.
It helps developers and DevOps guys find insights and bottlenecks in applications and trace information.
Its importance increases in real production environments to prevent downtime. Proper alerting should be in place.

How does Observability work?

To attain the ultimate state of observability, consider the following -

Logging Process

Logging is a mechanism to collect logs from various input sources. Usually, logs are in raw format. To gain real insights, parse these logs and apply queries to gain insights quicker. Usually, logs are sent to an output tool that organizes the logs. Logging defines what to log, how it should be logged, and how logs are shipped to an external system for aggregating. Debug mode is disabled for better logging as logging everything based on a debug level will become really expensive to manage, creating extra false positives, not-so-important alarms, and more difficulty in getting important data. Debug should be the default only while troubleshooting, not in real production environments

Monitoring Process

Monitoring is an activity performed by DevOps guys. It’s simply observing the state of an infrastructure/environment over a period of time. Monitor for three reasons:

Detecting Problems— usually by alerts or looking at issues on dashboards
Finding Resolution for Problem— finding root causes of issues and troubleshooting
Continuous Improvement— reporting and documenting

Tracing Working

Trace the calls between various applications. Priorities are defined as different service failures, and the one with the highest priority is caught and alerted immediately. Tracing shows what either happened in the past or what is happening at present. Tracing is a very important piece of it proactively. It also suggests what code we can add to the service to provide better insights into the application. There should be transparent visibility end to end for all transactions happening in the environment.

How Alerting Works?

Alerting helps define how to notify the Ops guys when an event occurs. False positives it is very important to remove them. There should be -

Alert only to important events
Self-healing infrastructure
Enable analytics when something has been done manually many times
Enable Automation to fix the problems

Monitoring of applications is the biggest aspect, As the speed and the accuracy expected is very high to address the issue on time. Click to explore about, Application Performance Management in DevOps

Benefits of Observability

Observability helps to understand what’s going on in production, improves the work of end users, and eliminates the need for debugging in a Production environment.
Monitors the performance of applications.
Helps in identifying the root causes of issues and helps in troubleshooting.
There are many intuitive dashboards to observe what is happening in real-time.
Allows Self-healing infrastructure.
It enriches the data and provides information faster. Encouraging developers to use tracing helps them see how their daily work maintains the application and improves its infrastructure.

Best Practices of Observability

Do not Monitor everything.
Monitor only things that are essential to fix when they fail.
Do not put Alerts on everything.
Put alerting only for critical events.
Do not store all logs and all data.
Store only logs that give insights about critical events.
Don't use default graphs.
Create a custom graph according to customer needs.
Create alerts based on Prometheus or Grafana metrics of running applications in an environment.

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List of Top Observability Tools

Logging
Fluentd
Logstash

Various Monitoring Tools

Prometheus
Grafana

Deployment tools

Containers and Orchestration tools such as Docker and Kubernetes.
Log Aggregator
AWS Cloudwatch

Alerting Tools

Slack
Pagerduty

Conclusion

Observability enables infrastructure. At first, applying this requires a slight change of mindset and new tools, but once proper Logging, Monitoring, and Alerting are in place, it will be beneficial on a daily basis and in the long term. Plan to deliver applications in modern ways, spend more time debugging, and add operational visibility tools to optimize the speed of troubleshooting and ultimately automate them.