Observability is a way to get insights into the whole infrastructure. It is essential for the operations team . Observability means assembling all fragments from logs, monitoring tools and organize them in such a way which gives actionable knowledge of the whole environment, thus creating an insight. It is 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 troubleshooting 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
Observability means service can explain any questions about what happening on the inside of the system just by observing the outside of the system, without entering new code to answer further questions. Software is enhancing exponentially extra complex. Observability 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 be 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 themselves 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 problem in production
There need to be data to explore
Quantitative examine can help you obtain the business case to address an issue
Retrospectives instil confidence that issues won't happen again
An Observability Platform for Monitoring, Logging, Tracing and Visualization of Big Data Cluster and Kubernetes with ML and Deep learning. Click to explore about, Observability for Kubernetes
Why is Application Observability important?
To run the Continuous integration (CI) and continuous delivery (CD) process entirely, there must be any kind of feedback. It doesn't make any sense to push out changes without acknowledging if continually they make things better or worse. The "Monitor" Part of the DevOps circuit gives 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 error 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 planned 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 decreases mean time to repair (MTTR)
Service level objective (SLO)
There is usually difficulty in the use of Service Level Agreement (SLA) and Service level objective (SLO). The SLA is a complete understanding of what service is to be given, how it is maintained, times, locations, costs, performance, and responsibilities of the people involved. SLOs are specific measured properties of the SLA such as availability, throughput, repetition, response time, or quality. Certain SLOs usually are meant to define 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 event and use a window and target percentage 99.9% of an event good in 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 Pillars of Observability?
What is Logging?
Log's: A records 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 analyze logs
Plain logs report force is a free-form text. This is also the usual standard format of logs.
Structured logs are much converted and advocated for in recent days. Typically, these logs are issued in the 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 is Metrics?
Numeric aggregation of data describing the behaviour 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 +inspect traces.
Open Telemetry:- A language- Neutral approach to tracing
Forks in performance flow like OS thread
A fan-out over channels or process boundaries
A service mesh is a process to manage how various elements of an application share data with each other.
The configurable infrastructure layer for Microservice application 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 gathering 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 about, Performance Testing Tools
Why Observability Matters?
Following are the reasons why Observability matters
Enables transparency across application deployed in the environment.
It helps in documenting the production environment and get the information needed to improve it.
It helps to understand what’s going on behind the scenes.
It allows catching unknown issues in time and helps understand how to handle them.
Detecting issues without observability in place is hard.
Allows feedback loops essential in DevOps movement.
Enabling Observability inside environment is very important.
It helps both Developers and DevOps guys to find insights, bottlenecks in applications, tracing information.
Observability 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 is a mechanism to collect logs from various input sources. Usually, logs are in raw format. To gain real insights, parse these logs, apply query-type to gain insights quicker. Usually, logs sent to an output tool which organizes the logs. Logging defines what to log and how it should be logged and how logs shipped to an external system for aggregating. Debug mode disabled for better logging as logging everything based on a debug level will become really expensive to manage, create 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 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 Problem – 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.
Trace the calls between various applications. Priorities defined to different failures of service and the one with the highest priority 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 Observability 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 in defining how to notify and the Ops guys notified when an event occurs. False positives, it is very important to remove them. There should be -
Alert only important events.
Enable analytics when something has been done manually many times.
Helps to understand what’s going on in production and make it work better for end users.
It 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 high to observe what is happening in Real-Time.
Allows Self-healing infrastructure.
Observability helps in enriching the data and provides information faster. Encouraging developers to use tracing helps them to see how what they’re doing on an everyday basis maintain Observability and improve overall application and its' infrastructure.
How to adopt Observability?
Observability is essential to discipline in DevOps. To Adopt Observability, Along with logs ingestion, Dev should add code to the applications to be able to provide more additional and insightful data to enable alerts. Devops run automated end-to-end tests on a daily basis, before and after deploying applications, to understand if deployment broke anything or not. Observability should be the responsibility of both Dev and Ops guys. Developer provides appropriate logging data, which makes sense and useful to get insights, proper error codes for requests. It reduces outages time while Ops teams troubleshoot. Both Dev and Ops guys add values to Observability to achieve full success.
What are the best practices of Observability?
Following Best Practices followed while allowing Observability for applications running in the environment -
Do not Monitor everything.
Monitor only things which 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 which 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.
Observability enables infrastructure. At first, applying this is little change of mindset, adding new tools in place but once proper Logging, Monitoring, Alerting is in place, it will be beneficial on a daily basis as well as in the long term. Plan to deliver applications in modern ways, spend more time to debug and adding operational visibility tools to optimize the speed of troubleshooting and ultimately automating them.