Peeking Into Patterns with Kibana "Sherlock"
- Kibana magnifies the tiny trails left by users, revealing paths through data forests. It's like Sherlock with a software license.
Site Reliability Engineer (SRE) with Kibana Salary in 2024
How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Site Reliability Engineer (SRE) developer with Kibana with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.
The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.
Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.
Trending Site Reliability Engineer (SRE) tech & tools in 2024
Site Reliability Engineer (SRE)
What is site reliability engineering?
At its core, SRE is a software-enabled practice that automates many tasks within IT infrastructure, such as system management and application monitoring. Organisations use SRE to maintain the reliability of software applications in the face of frequent updates coming from development teams. SRE improves the reliability of scalable software systems in which management (including updating and monitoring) of a large system with software would offer more sustainability than manually monitoring hundreds of machines.
Why is site reliability engineering important?
Site reliability refers to the stability and quality of service the application can provide once it is at the disposal of end users. Every so often, software maintenance either directly or indirectly impacts software reliability when, for example, a developer makes some changes impacting certain use cases to cause the application to crash.
The following are some benefits of site reliability engineering (SRE) practices:
- Improved collaboration: SRE helps the development and operations teams work together more effectively. When newly arrived code is released by the development team to the production environment so as to deliver new features or resolve urgent defects, it is often necessary for a developer to make a sudden change to an application. Meanwhile, the operations team needs to make sure that the resulting service is delivered correctly. Consequently, they must stand ready to immediately realise when changes cause issues. SRE practices allow the operations team to provide close monitoring of every update and respond quickly to the inevitable problems that changes engender.
- Enhanced customer experience: Based on an SRE model, organisations are able to put in place barriers that ensure mistakes with the software will never impede the experience of a customer. Take the software team that uses tools designed and developed by SRE teams to automate the entire software development lifecycle. Doing so enables the team to reduce the rate of mistakes, meaning less feature development time spent fixing so-called bugs.
- Improved operations planning: SRE teams recognise that failures in software are a real probability, so they plan for the right incident management strategy to limit the impact of the outage on the business and the end user. They are also able to more accurately assess the cost of the downtime and understand the nature of the business impact of the failure.
What are the key principles in site reliability engineering?
The following are some key principles of site reliability engineering (SRE):
Application monitoring
SRE teams recognise that errors are an inevitable part of deploying software. Rather than seeking a perfect solution, they monitor it based on service-level agreements (SLAs), service-level indicators (SLIs), and service-level objectives (SLOs). They can even monitor performance metrics while the application is continually deployed to production environments.
Gradual change implementation
SRE best practices call for the release of frequent but small batches that support resilience. SRE automation engines use regular but repeatable processes to perform the following:
- Reduce risks due to changes
- Provide feedback loops to measure system performance
- Increase speed and efficiency of change implementation
Automation for reliability improvement
SRE employs policies and processes that bake reliability into every step of the delivery pipeline. Some automatic problem-resolution strategies include:
- Developing quality gates based on service-level objectives to detect issues earlier
- Automating build testing using service-level indicators
- Making architectural decisions that ensure system resiliency at the outset of software development
What is observability in site reliability engineering?
It’s a journey that gets the software team ready for uncertainty once the software is live with end users – in the form of SRE tools that can detect anomalous software behaviours from early warning indicators, and (much more importantly) collecting data from the observed processes that would allow the developers to understand and diagnose the root cause of the problem. Here’s what that journey entails, as far as the collection of information goes:
- Metrics: A metric is a quantifiable value that reflects how an application is performing or the status of a system. SRE teams look at metrics to evaluate whether their software is slurping up their memory or acting up.
- Logs: SRE software creates highly detailed, timestamped event logs in reaction to specific events. When software engineers dig through such event logs for patterns, they’re often asked to trace the fault through itself.
- Traces: Traces are messages that log the specific code path to complete a function in a distributed system. Checking out an order cart, for instance, might involve all of the following:
- Tallying the price with the database
- Authenticating with the payment gateway
- Submitting the orders to vendors
They consist of ID, name, and time; used by programmers to spot latency problems and make applications perform smoother.
What is monitoring in site reliability engineering?
Monitoring is the act of looking for pre-defined metrics in a system. Development staff agree on a content set of parameters to monitor – the parameters they believe are most helpful at assessing the health or status of the application. Then they configure the monitoring tools to track whether those parameters deviate by a significant margin. SRE operations staff track those key performance indicators (KPIs) and report that information in a graph.
In SRE, software teams monitor these metrics to gain insight into system reliability:
- Latency: Latency is the period of time that elapses between when an application receives a request and provides a reply. For instance, a webform submission might take three seconds before it completes and redirects the user to an acknowledgment webpage.
- Traffic: For example, traffic is the number of users actively using your service. Software teams use traffic to budget computing resources so that your service level – your users’ ability to use your service in a timely, error-free fashion – is consistent for everyone.
- Errors: A failure, in turn, is characterised by an error: a condition in which an application doesn’t do what it’s supposed to do, or succeed in producing what is requested of it. A web page that refuses to load, a bid that fails to go through, the loss of some data – these are all examples of errors that SRE teams track and address automatically with special software.
- Saturation: Saturation describes how much the application is busy at any given point in time. Saturation typically reduces the performance of an application. Site reliability engineers monitor saturation and make sure it is below a certain threshold.
What are the key metrics for site reliability engineering?
SRE teams use the following metrics for measuring quality of service delivery and reliability:
- Service-level objectives: These objectives are referred to as service-level objectives (SLOs). The SLOs you establish are specific and quantifiable, and you have confidence that the software can deliver them at a reasonable cost to the other metrics, such as those below:
- Uptime, or the time a system is in operation
- System throughput
- System output
- Download rate, or the speed at which the application loads
An SLO promises actual delivery to the customer: for instance, the food delivery app launched by your company has an SLO of 99.95 per cent uptime.
- Service-level indicators: Service-level indicators (SLIs) are the actual measurements of the metric that an SLO defines. In the real world, you could get either exactly the same amount of the ‘up’ calls as the SLO demands or you might well get less than that, like in the case where your application was up 99.92 per cent of the time, short of the agreed-upon SLO.
- Service-level agreements: The specifics of these service-level agreements (SLAs) are spelled out in the legal clauses of what should happen when one or more of your service-level objectives (SLOs) aren’t being met. The SLA might say, ‘If your technical team doesn’t solve your customer’s problem within 24 hours of it being reported, then we have to refund your customer their money.’ That might mean that your SLOs should suggest a target for that area of your work, and your team should have to pay up if they don’t hit the objective.
- Error budgets: An error budget describes how much an SLO can be violated. For example, if the SLO specifies an uptime of 99.95 per cent, the corresponding error budget is 0.05 per cent. If the error budget for software downtime is exhausted, then the software team puts all their resources and attention into bringing the application back to a stable state by relieving the source of the problem.
How does site reliability engineering work
SRE holds that you should have site reliability engineers in a software team. The SRE team specify your key metrics, and establish what’s called an error budget; that is, what the system is willing to allow in terms of error. If your error budget, which represents your ability to make mistakes and survive, is low, then the development team is free to roll out more features. But if errors exceed the error budget, you pack up and put new changes on hold. You find and eliminate the problems you already have.
For example, a site reliability engineer (SRE) uses a service to monitor performance statistics and to look out for unusual behaviour from the application. If something is wrong, the SRE team submits a report to the software engineering team. Developers fix reported problems and release the new application.
DevOps
DevOps is a kind of software culture that overcomes the traditional boundary between the development (Dev) and the operation (Ops) teams. We can no longer find the development team and the operation engineer as a pair of barrels or brothers. They originally develop software, deploy and maintain software with systematic software tools, and follow the trend of business model update in software update release frequency and speed.
SRE compared to DevOps
SRE is operationalising DevOps. DevOps provides the philosophical basis of what needs to happen to continue to establish the necessary level of software quality against a backdrop of an ever-decreasing time-to-market window. Site reliability engineering provides the answers to what needs to happen to have DevOps succeed. SRE assures that the DevOps team delivers on DevOps the right way (speed-of-release balanced against stability of the code base).
What are the responsibilities of a site reliability engineer?
Site reliability engineer is a system expert of IT through the method of software reliability monitoring and observing and intervention in the production environment. When software problems arise, they use automation tools to quickly identify and solve them. Former system administrator or operation engineer with good codibility would be excellent at one of these jobs. The following is the role of site reliability engineer:
Operations
In addition to designing, site reliability engineers spend up to 50 per cent of their time doing ‘ops work’, which involves:
- Emergency incident response
- Change management
- IT infrastructure management
The engineers use SRE tools to automate several operations tasks and increase team efficiency.
System support
SREs interact with the development team to build features and stabilise production systems; SREs define an SRE process, collaborate with development to build new features and stabilise their production systems, and are on call where engineers are forced to make field changes; SREs write procedures so that customer support can run the production service; SREs build runbooks to assist customer support agents in responding to valid complaints.
Process improvement
Site reliability engineers enhance the software development cycle via after-action post-incident reviews. The SRE team maintains a shared knowledge base detailing software incidents, along with their respective solutions, which will be a useful asset when the software team has to deal with similar issues in the future.
What are the common site reliability engineering tools?
SRE teams use various classes of tools to support monitoring, observation and incident response:
- Container orchestrator: A container orchestrator is used by software engineers to run containerized applications on different computing platforms. The source code files and resources required by an application are bundled within a self-contained package known as a container. For instance, Amazon Elastic Kubernetes Service (Amazon EKS) is used by software developers to run and scale cloud applications.
- On-call management tools: An on-call management tool is a type of software that SRE teams use to schedule, organise, and manage support staff who have the ability to respond to reports of software issues SRE teams might use the software to ensure that support staff are on standby to receive notifications of software anomalies.
- Incident response tools: An incident is something that needs to be addressed through an incident response tool that provides an escalation pathway for reported software issues. An incident response tool provides different severity levels for reported issues, and allows the SRE team to take appropriate courses of action, as well as provide post-incident analysis report to prevent incidents of similar type.
- Configuration management tools: Configuration management tools are software that automates the workflow of software; SRE teams use tools to eliminate the repetitive tasks in order to be more productive. For example, site reliability engineers use AWS OpsWorks to automate the provisioning and management of servers in AWS environments.
Where is Kibana used?
Kibana Utilization Cases
Taming the Elastic Stack Jungle
- As a lion commands the savannah, Kibana tames wild Elastic Stack data. Watch it roar through logs and metrics!
Visualizing Victory
- It turns dreary data into vibrant visualizations, crafting charts more colorful than a painter's palette in spring.
The Dashboard DJ
- Blasting beats of business intelligence, Kibana mixes real-time dashboards like a DJ spinning vinyl at the data disco.
Kibana Alternatives
Grafana
Open-source analytics and monitoring solution often used for time-series data. Integrates with various data sources such as Graphite, Prometheus, and InfluxDB.
// Example of setting up a Grafana dashboard
dashboards:
- name: 'Production Overview'
org_id: 1
folder: 'Production'
type: 'file'
options:
path: /var/lib/grafana/dashboards/production.json
- Rich visualization options.
- Multi-data source support.
- Complex alerting system.
- Can be complex to set up.
- Steep learning curve for querying.
- Limited ad hoc reporting.
Splunk
Software for searching, monitoring, and analyzing machine-generated big data, via a web-style interface. Primarily used for log and events management.
// Example of a Splunk search query
index=main error 5* | stats count by host
- Powerful search language.
- Comprehensive data processing.
- Built-in user management.
- High cost for extensive log data.
- Complex setup and maintenance.
- Potential performance issues at scale.
Elasticsearch + Logstash + Beats (ELK Stack)
A suite of tools: Elasticsearch search and analytics engine, Logstash data processing pipeline, and Beats lightweight shippers for data.
// Filebeat configuration example to ship logs
filebeat.inputs:
- type: log
enabled: true
paths:
- /var/log/*.log
output.logstash:
hosts: ["localhost:5044"]
- Interoperable stack components.
- Real-time data processing.
- Scalable search capabilities.
- Requires coordination of multiple tools.
- Resource-intensive architecture.
- Steep learning curve for optimizing.
Quick Facts about Kibana
Birth of a Dashboard Dynamo: Kibana
Way back in 2013, when people still occasionally got lost using paper maps, Rashid Khan decided life could be easier and thus, Kibana was born. Initially a mere sidekick to Elasticsearch, Kibana quickly grew to become the visual heartbeat of the Elastic Stack, letting users create graphs easier than a toddler with a crayon.
The Version Evolution: Kibana's Milestones
Imagine going from drawing stick figures to painting the Mona Lisa. That's a bit like Kibana's journey from its version 1.0 release to its current state. Major milestones include 4.x introducing Dashboard-only mode, making everything a lot neater, and version 6.x, where it integrated with X-Pack, putting on its superhero cape with security and monitoring features.
Timeslicing through Data: Time Lion
Once upon a timeline, in 2017, Kibana introduced Time Lion, a flexible and robust tool for time series data—an innovation as exciting as finding out your coffee has the power to reheat itself every morning. Users could slice, dice, and visualize data over time without breaking a sweat. It was like giving data analysts a time machine, but with charts.
// Sample Kibana Timelion expression to calculate the moving average:
.es(index="your-data-*", metric="avg:price").movingaverage(window=10)
What is the difference between Junior, Middle, Senior and Expert Kibana developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities | Quality-wise |
|---|---|---|---|---|
| Junior | 0-2 | 50,000 - 70,000 |
| Close monitoring needed, may require revisions |
| Middle | 2-5 | 70,000 - 90,000 |
| Moderate supervision, understands best practices |
| Senior | 5-10 | 90,000 - 130,000 |
| High-quality self-sufficient work, minimal oversight |
| Expert/Team Lead | 10+ | 130,000 - 160,000+ |
| Exceptional quality, strategic thinker, leadership capability |
Top 10 Kibana Related Tech
Elasticsearch Query DSL
Picture yourself whispering sweet nothings into the ear of a search engine, and it gets you exactly what you want – that's the beauty of Elasticsearch's Query Domain Specific Language. It's the bread and butter for data retrieval in Kibana. Sure, you can start with a simple "Hello, World!" but soon you'll be scripting poetic Boolean queries like a data bard. Feel the power, embrace the syntax!{
"query": {
"match": {
"message": "Search me, maybe?"
}
}
}JavaScript and Node.js
The dynamic duo for Kibana plugin development! JavaScript is like the Swiss Army knife in the coder's toolkit – good for both quick fixes and crafting masterpieces. Combine it with Node.js, and you've got the environment to build the server-side of plugins that’ll make Kibana dance to your tune. So, grab your coding cape; it's time to go full superhero mode.const Kibana = require('kibana');
Kibana.server.plugins.create({
name: 'dancePlugin',
init(server, options) {
console.log('Let\'s make Kibana boogie!');
}
});React
The magic paintbrush for your Kibana plugins! React is the go-to for crafting gorgeous user interfaces that users want to bring home to meet their parents. Its component-based architecture is like LEGO for adults, letting you build snazzy features that feel as good as popping bubble wrap.import React from 'react';
const SuperButton = () => (
);
export default SuperButton;RESTful API Integration
The RESTaurant where Kibana likes to dine! RESTful APIs serve up the delicious data dishes Kibana craves. Knowing how to talk REST is like being fluent in 'data-deliciousness', enabling Kibana to digest information from various back-ends, just like a gastronaut exploring the universe of flavors!GET /api/delicious_data/nom_nom
{
"query": {
"match_all": {}
}
}Elastic Stack (ELK)
ELK is not just a majestic animal; it's a trinity of Elasticsearch, Logstash, and Kibana! Working with the Elastic Stack is like being part of a superhero team where everyone has cool data powers. Each plays a unique role in gathering (Logstash), storing (Elasticsearch), and visualizing (Kibana) the data. It's the Avengers of search & analytics!Logstash for Data Ingestion
Logstash is like a data-processing chef that preps your ingredients (logs, streams, you name it) to perfection before Kibana serves them up. Get the seasoning right with filters, and watch as your data comes to life, ready to be devoured by hungry analysts!input {
file {
path => "/var/log/apache2/access.log"
}
}
filter {
grok {
match => { "message" => "%{COMBINEDAPACHELOG}" }
}
}
output {
elasticsearch {
hosts => ["localhost:9200"]
index => "access_logs"
}
}Linux/Unix Environment
Ah, the old-school charm of Linux/Unix – it's the sturdy workbench where all Kibana crafting happens. Knowing your way around this environment is like having a black belt in data dojo; you can navigate, manipulate, and orchestrate the components needed to make Kibana hum.Git and Version Control
Forget "Ctrl+Z," Git is your time machine in the codingverse! This tool is like a safety net for when you walk the tightrope of code changes. It's the difference between "Oops, I did it again!" and "Aha! Just as I planned!” for managing your Kibana work wonders.Containerization with Docker
Docker – it's like Tupperware for your applications, only cooler and less about last night's leftovers. Containerizing with Docker keeps your Kibana setup as portable and consistent as a hipster's vinyl collection. Spin up a Kibana instance like a DJ drops a beat!docker run --name my-kibana -e ELASTICSEARCH_HOSTS=http://my-elasticsearch:9200 -p 5601:5601 -d kibana:7.12.0
Visualization Techniques
The art and science behind those dashboards! Knowing how to present your data with Kibana’s visualization tools is like being a data artist and scientist rolled into one. It’s not just about pretty charts; it's turning numbers into stories that captivate and inform.