Monitoring Your Machine(s) With TIG

TIG = Telegraf, InfluxDB, Grafana. Audience: developers, nerds, devops beginners…

Windows 10 machine while running a game

Monitor Your Own Host Within 15 Minutes

(This story/howto/tutorial assumes you have basic knowledge of linux, the command line and how computers work)

TIG

You may have heard of the “TICK” stack: Telegraf, InfluxDB, Chronograf, Kapacitor. I heavily use Telegraf and InfluxDB, but as visualization frontend, Grafana has become the standard in the environments I work in. Kapacitor can still be used alongside for specific purposes. For this article it’s not needed.

Quite a few years ago — out of curiosity and desire to learn and grow — I started playing with Telegraf and InfluxDB. I got hold of a few older machines (dual cores) on which I installed linux for my two kids. And since I wanted to know more about monitoring, I decided to monitor them (the linux machines ofc!) with Telegraf (a small data collection tool written in golang).

So before we can monitor anything we need to setup the target database and the frontend to see something.

Setup InfluxDB And Grafana

Since we want to focus more on gathering data, we’ll do this real quick.

Take the below docker-compose.yml file and launch it on a host of your preference. For playing around with it, I recommend launching it on your local machine. If you have a dedicated or virtual linux box, that will do fine too.

macOS Prerequisites:

• install homebrew : https://brew.sh/
• install docker : brew cask install docker

Linux Prerequisites:

• install docker : https://docs.docker.com/get-docker/

Windows Prerequisites:

• install docker : https://docs.docker.com/get-docker/

Docker Compose

Download the following and save it as docker-compose.yml file in a new directory.

## using version 2 because of https://docs.docker.com/compose/compose-file/#resources
	version: "2.4"
	services:
	  telegraf:
	    image: drpsychick/telegraf
	    restart: always
	    environment:
	      HOST_PROC: /rootfs/proc
	      HOST_SYS: /rootfs/sys
	      HOST_ETC: /rootfs/etc
	      TEL_AGENT_HOSTNAME: hostname = "myhostname"
	      TEL_OUTPUTS_INFLUXDB_0: "[[outputs.influxdb]]"
	      TEL_OUTPUTS_INFLUXDB_URLS: urls = ["http://localhost:8086"]
	      TEL_INPUTS_KERNEL_0: "[[inputs.kernel]]"
	      TEL_INPUTS_MEM_0: "[[inputs.mem]]"
	      TEL_INPUTS_SWAP_0: "[[inputs.swap]]"
	      TEL_INPUTS_PROCESSES_0: "[[inputs.processes]]"
	      TEL_INPUTS_SYSTEM_0: "[[inputs.system]]"
	      TEL_INPUTS_DISK_0: "[[inputs.disk]]"
	      TEL_INPUTS_DISKIO_0: "[[inputs.diskio]]"
	      TEL_INPUTS_NET_0: "[[inputs.net]]"
	      TEL_INPUTS_NETSTAT_0: "[[inputs.netstat]]"
	    volumes:
	      - "/proc:/rootfs/proc:ro"
	      - "/sys:/rootfs/sys:ro"
	      - "/etc:/rootfs/etc:ro"
	      - "/var/run/docker.sock:/var/run/docker.sock:ro"
	    cpu_percent: 1
	    mem_limit: 50m
	    network_mode: "host"
	  influxdb:
	    image: drpsychick/influxdb
	    restart: always
	    environment:
	      IFX_GLOBAL: reporting-disabled = true
	    ports:
	      - 8086:8086
	      - 8089:8089/udp
	    volumes:
	      - "influxdb:/var/lib/influxdb"
	    cpu_percent: 50
	    mem_limit: 1g
	    networks:
	      - monitoring
	  grafana:
	    image: grafana/grafana
	    restart: always
	    #environment:
	      #GF_INSTALL_PLUGINS: ## plugins to install
	    ports:
	      - 3000:3000
	    volumes:
	      - "grafana:/var/lib/grafana"
	    cpu_percent: 1
	    mem_limit: 200m
	    networks:
	      - monitoring

	networks:
	  monitoring:

	volumes:
	  influxdb:
	  grafana:

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What is GEEK

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How to Monitor Third Party API Integrations

Many enterprises and SaaS companies depend on a variety of external API integrations in order to build an awesome customer experience. Some integrations may outsource certain business functionality such as handling payments or search to companies like Stripe and Algolia. You may have integrated other partners which expand the functionality of your product offering, For example, if you want to add real-time alerts to an analytics tool, you might want to integrate the PagerDuty and Slack APIs into your application.

If you’re like most companies though, you’ll soon realize you’re integrating hundreds of different vendors and partners into your app. Any one of them could have performance or functional issues impacting your customer experience. Worst yet, the reliability of an integration may be less visible than your own APIs and backend. If the login functionality is broken, you’ll have many customers complaining they cannot log into your website. However, if your Slack integration is broken, only the customers who added Slack to their account will be impacted. On top of that, since the integration is asynchronous, your customers may not realize the integration is broken until after a few days when they haven’t received any alerts for some time.

How do you ensure your API integrations are reliable and high performing? After all, if you’re selling a feature real-time alerting, you’re alerts better well be real-time and have at least once guaranteed delivery. Dropping alerts because your Slack or PagerDuty integration is unacceptable from a customer experience perspective.

What to monitor

Latency

Specific API integrations that have an exceedingly high latency could be a signal that your integration is about to fail. Maybe your pagination scheme is incorrect or the vendor has not indexed your data in the best way for you to efficiently query.

Latency best practices

Average latency only tells you half the story. An API that consistently takes one second to complete is usually better than an API with high variance. For example if an API only takes 30 milliseconds on average, but 1 out of 10 API calls take up to five seconds, then you have high variance in your customer experience. This is makes it much harder to track down bugs and harder to handle in your customer experience. This is why 90th percentile and 95th percentiles are important to look at.

Reliability

Reliability is a key metric to monitor especially since your integrating APIs that you don’t have control over. What percent of API calls are failing? In order to track reliability, you should have a rigid definition on what constitutes a failure.

Reliability best practices

While any API call that has a response status code in the 4xx or 5xx family may be considered an error, you might have specific business cases where the API appears to successfully complete yet the API call should still be considered a failure. For example, a data API integration that returns no matches or no content consistently could be considered failing even though the status code is always 200 OK. Another API could be returning bogus or incomplete data. Data validation is critical for measuring where the data returned is correct and up to date.

Not every API provider and integration partner follows suggested status code mapping

Availability

While reliability is specific to errors and functional correctness, availability and uptime is a pure infrastructure metric that measures how often a service has an outage, even if temporary. Availability is usually measured as a percentage of uptime per year or number of 9’s.

AVAILABILITY %DOWNTIME PER YEARDOWNTIME PER MONTHDOWNTIME PER WEEKDOWNTIME PER DAY90% (“one nine”)36.53 days73.05 hours16.80 hours2.40 hours99% (“two nines”)3.65 days7.31 hours1.68 hours14.40 minutes99.9% (“three nines”)8.77 hours43.83 minutes10.08 minutes1.44 minutes99.99% (“four nines”)52.60 minutes4.38 minutes1.01 minutes8.64 seconds99.999% (“five nines”)5.26 minutes26.30 seconds6.05 seconds864.00 milliseconds99.9999% (“six nines”)31.56 seconds2.63 seconds604.80 milliseconds86.40 milliseconds99.99999% (“seven nines”)3.16 seconds262.98 milliseconds60.48 milliseconds8.64 milliseconds99.999999% (“eight nines”)315.58 milliseconds26.30 milliseconds6.05 milliseconds864.00 microseconds99.9999999% (“nine nines”)31.56 milliseconds2.63 milliseconds604.80 microseconds86.40 microseconds

Usage

Many API providers are priced on API usage. Even if the API is free, they most likely have some sort of rate limiting implemented on the API to ensure bad actors are not starving out good clients. This means tracking your API usage with each integration partner is critical to understand when your current usage is close to the plan limits or their rate limits.

Usage best practices

It’s recommended to tie usage back to your end-users even if the API integration is quite downstream from your customer experience. This enables measuring the direct ROI of specific integrations and finding trends. For example, let’s say your product is a CRM, and you are paying Clearbit $199 dollars a month to enrich up to 2,500 companies. That is a direct cost you have and is tied to your customer’s usage. If you have a free tier and they are using the most of your Clearbit quota, you may want to reconsider your pricing strategy. Potentially, Clearbit enrichment should be on the paid tiers only to reduce your own cost.

How to monitor API integrations

Monitoring API integrations seems like the correct remedy to stay on top of these issues. However, traditional Application Performance Monitoring (APM) tools like New Relic and AppDynamics focus more on monitoring the health of your own websites and infrastructure. This includes infrastructure metrics like memory usage and requests per minute along with application level health such as appdex scores and latency. Of course, if you’re consuming an API that’s running in someone else’s infrastructure, you can’t just ask your third-party providers to install an APM agent that you have access to. This means you need a way to monitor the third-party APIs indirectly or via some other instrumentation methodology.

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Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

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Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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