A Kubernetes monitoring tool built on electron.
Maestro is an open-source monitoring tool for keeping track of the health of your Kubernetes cluster. Maestro is lightweight and allows users to view key metrics at a glance. This tool leverages the K8s API to obtain important cluster data, and promQL queries to scrape key metrics and display them in a digestible format.
Users must have Prometheus installed on their Kubernetes cluster.
kubectl port-forward -n default svc/prometheus-kube-prometheus-prometheus 9090
npm install npm run webpack-start npm run start
Source code: https://github.com/oslabs-beta/maestro
#react-native #react #electron #kubernetes
Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.
According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.
(State of Kubernetes and Container Security, 2020)
And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.
(State of Kubernetes and Container Security, 2020)
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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.
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.
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 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.
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
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
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.
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.
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.
#monitoring #api integration #api monitoring #monitoring and alerting #monitoring strategies #monitoring tools #api integrations #monitoring microservices
Kubernetes is one of the most popular choices for container management and automation today. A highly efficient Kubernetes setup generates innumerable new metrics every day, making monitoring cluster health quite challenging. You might find yourself sifting through several different metrics without being entirely sure which ones are the most insightful and warrant utmost attention.
As daunting a task as this may seem, you can hit the ground running by knowing which of these metrics provide the right kind of insights into the health of your Kubernetes clusters. Although there are observability platforms to help you monitor your Kubernetes clusters’ right metrics, knowing exactly which ones to watch will help you stay on top of your monitoring needs. In this article, we take you through a few Kubernetes health metrics that top our list.
A crash loop is the last thing you’d want to go undetected. During a crash loop, your application breaks down as a pod starts and keeps crashing and restarting in a circle. Multiple reasons can lead to a crash loop, making it tricky to identify the root cause. Being alerted when a crash loop occurs can help you quickly narrow down the list of causes and take emergency measures to keep your application active.
#devops #kubernetes #monitoring #observability #kubernetes health monitoring #monitoring for kubernetes
Recently, Microsoft announced the general availability of Bridge to Kubernetes, formerly known as Local Process with Kubernetes. It is an iterative development tool offered in Visual Studio and VS Code, which allows developers to write, test as well as debug microservice code on their development workstations while consuming dependencies and inheriting the existing configuration from a Kubernetes environment.
Nick Greenfield, Program Manager, Bridge to Kubernetes stated in an official blog post, “Bridge to Kubernetes is expanding support to any Kubernetes. Whether you’re connecting to your development cluster running in the cloud, or to your local Kubernetes cluster, Bridge to Kubernetes is available for your end-to-end debugging scenarios.”
Bridge to Kubernetes provides a number of compelling features. Some of them are mentioned below-
#news #bridge to kubernetes #developer tools #kubernetes #kubernetes platform #kubernetes tools #local process with kubernetes #microsoft
Over the last few years, Kubernetes have become the de-facto standard for container orchestration and has also won the race against Docker for being the most loved platforms among developers. Released in 2014, Kubernetes has come a long way with currently being used across the entire cloudscape platforms. In fact, recent reports state that out of 109 tools to manage containers, 89% of them are leveraging Kubernetes versions.
Although inspired by Borg, Kubernetes, is an open-source project by Google, and has been donated to a vendor-neutral firm — The Cloud Native Computing Foundation. This could be attributed to Google’s vision of creating a platform that can be used by every firm of the world, including the large tech companies and can host multiple cloud platforms and data centres. The entire reason for handing over the control to CNCF is to develop the platform in the best interest of its users without vendor lock-in.
#opinions #google open source #google open source tools #google opening kubernetes #kubernetes #kubernetes platform #kubernetes tools #open source kubernetes backfired