Spark-Radiant is the Apache Spark Performance and Cost Optimizer. Spark-Radiant will help optimize performance and cost considering catalyst optimizer rules, enhance auto-scaling in Spark, collect important metrics related to a Spark job, Bloom filter index in Spark, etc.
Spark-Radiant is now available and ready to use. The dependency for Spark-Radiant 1.0.4 is available in Maven central. In this blog, I will discuss the availability of Spark-Radiant 1.0.4, and its features to boost performance, reduce cost, and the increase observability of the Spark Application. Please refer to the release notes docs for Spark-Radiant 1.0.4.
Prometheus has become the de facto standard for the monitoring and alerting of distributed systems and architecture. Designed to overcome shortcomings in existing monitoring solutions, including a lack of scalability and deployment challenges, its core architecture, data collection and discovery services, and third-party integrations help to derive greater value.
In this Refcard, we explore the core components of the Prometheus architecture and key concepts — then focus on getting up and running with Prometheus, including configuration and both collecting and working with data.
Without a crystal clear understanding of the processes when a team works on a software product, it can be tempting to think that all the problems stem from under-qualified QA engineers who click around randomly and ruin the hard work of the whole team. However, the value and purpose of the quality assurance process are not transparent without documentation. That's where a test plan and test strategy can help.
Even for those who are well aware of the processes, there's still the problem of measuring the quality QAs provide. If you don't measure quality, you don't have any control over the testing process or any ability to anticipate the results. So how should you know what exactly you paid for?
DORA Metrics and Beyond
In 2014 the DevOps Research and Assessment (DORA) team published their first State of DevOps report, identifying four metrics that can be used to measure engineering team performance.
Six months ago the Data Science Team at LinearB decided to continue where DORA left off, digging deeper into the data than ever before. For the first time in history, engineering teams are able to benchmark their performance against data-backed industry standards.
Spring Boot offers a metrics endpoint that you can use diagnostically to analyze the metrics gathered by the application.
Adding the Dependency Inside POM Is the First Step
A meter is an interface for gathering a bunch of estimations (which we separately call measurements) about your application. spring-measurements loads with an upheld set of Meter natives including Timer, Counter, Gauge, DistributionSummary, and LongTaskTimer. Note that diverse meter types bring about an alternate number of measurements. For instance, while there is a solitary metric that addresses a Gauge, a Timer estimates both the number of coordinated occasions and the absolute season of all occasions planned.
Objective data to measure software development is here, and it’s here to stay.
For a long time, the notion of using such data was thought to not really be possible. Thought leaders like Martin Fowler and Joel Spolsky basically said it couldn’t be done. Clearly, it’s a challenging task that frustrated software development managers everywhere. Shoot, I wrote an article way back when basically arguing that it is impossible to do.
Well, I’d continue to argue that it was impossible to do. But now, with the rise of tooling like git, Jira, and other project management tools, it started becoming clear that the data is there to enable us to get a closer, more data-driven look at what is going on inside software development projects. That data just had to be revealed.
Informing someone that you want to “measure” them is not a great way to start a conversation. Software developers, like all people, tend to look unfavorably upon having their performance closely measured. But measuring developers is one of the hottest trends for companies around the globe. So is it tyranny to measure people?
People are quick to note that numbers don’t tell the whole story and can become defensive at the notion their productivity should be quantified somehow. This resistance can become even more entrenched when teams become stacked against each other.
Introduction
Taking a proactive and efficient approach to Kubernetes cluster monitoring can help engineering teams identify and predict many critical problems like CPU outage, memory outage, storage issues well in advance of these issues taking a toll on a business. Companies of all sizes such as enterprises like CERN monitor petabytes of their Kubernetes cluster data to understand all their cluster workloads. Solving critical problems before they have the chance to make too significant an impact saves money, time, and reputation. The task is a challenge though as proper cluster monitoring can be a pain point for many companies as it’s important to be aware of what exactly we want to monitor in a cluster.
This article will discuss cluster monitoring fundamentals and how we can use Prometheus Operator to deploy Prometheus and Grafana to monitor a Kubernetes cluster.
What leads teams to game metrics within their organization?
On a recent episode of Dev Interrupted, I talked with agile expert Ray Elenteny, Principal Owner at Solutech Consulting, about how people game dev metrics and the underlying issues in culture & leadership that lead to it.
What is it like to work at Netflix as a developer? How do they think about culture, customers and engineering productivity?
In this incredible episode of Dev Interrupted, I bring in Kathryn Koehler, the Director of Productivity Engineering at Netflix, to chat about what makes Netflix so unique and why they are standardizing data-driven engineering today.
Introduction
Graphite is a free and open-source software. It is used as a time-series database monitoring tool, where you can collect, store and display time-series data in real-time. As you can monitor certain metrics of this data using Graphite, it has a very useful and simple dashboard used to visualize these metrics.
This article will show you how to display a metric on your Graphite dashboard.
Overview
During an application startup process, Spring Boot performs a lot of work in the background. This work involves creating Spring Application Context, creating various beans, auto-wiring, and auto-configuration of various components, and finally, starting the application. When a Spring Boot Application has a slow startup, it can be one or more beans and related dependencies taking longer to initialise and slowing down the entire process.
Profiling Spring Boot application doesn’t often help in diagnosing the startup issues. This is because there are a number of beans getting initialised and it is really difficult to figure out which ones are causing the latency. Spring Boot Application Startup Metrics are useful for such cases.
In the past, code and infrastructure were handled by completely separate organizations. Developers wrote code, while IT set up servers. Developers fixed bugs, while IT handled infrastructure maintenance. However, with the trend towards DevOps and the increased availability of Platform-as-a-Service (PaaS), there is an increasing overlap between Dev and IT. For developers, this can mean taking care of infrastructure, a task that is quite different from standard app dev.
As a full-stack developer, I've been pushed to handle more and more infrastructure-related responsibilities, including monitoring production applications. We just went live for HP Foundation at https://www.life-global.org with our Next.js-based learning management system. As a dev team lead, I have been researching how to best support and maintain the application through metrics.
Metrics are the golden ticket to knowing what’s going on with your system… or so everyone thinks. But there can be too much of a good thing. Are your metrics really doing you any favors? Are they letting you see into what your customers truly want from you? If not, you might have a problem. You might be fetishizing your metrics. The good news is you’re definitely not alone
Like The Hobbit’s dragon Smaug laying on his pile of gold, never spending and only hoarding, many of us often stockpile pretty, feel-good, but useless metrics that never make a difference. In fact, they could actually be clouding your ability to get the context and clarity you need from your metrics. In this blog post, we'll help you kick your fetish and move away from Smaug-ing up all your metrics.
When talking about time series data, the data will be very huge. The number of records increases based on the granularity level. If the granularity is minute, we will get 60 records for one minute for one instance.
For example, we want to store CPU percentage of a device for each minute. So let's assume we are getting data for the last 30 days.
Monitoring your MySQL database performance in real-time helps you immediately identify problems and other factors that could be causing issues now or in the future. It's also a good way to determine which components of the database can be enhanced or optimized to increase your efficiency and performance. This is usually done through monitoring software and tools either built-in to the database management software or installed from third-party providers.
Prometheus is an open-source software application used for event monitoring and alerting. It can be used along with a visualization tool like Grafana to easily create and edit dashboards, query, visualize, alert on, and understand your metrics. ScaleGrid provides full admin access to your MySQL deployments — this makes it easier to integrate the existing MySQL ecosystem of tools with your ScaleGrid MySQL deployments on AWS or Azure. Prometheus works well for recording any purely numeric time series, and also offers support for multi-dimensional data collection and querying. Grafana can be used with it to build dashboards that help visualize this data in a way that is easy to interpret and utilize.
