Scalyr updates: Usability on alerts, graphs, Live Tail, and more

Last month, we focused on usability improvements in our Scalyr Platform releases. These include better alerts, tweaks to the graph legend, live tail improvements, adding scrolling to the search fields sidebar and improving times and dates in search results.

This page shows the alerts and responses set up in Scalyr

Alerts

There are lots of usability goodies in this batch of alerts improvements. Amongst other changes, we made the display faster and filterable with a tabbed display that separates alerts by state (triggered, muted, etc.). This lets you zero in on stuff that matters and ignore the rest. And for those of you with lots of alerts, we’ve added infinite scrolling so that the page comes up much faster. Read more about alerts.

Graphs

We added a master checkbox on the legend so can click “all” or “none” to get to the view you need. We also added a mouseover so you can see on the legend what plot you’re looking at. Read more about graphs.

Live Tail

We heard your feedback on Live Tail! It now flows smoothly, giving you continuous updates with better viewability and the ability to start and stop easily.

Dates and times

We separated the date and time displays in your search results and now give you absolute times relative to UTC.

Field sidebar

We made the field sidebar scrollable instead of paginated and gave you the ability to filter on fields by name. Read more about fields sidebar.

Going forward

We are developing Scalyr with the engineering front line in mind, and with a focus on our three value pillars – fast, simple, and shareable.

In the coming months, we will focus on observability for container and serverless environments; enhance your stakeholders’ experience; and improve report creation, scheduling, and distribution.

Feedback

Your product (or any) feedback is always welcome. Please reach out to us at support@scalyr.com.

Log4j2 Configuration: A Detailed Guide to Getting Started

We covered basic logging for Java applications a while back. In that tutorial, we used log4j version 2, a logging framework from the Apache project. Let’s go one step further with Java application logging and look at log4j2 configuration.

Log4j’s capabilities have made it one of Java’s most popular logging frameworks. It can be configured for multiple logging destinations and a variety of log file formats. Log messages can be filtered and directed at the individual class level, giving developers and operations personnel granular control over application messages.

Let’s examine these mechanisms by configuring log4j with a command line Java application.

log4j2_configuration_guide_scalyr

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Get Started Quickly With Spring Boot Logging

Hot on the heels of the Get Started Quickly With Python Logging and Getting Started Quickly With C++ Logging articles, we’re headed across the coffee shop to look at logging in the context of the Java Spring Boot framework. While we’ve written already on the topic of Java logging in Get Started Quickly With Java Logging, the Spring Boot framework simplifies a lot of the plumbing involved in getting up and running. In this article we’ll learn how to:

  • Create a Spring Boot starter project
  • Use Gradle to build our application
  • Configure the default Spring Boot logger
  • Use Log4j2 with Spring Boot
  • Customize the logging configurations

Grab your Venti red-eye triple espresso with almond milk, and let’s get started!spring_boot_logging_scalyr

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Getting Started With the Rails Logger

Let’s continue our ongoing series on getting starting with loggers for different languages and platforms. Back in March, we covered logging with Ruby; now it’s time to take a look at the platform most often associated with that language, Rails.

We’ll start with a simple application with scaffolding for CRUD operations on a single record. We’ll look at Rails’ default logging configuration and how to use logging in an application. Then we’ll look at how logging can be improved and why you might want to improve it.

This tutorial uses Ruby v2.5.1 and Rails 5.2.0. You’ll need to have them installed to follow along. These instructions will use the command line to create and configure the application and will not rely on a specific IDE or editor. We’ll let Rails use SQLite for the backend database.rails_logger_scalyr

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Kubernetes: The Next VMware?

It’s been almost 10 years since VMware started selling ESX version 4.0. This set the path for VMware to dominate more than 75% of the virtualization market in 2017. Gartner considers this market “matured” since most of its revenue comes from maintenance instead of new licensing. Many companies have consolidated their workloads with virtualization, but there are new problems to solve.

Delivering, testing, deploying, and scaling applications are among these challenges. Teams that implement microservices also need to automate as much as possible to make them manageable. Kubernetes, Marathon, Swarm, and Nomad compose a new breed of tools that respond to these needs through orchestration. If you host on-premises or in the cloud, consider them to help your business more quickly deliver code to production.

Companies evolving towards data-driven decision-making often implement machine learning and business intelligence tools, looking for an edge in their markets. As information technology professionals, it’s our responsibility to make sure our businesses select tools that

  • perform in a reliable way;
  • allow quick deployment of new features;
  • scale properly in response to user demand; and
  • deploy new software in a safe and reproducible way.

In this article, I explain why I think Kubernetes is a market leader in the orchestration space and how it might steal VMware’s thunder in the not-so-distant future.

kubernetes_the_next-vmware_scalyr

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Getting Started Quickly With C++ Logging

Since publishing my article Get Started Quickly With Python Logging, I’ve been working on a couple of C++ projects where I’ve found a need for more robust logging solutions than a simple time stamp and message written to a file. Since we also have articles on logging for C# and Java, it made sense to continue the series with an article on C++ logging with Boost.Log. Specifically in this article we are going to:

  • Create a simple Visual Studio 2017 project.
  • Install the Boost libraries using the NuGet package manager.
  • Learn how to configure log output formatting.
  • Add custom attributes to our logger.

This article is a big one, so strap in and let’s get started.

getting_started_quickly_with_c++_logging_scalyr

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Modern software is great, but when it comes to observability, gird your loins!

This won’t come as a shock, but engineers who are on the hook to develop revenue-generating software are quickly moving away from traditional, monolithic architectures and delivering code faster than ever. This trend is especially evident among Scalyr customers, but we wanted to understand how it is generally unfolding and how it affects DevOps observability. We surveyed 155 software development practitioners in DevOps-focused organizations over the last couple of months, and just released our State of DevOps Observability Report. You can download the full report here or check out its summary in this infographic, but in this blog I’ll give you the Cliff’s Notes version of what we found.

Croped infographic on DevOps Observability

We confirmed that organizations really are shifting away from traditional, monolithic architectures. Three-quarters of survey respondents say they deliver at least some of their applications, and more than one-third deliver most of their applications, as microservices. They are also delivering software rapidly, with 71 percent of engineers pushing code into production at least weekly and nearly one-third doing so at least daily. Looking at all of our survey findings through the lens of these two trends, we realize that modern software delivery is putting pressure on DevOps observability.

Here are some of our findings:

Companies are delivering software in a modern way.

  • Three-quarters of respondents deliver some and more than one-third deliver most of their applications as microservices.
  • 71 percent of engineers push code at least once per week, and nearly one-third push code at least once per day.

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Containers and Kubernetes vs VMs vs Config Management

In today’s DevOps-centered world, it’s often easy to be taken in by the infrastructure solution of the hour—VMs for everyone! Or wait, now containers are where it’s at! Who needs config management anyway?

Those of us that are primarily developers probably haven’t had to think about infrastructure decisions as much as our friends on the operations side. Since we haven’t had to make those decisions in the past, it can be hard to figure out what’s out there and why you’d want to use things like VMs or containers. We need to consider what actual problem these solutions are trying to solve. So let’s start simply.

containers_kubernetes_vs_config_management

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Why Do Engineers Care About Logging?

Logging and log analysis are critical to an engineer’s skill set. Engineers rely on logs to track how their systems are running. Sometimes engineers fail to log important events. They often regret not having them in the logs.

See, engineers use logs for error resolution, security, and even for making improvements to the system. Having certain events in the logs and the tools to properly analyze them is what enables engineers to do these things. Let’s look at these ways in which logs are valuable in order to understand more about why engineers care so much about logging.

Scaylr_Engineers_Care_About_Logging_Keyboard

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