Organizations like yours are shifting from traditional, monolithic approaches to microservices architectures, container environments, and even serverless deployments. Doing this helps you focus engineering resources, decouple software creation from deployment, streamline quality assurance, and optimize the frequency of code pushes. While you are achieving gains in flexibility and alignment, these architectural changes make your operational visibility processes such as container monitoring and logging microservices crucial…and considerably more challenging. According to a recent survey, organizations that have adopted microservices or push code frequently spend the bulk of their time troubleshooting software issues. Yet, despite identifying query speed as their top priority in log management, those who deliver software this way, but still use traditional observability tools, spend a greater portion of their investigation time waiting for log management queries to complete.

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Microservices and containers have become critical architectural choices for delivering software. This is especially true when your application is customer facing and generates revenue. Your engineering team responsible for delivering that software is on the front line and must meet ever-increasing performance and usability requirements. This work is highly visible and sensitive to missteps, there is very little tolerance for error, and any problems that do occur affect revenue. The pressure is on, and you must move faster than ever.

Moving from monolithic applications to discrete, focused microservices can streamline your engineering process. You no longer compete for skilled, full-stack development talent but instead bring in the right individuals to focus on particular services or functions. You can also deploy those engineers to work on specific areas of the business so that they are more responsive to business changes, delivery cadence, and customer feedback. For example, an online food delivery company can organize its engineers into functional units such as ordering, payments, restaurant content, and delivery network, each of which can be provided by different services, with a different pace of software delivery, different interdependencies, and different tolerances for risk and failure.

Use Cases

Container monitoring

  • Track real-time health and performance
  • Monitor ephemeral containers
  • Have visibility across Kubernetes clusters

Continuous delivery

  • Troubleshoot issues faster
  • Search terabytes in seconds
  • Get to the root cause in record time

Parallel development

  • Share with all team members
  • No performance penalty or throttling
  • No need for admins or other specialized roles

These different functions can run in entirely separate, parallel software delivery pipelines – some fast and others slower – that completely decouple the design, development, testing, deployment, and monitoring of one service from that of all of the others. These architectural changes are a good thing. They help you move more quickly, be more flexible, and respond to your customers’ needs. This flexibility in turn means you can deliver software at a pace that fits your business. Our studies show that software developers who primarily deliver microservices are half again more likely to push code at least once per day as are those who do not.

Despite the many benefits of modern architectures, container monitoring and logging microservices can be extremely challenging. After all, deploying microservices, containers, and container orchestration tools such as Kubernetes vastly increases the amount of log data you produce. This is especially true if you deploy ephemeral containers, whereby you are constantly starting, stopping, and killing containers based on your service needs and scaling requirements. Regardless of how many services you are running at any given time, you still need to be able to monitor all of your microservices, preferably as a group and be able to aggregate them with logs from across your infrastructure and applications.

Why Scalyr


Go fast. Blazing-fast. Ingest logs and alert on them in real time. Perform split-second searches and visualizations, across your entire environment.

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Skip the learning curve. Point and click to search, pivot or visualize your data.  No query language expertise required.

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Using our time-series database and massive compute capacity, Scalyr will easily scale with your systems.  You also won’t break the bank as you grow with us.

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“One of the things I really value in Scalyr is the responsiveness. I can give it a really terrible-looking query with a bunch of regular expressions, and somehow it still comes back in under a second.”

Elena Tatarchenko, Senior Engineering Manager, Oracle

“We push many hundreds of gigabytes of logs to Scalyr, and I need to search back a few days sometimes, a month even. The fact that I can still do that is just great.”

Jeff Watts, Senior Engineering Manager, Periscope Data

“Asking how Scalyr helps is like asking how breathing helps with your life.”

Tim Kröger, Head of Engineering, Zalando

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