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Docker Daemon Logs Software Has Now

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I was initially interested in figuring out what log management looks like for any Docker users out there.

You can run this via the docker daemon and it will captures all the stdout stderr from the process youre running.

One of our clever engineers put together a quick Dockerfile for me to test this out.

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Because were repeating these build, push, and pull steps so many times, we should pay special attention to our container build steps.

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Watch Video Resources Blog Case Studies Free Webinars Documentation ROI Calculator Support Security Information GDPR Blog Login Start Free Trial Docker Performance Improvement: Tips and Tricks Lou Bichard April 4, 2019 Developer Tips, Tricks Resources Docker is now everywhere.

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Over the past few years, a lot of modern-day software has now moved to become packaged in a Docker container, and with good reason.

One of the biggest benefits touted about Docker containers is their speed.

You dont get lightning-fast performance out of the box without Docker performance tuning.

Were going to discuss some of the tips and tricks to ensure you are utilizing the real speed of containers.

Docker Daemon Logs Code Is Easy

Part 1 Optimizing the speed of containers before we ship (build-time configuration): Dockerfile optimization Docker context relevancy Pushpull latency cost Part 2 Optimizing your containers in production: Hostcontainer relationship Container performance data Leveraging APMs for easier performance data Tip: Find application errors and performance problems instantly with Stackify Retrace Troubleshooting and optimizing your code is easy with integrated errors, logs and code level performance insights.

Docker recap Its important to understand the nuances of how Docker works so we can ensure were leveraging its powerful features.

Simply put, Docker containers are a way of packaging and distributing software with simple instructions to run.

Containers will always run predictablyno matter where you choose to execute themas isolated and protected processes.

Some key points to remember about containers: Containers (nearly always) have hosts.

Containers need machines to run on, but dont expect them to run optimally out of the box.

You need to think about what resources the host has, and how its sharing these with the containers.

This means, among other things, as you build a Docker image (as part of your Dockerfile) its cached for performance.

Importantly though, these cached layers are additive, which means you can only add to them (more on this later).

The art of performance debugging When it comes to performance improvements, stick to the following guidelines: Optimize the bottleneck.

Take into account where your bottleneck is and optimize only at that point.

Optimizing up or downstream the bottleneck wont have an effect on the end user, or the consumer, of the system.

Be data driven. Gathering hard evidence (numbers) about system behavior before and after you run any performance analysis is essential.

With the introduction done, we can now discuss some fun stuff: making containers super fast.

Part 1: Docker build-ime performance When we work with containers, were typically packaging the software were working on into a container build.

Every time we change the software, well want to check that our new artifact is working as expected.

Then, when were satisfied that our software works, we push our code through a deployment pipeline.

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