Kafka: "git branch" for production streams

By Mark Bullet_white Comments Comments

I switched from SVN to Git for version control years back largely because it made experimenting a blast. Creating branches, viewing diffs, stashing - it was fun to veer off the highway and explore dirt roads.

Making it easy to experiment on my dev box is great, but rolling those changes to production is scary. Is there a way to compare the performance of application 1.4 (the steady workhorse) vs. application 2.0 (the silky smooth refactor) against live data?

Yes, there is. Lets explore.

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DevOpsDays Minneapolis

By Mark Bullet_white Comments Comments

Some of the best things about summer are BBQ, baseball and roadtrips. As part of my summer celebration, I'm hitting the open road and heading up to Minneapolis, MN for DevOpsDays MSP.

DevOpsDays MSP is a 2 day event on July 8th & 9th in downtown Minneapolis. This is their second year organizing and they are expecting over 450+ DevOps Professionals in attendance. The organizers have done a great job in lining up speakers and Scout is proud to be one of many sponsors.

I'm really excited to be attending, and for the first time presenting! Scout added StatsD support a couple months ago, we're seeing a lot of interest in custom metrics. I'll present an Ignite style talk on what StatsD is and how to get started.

For the record, yes, there will be baseball - Twins Vs. Tigers on Friday Night.

Any recommendations on a great BBQ spot in the twin cities?


An intro to the Docker Stats API

By Mark Bullet_white Posted in Examples Bullet_white Comments Comments

If I was teaching Server Health 101, I'd start with four key metrics:

  • CPU Usage
  • Memory Usage
  • Network I/O
  • Disk Utilization

The approach for fetching these metrics on Linux hosts is tried-and-true (hint: look in the /proc folder). However, I was curious about Docker Containers: where do I access these critical metrics? What tools does Docker provide? Are there any gotchas?

Read on for what I found.

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Docker ETP Program

By Mark Bullet_white Comments Comments

Our friends at Docker today announced that we are part of their new ETP (Ecosystem Technology Partner) program. We couldn't be more excited!

Docker has done a great job of creating an API that allows anybody to track performance metrics, container events and metadata about their containers. What we've done - is simplified that interface, and added Docker integration to the Scout dashboards and alerting that you already know and love.

We <3 Docker and the on-going shift towards isolated microservices. If you haven't already - check out these blogs we've written about Docker:

Questions? Comments? Reach out to us here or follow us on Twitter for more monitoring news.


Implementing Docker event monitoring from scratch

By Mark Bullet_white Posted in Examples Bullet_white Comments Comments

docker events dashboard

Docker's API provides a ton of functionality around containers and images - but there is a hidden secret, one that is easy to miss in the documentation: Docker's API has the capability to report host wide events! Container events like: die, restart & out of memory. With a simple GET request, these events are available for processing.

I'll take a look at how we can tap into this functionality, and how we can convert raw data into meaningful dashboards and alerts.

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Django Application Monitoring (APM) with StatsD

By Mark Bullet_white Posted in HowTo Bullet_white Comments Comments

Spring 2018 Update - This post covers instrumenting a Django app with StatsD. For a more full-featured APM solution, checkout Scout's Python Application Monitoring.

One of the basic tenets in DevOps is that we measure EVERYTHING.

StatsD is the open source darling that has quickly turned into our tool of choice for measuring "all the things". StatsD is logging for metrics.

Instrumenting your application code via StatsD is lightweight, both in terms of syntax and overhead. It's the missing swiss army knife in your measurement toolbelt.

However, there are two sides to the StatsD equation:

  • Instrumenting your code (easy)
  • Setting up infrastructure to view your metrics

...well that second step is pretty involved. I'm antsy to get some StatsD in place. Let's cheat: we'll use use Scout as our StatsD backend. We'll have a dashboard like this in minutes:


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