Monitorama pdx 2019: 5 neat tricks with prometheus

Any new windows created in the tmux session will have the updated ssh information. Already running shells are not updated how would tmux tell the shell to update? I've added a zsh alias to my workflow to pull in the updated value from a running shell. It wraps the show-environment tmux command. This is a shell alias because it needs to affect the running shell.

I hate running an update and seeing Permission denied. When I remember. It was funny. It really showed me the power of gradualism. What if you want to run it non-interactively? Yes, you can do this. And yes I have a use-case!

Monitoring Kubernetes with Prometheus – Tom Wilkie

This is an easy use case because I don't change anything in the editor window, that's handled by the --autosquash flag to rebase. Let the user edit that list before rebasing. The commit list format can be changed by setting the configuration option rebase.

A customized instruction format will automatically have the long commit hash prepended to the format. Overriding one of those will change the editor that is run during interactive rebase but also changes the editor used while in the rebase to change commit messages and etc. This editor is only used for the interactive rebase edit.

As an aside, this is a wonderful commit message. As usual, the environment variable takes precedence over the configuration variable.

It is envisioned that other "sequencer" based tools will use the same mechanism. No, not really.Discovered by Player FM and our community — copyright is owned by the publisher, not Player FM, and audio is streamed directly from their servers.

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Play later. Manage episode series Welcome to Player FM! Download the App! A weekly podcast on deploying and managing enterprise storage and data. The African Tech Roundup podcast delivers independent Africa-focused technology, digital and innovation insight and analysis. The show is produced by broadcaster and entrepreneur, Andile Masuku www.

Android Backstage, a podcast by and for Android developers. Hosted by developers from the Android engineering team, this show covers topics of interest to Android programmers, with in-depth discussions and interviews with engineers on the Android team at Google.Q: openstack has a technology called "cata"?

Q: i'm a pro-feminist man, and i understand why you can't depend on the repressed group to solve the problem, but if i use my voice then i'm going to be speaking for women and reinforce the problem, what can i do? Q: hubble's estimate was wrong because his data wasn't accurate, it seems in our world that our measuremens are very accurate, does that change our approach? Q: a comment - we can measure time accurately in computing, but most data in operations is very inaccurate and noisy.

Q: another comment - i'm struggling with eventual consistency of the cloud, as such you have to deal with eventual consistency, even in your monitoring.

Q: in your last example with the power laws, you found the peak after the fact, does it work ahead of time? Q: would human behavior play into your prediction? Q: you mentioned a huge graphite instance, what backend are you using? Q: how many syslog servers? Skip to content. Instantly share code, notes, and snippets. Code Revisions 26 Stars 11 Forks 2.

Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. Monitorama notes. Tuning 99 Resource Mgmt. Gilt Groupe: went from a handful of services to services over the course of a year "death star" microservice pattern: everything is calling everything else in one big tangled graph of dependencies how to you visualize this?

JVM metrics challenges with dynamic ephemeral cloud apps: dedicated hardware: arrives infrequently, disappears infrequently, sticks around for years, unique IPs and MAC addresses cloud assets: arrive in bursts, stick around for a few hours, recycles the IP and MACs of machines that were just shut down!

A: error rate, CPU time, response time, jmeter functional tests, business metrics, and you need to do the comparison on freshly spun up nodes e.Sold by: Amazon. Backed by the expertise and experienced guidance of the authors, this book provides everything you need to move your business forward.

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Monitorama PDX 2019: 5 Neat Tricks with Prometheus

Small to medium—sized business owners looking to run their own IT, system administrators considering migrating to Linux, and IT systems integrators looking for an extensible Linux infrastructure management approach. Puppet provides a way to automate everything from user management to server configuration. You'll learn how Puppet has changed in the latest version, how to use it on a variety of platforms, including Windows, how to work with Puppet modules, and how to use Hiera.

Puppet is a must-have tool for system administrators, and Pro Puppet will teach you how to maximize its capabilities and customize it for your environment. Install and configure Puppet to immediately start automating tasks and create reporting solutions. Skip to main content James Turnbull. Something went wrong. Please try your request again later. James Turnbull is the author of ten technical books about open source software and a long-time member of the open source community.

monitorama pdx 2019: 5 neat tricks with prometheus

He is the co-chair of the Velocity conference, a past president of Linux Australia, a former committee member of Linux Victoria, was Treasurer for Linux. He likes food, wine, books, photography, and cats. He is not overly keen on long walks on the beach or holding hands.

monitorama pdx 2019: 5 neat tricks with prometheus

Are you an author? Help us improve our Author Pages by updating your bibliography and submitting a new or current image and biography. Learn more at Author Central. Previous page. Kindle Edition. Pro Puppet. Next page. Blog post. I was an early adopter of Docker and have always been passionate about reducing friction in the development process, especially locally.

Quick performance wins for individual developers add up to substantial productivity improvements across a whole team. Intro to Distributed Tracing. I am especially interested in how long before it is something that more people can use without requiring deep knowledge and that they can quickly find useful. Reactions on this topic tend to be varied and divergent.

Building your first product road map. Building product road maps You have an amazing idea. The prototype is the first pass. Frank Robinson said when he coined the MVP term: … think big for the long term but small for the short term. Think big enough that the first product is a sound launching pad for it and its next generation and the road map that follows, but not so sm.

That Tattoo. So what prompted this?When it came to investigating these solutions, the biggest challenge was piecing together an appropriate architecture and configuration for your needs - most of the information for this is hidden in github issues for the project, or spread across different pieces of documentation.

With the python graphite components, we had reached the following architecture to meet our performance and HA requirements. Each configured server that the relay sends metrics to has a queue associated with it.

This queue allows for disruptions and bursts to be handled. Constraining the queue size limits memory consumption, but setting it too low risks you losing metrics. If you have a lot of concurrent connections you need to increase this to avoid connection refused errors on clients.

On the cache nodes we evaluated and decided to implement go-carbon. It supports all the same input methods as python carbon, plus a few more, and supports that carbonlink protocol that allows graphite-web to query the cache. And it has some neat features, like persisting the in-memory cache to disk during a restart, whereas python carbon-cache would just lose whatever metrics are in the cache during a process restart. These two settings configure how much CPU resources can be used - obviously, having more persister workers is important for throughput.

The number is dependant on the resolution of your data and how many metrics you receive in that period. In our current case, we are receiving about 1. During normal operation the cache size sits about 1.

The kernel will then take multiple datapoint writes, and coalesce them into a single disk write performed in the background. A really useful feature that python carbon supports is max creates per minute, and happily go-carbon started supporting it in 0.

The first time a metric is seen, then carbon will lay out a new whisper file. This means writing out the entire content of the file, which is a lot more IO than just updating a few datapoints. This absolutely kills the update performance of the carbon persister and can affect search performance. So, this setting will rate limit the number of new whisper files being created. Enough cores - 2x Xeon E 2. Obviously you need fast enough disks on the backend, but having a large disk buffer in RAM is vital for the operation of go-carbon.

This is because each write to a whisper file requires multiple reads. Also if your whisper files have multiple retention periods, the aggregated points are written to every write, requiring a read of all points in the highest precision that cover that aggregated period. Basically, more cache results in less disk reads.

Now we have our current architecture - go-carbon has replaced all the carbon components, as well as haproxy, on the cache nodes.Your browser does not seem to support JavaScript.

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As a result, your viewing experience will be diminished, and you may not be able to execute some actions. Please download a browser that supports JavaScript, or enable it if it's disabled i. As a longtime systems engineer, Blerim Sheqa knows all about using tools like Grafana to debug issues in infrastructures.

We think we found something in a graph that looks strange, but actually just the graph was strange. He came up with this one from a newspaper about a gun law in Florida. The graph seems to show that the rate of murders by guns dropped after the law was passed. We believe that the graph shows us a pretty normal load on the server, where it actually is pretty high, depending on the hardware obviously.

This graph of memory usage on a server is stacked, which causes problems. Is it increasing or is it decreasing? Lesson 3: Highlighting the important parts of a graph is pretty important for data visualization.

Is it many requests? Are they going up or down? What is happening here? So just by looking at averages, we cannot tell any details about the behavior. You should make use of that when you have something like CPUs or network statistics. Graphs should always be readable for everyone in your organization, not just the person who created it.

The following load graph has annotations about when monitoring sent an alert because of a failing service. At which point and at which time frame do I have to look at the graphs? On the other hand, there is such a thing as too many annotations and contexts.Hi there!

Here's some tech reading for your Summer break As usual, a hat tip to Hacker News and Twitter feeds, which are my usual sources. Z-Score: What's the Difference? Con - Speling werds egspressively with rrkurrent nuril nedwirques! Justin Lee, Red Hat. By Ashwin Jayaprakash. Topics: datajavasystemtech. Post a Comment. Falling into the future at light speed. Sunday, August 11, Summer tech reading. By Ashwin Jayaprakash Topics: datajavasystemtech. Newer Post Older Post Home. Subscribe to: Post Comments Atom.

Who am I? Ashwin Jayaprakash View my complete profile.

monitorama pdx 2019: 5 neat tricks with prometheus

Disclaimer The content on this blog are my own and do not necessarily reflect the views of my current or previous employers. Blog topics general tech java travel data dads pictures 61 note to self 50 event processing 42 ashwin-ism 39 streamcruncher 39 system 33 books 15 biz 14 productivity Blog feed Atom All topics Tech topics General topics. Sebastian Blessing - Run, actor, run - YouTube. Analysis Process - Criterion. Comparing Functions - Criterion. Summarizing quantitative data Statistics and probability Khan Academy.

T-Score vs. Violin Plot - Learn about this chart and tools to create it. Elasticsearch Survival Guide for Developers. How John Deere uses Flink to process millions of sensor measurements per second - Ververica. Stories of My Experiments with "Distroless" Containers. Why did we ditch Jenkins for Argo? Understanding real-world concurrency bugs in Go — the morning paper.