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camel_gopher

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medium.com 2y ago

Nuclear power revisited – SMRs come of age

camel_gopher
2pts1
www.circonus.com 7y ago

On Job Scheduling – How we manage concurrency with libmtev

camel_gopher
3pts0
www.circonus.com 7y ago

Tuning AWS EC2 Instances with CloudWatch Metric Analysis

camel_gopher
4pts0
www.circonus.com 7y ago

Which block I/O scheduler is the best? We asked eBPF

camel_gopher
4pts0
www.circonus.com 7y ago

The Internet of Things and Air Quality Monitoring During Wildfires

camel_gopher
6pts0
www.circonus.com 7y ago

The Problem with Percentiles – Aggregation Brings Aggravation

camel_gopher
5pts0
www.circonus.com 7y ago

A Guide to Service Level Objectives, Part 3: Quantifying Your SLOs

camel_gopher
4pts0
www.circonus.com 7y ago

A Guide to Service Level Objectives, Part 2: It All Adds Up

camel_gopher
7pts0
www.circonus.com 7y ago

Latency SLOs done right

camel_gopher
7pts1
www.circonus.com 7y ago

TSDBs at Scale – Part One

camel_gopher
8pts0
www.circonus.com 8y ago

Comprehensive Container-Based Service Monitoring with Kubernetes and Istio

camel_gopher
8pts0
www.circonus.com 8y ago

Cassandra Query Observability with Libpcap and Protocol Observer

camel_gopher
7pts0
www.circonus.com 8y ago

Effective Management of High Volume Numeric Data with Histograms

camel_gopher
7pts0
www.circonus.com 8y ago

Linux System Monitoring with eBPF

camel_gopher
72pts3
www.circonus.com 8y ago

Grafana Heatmaps with the IRONdb Data Source

camel_gopher
15pts0
medium.com 8y ago

Fun with air quality sensors and IoT systems monitoring

camel_gopher
10pts0
heinrichhartmann.com 8y ago

Circonus monitoring on a Raspberry pi

camel_gopher
1pts0
www.youtube.com 8y ago

Solving the Technical Challenges of Time Series Databases at Scale

camel_gopher
6pts0
www.getlytics.com 11y ago

Lytics raises 7M for adaptive digital marketing

camel_gopher
1pts0

You can communicate like this and have it be effective if you have an established good relationship with the recipient. That’s why team cohesiveness is important.

Context of whom you are communicating with is also important. That’s the trade off of approaches like these rules. In some situations they are fine. In others not so much.

A rule that has suited me well is to take an estimate, double it, and increase by an order of magnitude for inexperienced developers. So a task the say would take two weeks ends up being 4 months. For experienced developers, halve the estimate and increase by an order of magnitude. So your 10 days estimate would be 5 weeks.

You have to be careful of the perceived politics around this. Tall poppies get cut down. I still don’t totally understand why but sometimes taking initiative doesn’t sit well with the folks who want their trains to run on time.

Nearly all time series databases store single value aggregations (think p95) over a time period. A select few store actual serialized distributions (Atlas from Netflix, Apica IronDB, some bespoke implementations). Latency tooling is sorely overlooked mostly because the good tooling is complex, and requires corresponding visualization tooling. Most of the vendors have some implementation of heat map or histogram visualization but either the math is wrong or the UI can’t handle a non trivial volume of samples. Unfortunately it’s been a race to the bottom for latency measurement tooling, with the users losing.

Source: I’ve done this a lot

You have to be able to get the prescription. HMOs (Kaiser specifically) will generally not provide any sort of preventative care in this area unless your numbers are very high. You can’t get access to a cardiologist unless you’ve already had an adverse event.

If you can get time off work and have a PPO, you can get the preventative care.