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Should you use these metrics and draw immediate conclusion if you team is amazing or sucks? Metrics without the context are a huge danger, but can trigger valuable investigations and gaining more understanding.

Time to restore service: The time it takes to restore service in production after an incident.

Change failure rate: The share of incidents, rollbacks, and failures out of all deployments.

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Change lead time: The time it takes to get committed code to run in production.

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Deployment frequency: How often a software team pushes changes to production.

Introducing DORA metrics - read more here. Can you measure good engineering? The main point the book is trying to make is that you can avoid sacrificing quality and focusing on the right things you can increase velocity of your organization. “Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations” by Nicole Forsgren, Jez Humble and Gene Kimĭata-driven approach to best practices in engineering. I think it applies to marketing and other “scalable” roles, but that’s not my place to comment. Why your designers should not have to jump through ridiculous hoops to get their best work done and why your product people should be empowered to take risky but calculated decisions. Why it’s worth paying the top dollar to get the best possible engineers.

My recommendation is to take the ideas here with a giant grain of salt, it’s likely not something that applies to your seed-stage B2B SaaS startup where cash is in limited supply and you have commission based roles.īecause I think it helps to think about how you should treat product, engineering and design roles.

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The book is full of anecdotes of the CEO and interviews from the various employees that make the case for the above. Make sure that they know they own their shit, they are expected to take risks and will get fired if they don’t get results on time.Remove redundant bureaucracy because you hired the “best” and they hate stupid rules that don’t bring any value.Then you pay top of the market salaries to those who are left and recruit the “best” people.First you aggressively fire anyone that is not a “top performer”.

The core idea is that an exceptional company increases talent density and establishes a culture of freedom and responsibility.Īllow me to put it less diplomatically in steps:

It’s about the (in)famous Netflix culture. But if you do things this way then you will be getting the maximum performance possible from your data.įor further gains, consider 'pre-aggregating' information in other collections, based on periodically running the base forms of aggregation over the raw 'log' data that you have.“No Rules Rules: Netflix and the Culture of Reinvention” by Erin Meyer and Reed Hastings Please note that the indexes can only ever be used in the initial $match stage of the aggregation pipeline, so this is importantly where you select your data and reduce your working set. So it is the 'date aggregation operators' that allow you to split that BSON date into the components that you want (in this case day) so that all the timestamp values contained within those boundaries are subject to the other aggregation operations on the other fields that you have. So for index deployment to optimize you want the following indexes created, or otherwise specified with the equivalent annotation on your class: db.collection.ensureIndex(

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