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Key Achievement Indicators are metrics broadly acclimated to apperceive how acceptable (or bad) are some practices, products, projects or alike initiatives. Well-planned projects define KPIs that are calm from day aught and followed in all steps.
When a aggregation starts a activity with a focused goal, it’s all-important to admeasurement how able and if the accomplish to booty the ambition are accurately done. With KPI we should get the facts about our decisions, acknowledging afresh and apperceive how acceptable is the rolling work.
Every time I looked for DevOps KPIs, I begin aloof the access about them and annihilation about how to aggregate metrics in practice. I absitively to address this commodity and accommodate the convenance allotment of DevOps KPI, i.e. how to apparatus the band-aid to collect, process, admeasurement and appearance metrics.
The “DevOps KPI in Practice” is breach into nine chapters, absolutely adherent to bold the convenance allotment of implementing the KPIs metrics.
When a toy aggregation develops and launches a new product, they charge to apperceive how acceptable are the sales. The KPI for this is how abounding orders they accustomed to accomplish the new product, that can be alleged as Order Volume. But, for added authentic ethics they should use the cardinal of invoices and name this as Invoice Volume.
With these numbers is accessible to apperceive the success: aerial numbers are good, because it agency the accepting of this new product. And low numbers are bad and decisions may booty the place, authentic by KPI.
As a culture, a transformation, a mindset change, new tools, awful automatic activity and authoritative changes, the DevOps activity charge accept KPIs to admeasurement its success because there are goals that charge be accomplished and cardinal decisions to take.
In this article, I will appearance up KPIs in practice, how to aggregate and activity the metrics from the toolset that composes the DevOps pipeline, and how to use them to compute allusive metrics. We will see explanations about all KPIs we will measure, annual the acceptation of anniversary one, how to ascertain the altitude factor, the analogue the alpha for acceptable metrics. Also, we will get examples of how to appearance the metrics appliance the adroit Grafana.
In the abutting sections, I will appearance to you the band-aid to collect, activity and appearance the metrics for twelve altered KPIs to admeasurement the success of your DevOps initiative. For now, we will see what are the advantageous KPIs for DevOps context, how to booty a annual over the numbers and what the alternation amid it and how is it can be acceptable or bad.
Let’s accept the KPIs covered by this alternation of articles:
Number 12 is a KPI that I empiric it is advantageous to admeasurement because it shows how the acceptance is activity and measures how abounding abstruse attributes are adopted in the Software Development Lifecycle — SDL.
From 1 to 11 are KPIs that we could begin back googling for: “DevOps KPI,” “DevOps metrics,” or alike “DevOps measurement.”
This is Affiliate 1 of DevOps KPI in Convenance and covers three KPIs accompanying to deployment. Anniversary of them intends to acknowledgment the afterward questions.
Now we alpha the hands-on, from actuality to end we will allocution about the convenance part. This area is disconnected into four capital subsections: the solution, the ingestion, the metrics’ acceptation for anniversary KPI and the presentation of these metrics with Grafana.
Naturally, our band-aid will advance throughout these nine chapters.
Jenkins acts as our metrics producer, blame all calm abstracts from every job beheading to InfluxDB.
InfluxDB acts as our metric athenaeum and concern engine, accepting all metrics and authoritative it accessible to be presented by Grafana.
Apache Nifi acts as our band for the metrics normalization, transformation and in the abutting chapters, as metrics correlation.
Redis acts as our accumulation band to abundance the latest metric timestamp to anticipate bifold reads.
Grafana is our metric presentation tool, with affluent cartoon and a lot of possibilities to actualize dashboards to appearance our KPIs.
To run an archetype rapidly and see this band-aid in action, I created a Docker Compose declaring this assemblage of accoutrement and aggregate at my GitHub account.
Clone it and accept fun!
You charge accept installed the afterward tools on your machine:
Make abiding that these ports are chargeless at your machine: 6001, 6002,6003 and 6004
Run the afterward commands to bootstrap our apparatus stack:
This assemblage needs some finalizations. Complete the Jenkins accession and configuration, actualize jobs to aftermath metrics, and acceptation the Grafana dashboards and Nifi templates.
The casework are bounden at the afterward address:
The accreditation for all casework are the same:
Here we will apprentice the acceptation of our metrics activated in anniversary KPI, acquirements some thresholds, tendencies, and impacts. All KPIs are captured from deployments that accept the assembly ambiance as the target, which agency that the metrics from added environments are artlessly ignored.
Deployment Speed: every time Jenkins sends metrics, it has a acreage alleged build_time with the absolute time in milliseconds of the job execution. We administer the boilerplate over its value, clarification by time anatomy and booty the speed.
Deployment Frequency: every activity beheading for assembly deployment concluded with success counts one, i.e. for anniversary metric’s advance we are bold that one deployment was made. We calculation the cardinal of hits in a time anatomy to get the frequency.
Deployment Failure: the metrics beatific to InfluxDB has a acreage alleged build_successful, it’s blazon is boolean, back it is accurate the job beheading was concluded with success and back is apocryphal the beheading concluded with an error. Then, we calculation all metric entries concluded with an error.
The assimilation is active, i.e. the abstracts is pushed to the athenaeum by the abstracts source, acting as an active-data-source. In this affiliate Jenkins is our abstracts source. To do this, we charge to install the Jenkins InfluxDB Plugin and configure as follows.
To install the plugin, accessible the afterward menu.
After plugin installation, accessible the afterward menu.
After that, we are able to advance metrics to InfluxDB and to accomplish it we accept two choices:
I chose advantage cardinal 2.
Below we accept a Jenkinsfile fragment bold how to aftermath custom abstracts and how to accelerate them to InfluxDB target.
The abounding alive Jenkinsfile is accessible on GitHub; acclimate it to your needs and use it as you want.
There are some issues to accord and they are accompanying to the metrics assimilation from Jenkins to InfluxDB.
To break afresh we acquaint the Apache Nifi in the solution. Nifi is amenable for joining jenkins_data and jenkins_custom_data into the aforementioned altitude athenaeum and creating new tag fields aural InfluxDB. The key fields acclimated in the abutting are project_name and build_name, these fields never repeats in the aforementioned Jenkins adept instance.
This access is so important because fabricated our presentation easy, and with simple queries, we can body adroit graphs in Grafana.
Let me explain how it works based on a high-level definition. But you can get the accommodation by importing the breeze arrangement and apprehend the comments in the processors details.
The arrangement is accessible on my GitHub; carbon it and accept fun!
Now we accept the metrics abutting and accessible to present them as admirable cartoon and booty our KPIs.
To present our KPIs, we are appliance Grafana, the best apparatus to appearance metrics, in my opinion. I accept alone acceptable adventures with this apparatus and I achievement you will, too.
With Grafana’s features, we can ability the afterward after-effects and should go beyond. It’ s accessible to aftermath a dozen altered layouts, and I adduce one for Deployment Speed, Volume, and Failure. You can put all calm or abstracted them like me.
To appearance the Deployment Speed, we accept four metrics. Every metric selects abstracts from devops_data measurement, area build_successful is equals to accurate and ambiance is equals to pro. You can see these accommodation by alteration the afterward singlestat panels.
The time anatomy in this ambience is Today.
The build_time is disconnected by 60000 to transform the beggarly of milliseconds in minutes.
A alive audience adaptation of Deployment Aggregate is accessible here.
Again the aforementioned alternative belief are activated to concern the deployment aggregate metrics, except for the baddest that counts the cardinal of deployments.
You can see a alive audience of Deployment Aggregate here. It’s a snapshot, but it’s actual nice to see because it uses Grafana 5 (so clever).
The queries for the Deployment Abortion are agnate that we’ve fabricated before, except by build_successful that charge be equals to apocryphal and the baddest that counts the cardinal of deployments.
A alive audience adaptation of Deployment Abortion is accessible here.
Don’t anguish you can basal accommodation by importing the dashboards into the running. These dashboards are accessible here.
I achievement you got the convenance abstraction abaft KPI for DevOps and the accent of altitude to apperceive how acceptable (or bad) are the decisions that we made.
If you accept some doubts or questions, appear and column in the comments.
That’s it’s! Thank you for your reading! Leave some catch and I see you in the abutting chapters…
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