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[2650.38 --> 2654.36] of have to use one of the tools and, and load your project's data into it. |
[2654.36 --> 2656.72] Um, and then you can, then you can access it. |
[2656.72 --> 2657.10] I guess better questions. |
[2657.18 --> 2657.72] How does it work? |
[2658.24 --> 2659.16] How do I use chaos? |
[2659.66 --> 2659.82] Yeah. |
[2659.86 --> 2660.02] Yeah. |
[2660.10 --> 2661.76] So, so we have two tools. |
[2661.84 --> 2666.20] So we have, we have Augur, which I use within, within VMware myself. |
[2666.76 --> 2670.86] Um, so the way Augur works is it's, it's a, on the backend, it's a Postgres database. |
[2671.32 --> 2675.12] So basically what it does is it pulls, it has a bunch of workers that pull data from |
[2675.12 --> 2678.80] GitHub, for example, and puts it in a very nicely structured Postgres database. |
[2678.80 --> 2681.86] And then there's also, they're doing some work on the front end. |
[2681.96 --> 2683.96] So they're kind of making some changes in the front end. |
[2684.02 --> 2685.70] It's a little bit less, less mature. |
[2686.08 --> 2689.90] But the reason I picked Augur was because there were four metrics that I wanted to measure |
[2689.90 --> 2691.60] that I wanted our maintainers to look at. |
[2691.80 --> 2695.76] And so because it's just a Postgres database in the backend, I can just write a whole bunch |
[2695.76 --> 2698.26] of Python scripts that generate the four charts that I want. |
[2698.66 --> 2700.20] And then we display those internally. |
[2700.20 --> 2702.86] We have a little internal dashboard that we use for that. |
[2703.16 --> 2703.30] Yeah. |
[2703.74 --> 2706.10] And then we also, we also use the Paturgia. |
[2706.86 --> 2707.46] Say what? |
[2707.46 --> 2712.56] So it's Grimoire Lab is one of the, and there's a company called Paturgia that does a lot |
[2712.56 --> 2713.58] of the work on Grimoire Lab. |
[2713.76 --> 2713.90] Okay. |
[2713.92 --> 2715.14] So that's the other piece of software. |
[2715.58 --> 2718.22] And it's, it uses the elk stack. |
[2718.34 --> 2725.08] So basically Elasticsearch, although they're migrating to OpenSearch and a fork of Kibiter. |
[2725.52 --> 2730.14] So it's, it's more, more of that style. |
[2730.20 --> 2731.24] So it's not a relational database. |
[2731.24 --> 2733.22] It's like a, you know, an elastic database. |
[2733.42 --> 2737.10] So you, you can run, you can run queries, but it's got like really big dashboards. |
[2737.46 --> 2738.48] That people can use. |
[2739.04 --> 2743.04] So that I think is great for community managers who really want to dig in on their individual |
[2743.04 --> 2745.32] project and want to know every little bit about it. |
[2745.56 --> 2748.36] Because the dashboards have all this, all this stuff already in them. |
[2748.36 --> 2750.40] And then you can write custom queries around it. |
[2750.40 --> 2754.90] So like Augur is more powerful if you want to write like Postgres database queries and display |
[2754.90 --> 2755.52] stuff yourself. |
[2755.88 --> 2758.40] Although they are working on the front end and it's looking really, really cool. |
[2758.56 --> 2761.60] So like, don't, I don't want to diss the Augur front end because there's some awesome stuff |
[2761.60 --> 2761.90] happening. |
[2762.58 --> 2767.26] And then the other one has like a more, more robust dashboard, but it's, it's confusing for |
[2767.26 --> 2767.68] a lot of people. |
[2767.68 --> 2770.78] Like they don't know how to write those queries because they're not relational database queries. |
[2770.92 --> 2771.50] They're different. |
[2771.50 --> 2774.00] Um, so it just kind of depends on what you want. |
[2774.36 --> 2776.28] How did you get to those four metrics? |
[2776.50 --> 2778.46] Why are those the ones that are important to your team? |
[2778.94 --> 2779.34] Yeah. |
[2779.48 --> 2780.26] So I picked them. |
[2780.48 --> 2782.24] Recount them for us and then why? |
[2782.78 --> 2783.12] Yeah, sure. |
[2783.66 --> 2784.06] Yeah. |
[2784.12 --> 2790.32] So the four metrics are response time for, uh, I picked pull requests, uh, response time |
[2790.32 --> 2790.96] for pull requests. |
[2791.30 --> 2796.56] And so our guideline internally is that if someone submits a pull request, we should have a human |
[2796.56 --> 2798.08] respond to it within two business days. |
[2798.08 --> 2802.34] So I exclude the bots and then I look at how many business days it took us to respond. |
[2802.80 --> 2804.30] And then I chart that over time. |
[2805.10 --> 2814.60] Um, and then I look at, um, change request closure ratio, which is, is basically, um, in |
[2814.60 --> 2820.14] a given month, there are a total of a hundred open pull requests during that month. |
[2820.28 --> 2821.94] Did you close 90 of them? |
[2822.06 --> 2823.42] Did you close 50 of them? |
[2823.52 --> 2827.54] And how big is the gap between the number of pull requests and the number of pull requests |
[2827.54 --> 2827.88] you close? |
[2827.88 --> 2832.22] So this is kind of the pull request backlog and whether you're keeping up with pull requests. |
[2832.60 --> 2837.90] So, so response time is good because like new, new contributors want a response to their |
[2837.90 --> 2838.36] contribution. |
[2838.64 --> 2840.18] Everybody wants a response to their contribution. |
[2840.84 --> 2845.00] Um, the pull request backlog is good because it shows that people are either merging pull |
[2845.00 --> 2846.74] requests or closing them without merge. |
[2846.74 --> 2846.84] It's like throughput. |
[2846.84 --> 2847.68] Yeah. |
[2847.68 --> 2847.74] Yeah. |
[2848.26 --> 2850.32] Because you don't want a huge backlog of pull requests. |
[2850.52 --> 2851.62] I look at release frequency. |
[2851.62 --> 2857.50] So I want to make sure that the, when they release bug fixes and security fixes that they actually |
[2857.50 --> 2859.12] land in a release in a timely manner. |
[2859.12 --> 2862.46] So those are not just like big releases, but like individual point releases. |
[2862.46 --> 2867.52] And then I also look at contributor risk, which is kind of a bus factor type metric. |
[2867.52 --> 2873.38] So I look at, does a project, and these are VMware owned projects that we run these metrics on. |
[2873.38 --> 2880.24] Um, I look at, you know, are there three people who are contributing 50% of the contributions to the project? |
[2880.24 --> 2885.28] Or is it one person who's contributing like 98% in which case that's, that's not good. |
[2885.36 --> 2895.44] But if you have a large number of people who are contributing across the project, then if one person left the company or retired or decided they didn't want to do it anymore, then the project can more easily continue. |
[2895.84 --> 2900.70] So I picked those because I thought it was a representative sample of, of things that a lot of people care about. |
[2900.84 --> 2906.00] And then what I want the projects to do and the maintainers to do is then drill down and have other metrics. |
[2906.00 --> 2910.20] So like I said, we have a team using the Grimoire Lab tools for their metrics. |
[2910.80 --> 2917.48] And then we have other teams that are doing like, you know, custom stuff out of the GitHub API, for example, to measure other things that they want to care about. |
[2917.82 --> 2919.38] What metrics hit your cutting room floor? |
[2919.74 --> 2922.56] What metrics was important, but didn't make the cut? |
[2923.14 --> 2923.70] That's a good question. |
[2923.78 --> 2925.26] I didn't really, I didn't really approach it that way. |
[2925.32 --> 2927.24] I just picked the four that I thought were important. |
[2927.64 --> 2928.52] So you just only chose four. |
[2928.64 --> 2929.22] I chose four. |
[2929.50 --> 2930.52] Drill was the first time. |
[2930.52 --> 2931.04] No requirements. |
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