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• The need for automation in the world of Kubernetes and cloud native. |
• The spectrum of internal platforms, from simple Docker file management to full Heroku-style interfaces. |
• The challenges of debugging and understanding complex systems like Kubernetes. |
• The benefits of having multiple tools and services for monitoring and logging, such as Grafana Cloud and Honeycomb. |
• Importance of observability in understanding application behavior |
• EBPF and Pixie for deep insights into system performance |
• Redundancies and failovers to minimize downtime |
• Debugging challenges and the need for visibility into complex systems |
• Observability and metrics as crucial investments, especially with Kubernetes |
• The difficulty of building observability infrastructure without automation tools |
• Feeding observability data back into automation for automated rollouts and error detection |
• Simplifying Kubernetes setup through public sharing of configuration and components |
• Inefficiencies in IT teams lead to wasted resources (20% CPU and memory usage) |
• CIOs seek to reduce infrastructure costs by consolidating VMs into one massive cluster |
• Kubernetes adoption was initially driven by cost savings, but operators saw benefits in its API and extensibility |
• Operators found that large clusters can be difficult to manage and upgrade, leading to the concept of "fleets" (small, homogeneous clusters) |
• Running small clusters per availability zone or workload type improves manageability and reduces costs |
• Stateful workloads pose challenges for Kubernetes and should be handled with caution, either by externalizing databases or using teams to manage them |
• Issues with PostgreSQL replication and networking |
• Use of Crunchy data and Zalanda operators |
• Comparison of managed vs self-managed PostgreSQL services |
• Data backup and restore procedures |
• Downtime and availability considerations for single instance PostgreSQL |
• Trade-offs between feature complexity and simplicity in system design |
• Serving stale content when origin is down |
• Managed services for databases vs running own infrastructure |
• Pitfalls of running multiple stateful sets in Kubernetes |
• Technical debt and complexity associated with scaling database operations |
• Importance of launching when necessary, rather than prematurely adopting complex solutions |
• Delay Kubernetes adoption as long as possible |
• Use existing managed platforms instead of managing Kubernetes installations |
• Focus on automation and education when adopting Kubernetes |
• Be prepared to spend "innovation points" on learning and implementing Kubernetes |
• Kubernetes is more complicated than expected, with many configuration options and best practices. |
[0.22 --> 5.62] You are listening to ShipIt, a podcast about operations, infrastructure, and the people |
[5.62 --> 7.00] that are Kubernetes-ing. |
[8.04 --> 8.56] Kubernetes-ing. |
[9.28 --> 10.78] K-T-ing, you know what I mean. |
[11.44 --> 16.62] I'm your host, Gerhard Lassil, and in this episode, I'm joined by Tamer Saleh, founder |
[16.62 --> 20.40] of Super Orbital and former VP of Engineering at Pivotal. |
[20.86 --> 25.28] Many years ago, we both used to work in the same London office on Cloud Foundry, and nowadays |
[25.28 --> 26.44] we are into Kubernetes. |
[26.44 --> 31.32] We start with table tennis, remote work, and then we spend the rest of the time talking |
[31.32 --> 33.98] about the challenges that teams have with Kubernetes. |
[34.60 --> 39.26] Tamer and his Super Orbital team are deeply experienced in this topic, and they help teams |
[39.26 --> 44.78] at companies like Bloomberg, Shopify, and federal US agencies tackle hard Kubernetes and DevOps |
[44.78 --> 47.22] problems through engineering and training. |
[47.66 --> 50.40] So why do companies need Kubernetes in the first place? |
[50.80 --> 52.80] Which are the right reasons for choosing it? |
[53.14 --> 54.60] Is Kubernetes even a platform? |
[54.60 --> 59.54] My favorite, I'm doing Kubernetes wrong, but it works better than when I was doing |
[59.54 --> 59.98] it right. |
[60.24 --> 61.16] So what's up with that? |
[61.58 --> 63.22] This last one was a lot of fun. |
[63.54 --> 66.76] And, as your request, we left the entire minute of laughter in. |
[67.08 --> 70.66] Big thanks to our partners Fastly, LaunchDarkly, and Linode. |
[71.02 --> 72.82] Thank you for the great bandwidth Fastly. |
[73.26 --> 75.34] You can learn more at Fastly.com. |
[75.86 --> 80.10] Ship new features with confidence by getting your feature flags powered by LaunchDarkly.com. |
[80.10 --> 83.70] And thank you, Linode, for keeping our Kubernetes fast and simple. |
[84.20 --> 88.28] Run your setup as we do via Linode.com forward slash changelog. |
[88.28 --> 98.70] This episode is brought to you by Honeycomb. |
[99.16 --> 103.76] Honeycomb is built on the belief that there's a more efficient way to understand exactly what |
[103.76 --> 105.84] is happening in production right now. |
[105.84 --> 109.96] When production is running slow, it's hard to know exactly where problems originate. |
[110.26 --> 114.18] Is it your application code, your users, or the underlying systems? |
[114.18 --> 119.02] Teams who don't use Honeycomb scroll through endless dashboards guessing at what they mean. |
[119.26 --> 123.56] They deal with alert floods, guessing which ones matter, and go from tool to tool to tool, |
[123.86 --> 125.72] guessing at how the puzzle pieces all fit together. |
[126.04 --> 129.70] It's this context switching and tool sprawl that are slowly killing your teams and your |
[129.70 --> 130.16] business. |
[130.56 --> 135.06] With Honeycomb, you get a fast, unified, and clear understanding of the one thing driving |
[135.06 --> 136.44] your business, production. |
[136.88 --> 140.94] Honeycomb quickly shows you the correct source of issues, discover hidden problems, even in |
[140.94 --> 145.24] the most complex stacks, understand why your app feels slow to only some users. |
[145.66 --> 148.12] With Honeycomb, you guess less and no more. |
[148.56 --> 153.04] Join the swarm and try Honeycomb free today at honeycomb.io slash changelog. |
[153.04 --> 156.02] Again, honeycomb.io slash changelog. |
[156.02 --> 172.60] We are going to ship in three, two, one. |
[186.02 --> 190.50] It's been several years since we worked together, 2016, 2017. |
[191.44 --> 196.08] And I think it's been too long since you and me played the game of table tennis. |
[196.54 --> 197.14] How's your game? |
[197.24 --> 201.20] I was so bad at table tennis. |
[202.16 --> 203.06] That's not true. |
[203.46 --> 204.02] That's not true. |
[204.16 --> 205.18] I've seen the improvement. |
[206.00 --> 208.26] I've seen those years in which you really improved. |
[208.66 --> 211.02] And the last games that we've had were really good. |
[211.12 --> 212.06] So I enjoyed them. |
[212.20 --> 212.92] It was a lot of fun. |
[212.92 --> 217.04] I don't know if you know this, it was never official, but it always kind of seemed like |
[217.04 --> 221.86] your seniority at Pivotal would directly correlate with how good you were at table tennis. |
[222.84 --> 223.24] Yes. |
[224.52 --> 226.90] I knew that, but I never mentioned it to anyone. |
[227.04 --> 228.60] I think it was like a little thing. |
[228.74 --> 229.00] Yes. |
[229.68 --> 233.50] I'm pretty sure most of my engineers let me win just to make me feel better. |
[233.86 --> 234.58] I'm sorry. |
[235.02 --> 235.80] Not me. |
[236.98 --> 238.54] No, we had some great games. |
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