Start by defining each resource at a high level, then contrast their roles: Pod as the atomic unit, Deployment as a controller for Pods, and Service as a stable network endpoint. Use a concrete example like a web app to illustrate how they work together, and highlight trade-offs such as self-healing and scalability.
Pro tip: Emphasize that Deployments manage Pods via ReplicaSets, enabling rolling updates and rollbacks, while Services abstract Pod IPs with selectors—this shows you understand the operational layer beyond just definitions.
Explain that a Pod is the smallest deployable unit, encapsulating one or more containers that share network and storage. Mention that Pods are ephemeral and not self-healing.
Describe a Deployment as a higher-level controller that manages ReplicaSets, which in turn manage Pods. Highlight features like declarative updates, scaling, and self-healing.
Clarify that you rarely create Pods directly; Deployments ensure desired state, handle rolling updates, and provide rollback. Pods are the 'what', Deployments are the 'how many and how to update'.
Explain that a Service provides a stable IP and DNS name for a set of Pods, using selectors to route traffic. Mention types like ClusterIP, NodePort, LoadBalancer.
Walk through a simple scenario: a Deployment creates 3 Pods running a web server; a Service load-balances traffic to them. This shows how they interact in a real system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by describing a systematic, layer-by-layer debugging process for a Pod that won't start, emphasizing the use of kubectl commands to inspect events, logs, and configuration. Highlight how you would interpret common error states and adjust your approach based on findings, while communicating clearly and efficiently.
Pro tip: Mention that you always check the Pod's events first with `kubectl describe pod` because it often reveals the root cause (e.g., image pull errors, scheduling issues) without needing to dig deeper. Also, note that you keep a mental checklist of common failure modes to speed up diagnosis.
Use `kubectl get pod <pod-name>` and `kubectl describe pod <pod-name>` to see the current state and recent events. Look for error messages like ImagePullBackOff, CrashLoopBackOff, or scheduling failures.
If the container started but crashed, use `kubectl logs <pod-name>` (add `--previous` if it restarted) to see application errors. For multi-container pods, specify the container with `-c`.
Check the Pod spec for issues: `kubectl get pod <pod-name> -o yaml`. Look for misconfigured environment variables, missing ConfigMaps/Secrets, volume mount problems, or incorrect resource requests/limits.
Ensure nodes have capacity and are healthy: `kubectl get nodes`, `kubectl describe node <node-name>`. Also check for network policies, service account permissions, or admission webhooks that might block the Pod.
Based on findings, fix the issue (e.g., correct image name, adjust resources, update ConfigMap) and redeploy. If needed, exec into a debug container or use `kubectl debug` to further investigate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I had the definitions down but the prioritization part got messy.
Start by clearly defining SLI, SLO, and SLA with concrete examples, then outline a structured incident response process for SLO breaches that includes detection, triage, mitigation, and postmortem. Emphasize prioritization based on user impact, error budget burn rate, and business criticality.
Pro tip: Frame your answer around the error budget concept: it turns reliability into a data-driven trade-off between feature velocity and stability, which resonates with TikTok's fast-paced, metrics-driven culture.
Define SLI as a quantitative measure of service behavior (e.g., request latency, error rate), SLO as an internal target for an SLI (e.g., 99.9% availability), and SLA as a contractual agreement with consequences for missing SLOs.
Confirm the SLO breach using monitoring and alerting tools, and assess the scope, severity, and user impact. Check if it's a false positive or a real incident.
Assemble the incident response team, prioritize mitigation actions (e.g., rollback, scaling, failover) to restore service within the error budget, and communicate status to stakeholders.
Rank issues by user impact, error budget burn rate, and business criticality. Focus on high-impact, fast-burning issues first, and consider pausing feature releases if the budget is exhausted.
Perform a blameless postmortem to identify root causes, implement corrective actions, and adjust SLOs or error budgets if needed to better reflect user expectations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the hardest question and honestly the most interesting.
Start by clarifying the scope and impact of the latency issue, then systematically walk through each layer (network, load balancer, database, cache, service) using a top-down or bottom-up approach. For each layer, describe the key metrics you'd check and the experiments you'd run to isolate the bottleneck, emphasizing data-driven diagnosis and iterative hypothesis testing.
Pro tip: Always correlate metrics across layers and use distributed tracing to pinpoint the exact service and operation causing latency, rather than guessing. Mention the importance of establishing a baseline and comparing against it to identify anomalies.
Ask clarifying questions to understand the latency issue: when did it start, what's the impact, is it affecting all requests or specific endpoints, and what's the expected latency? This helps narrow down the investigation.
Look at overall service latency metrics (p50, p95, p99), error rates, and throughput. Use distributed tracing to identify which service or component is contributing most to the latency.
Starting from the network, check for packet loss, retransmissions, and DNS resolution times. Then examine load balancer metrics (request rate, latency, error rates, backend health). For the database, check query latency, slow queries, connection pool usage, and replication lag. For the cache, check hit rate, eviction rate, and latency. Finally, inspect the service itself: CPU, memory, GC pauses, thread pools, and external calls.
Based on hypotheses, run experiments such as: canary deployments to test changes, load testing to reproduce, enabling debug logging, or using chaos engineering to simulate failures. Compare results to baseline to confirm root cause.
Once root cause is identified, apply immediate mitigation (e.g., scaling, rollback, caching) and propose long-term fixes (e.g., code optimization, infrastructure changes, monitoring improvements).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the core differences between SQL and NoSQL databases in terms of data model, schema, scalability, and consistency. Then, discuss specific scenarios where NoSQL is preferred, such as handling large volumes of unstructured data, need for horizontal scalability, and flexible schema. Finally, tie your answer to TikTok's scale and real-time data needs to show relevance.
Pro tip: Emphasize that the choice is not about one being better, but about trade-offs; mention that many large-scale systems use a polyglot persistence approach, combining both SQL and NoSQL databases where appropriate.
Briefly explain that SQL databases are relational, table-based, with fixed schemas and ACID transactions, while NoSQL databases are non-relational, distributed, with flexible schemas and BASE properties.
Contrast them on data model, scalability (vertical vs. horizontal), consistency (strong vs. eventual), and schema flexibility.
List situations where NoSQL excels: large-scale, low-latency applications, unstructured or semi-structured data, rapid development with evolving schemas, and high write throughput.
Connect to TikTok's needs: massive user base, real-time feeds, user-generated content, and global distribution, which often favor NoSQL solutions like Cassandra, MongoDB, or Redis.
Mention that SQL is still preferred for complex transactions and strong consistency, and that the best choice depends on specific requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.