I started with the data model which felt right but I spent too long on it and barely had time for the dispatch path.
Start by clarifying functional and non-functional requirements, then propose a high-level architecture with core components like job queue, scheduler, and workers. Finally, detail the dispatch mechanism, including how workers pull jobs and handle failures.
Pro tip: Emphasize idempotency and at-least-once delivery semantics to show you understand real-world reliability concerns. Also, discuss how you would monitor and scale the system, as Oracle values operational excellence.
Ask questions to understand job types (e.g., one-time, recurring), scale (jobs per second), latency, durability, and failure handling. Define functional requirements like job submission, scheduling, execution, and monitoring.
Outline components: API for job submission, persistent job store, scheduler service, job queue, worker pool, and monitoring. Explain how they interact and the data flow.
Describe how jobs are stored (e.g., relational DB for metadata, queue for pending jobs). Include fields like job ID, payload, schedule time, status, retry count, and dependencies.
Explain how the scheduler picks jobs (e.g., priority, FIFO, cron) and dispatches them to workers. Discuss push vs. pull models, and how to ensure exactly-once or at-least-once execution.
Cover fault tolerance (retries, dead-letter queues), scaling workers horizontally, and handling failures (worker crashes, network issues). Mention monitoring and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through at-least-once delivery with idempotency keys and it landed okay.
Start by clarifying the requirements: what types of jobs, expected scale, and failure semantics. Then describe a design that separates retry logic from dependency management, using a state machine for job states and a DAG for dependencies. Discuss trade-offs between simplicity and robustness, and how to handle partial failures and idempotency.
Pro tip: Emphasize idempotency and exactly-once semantics: in distributed systems, retries can cause duplicate execution, so designing jobs to be idempotent is crucial. Also, mention that dependency resolution should be event-driven to avoid polling overhead.
Ask about job types (batch, streaming), scale (jobs per second), failure handling expectations, and whether exactly-once or at-least-once semantics are needed. This shows you don't jump to solutions.
Describe retry policies: exponential backoff with jitter, max retries, dead-letter queues. Explain how to track retry counts and avoid infinite loops. Mention idempotency keys to prevent duplicate side effects.
Use a directed acyclic graph (DAG) to represent dependencies. Explain how to detect cycles, topologically sort, and trigger dependent jobs only after all parents succeed. Discuss handling of failed dependencies (e.g., skip or fail downstream).
Explain how retries affect dependency scheduling: a job's retry should not block independent jobs, but dependent jobs must wait until the job succeeds or is permanently failed. Use a state machine (PENDING, RUNNING, RETRYING, SUCCEEDED, FAILED) to manage transitions.
Compare approaches: centralized scheduler vs. distributed workers, polling vs. event-driven. Mention how to scale (sharding, partitioning) and ensure fault tolerance (persistence, leader election). Highlight trade-offs between consistency and availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Leader election was the answer they were fishing for and I got there, but I framed it as an afterthought rather than a core design decision.
Start by clarifying the scheduler's role and current architecture, then discuss scaling dimensions (horizontal vs vertical) and HA strategies (replication, failover). Emphasize trade-offs like consistency vs availability and how you'd handle stateful components.
Pro tip: Mention that scaling the scheduler often requires decoupling state from compute, and that using a distributed consensus system like Raft or Paxos for leader election is key to HA. Also, highlight the importance of idempotent job execution to handle retries safely.
Ask about the scheduler's workload (e.g., number of jobs, frequency), consistency needs, and latency requirements. This shows you tailor solutions to specific needs.
Discuss horizontal scaling by partitioning jobs across multiple scheduler instances (e.g., by job type or hash), and vertical scaling for short-term gains. Mention using a distributed queue or sharding.
Explain active-passive or active-active setups with leader election (e.g., using ZooKeeper, etcd) and automatic failover. For active-active, ensure idempotency and conflict resolution.
Describe how to persist scheduler state (e.g., job metadata, locks) in a replicated database or distributed store like Cassandra, ensuring durability and consistency.
Acknowledge trade-offs like increased complexity vs. scalability, and the need for monitoring, alerting, and chaos testing to validate HA.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one I actually had a decent answer for.
Start by explaining the mechanics of a missed scheduled run in a distributed scheduler, then discuss the trade-offs between catch-up and skip based on job semantics, idempotency, and business impact. Conclude with a decision framework that considers factors like job criticality, data freshness requirements, and system load.
Pro tip: Emphasize that the decision should be configurable per job, and mention that you'd implement monitoring and alerting for missed runs to detect issues early. This shows you think about operational maturity and not just the immediate fix.
Describe how the scheduler detects a missed run (e.g., due to downtime, overload, or misconfiguration) and the typical outcomes: the run is either skipped, queued for catch-up, or triggers an alert.
Clarify that catch-up means executing the missed run(s) as soon as possible, while skip means ignoring the missed run and waiting for the next scheduled time.
Discuss the pros and cons of each approach: catch-up ensures data completeness but may cause resource contention or duplicate processing; skip avoids overload but may lead to stale data or missed SLAs.
Evaluate factors such as idempotency, criticality, data dependencies, and business requirements to decide which approach is appropriate for a given job.
Outline a policy: for idempotent, critical jobs, catch-up; for non-idempotent or non-critical jobs, skip; and make it configurable with monitoring and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rattled off queue depth, job duration, and success rate.
Start by clarifying the system's critical user journeys and business goals, then propose a layered observability strategy covering metrics, logs, and traces. Focus on actionable metrics that tie directly to reliability, performance, and user experience, and explain how you'd use them for alerting and debugging.
Pro tip: Tie every metric to a specific failure mode or business outcome, and mention how you'd avoid alert fatigue by setting thresholds based on SLOs. Show you understand that observability is not just about collecting data but about enabling fast diagnosis and data-driven decisions.
Ask about the system's key user journeys, business objectives, and non-functional requirements (e.g., latency, availability). This ensures your observability plan aligns with what matters most.
Outline how you'd instrument metrics, logs, and distributed traces. Explain what each pillar provides and how they complement each other for debugging and monitoring.
Propose specific metrics for infrastructure (CPU, memory), application (request rate, error rate, latency), and business (conversion, revenue). Use frameworks like RED (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors).
Explain how you'd set SLOs and alerts based on symptoms (e.g., high latency) rather than causes (e.g., high CPU). Describe dashboards for different audiences (on-call, product, executives).
Mention specific tools (e.g., Prometheus, Grafana, ELK, Jaeger) and how you'd evolve the observability stack as the system scales. Emphasize continuous improvement based on incidents and feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pull vs push is one of those trade-offs where both answers are correct depending on context and I tried to say exactly that.
Start by defining both models clearly: pull-based (workers request jobs when ready) and push-based (dispatcher assigns jobs to workers). Then compare them across key dimensions like load balancing, fault tolerance, latency, and complexity, and conclude with when to use each, ideally tying it to Oracle's scale and reliability needs.
Pro tip: Mention that many real-world systems use a hybrid approach, such as push-based dispatch with pull-based backpressure, to get the best of both worlds. This shows you understand practical trade-offs beyond textbook definitions.
Briefly explain pull-based (workers poll or request work) and push-based (dispatcher sends work to workers) dispatch, including the direction of communication.
Analyze differences in load balancing, latency, throughput, fault tolerance, and complexity. For example, pull-based naturally handles slow workers, while push-based can reduce latency but requires careful load tracking.
Highlight scenarios where each model excels or fails, such as push-based causing overload on slow workers, or pull-based adding polling overhead and potential idle time.
Give examples like Kafka (pull-based consumers) vs. traditional message queues (push-based), and mention hybrid approaches used in large-scale systems.
Summarize when to choose each model based on requirements like scalability, latency sensitivity, and operational complexity, and note that the choice often depends on the specific use case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.