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Google·Software Engineer·Onsite - Coding / Algorithms·Senior

Senior
Jun 2026

Summary

Google SWE interview, algorithmic round focused on interval scheduling with a server capacity twist. The problem had a few layers that made it trickier than a standard meeting rooms question, and the follow-up pushed into system design territory.

Questions Asked (2)

Q1

You have a cluster of servers running daily jobs. Given a list of job intervals for a single day, where each interval has a job ID, start time, and end time, compute the minimum number of servers needed. The catch: multiple intervals sharing the same job ID can overlap on the same server without consuming extra capacity. Intervals are half-open ranges and jobs that run past midnight are truncated to the current day.

Algorithms & Data StructuresSystem Design
Author's notes

Looked like a classic meeting rooms problem until the same-job-ID overlap rule hit me.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and edge cases, especially the handling of overlapping intervals with the same job ID. Then, propose an algorithm that groups intervals by job ID and merges overlapping intervals within each job, followed by a sweep-line or priority queue approach to compute the maximum concurrent servers needed across all jobs.

Pro tip: Mention that you would validate the solution with edge cases like jobs that start and end at the same time, jobs truncated at midnight, and jobs with identical IDs but disjoint intervals. This shows attention to detail and robustness.

1. Clarify Requirements and Edge Cases

Ask questions to confirm: half-open intervals, truncation at midnight, and that overlapping intervals with the same job ID can share a server. Also discuss input size and whether intervals are sorted.

2. Preprocess Intervals by Job ID

Group intervals by job ID. For each job, merge overlapping intervals to form a set of non-overlapping intervals representing the job's total occupied time.

3. Compute Maximum Concurrency

Collect all merged intervals from all jobs. Use a sweep-line algorithm (or priority queue) to find the maximum number of overlapping intervals at any point in time, which equals the minimum servers needed.

4. Analyze Complexity and Optimize

Discuss time and space complexity. If needed, optimize by sorting events and using a min-heap to track end times, achieving O(N log N) time.

5. Test and Validate

Walk through examples, including edge cases like midnight truncation, same job ID with overlapping intervals, and no jobs. Verify the algorithm produces correct results.

Key Points to Mention

  • Grouping intervals by job ID and merging overlapping intervals within each job.
  • Using a sweep-line algorithm or priority queue to compute maximum concurrency.
  • Handling half-open intervals correctly (e.g., [start, end) means end time is exclusive).
  • Truncating jobs that run past midnight to the current day (e.g., end time becomes 24:00).
  • Time complexity: O(N log N) due to sorting, where N is total number of intervals.
  • Edge cases: jobs with same start and end time, jobs with zero duration, and jobs that exactly touch at boundaries.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Follow-up: some jobs are long-running and span multiple days without restarting. For those, you want a dedicated server that isn't shared with other jobs. How would you detect which jobs need a dedicated server and how does that change your total server count calculation?

System DesignTechnical Trade-offs
Author's notes

This one I fumbled a bit.

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AI HintsAI Generated

Suggested Approach

First, define criteria to identify long-running jobs, such as expected duration exceeding a threshold or requiring persistent state. Then, explain how to allocate dedicated servers for those jobs and adjust the total server count by adding the dedicated servers to the shared pool calculation, while considering utilization and peak load.

Pro tip: Mention that dedicated servers can be underutilized, so consider bin-packing or time-sharing among long-running jobs if they don't require full isolation. Also, highlight the importance of monitoring and dynamic adjustment to avoid over-provisioning.

1. Define criteria for dedicated servers

Establish thresholds for job duration (e.g., >24 hours) and resource requirements (e.g., high memory, persistent connections) that necessitate a dedicated server.

2. Detect qualifying jobs

Use job metadata, historical data, or runtime analysis to classify jobs as long-running and flag them for dedicated allocation.

3. Calculate dedicated server needs

Estimate the number of dedicated servers required based on the count of long-running jobs and their resource profiles, considering peak concurrency.

4. Adjust total server count

Add the dedicated servers to the shared pool calculation, ensuring that shared servers are sized for the remaining jobs without the long-running ones.

5. Optimize and validate

Consider bin-packing or time-sharing for dedicated servers if jobs don't fully utilize them, and validate the model with monitoring and feedback loops.

Key Points to Mention

  • Threshold-based detection using job duration and resource requirements
  • Impact on capacity planning: dedicated servers increase total count but may reduce contention on shared servers
  • Utilization trade-offs: dedicated servers may be underutilized, so consider consolidation
  • Dynamic allocation: jobs may change over time, so periodic re-evaluation is needed
  • Cost implications: dedicated servers can be more expensive, so balance isolation with efficiency
  • Monitoring and metrics: track job runtimes and server utilization to refine criteria

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.