Day 87: Debug & Optimize Caching System

Day 87 — Debug & Optimize Caching System

Coding problem

Solution outline (coding)

• Reproduce the bug with minimal input; add logging around cache hits/misses.
• Check TTL, stampede, eviction policy, and key collision handling.
• Optimize hot path after correctness — measure before micro-optimizing.

Time complexity: Varies — debugging session.

Space complexity: Varies.

class ReviewDay:
  """Practice / review: Debug & Optimize Caching System."""

  def practice_plan(self):
    return [
      "Pick 2–3 problems from this phase; re-solve timed without notes.",
      "For each: pattern name, time/space complexity, one alternative approach.",
    ]


# Input:  (your choice of problems from this week or phase)
# Output: a short list of gaps to drill before the next session

SQL interview practice

1. Interview question

Companies / track: OpenAI

OpenAI / API-scale products: SQL often covers usage logs, experiments, and evaluation pipelines—clarity on keys and grain matters as much as syntax.

What you are asked to write (SQL prompt):

Frame this as metrics work for **OpenAI**-scale surfaces (ads, product, or engagement — as the tables suggest).
cache_events(key, ts, event_type, latency_ms). BigQuery: debug cache by computing hit/miss ratios, stale reads, and P99 latency before/after config change.

Tables implied by the prompt:

• cache_events(key, ts, event_type, latency_ms)

Engine: BigQuery — use its date, array, and approximate functions as documented.

2. Solution outline

• Clarify out loud: result grain (one row per what?), join keys, time zone, and any ORDER BY / LIMIT / tie-breakers.
• Map Debug / Design to SQL: say the relational equivalent (e.g. hash map → GROUP BY + key; two pointers → ordered window + filter).
• Rates: SAFE_DIVIDE or NULLIF; define numerator and denominator.
• Percentiles: approx vs exact; say so if you use APPROX_QUANTILES.
• Structure: CTEs (WITH) — one step per CTE; validate on a tiny slice (counts, nulls, duplicates).

SELECT event_type,
  COUNTIF(event_type = 'hit') / COUNT(*) AS hit_ratio,
  APPROX_QUANTILES(latency_ms, 100)[OFFSET(99)] AS p99
FROM cache_events
GROUP BY event_type;