Day 35: Review – Week 5

Day 35 — Review – Week 5

Coding problem

Solution outline (coding)

• Review graph BFS, topo sort, union-find — pick two implementations you would not write bug-free first try.
• Timed redo: islands + one of course schedule / accounts merge.
• Document template: when to use BFS vs DFS vs UF.

Time complexity: Varies.

Space complexity: Varies.

class ReviewDay:
  """Practice / review: Review – Week 5."""

  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: Review / mixed (see weekly theme)

This is a review / mixed day. Expect SQL that blends data quality, funnels, and metric definitions—the same mix you see across consumer tech and ads analytics.

What you are asked to write (SQL prompt):

Review / mixed week — use the same tables and deliverables as in a standard onsite SQL round.
For all graph-based datasets, design BigQuery queries to monitor component size distribution and emergence of giant components over time.

Tables implied by the prompt:

• Infer schemas from the prompt and state them before coding.

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 Review to SQL: say the relational equivalent (e.g. hash map → GROUP BY + key; two pointers → ordered window + filter).
• Structure: CTEs (WITH) — one step per CTE; validate on a tiny slice (counts, nulls, duplicates).

SELECT run_date, APPROX_QUANTILES(component_size, 100)[OFFSET(50)] AS p50_comp_size
FROM graph_component_stats
GROUP BY run_date;