2 min lesson
SQL & Python craft for scale
Explain what the example in "SQL & Python craft for scale" is doing and why it matters.
Step 1 of 3
Cursor publishes no interview stages, but the posting's charter points at SQL on event data and applied stats. Rehearse as if you'll be asked to pull p95 latency by model from a billion-row table without melting the warehouse, and to make that number rerunnable tomorrow.
Prepare three skills: window functions for sessionization and funnels, percentile aggregations for latency, and cost-aware querying so your scan touches partitions instead of the whole table. Show all three on one realistic problem and you've covered the ground the charter implies.
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Advanced table
Cost-aware querying on billions of rows
Cost-aware querying on billions of rows
- Technique
- Partition pruning
- What it buys
- Scan days, not years
- When to reach for it
- Always: filter on the partition key first
- Technique
- Pre-aggregation / rollups
- What it buys
- Dashboards read a small table
- When to reach for it
- Repeated, well-known reliability questions
- Technique
- Sampling
- What it buys
- Fast exploratory answers
- When to reach for it
- Iterating on a hypothesis, not a final number
- Technique
- Approximate quantiles
- What it buys
- Tail metrics at scale
- When to reach for it
- p95/p99 over huge groups
- Technique
- Column pruning
- What it buys
- Less I/O on wide event tables
- When to reach for it
- Select only the columns you need
| Technique | What it buys | When to reach for it |
|---|---|---|
| Partition pruning | Scan days, not years | Always: filter on the partition key first |
| Pre-aggregation / rollups | Dashboards read a small table | Repeated, well-known reliability questions |
| Sampling | Fast exploratory answers | Iterating on a hypothesis, not a final number |
| Approximate quantiles | Tail metrics at scale | p95/p99 over huge groups |
| Column pruning | Less I/O on wide event tables | Select only the columns you need |
On billions of rows, the query plan is part of the analysis, and a correct number you can't afford is not an answer.
Python for log analysis and detection
SQL gets you aggregates; Python gets you the modeling SQL can't express. Pull a manageable slice with pandas or polars, run hypothesis tests with scipy/statsmodels and prototype lightweight detection, such as a change-point or anomaly check, before anyone builds the production version.
from statsmodels.stats.proportion import proportions_ztest
# successes / totals for control arm vs canary arm, same window
successes = [control_success, canary_success]
totals = [control_total, canary_total]
stat, pval = proportions_ztest(count=successes, nobs=totals)
delta = canary_success / canary_total - control_success / control_total
print(f"success-rate delta={delta:+.3%} p={pval:.4f}")
# A real regression should clear significance AND be large enough to matter
# Pre/post needs a time-matched baseline; cluster by user or bootstrap when attempts correlateAt billions of interactions, a 0.01% change is statistically significant and operationally meaningless. Always pair the p-value with the effect size and a materiality bar: does this move the error budget, and does it affect a meaningful number of users? Reporting significance alone in this role reads as junior.