Window Functions and Time Series Analytics Questions
Advanced SQL window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and aggregate functions (SUM, COUNT, AVG) with OVER and partition clauses. Using window functions to solve practical problems: ranking users or events within segments, calculating running totals and cumulative metrics, identifying trends and transitions over time, detecting patterns in user behavior sequences. Applications: cohort retention analysis (calculating retention rates across cohorts), user lifetime value trends, engagement metrics over time windows, and sequential user actions.
EasyTechnical
0 practiced
Explain the difference between PARTITION BY in window functions and GROUP BY. Provide examples where each is appropriate and show a SQL snippet that uses both in the same query. Highlight when PARTITION BY preserves row-level detail.
HardTechnical
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Explain pitfalls when using RANGE BETWEEN INTERVAL for time-based windows in SQL engines that interpret RANGE logically rather than physically. Describe how non-uniform timestamp distributions and timezone-aware timestamps can lead to surprising window sizes and provide mitigation patterns.
HardTechnical
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COUNT(DISTINCT) over a window is unsupported and too slow for your dataset. Design a batch architecture that computes daily cumulative unique users per product across 5 years with incremental updates. Include storage format, how to maintain and merge sketches (e.g., HLL), backfill approach, and how to produce exact counts for small cohorts.
HardTechnical
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A complex analytics query computes several window aggregates over the same partition and ordering but the engine recomputes the partition multiple times, causing large shuffles. Describe how you'd rewrite the SQL or use materialization to reduce repeated work. Include engine-specific tactics for Spark and BigQuery.
MediumTechnical
0 practiced
When using Spark SQL to compute window functions over very large event tables, what execution and memory considerations should you account for? Describe specific strategies such as repartitioning, broadcast joins, adjusting shuffle partitions, and avoiding skew. Provide example Spark SQL or DataFrame snippets illustrating repartitioning before windowing.
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