Analytics Infrastructure and Query Performance Questions

Designing analytics data infrastructure and optimizing query performance for analytics workloads. Includes data modeling for analytics, columnar versus row storage trade offs, clustering and partitioning strategies, indexing and materialized views, caching and result reuse, profiling and tuning slow queries, cost and latency trade offs for large scale analytics, and considerations for ingest pipelines and analytical storage choices.

EasyTechnical

22 practiced

You need to choose between using an OLAP cube pre-aggregation layer or relying on ad-hoc SQL on a columnar warehouse for business reporting. List pros and cons for both approaches and recommend an approach for a product analytics team that needs near-real-time metrics updated every 5 minutes.

HardTechnical

18 practiced

Design a testing strategy to validate that a new compaction algorithm for your analytics store does not change query results and improves latency. Include unit tests, integration tests, benchmarks, and safety rollbacks.

HardTechnical

18 practiced

Write a parameterized SQL query (in the dialect of your choice) that computes a 7-day rolling retention metric per cohort for user sign-ups, optimized to avoid scanning entire tables for each cohort. Describe indexing/partitioning that makes this query efficient.

Sample Answer

Approach: compute cohorts by signup_date and join a pre-aggregated activity table bucketed by day to avoid repeated full scans. Use parameterized SQL with windowing and efficient partition pruning.

Example (Postgres/Postgres-compatible SQL):

sql

WITH signups AS (
  SELECT user_id, DATE(signup_ts) AS signup_day
  FROM users
  WHERE signup_day BETWEEN $1 AND $2
), activity AS (
  SELECT user_id, DATE(event_ts) AS event_day
  FROM events
  WHERE event_day BETWEEN $3 AND $4
)
SELECT s.signup_day AS cohort_day,
       a.event_day,
       COUNT(DISTINCT a.user_id) AS retained_users,
       COUNT(DISTINCT s.user_id) AS cohort_size,
       ROUND( COUNT(DISTINCT a.user_id)::numeric / GREATEST(COUNT(DISTINCT s.user_id),1), 4) AS retention
FROM signups s
JOIN activity a ON s.user_id = a.user_id
  AND a.event_day BETWEEN s.signup_day AND s.signup_day + INTERVAL '6 days'
GROUP BY cohort_day, a.event_day
ORDER BY cohort_day, a.event_day;

Partitioning/indexing: partition users and events by event_date (daily) and create composite indexes: events(event_day, user_id) and users(signup_day, user_id). Also maintain a daily pre-aggregated active_users(user_id, event_day) or bloom-filtered sketches for heavy tables to speed joins and reduce scans.

MediumTechnical

22 practiced

Describe the role of statistics and histograms in query optimizers. How can stale or missing statistics lead to poor query plans, and what strategies can you use to maintain accurate statistics for large, rapidly changing datasets?

MediumTechnical

23 practiced

Compare and contrast partitioning strategies: range, hash, and list partitioning. For each, provide a concrete example workload where it's the preferred strategy and explain why.

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