Seemed straightforward at first and I kind of rushed it.
Start by clarifying the definitions of DAU, WAU, and MAU in terms of the clickstream events table, then write separate queries that count distinct users per day, week, and month. Use date functions to truncate event timestamps to the appropriate granularity and join with the users table if needed to filter valid users.
Pro tip: Mention that DAU/WAU/MAU are typically computed on a rolling basis (e.g., last 7 days) rather than calendar weeks, and clarify the time zone and event type (e.g., only 'click' events) to avoid ambiguity.
Confirm what constitutes an active user (e.g., any event vs. specific event type) and the time windows (calendar vs. rolling). Also check if the users table is needed to filter out bots or inactive accounts.
Locate the user_id and event_timestamp columns in the clickstream table. Determine the SQL date functions to truncate timestamps to day, week, and month (e.g., DATE_TRUNC, DATE_FORMAT).
Count distinct user_id per day by grouping on the truncated date. Optionally join with users table to include only valid users.
Similarly, count distinct user_id per week and per month using appropriate date truncation. For rolling metrics, use a window function or self-join to count distinct users in the last 7 or 30 days.
If needed, combine the three metrics into a single query using conditional aggregation or UNION ALL. Mention indexing on user_id and event_timestamp for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one took me a minute to set up correctly.
First, clarify the schema and definitions of D1 and W1 retention (e.g., D1 = users who return the day after signup, W1 = users who return within 7 days). Then, write a SQL query that identifies cohorts by signup date, calculates retention based on activity dates, and aggregates results. Use a consistent date logic and handle edge cases like time zones.
Pro tip: Mention that you would validate the retention definition with stakeholders and consider using a calendar date table to handle gaps in activity data, ensuring accurate cohort sizes.
Confirm what D1 and W1 retention mean (e.g., D1: user returns exactly 1 day after signup; W1: user returns within 7 days). Identify key tables: users (with signup date) and events (with user_id and activity date).
Group users by their signup date to form daily cohorts. For each cohort, calculate the total number of users.
For each cohort, determine which users performed the desired action (e.g., logged in) on the target day(s). Use date arithmetic (e.g., DATE_ADD) to compare activity date to signup date.
Join cohort sizes with retained user counts, compute retention rate as retained users divided by cohort size, and format the output (e.g., by cohort date).
Consider indexing, use CTEs for readability, and validate results with sample data or known benchmarks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining the concept of sessionization: grouping user events into sessions based on a timeout (e.g., 30 minutes of inactivity). Then describe a SQL approach using window functions to identify session boundaries and assign session IDs, and finally list key metrics like session duration, page views, and conversion rate.
Pro tip: Mention that sessionization can be done efficiently with a single pass using window functions, and consider edge cases like multiple sessions per user per day and handling of late-arriving data.
Explain that a session starts with the first event and ends after a period of inactivity (e.g., 30 minutes). Use the LAG function to compare timestamps and flag new sessions when the gap exceeds the threshold.
Use a cumulative sum of the new session flags to assign a unique session ID per user. This can be done with SUM() OVER (PARTITION BY user_id ORDER BY event_time).
Aggregate events by session ID to calculate metrics such as session start/end time, duration, number of events, and distinct pages visited.
From session-level data, compute higher-level metrics like bounce rate, conversion rate, and average session duration per user or cohort.
Discuss performance considerations (e.g., partitioning, indexing) and edge cases like sessions spanning midnight or multiple sessions in a short period.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the idempotency framing.
Start by clarifying the requirements: what aggregation tables, what grain, what source data, and how late-arriving data is handled. Then describe a two-phase approach: first compute the incremental delta using window functions to identify new or changed records, then apply the delta to the aggregation table using an idempotent MERGE or INSERT ... ON CONFLICT pattern. Emphasize how the design ensures exactly-once semantics and handles reruns safely.
Pro tip: Mention that idempotency is best achieved by making the job deterministic and keyed on a natural business key plus a processing date, so rerunning the same partition produces the same result. Also note that using MERGE with a source that includes only the latest version per key (via ROW_NUMBER) avoids duplicate updates and is more efficient than deleting and reinserting.
Ask about the aggregation grain, source tables, update frequency, data volume, and how late or out-of-order data is handled. Confirm whether the job must be exactly-once and what the SLA is.
Use window functions like ROW_NUMBER() or RANK() to deduplicate source data and select the latest record per key, and use LAG/LEAD or date filters to identify only new or changed rows since the last run. This minimizes the delta to process.
Use MERGE (or INSERT ... ON CONFLICT DO UPDATE) to upsert the delta into the aggregation table, matching on the aggregation key. Ensure the merge condition includes a timestamp or version to avoid overwriting newer data with older data.
Explain how to handle late data by either reprocessing affected partitions or using a merge that updates aggregates for the impacted keys. Show how the same logic can be rerun for a given date range without duplicating results.
Compare MERGE vs. INSERT patterns in terms of performance, locking, and complexity. Mention partitioning, indexing, and monitoring to ensure the job scales and is observable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said something about using ROW_NUMBER partitioned by a natural key to deduplicate, and watermarks for late events.
Start by clarifying the pipeline's requirements and constraints, then explain how you detect duplicates and late data using techniques like event-time processing, watermarks, and idempotent operations. Finally, discuss trade-offs between correctness, latency, and cost, and how you handle late data with strategies like allowed lateness, side outputs, or reprocessing.
Pro tip: Emphasize that you design for idempotency and exactly-once semantics from the start, and mention how you monitor and alert on duplicate and late data rates to catch issues early.
Ask about data sources, volume, latency requirements, and business impact of duplicates or late data. This shows you understand the problem context before diving into solutions.
Explain methods like unique event IDs, deduplication windows, and event-time vs processing-time comparison. Mention monitoring metrics like duplicate rate and lateness distribution.
Describe idempotent writes, deduplication keys, and exactly-once processing frameworks (e.g., Flink, Kafka). Discuss trade-offs between at-least-once and exactly-once.
Cover strategies like watermarks with allowed lateness, side outputs for late events, and reprocessing or backfilling. Explain how you decide when to drop vs. process late data.
Summarize trade-offs between correctness, latency, and cost. Highlight the importance of monitoring, alerting, and iterative improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Used json.loads in a loop with some try/except for malformed records, then pandas for the normalization and pyarrow to write Parquet.
Start by clarifying the schema and partitioning strategy, then outline a lightweight pipeline using pandas and pyarrow to parse JSON, flatten nested fields, and write partitioned Parquet. Emphasize trade-offs around memory, schema evolution, and partition granularity.
Pro tip: Mention that you'd first sample the JSON to infer a schema and handle edge cases like missing keys or type inconsistencies, and use pyarrow's dataset API for efficient partitioned writes without loading everything into memory.
Ask about the expected JSON structure, partitioning keys (e.g., date, region), and volume of data to determine if a streaming or batch approach is needed.
Use pandas.json_normalize to flatten nested JSON, handle missing fields, and enforce a consistent schema across records.
Leverage pyarrow to write Parquet files partitioned by the chosen keys, using a dataset writer to avoid loading all data into memory.
Check output for schema consistency, partition pruning efficiency, and file sizes; consider compression and row group sizing for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining a testing strategy that covers both happy paths and edge cases for each data-cleaning function. Emphasize the use of a testing framework like pytest or unittest, and describe how you would structure tests to be fast, isolated, and maintainable. Conclude by mentioning how you would integrate these tests into a CI pipeline to catch regressions early.
Pro tip: Use parameterized tests to efficiently cover multiple input-output scenarios, and always include tests for malformed or unexpected data to ensure robustness. This demonstrates foresight and a quality-first mindset that Meta values.
Break down the data-cleaning script into individual functions or methods, such as handling missing values, type conversions, or deduplication. Each unit should be tested in isolation to pinpoint failures.
For each unit, specify the expected output for typical inputs and enumerate edge cases like empty datasets, null values, or unexpected formats. This ensures comprehensive coverage.
Select a framework like pytest or unittest, and write test functions that assert the expected outcomes. Use fixtures or setup methods to create consistent test data.
Execute the tests locally and use coverage tools to identify untested code paths. Aim for high coverage but prioritize critical paths.
Configure the tests to run automatically on each commit or pull request using a CI service like GitHub Actions or Jenkins. This ensures early detection of regressions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.