Answer to the question

Utilize a CTE (Common Table Expression) combined with the ROW_NUMBER() window function to tag duplicates deterministically. Partition the dataset by the unique logical key columns (sensor_id, granularity), and apply an ORDER BY clause that reflects your retention priority: signal_strength DESC followed by timestamp ASC, and crucially, the PRIMARY KEY (e.g., log_id) as a final tie-breaker to guarantee determinism. The outer query then deletes all records where the assigned row number exceeds one, ensuring only the highest-priority row per group survives.

WITH RankedLogs AS (
  SELECT log_id,
         ROW_NUMBER() OVER (
           PARTITION BY sensor_id, granularity 
           ORDER BY signal_strength DESC, timestamp ASC, log_id ASC
         ) AS priority_rank
  FROM telemetry_logs
)
DELETE FROM telemetry_logs
WHERE log_id IN (
  SELECT log_id 
  FROM RankedLogs 
  WHERE priority_rank > 1
);

Situation from life

An industrial IoT platform ingested high-frequency vibration data from manufacturing robots into a table named machine_telemetry. Due to MQTT broker retries during network partitions, approximately forty percent of the table consisted of duplicate recordings sharing the same robot_id and time_bucket, yet differing slightly in payload checksums. Reporting dashboards were double-counting operational hours, skewing maintenance schedules.

Solution 1: Correlated Self-Join. One approach involved joining the table to itself on robot_id and time_bucket, deleting rows where the surrogate key was greater than the partner's key. This method required no window functions. However, its time complexity approached O(N²), causing severe performance degradation on the 300-million-row dataset, and it handled NULL values in composite keys incorrectly by failing to match them.

Solution 2: Staging Table with Grouping. Engineers considered creating a temporary table housing only the surviving log_ids identified via GROUP BY and MIN() aggregations, then truncating the original and re-inserting. While logically sound, this required significant temporary storage space, necessitated DDL privileges not available in the restricted production environment, and created a brief window where data appeared missing to concurrent readers.

Solution 3: Window Function CTE. The team implemented the ROW_NUMBER() strategy, partitioning by the duplicate key (robot_id, time_bucket) and ordering by signal quality metrics. This solution executed as a single atomic transaction, preventing data inconsistency during the cleanup. It processed the entire backlog in under four minutes and reduced storage costs by forty percent without taking the table offline.

What candidates often miss

Why must a truly unique primary key always serve as the final column in the ORDER BY clause of a deduplication window function, even when business logic seemingly dictates ordering solely by a non-unique timestamp?

In ANSI SQL, the ordering of rows that possess identical values for all specified ORDER BY keys is non-deterministic. If two duplicate records share the exact same timestamp and signal_strength, the database engine is free to arrange them arbitrarily. Consequently, executing the deletion logic multiple times might randomly select different rows for preservation, leading to inconsistent results and potential loss of critical data. Appending the PRIMARY KEY ensures a total ordering, guaranteeing idempotent and reproducible deletions.

How does ANSI SQL treat NULL values within a PARTITION BY clause compared to standard equality predicates in a join condition, and why does this distinction endanger deduplication accuracy?

Within GROUP BY or PARTITION BY clauses, ANSI SQL treats NULL values as indistinguishable and groups them together (effectively, NULL equals NULL for aggregation). Conversely, in WHERE clauses or join predicates (ON t1.x = t2.x), the expression NULL = NULL evaluates to UNKNOWN, not TRUE. Therefore, if deduplicating via a self-join, rows with NULL values in the matching columns will never be recognized as duplicates, causing them to survive erroneously. To handle NULLs correctly in joins, you must employ the IS NOT DISTINCT FROM syntax (ANSI SQL:1999).

When removing millions of duplicates in a single transaction, what specific concurrency and resource hazard threatens production stability, and what ANSI SQL technique mitigates this risk?

A monolithic DELETE statement acquires EXCLUSIVE LOCKS on every affected row, potentially escalating to a table-level lock that blocks all concurrent inserts and reads. Furthermore, it generates massive TRANSACTION LOG growth, risking disk exhaustion or recovery failures. To mitigate this while adhering to ANSI SQL, one must process the deletions in batches. This involves iteratively deleting a limited subset identified by FETCH FIRST n ROWS ONLY within a subquery or using a scrollable cursor, committing each small transaction independently to release locks and truncate log segments progressively.