Performance hinges on a few guiding principles. Composite indexes on the partition and order columns let the engine avoid a Sort step — EXPLAIN plans in PostgreSQL show a WindowAgg operator without an upstream Sort when the index supports the window. CTE materialization can be forced with WITH x AS MATERIALIZED (...) in PostgreSQL 12+, which changes whether the planner can inline the CTE and may either help or hurt performance. Windows over joined data often force materialization of the join, so windowing first in a CTE and then joining is frequently faster than computing windows after the join. Large partitions can spill to disk because each partition must fit in memory for the sort and buffer; raising work_mem in PostgreSQL, clustering Snowflake tables on partition keys, or partitioning and clustering BigQuery tables by date and partition columns all help.
Engine support varies meaningfully and should drive query portability choices. QUALIFY, which filters on window functions without subquery nesting, is supported in Snowflake, BigQuery, Databricks, and Teradata but not natively in PostgreSQL, MySQL, or SQL Server. RANGE with INTERVAL frames for rolling time-based metrics is supported in PostgreSQL, BigQuery, and DuckDB but not in older MySQL or SQL Server, which require ROWS over pre-aggregated daily counts. MySQL gained window function support only in 8.0; SQLite added it in 3.25 in 2018; ClickHouse has supported windows since 21.x with some unique syntax; DuckDB offers the richest open-source support including QUALIFY and RANGE intervals. PostgreSQL's DISTINCT ON (g) ORDER BY g, ts DESC is a concise shortcut for keeping first-per-group without an explicit ROW_NUMBER subquery.
Several common mistakes are worth flagging. Filtering rows with a window function inside WHERE is invalid — the fix is QUALIFY, a CTE or subquery, or DISTINCT ON in PostgreSQL. Forgetting a tiebreaker in the ORDER BY of a ranking window produces non-deterministic results, so a unique id or sequence should always be appended. Wrapping a UNION ALL in a subquery is required before applying a window over the combined result, otherwise the parser may error or scope unexpectedly. Updating or deleting rows using a window function follows the pattern of computing the window in a subquery and joining back to the target table, useful for deduplication and backfills. For data engineering workflows, DBT models in the mart layer commonly use window functions for ranking, deduplication, and retention metrics, and Spark SQL uses the same syntax via the Window API for distributed execution with partition-based parallelism.