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The pattern in one picture
Keep the source timestamp as the single source of truth. Add a generated column, here called stats_date, derived from it by a deterministic rule. Index stats_date. Daily queries then filter or group on that key, for example WHERE stats_date = '2026-10-05'.
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A generated column is computed from other columns in the same row. You cannot write arbitrary values into it, so the key cannot drift from the timestamp. That consistency is the main benefit over a hand-maintained date column.
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Decide what “a day” means first
The index is only as correct as the expression behind it. Settle these before writing DDL:
#1 Best Overall
- Storage type. Is the source a timestamp without a zone, a timestamp with a zone, a Unix integer, or a string?
- Reporting timezone. A “day” in UTC is a different set of rows from a “day” in a local zone. The derivation must use the zone your report uses.
- Daylight-saving transitions. In a local zone, some days are 23 or 25 hours long. A fixed-offset rule will misassign rows near those boundaries.
- Stability. Indexed and generated expressions must be deterministic (SQLite) or immutable (PostgreSQL). Don’t build the key from the current time or from a session setting that can change. A rule that depends on a mutable session timezone is a common way to fail this requirement.
What each engine requires
PostgreSQL
PostgreSQL’s “Generated Columns” documentation covers stored and virtual generated columns. The rule that matters here is quoted directly: “The generation expression can only use immutable functions and cannot use subqueries or reference anything other than the current row in any way.” The timestamp-to-date conversion therefore has to be expressed in an immutable form. Converting a zone-aware timestamp with a fixed, explicit zone name is the usual route. Relying on the session’s timezone setting is not. The documentation I relied on doesn’t specify your PostgreSQL version, so confirm what your release supports for virtual versus stored columns.
-- Illustrative PostgreSQL sketch (timestamptz source, fixed reporting zone)
ALTER TABLE events
ADD COLUMN stats_date date
GENERATED ALWAYS AS ((created_at AT TIME ZONE 'UTC')::date) STORED;
CREATE INDEX events_stats_date_idx ON events (stats_date);If PostgreSQL rejects the expression as non-immutable, that is the rule above working as designed. Adjust the expression rather than hunting for a way around it.
Rank #2
MySQL
MySQL’s manual describes generated columns as a way to simulate functional indexes. It warns that a stored generated value and its index each occupy storage, so you pay twice. For the optimizer to use the index on an expression you wrote in the query, the manual (8.4, “Optimizer Use of Generated Column Indexes”) states: “For a query expression to match a generated column definition, the expression must be identical and it must have the same result type.” Behavior can vary by server version, so read the manual for the version you deploy.
-- Illustrative MySQL sketch
ALTER TABLE events
ADD COLUMN stats_date DATE
GENERATED ALWAYS AS (DATE(created_at)) STORED,
ADD INDEX idx_stats_date (stats_date);The simplest way to avoid the matching question is to query stats_date directly.
Rank #3
SQLite
SQLite offers two routes. Stored generated columns use ordinary indexes, and virtual generated columns produce expression indexes. You can also index an expression directly, without any generated column. The planner considers an expression index when the same expression appears in WHERE or ORDER BY, apart from minor syntax differences. The documentation says, “The query planner does not do algebra”, so a rewritten but mathematically equivalent expression will not match. Functions used in an index must be deterministic.
- Generated columns need SQLite 3.31.0 or later.
- Expression indexes need SQLite 3.9.0 or later.
- Older versions can fail to open a schema that uses generated-column syntax. Check the version embedded in your application and in every tool that opens the file, including command-line shells and GUI browsers.
SQLite also documents a rare maintenance case. If an expression that was assumed deterministic changes behavior across software or platform versions, the index can disagree with the data. REINDEX is the documented repair. This is an edge case, not a reason to expect routine corruption.
Rank #4
Make the query match the index
This is where most disappointments come from. There are three ways to write the filter:
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WHERE stats_date = ?is the most direct form and doesn’t depend on expression matching. - Repeat the expression. Where the engine recognizes indexed expressions, the query’s expression must match the definition. SQLite wants the same expression apart from minor syntax. MySQL wants it identical, with the same result type.
- Don’t wrap the indexed column in extra arithmetic or casts. Anything the planner would have to simplify algebraically won’t be simplified.
An alternative: a range on the raw timestamp
You may not need a generated column. A plain index on the timestamp can serve a half-open range for each day:
WHERE created_at >= :day_start AND created_at < :next_day_startThe day boundaries must follow the same timezone definition as your report. The sources I used give no engine-specific comparison between this and a generated date key, so don’t assume either is faster. Test both on your data. The range form avoids an extra column and its storage. The generated key makes the intent explicit and works well for grouping and for joins to calendar tables.
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- Run your engine’s plan tool, such as
EXPLAIN, on the real daily query. Confirm the plan names your new index and doesn’t scan the whole table. - If it doesn’t, compare the query’s expression with the column definition. Look for a type mismatch, an altered expression, or a function the engine doesn’t treat as stable.
- Benchmark latency and resource use before and after, on data that resembles production in size and date distribution. The documentation I used publishes no speedup figure for this workload, so only your own measurement counts.
- Count the costs. Each insert or update must compute and maintain the key. The index takes disk and memory. In MySQL, stored values and their index both consume space.
- Consider selectivity. If one day matches a large share of the table, or the report touches nearly every day, an index may not beat a scan.
Choosing between the options
| Axis | Generated date key + index | Raw timestamp range + index |
|---|---|---|
| Engine requirements | Generated-column support; expression must satisfy immutable/deterministic rules | Ordinary index only |
| Query writing | Filter on the key, or match the expression exactly | Compute day start and end in the reporting zone |
| Timezone handling | Fixed inside the column definition | Applied per query by the application |
| Storage and write cost | Extra column (if stored) plus index | Index only |
| Performance | Not established by the sources; measure | Not established by the sources; measure |
Both approaches are legitimate. Pick the generated key if many queries group by day or need one authoritative definition of the day. Pick the range if you only need to fetch rows for a day and want minimal schema change.
Further reading
For broader background on how indexes are chosen and used, Markus Winand’s SQL Performance Explained is available in a free web edition at Use The Index, Luke. It covers indexing in general, not this specific pattern.
The Bottom Line
Index a generated date key only after you’ve fixed the timezone and day boundary and confirmed your engine accepts the expression. Then query the key directly and check the plan. Compare the result against a plain timestamp-range index on your own data, because no published figure will tell you which one wins for your workload.
Quick Recap
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