postgres generate series: Examples, How It Works, Best Practices

The Postgres function often searched as postgres generate series is a powerful set-returning feature that creates rows on the fly for integers, dates, and timestamps. It is ideal for time series analysis, filling missing days, building calendars, and testing. Yet, hand-writing these queries can be tricky: mixing types (date vs timestamp), picking inclusive boundaries, choosing correct step intervals, and avoiding performance pitfalls. With AI2sql, you describe the range and business goal in plain English, and you get production-ready SQL for PostgreSQL or an equivalent in BigQuery, Snowflake, SQL Server, MySQL, and more. This short guide shows how to use generate_series effectively, how to avoid common mistakes, and how AI2sql eliminates guesswork so you can move from question to correct SQL in minutes. For teams adopting multiple warehouses, we include side-by-side examples across engines. If you want a deeper product overview, see our PostgreSQL integration and the AI2sql platform.

postgres generate series Overview (dialect specifics)

In PostgreSQL, generate_series returns a set of values from start to stop with a given step. It supports: integers and bigints with integer step; dates with interval step; timestamps with interval step. Boundaries are inclusive when the endpoint lands exactly on the step. Steps can be negative to count down. Typical uses: calendar tables, session activity by hour, pagination offsets, sample data generation, backfilling missing days for analytics. Tip: when joining generated dates to a fact table, LEFT JOIN preserves empty days for accurate reporting.

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Connect Your Database to AI2sql (steps)

1) Open AI2sql and sign in. 2) Choose PostgreSQL, enter host, port, database, and a read-only user. 3) Allow schema introspection so AI2sql can reference your actual tables and columns. 4) Describe your goal in plain English, e.g., create a daily series for the last 30 days and join to orders. 5) Review generated SQL, run in your editor, or let AI2sql format, explain, and validate it. For multi-warehouse teams, switch engines and re-prompt to get equivalent logic for BigQuery or Snowflake.

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Prompts to SQL for postgres generate series

Below are practical, runnable snippets. Each includes a brief business context and the exact SQL. Where useful, we show equivalents in other warehouses.

Example 1: postgres generate series for daily revenue baseline (PostgreSQL) — Create a daily calendar for the last 30 days and join to orders to fill missing dates.

WITH days AS (   SELECT gs::date AS day   FROM generate_series(current_date - interval '29 days', current_date, interval '1 day') AS gs ) SELECT d.day, COALESCE(SUM(o.amount), 0) AS revenue FROM days d LEFT JOIN orders o   ON o.order_date::date = d.day GROUP BY d.day ORDER BY d.day;

Prompt: create a daily series for the last 30 days and left join to orders to avoid gaps.

Example 2: Hourly active users with gaps filled (PostgreSQL) — Build an hourly time series for the past 24 hours and join to events.

WITH hours AS (   SELECT gs AS hour_ts   FROM generate_series(date_trunc('hour', now()) - interval '23 hours',                         date_trunc('hour', now()),                         interval '1 hour') AS gs ) SELECT h.hour_ts, COALESCE(COUNT(e.user_id), 0) AS active_users FROM hours h LEFT JOIN events e   ON date_trunc('hour', e.occurred_at) = h.hour_ts GROUP BY h.hour_ts ORDER BY h.hour_ts;

Prompt: hourly series for the last 24 hours, join events, count per hour.

Example 3: Business days only (PostgreSQL) — Generate weekdays for current month to schedule operations.

WITH days AS (   SELECT gs::date AS day   FROM generate_series(date_trunc('month', current_date)::date,                         (date_trunc('month', current_date) + interval '1 month - 1 day')::date,                         interval '1 day') AS gs ) SELECT day FROM days WHERE EXTRACT(dow FROM day) BETWEEN 1 AND 5 ORDER BY day;

Prompt: create a series of business days this month, Monday to Friday only.

Example 4: Pagination offsets (PostgreSQL) — Precompute page offsets for a UI that paginates 10 rows per page.

SELECT offset FROM generate_series(0, 90, 10) AS offset ORDER BY offset;

Prompt: emit 0,10,20,...,90 for pagination.

Example 5: BigQuery equivalent for date series (BigQuery) — Create a last-30-days calendar and join to orders.

WITH days AS (   SELECT day   FROM UNNEST(GENERATE_DATE_ARRAY(DATE_SUB(CURRENT_DATE(), INTERVAL 29 DAY), CURRENT_DATE(), INTERVAL 1 DAY)) AS day ) SELECT d.day, COALESCE(SUM(o.amount), 0) AS revenue FROM days d LEFT JOIN `project.dataset.orders` o   ON DATE(o.order_date) = d.day GROUP BY d.day ORDER BY d.day;

Prompt: BigQuery last 30 days series and join to orders.

Example 6: Snowflake equivalent for date series (Snowflake) — Emit the last 30 days and left join to orders.

WITH days AS (   SELECT DATEADD(day, seq4(), DATEADD(day, -29, CURRENT_DATE())) AS day   FROM TABLE(GENERATOR(ROWCOUNT => 30)) ) SELECT d.day, COALESCE(SUM(o.amount), 0) AS revenue FROM days d LEFT JOIN analytics.orders o   ON CAST(o.order_date AS date) = d.day GROUP BY d.day ORDER BY d.day;

Prompt: Snowflake last 30 days series and join to orders.

Example 7: SQL Server hourly series via recursive CTE (SQL Server) — 24-hour hourly buckets for event counts.

WITH n AS (   SELECT 0 AS k   UNION ALL   SELECT k + 1 FROM n WHERE k < 23 ) SELECT DATEADD(hour, k, DATEADD(hour, DATEDIFF(hour, 0, GETDATE()) - 23, 0)) AS hour_ts,        COUNT(e.user_id) AS active_users FROM n LEFT JOIN dbo.events e   ON DATEADD(hour, DATEDIFF(hour, 0, e.occurred_at), 0) = DATEADD(hour, k, DATEADD(hour, DATEDIFF(hour, 0, GETDATE()) - 23, 0)) GROUP BY DATEADD(hour, k, DATEADD(hour, DATEDIFF(hour, 0, GETDATE()) - 23, 0)) ORDER BY hour_ts OPTION (MAXRECURSION 1000);

Prompt: SQL Server hourly series for last 24 hours with event counts.

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Common Errors and Fixes

• Type mismatch: function generate_series(date, date, integer) does not exist. Fix: use an interval step for dates, e.g., interval '1 day'.

• Wrong timestamp vs date granularity: joining timestamp facts to date series without truncation drops matches. Fix: cast or truncate, e.g., order_date::date or date_trunc('hour', ts).

• Off-by-one endpoints: inclusive boundaries only include the stop when divisible by the step. Fix: verify series stop or use an extra LEAST/GREATEST guard.

• Time zone drift: now vs current_date and user session zones can misalign. Fix: standardize with date_trunc and AT TIME ZONE if needed.

• Excessive rows: large ranges create heavy scans. Fix: restrict bounds, use WHERE filters, or prebuild a date dimension.

• Descending ranges: if stop is less than start with positive step, no rows. Fix: use a negative step, e.g., interval '-1 day'.

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FAQs: postgres generate series with AI2sql

Does AI2sql handle multiple engines? Yes, describe your goal once and get SQL for Postgres or equivalents for BigQuery, Snowflake, SQL Server, MySQL, and more. Can I customize boundaries and step? Yes, specify start, stop, and step in your prompt; AI2sql produces the correct signature per dialect. How do I fill missing dates in reports? Generate a calendar series and LEFT JOIN your fact table; AI2sql can also add COALESCE for zero-filling. Will AI2sql explain the query? Use the built-in SQL explainer and validator to see a human-readable breakdown and catch issues before running.

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Conclusion

PostgreSQL generate_series is the fastest way to build integers, dates, and timestamps on demand, powering reports, cohorts, and backfills. The details matter: data types, inclusive endpoints, intervals, and joins. AI2sql turns your intent into precise SQL and ports the idea to other engines like BigQuery and Snowflake when needed. Connect your database, describe the desired range and aggregation, and copy the result into your workflow. Save time on boilerplate, avoid edge-case bugs, and stay focused on analysis. Try AI2sql Free and generate correct series-based queries from plain language at https://builder.ai2sql.io/ — Try AI2sql Free – Generate postgres generate series Solutions.