unleash-server/dist/migrations/20250813125755-backfill-lifecycle-trends-fix.js

Unleash is an enterprise ready feature flag service. It provides different strategies for handling feature flags.

"use strict";
exports.up = function (db, cb) {
    db.runSql(`
            DELETE FROM lifecycle_trends;

            WITH current_sunday AS (
                SELECT date_trunc('day', now() AT TIME ZONE 'UTC')
                           - ((extract(dow from date_trunc('day', now() AT TIME ZONE 'UTC'))::int + 7) % 7) * INTERVAL '1 day' AS sunday_date
                ),
            week_ranges AS (
            SELECT
                to_char(week_start, 'YYYY') || '-' ||
                lpad((floor(extract(epoch from (week_start - (
                date_trunc('year', week_start)
                - extract(dow from date_trunc('year', week_start))::int * INTERVAL '1 day'
                ))) / 604800) + 1)::text, 2, '0') AS id,
                week_start,
                week_start + INTERVAL '7 days' AS week_end,
                week_start + INTERVAL '7 days' + INTERVAL '1 hour' AS created_at
            FROM current_sunday,
                generate_series(
                sunday_date - INTERVAL '52 weeks',
                sunday_date - INTERVAL '1 week',
                INTERVAL '1 week'
                ) AS week_start
            ),
            feature_data AS (
                SELECT
                    fl.created_at AT TIME ZONE 'UTC' AS lifecycle_time,
                    fl.stage,
                    f.type AS flag_type,
                    f.project,
                    fl.feature
                FROM feature_lifecycles fl
                    JOIN features f ON f.name = fl.feature
                    JOIN projects p ON f.project = p.id
                    JOIN feature_types ft ON ft.id = f.type
            ),
            latest_stage_on_week AS (
                SELECT DISTINCT ON (fl.feature, wr.id)
                    wr.id AS week_id,
                    fl.feature,
                    fl.stage AS stage_on_week,
                    fl.created_at
                FROM week_ranges wr
                    JOIN feature_lifecycles fl ON fl.created_at <= wr.week_end
                ORDER BY wr.id, fl.feature, fl.created_at DESC
            ),
            weekly_counts AS (
                SELECT
                    wr.id,
                    wr.created_at,
                    fd.stage,
                    fd.flag_type,
                    fd.project,
                    COUNT(DISTINCT CASE WHEN fd.lifecycle_time >= wr.week_start AND fd.lifecycle_time < wr.week_end THEN fd.feature END) AS new_flags_this_week,
                    COUNT(DISTINCT CASE WHEN fd.lifecycle_time < wr.week_start
                        AND lsbw.feature IS NOT NULL
                        AND lsbw.stage_on_week = fd.stage
                            THEN fd.feature
                        END) AS flags_older_than_week
                FROM week_ranges wr
                    JOIN feature_data fd ON fd.lifecycle_time < wr.week_end
                    LEFT JOIN latest_stage_on_week lsbw ON lsbw.feature = fd.feature AND lsbw.week_id = wr.id
                GROUP BY wr.id, wr.created_at, fd.stage, fd.flag_type, fd.project
            )
            INSERT INTO lifecycle_trends (
                id,
                stage,
                flag_type,
                project,
                flags_older_than_week,
                new_flags_this_week,
                created_at
            )
            SELECT
                id,
                stage,
                flag_type,
                project,
                flags_older_than_week,
                new_flags_this_week,
                created_at
            FROM weekly_counts
            ORDER BY id, project, flag_type, stage;
        `, cb);
};
exports.down = function (db, cb) {
    db.runSql(``, cb);
};
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