Find numbers that appear at least 3 times consecutively in the logs table.
This challenge now meets the current minimum standard for sitemap inclusion and independent indexing.
Find numbers that appear at least 3 times consecutively in the logs table.
This is a row-sequence problem, not just a counting problem. Similar logic shows up in fraud detection, uptime monitoring, anomaly streaks, and event-stream analysis where you care about repeated behavior across adjacent records rather than total frequency.
idnumSELECT DISTINCT l1.num AS ConsecutiveNums
FROM logs l1
JOIN logs l2
ON l1.id = l2.id - 1
JOIN logs l3
ON l2.id = l3.id - 1
WHERE l1.num = l2.num
AND l2.num = l3.numThis is the classic solution. It models the streak directly by lining up three neighboring rows and checking whether all three contain the same number.
WITH numbered AS (
SELECT
id,
num,
LAG(num) OVER (ORDER BY id) AS prev_num,
LEAD(num) OVER (ORDER BY id) AS next_num
FROM logs
)
SELECT DISTINCT num AS ConsecutiveNums
FROM numbered
WHERE num = prev_num
AND num = next_numThe window-function version is easier to generalize to longer streak analysis. It centers each row and checks whether the previous and next values match the current value.
Do not solve this with `GROUP BY num HAVING COUNT(*) >= 3`. That only proves a number appears three times somewhere in the table, not that the appearances are consecutive. Another trap is assuming ID gaps do not matter when the sequence condition depends on adjacent rows.
Because the question is about ordering and adjacency. A number can appear many times overall but still never form a run of three consecutive rows.
Use LAG and LEAD when you expect follow-up questions about longer streaks, breakpoints, or more complex sequence analysis. It scales better conceptually than adding more self joins.
Use these guides when you want the fuller explanation behind the SQL pattern this challenge is testing.
Master ROW_NUMBER, RANK, and running totals with interactive visualizations. See exactly how window functions process your data row by row.
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