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#550
Medium Database

Game play analysis iv

Database
40.8% acceptance
Feb 27, 2026
1476
249
Table: Activity +--------------+---------+ | Column Name | Type | +--------------+---------+ | player_id | int | | device_id | int | | event_date | date | | games_played | int | +--------------+---------+ (player_id, event_date) is the primary key (combination of columns with unique values) of this table. This table shows the activity of players of some games. Each row is a record of a player who logged in and played a number of games (possibly 0) before logging out on someday using some device. Write a solution to report the fraction of players that logged in again on the day after the day they first logged in, rounded to 2 decimal places. In other words, you need to determine the number of players who logged in on the day immediately following their initial login, and divide it by the number of total players. The result format is in the following example.

Solution

SQL
LeetCode
solution.sql
#
# Table: Activity
# +--------------+---------+
# | Column Name  | Type    |
# +--------------+---------+
# | player_id    | int     |
# | device_id    | int     |
# | event_date   | date    |
# | games_played | int     |
# +--------------+---------+
# (player_id, event_date) is the primary key (combination of columns with unique values) of this table.
# This table shows the activity of players of some games.
# Each row is a record of a player who logged in and played a number of games (possibly 0) before logging out on someday using some device.
# Write a solution to report the fraction of players that logged in again on the day after the day they first logged in, rounded to 2 decimal places. In other words, you need to determine the number of players who logged in on the day immediately following their initial login, and divide it by the number of total players.
# The result format is in the following example.
# Example 1:
# Input:
# Activity table:
# +-----------+-----------+------------+--------------+
# | player_id | device_id | event_date | games_played |
# +-----------+-----------+------------+--------------+
# | 1         | 2         | 2016-03-01 | 5            |
# | 1         | 2         | 2016-03-02 | 6            |
# | 2         | 3         | 2017-06-25 | 1            |
# | 3         | 1         | 2016-03-02 | 0            |
# | 3         | 4         | 2018-07-03 | 5            |
# +-----------+-----------+------------+--------------+
# Output:
# +-----------+
# | fraction  |
# +-----------+
# | 0.33      |
# +-----------+
# Explanation:
# Only the player with id 1 logged back in after the first day he had logged in so the answer is 1/3 = 0.33
#

# Write your MySQL query statement below

SELECT ROUND(COUNT(DISTINCT a2.player_id) / COUNT(DISTINCT a1.player_id), 2) AS fraction
FROM Activity a1
LEFT JOIN Activity a2 ON a1.player_id = a2.player_id
  AND a2.event_date = DATE_ADD(a1.event_date, INTERVAL 1 DAY)
WHERE (a1.player_id, a1.event_date) IN (
  SELECT player_id, MIN(event_date) FROM Activity GROUP BY player_id
);