Abstract / Summary

Given a list of game play records, list the player IDs with the most games played.

Note

The full problem description, including helpful examples, can be found on LeetCode.

This problem is ranked as

and has an acceptance rate of about 77% (700k+ submissions).

Typical patterns to solve this problem:

• Database

Every single line of code was written by me, 100% from scratch, between August 2023 and August 2024.
However, two years later I set up this website and used various AI tools to generate most of the walkthrough / explanations below.

See this dedicated page for more details.

Overview

The provided SQL statement extracts the earliest login date for each distinct player from the Activity table. By grouping rows on player_id and applying the MIN aggregate function to event_date, the query produces a result set where each row contains a player identifier together with the date of that player’s first recorded activity.

Key Steps

The query begins by selecting two columns: player_id and the computed alias first_login. The alias is derived from the MIN(event_date) expression, which evaluates the smallest (chronologically earliest) date value within each group. The FROM Activity clause designates the source table containing raw activity logs. The GROUP BY player_id clause instructs the database engine to partition the rows by player identifier, ensuring that the aggregation operates independently for each player. The final output consists of one row per player, pairing the identifier with the earliest associated event date.

SELECT player_id, MIN(event_date) AS first_login
  FROM Activity
 GROUP BY player_id

Edge Cases Handled

When a player appears multiple times in the Activity table, the aggregation correctly isolates the earliest timestamp regardless of the order in which rows are stored. If a player has only a single activity record, the MIN function simply returns that date, satisfying the requirement without additional logic. Players that never appear in the Activity table are naturally excluded from the result set, as the GROUP BY operation only processes existing rows. In scenarios where multiple rows share the exact same earliest event_date for a player, the MIN function still returns that date, and the grouping guarantees a single output row per player, thereby handling ties gracefully.

Performance Optimizations

The query leverages the built-in MIN aggregate, which is optimized by most relational database engines to scan each group only once, minimizing CPU overhead. When an index exists on the combination of player_id and event_date, the database can perform an index-only scan, retrieving the smallest event_date for each player_id directly from the index structure without accessing the full table rows. This reduces I/O and accelerates grouping. Additionally, the use of a simple GROUP BY without additional joins or subqueries allows the optimizer to apply hash or sort-based aggregation strategies efficiently, depending on data distribution and available memory. The query’s straightforward structure enables the planner to choose the most effective execution path, such as streaming aggregation when the data is already ordered by player_id.

Imagine a giant toy box called Activity that is filled with cards. Each card tells a story about a time a player logged into a game. Some players have a whole stack of cards because they play every single day. This code helps sort through all those cards by putting them into separate piles based on the name of the player. Once the cards are in their own little piles, the computer looks at every pile to find the very first date written on any of the cards in that stack. It writes down the name of the friend and the earliest date found in their pile so that it is easy to see exactly when each person started playing for the first time.

Several LLMs ranked my code quality on a scale from 1 (very poor) to 10 (outstanding):

not analyzed yet

I wrote a basic crawler to find other solutions to that problem.
Obviously, I did that after I solved the problem myself.

In general, AlgoMonster and WalkCCC solutions are high-quality and always worth a look.
I haven't checked most of the other solutions, but they might be helpful as well.

And of course, the LeetCode forum is always a good place (or even the best) to look for alternative approaches.

(click on a column header to sort the table)

	author
author	solutions
www	AlgoMonster		C++	Java	Python	TS
	WalkCCC	SQL
	leetcode.ca	SQL			Python
	LeetcodeHelp		C++	Java	Python
	Progiez		C++	Java	Python
github	everthis	SQL
	suxrobGM	SQL
AlgoMonster	C++, Python, Java, TypeScript
WalkCCC	SQL
leetcode.ca	Python, SQL
LeetcodeHelp	C++, Java, Python
Progiez	C++, Java, Python
everthis	SQL
suxrobGM	SQL

There are a few extra pages giving you some insights into my setup:

• Tech Stack - my development environment and tools
• AI - how AI helped me create this website (NOT solving the problems)
• Progress - progression over time
• Quality - code quality