1683. Invalid Tweets Solution & Explanation

The problem

Table: Tweets

+----------------+---------+
| Column Name    | Type    |
+----------------+---------+
| tweet_id       | int     |
| content        | varchar |
+----------------+---------+
tweet_id is the primary key (column with unique values) for this table.
content consists of characters on an American Keyboard, and no other special characters.
This table contains all the tweets in a social media app.

Write a solution to find the IDs of the invalid tweets. The tweet is invalid if the number of characters used in the content of the tweet is strictly greater than 15.

Return the result table in any order.

The result format is in the following example.

Example 1:

Input: 
Tweets table:
+----------+-----------------------------------+
| tweet_id | content                           |
+----------+-----------------------------------+
| 1        | Let us Code                       |
| 2        | More than fifteen chars are here! |
+----------+-----------------------------------+
Output: 
+----------+
| tweet_id |
+----------+
| 2        |
+----------+
Explanation: 
Tweet 1 has length = 11. It is a valid tweet.
Tweet 2 has length = 33. It is an invalid tweet.

Approach overview

Problem Overview: The Invalid Tweets problem asks you to identify tweets whose content exceeds the platform’s character limit. Given a table Tweets(tweet_id, content), return the tweet_id for every tweet where the content length is greater than 15 characters.

Approach 1: SQL Query Filtering (O(n) time, O(1) space)

The most direct solution is a single SQL filter. Scan the Tweets table and compute the string length for each content value using a built-in function such as LENGTH() or CHAR_LENGTH(). Any row where the length exceeds 15 is considered invalid. The query engine performs a linear scan over the table, evaluates the condition, and returns the matching tweet_id. This approach is optimal for database problems because it pushes the filtering logic directly to the database engine and avoids unnecessary processing in application code.

Databases are optimized for operations like filtering and projection, so a simple condition such as WHERE LENGTH(content) > 15 executes efficiently. Time complexity is O(n) because each row is checked once, and extra space is O(1) since no additional data structures are created.

Approach 2: Iterative Filtering in Application Code (O(n) time, O(1) space)

If the tweets are already loaded into memory as objects or records, you can iterate through them and check the length of the content string. For each tweet, compute len(content) (or equivalent) and append the tweet_id to the result list when the value exceeds 15. This works the same way across languages such as Python, Java, C++, C#, or JavaScript.

The algorithm is straightforward: iterate over the collection once, evaluate a length condition, and collect the matching IDs. Each tweet is processed exactly one time, giving O(n) time complexity. Only a small output list is maintained, so auxiliary space remains O(1) aside from the result itself.

This approach appears frequently in introductory problems involving string processing and basic array iteration. The SQL variant falls under core database querying patterns.

Recommended for interviews: Interviewers typically expect the SQL filtering approach when the problem is presented as a database query. Writing a concise condition such as WHERE LENGTH(content) > 15 demonstrates that you understand how to leverage built‑in database functions efficiently. The iterative approach still shows correct reasoning about string length checks, but pushing the logic into SQL is the cleaner and more idiomatic solution.

Approach 1: SQL Query Approach

This approach involves using SQL to filter out invalid tweets directly from the database. The key is to use the WHERE clause to check the length of the tweet content and return those with a length strictly greater than 15.

MySQL

SELECT tweet_id FROM Tweets WHERE LENGTH(content) > 15;

The SQL solution utilizes the LENGTH function to determine the length of each tweet's content. The WHERE clause effectively filters these lengths and retrieves the tweet IDs of any entries with a content length greater than 15.

Complexity

Time Complexity: O(n), where n is the number of tweets in the table. This is due to the need to calculate the length for each tweet.
Space Complexity: O(1), as we are using in-place operations without any additional data structures.

Try this approach in the editor

Approach 2: Iterative Filtering Approach

This approach reads all tweets and checks their content length programmatically. It uses an iterative process to filter out the invalid tweets by measuring the length of each tweet's content and collecting the IDs of those that are longer than 15 characters.

Python

def find_invalid_tweets(tweet_ids, contents):
    print("+----------+")
    print("| tweet_id |")
    print("+----------+")
    for id, content in zip(tweet_ids, contents):
        if len(content) > 15:
            print(f"| {id}        |")
    print("+----------+")

Java

import java.util.List;

public class InvalidTweets {
    public static void findInvalidTweets(List<Integer> tweetIds, List<String> contents) {
        System.out.println("+----------+\n| tweet_id |\n+----------+");
        for (int i = 0; i < contents.size(); i++) {
            if (contents.get(i).length() > 15) {
                System.out.println("| " + tweetIds.get(i) + "        |");
            }
        }
        System.out.println("+----------+");
    }
}

C++

#include <iostream>
#include <vector>

void findInvalidTweets(const std::vector<int>& tweetIds, const std::vector<std::string>& contents) {
    std::cout << "+----------+\n| tweet_id |\n+----------+\n";
    for (size_t i = 0; i < contents.size(); ++i) {
        if (contents[i].length() > 15) {
            std::cout << "| " << tweetIds[i] << "        |\n";
        }
    }
    std::cout << "+----------+\n";
}

JavaScript

function findInvalidTweets(tweetIds, contents) {
    console.log("+----------+\n| tweet_id |\n+----------+");
    contents.forEach((content, index) => {
        if (content.length > 15) {
            console.log(`| ${tweetIds[index]}        |`);
        }
    });
    console.log("+----------+");
}

#include <stdio.h>
#include <string.h>

void findInvalidTweets(int tweetIds[], char* contents[], int n) {
    printf("+----------+\n| tweet_id |\n+----------+\n");
    for (int i = 0; i < n; ++i) {
        if (strlen(contents[i]) > 15) {
            printf("| %d        |\n", tweetIds[i]);
        }
    }
    printf("+----------+\n");
}

using System;
using System.Collections.Generic;

public class Tweets {
    public static void FindInvalidTweets(List<int> tweetIds, List<string> contents) {
        Console.WriteLine("+----------+\n| tweet_id |\n+----------+");
        for (int i = 0; i < contents.Count; i++) {
            if (contents[i].Length > 15) {
                Console.WriteLine("| {0}        |", tweetIds[i]);
            }
        }
        Console.WriteLine("+----------+");
    }
}

The Python solution makes use of the zip function to pair tweet IDs with their content. It checks if the content length is over 15 characters and prints IDs of invalid tweets.

Complexity

Time Complexity: O(n), as each tweet's content length must be evaluated.
Space Complexity: O(1), no significant extra space used.

Try this approach in the editor

Complexity comparison

Approach	Complexity
SQL Query Approach	Time Complexity: O(n), where n is the number of tweets in the table. This is due to the need to calculate the length for each tweet. Space Complexity: O(1), as we are using in-place operations without any additional data structures.
Iterative Filtering Approach	Time Complexity: O(n), as each tweet's content length must be evaluated. Space Complexity: O(1), no significant extra space used.

Complexity analysis

Approach	Time	Space	When to Use
SQL Query Filtering	O(n)	O(1)	When solving directly in SQL or querying a database table
Iterative Filtering in Application Code	O(n)	O(1)	When tweets are already loaded into memory as objects or arrays

Frequently asked questions

Is Invalid Tweets easy or hard?

Invalid Tweets is classified as an Easy problem. The task focuses on basic filtering using SQL string length functions or simple iteration logic, making it a common warm‑up question for database practice.

Invalid Tweets Python/Java solution

In Python or Java, iterate through a collection of tweets and check if the string length of the content exceeds 15. If it does, add the tweet_id to the result list. This approach processes each tweet once and runs in O(n) time.

How to solve Invalid Tweets in O(n)?

Perform a single pass over all tweets and check the length of the content field. In SQL, write a query like SELECT tweet_id FROM Tweets WHERE LENGTH(content) > 15. Each row is evaluated once, giving linear O(n) time complexity.

What is the best approach for Invalid Tweets?

The best approach is a simple SQL filter using LENGTH(content) > 15. It scans the Tweets table once and returns the tweet_id values that exceed the character limit. This runs in O(n) time with O(1) extra space and leverages the database engine’s optimized filtering.

Is Invalid Tweets asked at Google/Amazon/Meta?

Invalid Tweets is a basic SQL filtering problem commonly used in coding platforms to test database fundamentals. Variants of similar query‑filter tasks appear in interviews at companies like Amazon and Meta when assessing SQL skills.

What data structure is used in Invalid Tweets?

The database version operates on a relational table and relies on SQL string functions. In application code implementations, the tweets are typically stored in arrays or lists and processed using simple string length checks.

What is the time complexity of Invalid Tweets?

The solution runs in O(n) time because each tweet row is evaluated once to compute the content length and check the condition. No nested operations are required. Space complexity is O(1) since only a small result set is returned.

Invalid Tweets solution and explanation

The problem

Approach overview

Approach 1: SQL Query Approach

Approach 2: Iterative Filtering Approach

Complexity comparison

Complexity analysis

Frequently asked questions

Comments

Invalid Tweets solution and explanation

The problem

Approach overview

Approach 1: SQL Query Approach

Approach 2: Iterative Filtering Approach

Complexity comparison

Complexity analysis

Frequently asked questions

Comments

Related problems