What is the time complexity of Find Consecutive Integers from a Data Stream?

The optimal solution has a time complexity of O(1).

What data structures are used to solve Find Consecutive Integers from a Data Stream?

This problem uses Hash Table, Design, Queue, Counting, Data Stream. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Find Consecutive Integers from a Data Stream?

Insight 1: We only need to keep track of the count of consecutive occurrences of the target value at the end of the stream. Insight 2: A simple counter is sufficient to solve this problem efficiently because we don't need to store the actual stream of numbers.

Which companies ask LeetCode 2526. Find Consecutive Integers from a Data Stream in interviews?

This problem is frequently asked at Intel.

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Find Consecutive Integers from a Data Stream - LeetCode 2526 Solution

Find Consecutive Integers from a Data Stream - Complete Solution Guide

Find Consecutive Integers from a Data Stream is LeetCode problem 2526, a Medium level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

For a stream of integers, implement a data structure that checks if the last k integers parsed in the stream are equal to value . Implement the DataStream class: DataStream(int value, int k) Initializes the object with an empty integer stream and the two integers value and k . boolean consec(int num) Adds num to the stream of integers. Returns true if the last k integers are equal to value , and false otherwise. If there are less than k integers, the condition does not hold true, so returns fals

Detailed Explanation

The problem requires us to implement a data structure that processes a stream of integers. The `DataStream` class should be initialized with a `value` and a `k`. The `consec(num)` method adds a new integer `num` to the stream and checks if the last `k` integers in the stream are all equal to the initialized `value`. It returns `true` if the last `k` integers are all equal to `value`, and `false` otherwise. If the stream has fewer than `k` integers, the method should return `false`.

Solution Approach

The solution uses a counter (`count`) to keep track of the number of consecutive integers equal to the target `value` at the end of the stream. Each time a new number `num` is added to the stream using the `consec` method, we check if `num` is equal to `value`. If it is, we increment the counter. Otherwise, we reset the counter to 0. Finally, we return `true` if the counter is greater than or equal to `k`, indicating that the last `k` integers are equal to `value`, and `false` otherwise.

Step-by-Step Algorithm

Step 1: Initialize the `DataStream` object with the target `value` and required consecutive count `k`.
Step 2: Initialize a counter `count` to 0. This counter will store the number of consecutive occurrences of `value` at the end of the data stream.
Step 3: In the `consec(num)` method, check if the input `num` is equal to the target `value`.
Step 4: If `num == value`, increment the `count`. Otherwise, reset the `count` to 0 because the consecutive sequence is broken.
Step 5: Return `true` if the `count` is greater than or equal to `k` (meaning the last `k` numbers are consecutively equal to value), and `false` otherwise.

Key Insights

Insight 1: We only need to keep track of the count of consecutive occurrences of the target value at the end of the stream.
Insight 2: A simple counter is sufficient to solve this problem efficiently because we don't need to store the actual stream of numbers.
Insight 3: Resetting the counter when a different number is encountered is crucial for maintaining the consecutive nature of the check.

Complexity Analysis

Time Complexity: O(1)

Space Complexity: O(1)

Topics

This problem involves: Hash Table, Design, Queue, Counting, Data Stream.

Companies

Asked at: Intel.

Find Consecutive Integers from a Data Stream | LeetCode 2526

Medium

Hash Table Design Queue Counting Data Stream

Time: O(1)Space: O(1)

Solution Code

Available in:

class DataStream {

    private int value;
    private int k;
    private int count;

    public DataStream(int value, int k) {
        this.value = value;
        this.k = k;
        this.count = 0;
    }
    
    public boolean consec(int num) {
        if (num == this.value) {
            this.count += 1;
        } else {
            this.count = 0;
        }
        
        return this.count >= this.k;
    }
}

/**
 * Your DataStream object will be instantiated and called as such:
 * DataStream obj = new DataStream(value, k);
 * boolean param_1 = obj.consec(num);
 */

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Problem Info

Description

Problem 2526: Find Consecutive Integers from a Data Stream - Detailed explanation and solution approaches.

Topics

Hash Table Design Queue Counting Data Stream

Companies

Intel

Stats

Difficulty:Medium

Languages:4

Companies:1

Problem #:2526

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

5 steps

1
Step 1: Initialize the `DataStream` object with the target `value` and required consecutive count `k`.
2
Step 2: Initialize a counter `count` to 0. This counter will store the number of consecutive occurrences of `value` at the end of the data stream.
3
Step 3: In the `consec(num)` method, check if the input `num` is equal to the target `value`.
4
Step 4: If `num == value`, increment the `count`. Otherwise, reset the `count` to 0 because the consecutive sequence is broken.
5
Step 5: Return `true` if the `count` is greater than or equal to `k` (meaning the last `k` numbers are consecutively equal to value), and `false` otherwise.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(1)

⚡ Constant time - Super fast! Same speed regardless of input size.

💾

Space Complexity

O(1)

💪 Constant space - Uses same memory regardless of input!

📝

Why?

The `consec` method performs a constant number of operations (comparison, increment/reset counter, and comparison), regardless of the input stream size. Therefore, the time complexity is O(1). The solution only uses a fixed number of integer variables (value, k, and count), so the space complexity is also O(1).

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Queue-based approach

Store the last `k` elements in a queue. In each call to `consec`, add the new number to the queue and remove the oldest if the queue size exceeds `k`. Then, iterate through the queue to check if all elements are equal to `value`.

Time: O(k) for the consec operation due to iterating through the queue.Space: O(k) to store the queue of at most `k` elements.

Trade-offs: This approach uses more space and time compared to the counter-based solution. It's useful if you need to access the actual last `k` elements, but it's less efficient for just checking consecutive equality.

✨

Key Insights

1Insight 1: We only need to keep track of the count of consecutive occurrences of the target value at the end of the stream.
2Insight 2: A simple counter is sufficient to solve this problem efficiently because we don't need to store the actual stream of numbers.
3Insight 3: Resetting the counter when a different number is encountered is crucial for maintaining the consecutive nature of the check.

📚

Key Terms Explained

For Students

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

Queue

FIFO structure - First In, First Out. Like a line at a store!

Edge Cases

Edge case 1: `k = 1`. The solution correctly handles this case because it will immediately return true if the current number is equal to the value.
Edge case 2: The first few numbers in the stream are not equal to the value. The counter `count` will remain at 0 until the target value appears, and the function will return `false` until `count >= k`.
Edge case 3: A very long stream with many consecutive values followed by a single different value. The `count` will be reset, ensuring correct behavior even in long streams.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to reset the counter to 0 when a number different from `value` appears in the stream. This would lead to incorrect results.

✗Common mistake 2: Returning `true` prematurely when the stream has fewer than `k` integers, without checking the `count` against `k`.

✗Common mistake 3: Using a list or array to store the stream and iterating to check. This results in higher time complexity than necessary.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Monitoring network traffic for consecutive packets from the same source with specific characteristics to detect denial-of-service attacks.

🏦Real-world scenario 2: Analyzing financial data to identify consecutive days where a stock price meets a certain condition, such as consecutive gains or losses above a threshold. This can be useful in algorithmic trading.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

Hash tables trade space for time. Before using one, consider if the problem constraints allow the extra memory. In interviews, always mention this trade-off.

🎯 Interview Tip

Medium problems are the bread and butter of technical interviews. Practice explaining your approach before coding.

⚠️ Watch Out

Don't forget to handle key collisions and null/undefined keys. Consider what happens with duplicate values.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: June 7, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 14

I was asked this exact problem at my Google interview. The interviewer wanted me to optimize space complexity.

@InterviewProJul 7

❓

Frequently Asked Questions

What are the trade-offs of using a hash table?

Hash tables provide O(1) average-case lookup, insertion, and deletion, making them ideal for frequency counting and duplicate detection. However, they use O(n) extra space and may degrade to O(n) time in worst-case scenarios with hash collisions. Always consider if the space trade-off is acceptable.

What techniques are commonly tested in medium-difficulty problems?

Medium problems often require combining multiple techniques: two-pointer with hash maps, BFS/DFS with state tracking, dynamic programming with optimization, or divide-and-conquer approaches. They test your ability to recognize patterns and choose the right combination of techniques.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Find Consecutive Integers from a Data Stream"?

For this medium-level Hash Table problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether design techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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