#2526

Medium

auto_awesomeQueue-driven state processing

LeetCode Problem Workspace

Find Consecutive Integers from a Data Stream

Design a data stream checker to identify consecutive integers from a stream, focusing on handling state transitions efficiently.

hash table design queue counting data stream

Problem Statement

Given a data stream of integers, design a data structure that can determine if the last k integers in the stream are equal to a specific value. The DataStream class should support initialization with two integers: value (the target value to check for) and k (the number of consecutive integers to check).

The class should provide a method consec(num) that returns true if the last k integers are all equal to value, and false otherwise. The method processes integers as they arrive in the stream and must maintain performance despite potentially large streams and many method calls.

Examples

Example 1

Input: See original problem statement.

Output: See original problem statement.

Input ["DataStream", "consec", "consec", "consec", "consec"] [[4, 3], [4], [4], [4], [3]] Output [null, false, false, true, false]

Explanation DataStream dataStream = new DataStream(4, 3); //value = 4, k = 3 dataStream.consec(4); // Only 1 integer is parsed, so returns False. dataStream.consec(4); // Only 2 integers are parsed. // Since 2 is less than k, returns False. dataStream.consec(4); // The 3 integers parsed are all equal to value, so returns True. dataStream.consec(3); // The last k integers parsed in the stream are [4,4,3]. // Since 3 is not equal to value, it returns False.

Constraints

1 <= value, num <= 109
1 <= k <= 105
At most 105 calls will be made to consec.

Solution Approach

Queue-driven State Processing

Implement a queue to store the most recent integers up to size k. This allows checking if the last k integers match the specified value in constant time. When a new integer arrives, the queue will discard the oldest value and check if all integers in the queue are equal to the target value.

Efficient Sliding Window

Use a sliding window approach, adjusting the window size dynamically as new integers are processed. By keeping track of the number of consecutive target values in the window, the consec() method can return a result in constant time without needing to scan all integers.

Optimized with Hash Table

An additional optimization can involve using a hash table to track the counts of the values in the queue. This would allow for faster checks and potentially better performance in some cases, especially with varying input distributions.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

The time complexity of the consec() method is O(1) when using a queue to store up to k integers, as checking whether all integers in the queue are equal to a specific value is constant time. The space complexity is O(k) because we need to store the most recent k integers in memory at any given time.

What Interviewers Usually Probe

Looking for efficient use of data structures to maintain state in a dynamic stream.
Testing knowledge of sliding window techniques and queue management.
Evaluating how the candidate manages performance under constraints.

Common Pitfalls or Variants

Common pitfalls

Failing to handle stream updates correctly when the queue size exceeds k.
Not maintaining constant time checks for consec() or using inefficient data structures.
Overcomplicating the solution by introducing unnecessary logic or data structures.

Follow-up variants

Modify the solution to handle multiple different values to check for in the stream.
Adapt the solution to track consecutive increasing or decreasing sequences of integers.
Change the constraints to allow for larger values of k and test performance at scale.

FAQ

How does the queue help solve this problem?

The queue stores the most recent k integers, allowing efficient checking of consecutive values and enabling constant-time updates as the stream progresses.

What is the best way to handle performance when k is large?

A sliding window approach using a queue ensures constant-time processing for each new integer, making it efficient even for large values of k.

What happens if we have to track multiple consecutive values?

The solution can be extended to track different target values by adjusting the data structures or adding additional logic to the queue management.

What is the time complexity of the `consec()` method?

The time complexity of consec() is O(1) because it only needs to check whether all values in the queue match the target value.

Can this solution be optimized further?

While the current solution is efficient, further optimizations could include using a hash table to track value counts or adjusting the sliding window logic for specific input patterns.

terminal

Solution

Solution 1: Counting

We can maintain a counter $\textit{cnt}$ to record the current number of consecutive integers equal to $\textit{value}$.

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class DataStream:
    def __init__(self, value: int, k: int):
        self.val, self.k = value, k
        self.cnt = 0

    def consec(self, num: int) -> bool:
        self.cnt = 0 if num != self.val else self.cnt + 1
        return self.cnt >= self.k


# Your DataStream object will be instantiated and called as such:
# obj = DataStream(value, k)
# param_1 = obj.consec(num)

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